Huazhi Future Releases Lingyan Miaoyu(Lingyan) Large Language Model Application White Paper: Full-Stack AI from Computing Power to Agents

Huazhi Future (Chongqing) Technology Co., Ltd. has officially released the Huazhi Future Lingyan Miaoyu Large Language Model Application White Paper (Official Edition V1.0, July 2026). The white paper systematically explains the company's full-stack AI system — the Xingchen Distributed Intelligent Computing Center, the Tianyu Intelligent Computing Platform, the Lingyan Miaoyu large language model built on the LingYu Architecture, and the ALRO Unified Model Access Portal — and combines nine key industry scenarios with typical practices to propose a three-stage implementation methodology for enterprise AI transformation.

Key Facts at a Glance

• 90-day standardized delivery: the Xingchen Distributed Intelligent Computing Center supports rapid delivery within 90 days with an on-demand expandable "1+N" deployment mode.
• PUE as low as 1.15, up to 80% green electricity: cold plate liquid cooling, intelligent temperature control, and green energy collaboration cut long-term training and inference costs.
• 205+ models, one portal: the ALRO Unified Model Access Portal aggregates 205+ mainstream models with unified invocation, unified formats, and unified permission management.
• Resource utilization above 70%: the Tianyu Intelligent Computing Platform raises computing utilization to over 70% via intelligent task scheduling.
• Lingyan Miaoyu model: 9B parameter specification, unified text / image / video / audio processing, and ScopeEdit controllable semantic editing.
• 800VDC power architecture: high-voltage DC power supply increases overall energy efficiency by 15% to 20%.
• High-density clusters: a single cabin accommodates 32 B300 servers, meeting ten-thousand-card training and high-concurrency inference needs.
• Three-stage methodology: Planning & Preparation → Model Deployment → Iterative Optimization.

About This White Paper

• Document Title: Huazhi Future Lingyan Miaoyu Large Language Model Application White Paper
• Publishing Entity: Huazhi Future (Chongqing) Technology Co., Ltd.
• Version: Official Edition V1.0
• Release Date: July 2026
• Target Audience: Government agencies, enterprise executives, technical leaders, industry partners, and industry researchers

Executive Summary

Large language models are evolving from general conversational capabilities into enterprise-grade intelligent infrastructure, while AI agents are becoming the core execution units connecting models, knowledge, tools, and business processes. Centering on the full-stack path of "Computing Power — Scheduling — Model — Access — Application," this white paper systematically elaborates on the technical system of Huazhi Future's Lingyan Miaoyu Large Language Model, LingYu Architecture, Xingchen Distributed Intelligent Computing Center, Tianyu Intelligent Computing Platform, and ALRO Unified Model Portal, and combines nine key industry scenarios with typical practices to propose a three-stage implementation methodology for enterprise AI transformation.

Core Conclusions

• The focus of competition in enterprise-grade large models is shifting from a single parameter scale to system synergy across model capabilities, inference computing power, data governance, agent orchestration, and security operations.
• Huazhi Future builds end-to-end full-stack capabilities with the Xingchen Intelligent Computing Center, Tianyu Scheduling Platform, Lingyan Miaoyu Model, ALRO Model Portal, and Industry Agents.
• The LingYu Architecture achieves being "reusable, orchestrable, governable, and measurable" through its model layer, agent execution layer, tools & knowledge layer, computing power layer, and security layer.
• Industry implementation should cut in from high-value, quantifiable scenarios, sequentially completing planning & preparation, model deployment, and iterative optimization, ultimately forming an agent-driven business closed loop.

Industry Outlook: 2026 Enterprise-Grade Large Model Implementation Trends

In 2026, the global and domestic artificial intelligence industry is undergoing a profound structural transformation. Large language models have evolved from early general conversational and text-generation tools into enterprise-grade intelligent infrastructure. For enterprise users, the focus of AI application has shifted from simple "cost reduction and efficiency enhancement" in a single link to comprehensive "business remodeling and intelligent decision-making" across the entire value chain.

[Figure 1-1 | Enterprise-Grade Large Model Implementation Trend Diagram]

Against this backdrop, the deployment of enterprise-grade large language models is characterized by four major trends: the restructuring of computing power architectures, the widespread adoption of AI agents, the explosive growth of inference-side computing demand, and the rapidly increasing value of data as a strategic asset. Together, these trends form the broader context in which the Lingyan Miaoyu Large Language Model and the LingYu Architecture were developed.

1.1 Global and China AI Industry Trends

After experiencing the first stage of algorithm breakthroughs and model scaling, the AI industry in 2026 is entering a new cycle driven by "both computing power and applications." Large models are gradually transitioning from technical hotspots to deep embedding in enterprise production systems, with intelligent computing power becoming the core infrastructure supporting this transformation. Globally, cloud vendors, chip manufacturers, and model companies are competing in a new round around "training computing power + inference computing power + intelligent agent platforms". Meanwhile, China is systematically advancing the construction of intelligent computing power systems, the marketization of data elements, and the "AI+" action at the national level, providing dual guarantees of policy and infrastructure for the implementation of enterprise-grade large models.

1.1.1 Evolution of the Global Intelligent Computing Market

The global intelligent computing market is rapidly evolving from a "computing supply-driven" model to a "task-driven" model. Over the past decade, cloud computing and GPU clusters primarily served offline training tasks, where computing power was treated as a general resource. Entering the large model era, computing power has begun to be configured in a structured manner around specific model families, inference scenarios, and industry applications.

On one hand, training-side computing power remains highly concentrated in the hands of a few hyperscale cloud vendors and leading model companies, forming a dual-layer structure of "supercomputing centers + dedicated AI clusters" to support the continuous iteration of models with tens of billions or hundreds of billions of parameters. On the other hand, inference-side computing power is exhibiting stronger distributed characteristics: cloud-side inference clusters, edge inference nodes, and terminal-side acceleration chips together constitute an application-oriented intelligent computing network.

Meanwhile, the competitive dimensions of the global computing market are also changing. Price is no longer the sole variable; energy efficiency ratio, latency, model compatibility, and agent orchestration capabilities have become key indicators for enterprises evaluating computing services. "Computing-as-a-Service" and "Agent-as-a-Service" centered around large model inference are becoming new infrastructure formats, providing enterprises with integrated capabilities ranging from model invocation and task orchestration to application monitoring.

1.1.2 Construction of China's Intelligent Computing System

The construction of China's intelligent computing system exhibits a three-tier collaborative pattern of "national top-level planning + local computing clusters + enterprise intelligent computing platforms". Through national plans such as the "East-Data-West-Computing" project, the national integrated computing network, and intelligent computing infrastructure, traditional IDCs and cloud data centers are being upgraded into intelligent computing centers oriented towards AI tasks. Local governments are constructing multi-location intelligent computing clusters based on regional industrial layouts, forming a hierarchical structure of "national hub nodes + regional computing centers".

In this framework, computing power is no longer just a simple stack of "computer rooms + servers," but is deeply coupled with data resources, algorithm capabilities, and industry application platforms. Specialized computing power pools, inference acceleration clusters, and multi-modal training platforms for large models are gradually becoming part of new infrastructure, providing on-demand intelligent computing services for industries such as government affairs, finance, manufacturing, transportation, and healthcare.

At the same time, China is also accelerating exploration in computing power scheduling and computing power governance. Cross-region computing power scheduling, computing power resource catalogs, and the standardization of computing power services are transitioning intelligent computing from "isolated resources" to fundamental capabilities that are "orchestrable, transacting, and supervisable," providing a more stable and controllable foundation for the implementation of enterprise-grade large models.

1.1.3 Explosion of Inference-Side Computing Power and the Arrival of the Agent Era

If training computing power determines the "upper limit" of a model, inference computing power determines the "speed and scale" of enterprise applications. As large models move from laboratories to production environments, inference-side computing power is experiencing explosive growth: enterprises no longer care solely about the cost of a single inference, but have begun to focus on "how many agents are embedded in each business process, and how many requests each agent can respond to per second".

The explosion of inference-side computing power directly catalyzed the arrival of the agent era. Enterprises no longer simply "invoke a model," but instead build new application formats where "multiple agents collaborate to complete tasks": customer service agents, risk control agents, operation agents, city governance agents, clinical assistant agents... Behind these agents lies an orchestrable inference computing power network.

During this process, the forms of inference computing power are also changing. Cloud-side inference clusters are responsible for unified scheduling of high-concurrency and multi-scenario tasks; edge nodes undertake inference tasks for low-latency and local data-sensitive scenarios; and terminal-side acceleration chips realize lightweight agents on terminal devices. Inference computing power is no longer just about "computing fast," but must "compute correctly, steadily, and securely," while closely integrating with task boundaries, permission controls, and semantic scope management of agents.

1.1.4 Deepening Trend of Data Element Marketization

In the era of large models and agents, data has upgraded from "training material" to "tradable production factors". China's exploration of data element marketization is moving from institutional design to substantive implementation: links such as data rights confirmation, data pricing, data trading, data security, and cross-border data flow are gradually forming an institutional closed-loop.

For enterprises, this means data is no longer merely an internal asset, but can participate in broader circulation and collaboration through compliant methods. Industry data spaces, regional data platforms, and professional data service providers jointly constitute a new data ecosystem, providing high-quality, governable, and traceable data sources for large models.

At the same time, the marketization of data elements also forces enterprises to improve their data governance capabilities. Only by achieving data standardization, structuring, and traceability can enterprises maximize their value in knowledge base construction, RAG retrieval, and multi-modal data fusion. In the future, the competition among large models will not only lie in parameter scale and inference speed, but more importantly, in "who possesses higher-quality and more usable data elements," which will directly determine the performance upper limit of agents in industry scenarios.

1.2 National Policy System and Agent Strategy

While technology and industries are accelerating their evolution, China is providing directional guidance and institutional guarantees for the implementation of large models and agents through a systematic policy system. From the "AI+" action, computing power interconnectivity, and the two-way empowerment of energy and AI, to basic data systems and industry data space construction, national-level top-level design is pushing AI from a single-point technological innovation into a new stage of "infrastructure-ization, factorization, and platformization".

1.2.1 "AI+" Action and Agent Popularization Goals

The core of the "AI+" action lies not only in "applying AI in more industries," but in promoting the upgrade of AI from a tool-type technology to an "embedded intelligent infrastructure". Under this framework, agents are regarded as key carriers connecting large models with business scenarios: they must possess the abilities to understand natural language, invoke tools, and access knowledge bases, while also being able to assume measurable task roles in specific industry processes.

The goals proposed at the policy level include not only achieving "AI+" application coverage in key industries, but also emphasizing the formation of replicable and promotable agent application models. For example, promoting "urban safety agents" and "comprehensive law enforcement agents" in city governance; promoting "risk control agents" and "intelligent traffic/fund allocation agents" in the financial sector; and promoting "clinical assistant agents" and "medication risk agents" in the medical field.

This means that future policy support will not be restricted to model research and development and computing power construction, but will increasingly focus on specific directions such as "agent platform construction," "industry agent standards," and "agent safety and ethical norms," providing institutional "guardrails and runways" for enterprises to build and operate agents.

1.2.2 Computing Power Interconnectivity Action Plan

The essence of the computing power interconnectivity action plan is to transform dispersed computing resources into a "schedulable national intelligent computing network". In the era of large models and agents, a single data center can no longer meet cross-regional and cross-industry computing demands; computing power needs to possess the capability of "cross-regional transmission, on-demand allocation, and unified scheduling" just like electricity.

National policies are promoting the construction of cross-regional computing power scheduling platforms to break down "computing islands" and achieve efficient matching between Eastern computing demand and Western green computing supply. For enterprises, this means that in the future, they can call cross-regional and heterogeneous computing resources on-demand through a unified computing access portal (such as the Tianyu Intelligent Computing Platform), thereby significantly reducing the physical cost of large model training and large-scale concurrent inference, and enhancing the elasticity and robustness of computing usage.

1.2.3 Two-Way Empowerment Mechanism between Energy and AI

The synergistic development of energy and AI is becoming an important direction for new infrastructure construction. On the one hand, large model training and intelligent computing center operations consume a massive amount of electricity, raising higher requirements for green energy supply, liquid cooling heat dissipation, and intelligent microgrid technologies. On the other hand, AI algorithms are deeply empowering power systems, feeding back into the efficiency improvement of the energy industry through means such as smart grid scheduling, new energy power generation prediction, and comprehensive energy management.

Against the backdrop of the "dual carbon" goals, policies encourage intelligent computing centers to prioritize the consumption of green electricity and promote the low-carbon transformation of computing infrastructure through PUE (Power Usage Effectiveness) index management. Huazhi Future's practice of the "green computing power system" through the Xingchen Distributed Intelligent Computing Center is precisely a concrete response to the national energy-AI synergy policy, achieving a balance between high performance and low energy consumption in intelligent computing centers.

1.2.4 Basic Data Systems and Industry Data Spaces

The gradual improvement of basic data systems has paved the way for enterprise data sharing and the compliance of large model training. The implementation of national policies regarding data property rights, circulation and trading, and benefit distribution provides enterprises with clear legal basis and operational norms when conducting data assetization transformations.

At the same time, the construction of "industry data spaces" encourages upstream and downstream supply chains as well as governments and enterprises to break down barriers, achieving cross-institutional joint modeling and knowledge sharing under the premise of ensuring data security. For the Lingyan Miaoyu large language model and its industry applications, the construction of data spaces provides a rich and compliant industry corpus, enabling large models to better understand the professional knowledge and deep business logic of specific industries.

1.3 Technological Evolution: From Centralization to Cloud-Edge-Device Synergy

With the continuous deepening of large model and agent applications, the architecture that solely relies on centralized cloud clusters can no longer meet the demands of all business scenarios. Practical challenges such as low latency, data privacy, localized interaction, and high concurrency are driving the AI technology architecture to profoundly evolve towards "cloud-edge-device synergy".

1.3.1 Core Logic of Cloud-Edge-Device Collaborative Architecture

The essence of the cloud-edge-device collaborative architecture is to reasonably stratify and dynamically deploy the capabilities of large models and agents based on task computational complexity, latency sensitivity, data privacy levels, and bandwidth costs.

Cloud Side (Core Brain and Training Base): Deploys ultra-large-scale foundation models and industry general models, bearing full-data training, complex multi-modal reasoning, cross-business agent orchestration, and large-scale knowledge base retrieval. As the "central brain" of the entire system, the cloud side handles highly complex, long-chain comprehensive tasks.

Edge Side (Regional Hub and Local Inference): Deploys medium-scale industry vertical models or quantized models, combined with edge intelligent computing nodes, to handle localized, low-latency, and semi-offline businesses for specific regions or industries. The edge side can effectively filter sensitive data, reduce cloud bandwidth pressure, and ensure business continuity under unstable network conditions.

Device Side (Ubiquitous Access and Lightweight Interaction): Deploys lightweight models and device-side agents on smart terminals, IoT devices, and mobile office equipment, achieving millisecond-level local response, keeping private data on the device, and enabling basic AI interaction in network-free or weak-network environments.

This multi-level collaborative architecture not only optimizes computing power costs, but also solves data privacy protection and compliance bottlenecks from the physical architecture level.

1.3.2 Heterogeneous Computing Power Management and Dynamic Scheduling

[Figure 1-2 | Cloud-Edge-Device Collaborative Architecture]

In the practice of cloud-edge-device synergy, the management and dynamic scheduling of heterogeneous computing power is one of the biggest pain points in technology implementation. Differences exist in chip architectures, instruction sets, memory capacities, and communication protocols among different vendors. How to achieve "write once, adapt to multiple ends" and "on-demand scheduling and intelligent slicing of computing resources" has become a key criterion for measuring the maturity of an enterprise's technical foundation.

Through the Tianyu Intelligent Computing Platform, Huazhi Future has built a heterogeneous computing power management and scheduling mechanism across clouds, edges, and devices. It can automatically distribute inference tasks to optimal physical nodes based on the real-time load, computing cost, and response latency during agent operation, maximizing the efficiency of computing resources.

As large models and agents move into the deep water zone of business, relying solely on centralized cloud inference can no longer cover all scenarios. On the one hand, scenarios such as government affairs, finance, and industry have higher requirements for data security and latency, and some tasks need to complete inference and decision-making locally or at the edge. On the other hand, the intelligence level of terminal devices is continuously improving. Vehicular terminals, industrial gateways, cameras, and mobile devices all possess certain computing and storage capabilities, laying the foundation for hosting lightweight models or specific task agents. Consequently, the cloud-edge-device collaborative architecture has become the mainstream technological path for the implementation of enterprise-grade large models.

On the cloud side, foundation large models such as Lingyan Miaoyu undertake "heavyweight tasks" such as general inference, complex multi-modal understanding, and cross-industry knowledge fusion, providing unified capabilities to various business systems through APIs and Agent platforms. On the edge side, lightweight models or agents tailored for specific scenarios are deployed, such as video inspection Agents in city governance, anti-three-violations visual models in park security, and railway network anomaly detection models in transportation systems. These models can complete real-time inference at edge nodes and report summary results or risk events to the cloud. On the device side, they are mostly agents oriented toward interaction and experience, such as mobile assistants, vehicle voice assistants, and companion learning Agents in educational scenarios, which achieve low-latency responses and personalized memory through a combination of local caching and cloud inference.

The core of cloud-edge-device synergy is not just "multi-layer deployment," but "task stratification and semantic coordination". The cloud-side large model is responsible for complex reasoning and knowledge fusion, edge-side models for real-time perception and local decision-making, and device-side agents for interaction and personalized experience, with the three collaborating through unified task orchestration and semantic protocols. This architecture enables Lingyan Miaoyu to build a complete intelligent application closed-loop in scenarios such as city safety, smart education, and smart healthcare through the approach of "cloud brain + edge perception + device interaction".

1.3.3 Wide-Area RDMA and Cross-Domain Scheduling Technology

To support distributed intelligent computing centers and cloud-edge-device collaborative architectures, physical distribution of computing resources alone is not enough; more crucially, it requires achieving efficient data and computing flows across wide-area network environments. Traditional TCP/IP networks easily become bottlenecks in cross-regional and large-scale cluster scenarios—especially during large model training and multi-modal inference where parameter synchronization, feature exchange, and task migration demand higher network latency and bandwidth. The maturation of wide-area RDMA technology enables computing networks across data centers and cities to operate under lower latency and higher throughput conditions.

The core advantage of RDMA lies in "bypassing the kernel for direct memory access," which reduces data copying and context switching within the network stack, thereby significantly lowering latency and CPU overhead. When RDMA capabilities extend from local area networks to wide-area networks, distributed training, cross-domain inference, and remote disaster recovery can all run on a unified, high-performance network. For enterprise-grade large models, this means parameter synchronization, model hot-standby, and task migration can be carried out across multiple intelligent computing centers without severe performance degradation caused by network bottlenecks.

Cross-domain scheduling technology acts as the "computing orchestration layer" running atop the high-performance network. It comprehensively evaluates factors such as task type, data location, computing load, energy consumption costs, and compliance requirements to determine which region, cluster, or type of computing resource should execute a specific training or inference task. For large model platforms like Lingyan Miaoyu that serve multi-industry and multi-regional clients, cross-domain scheduling can dispatch critical tasks to highly reliable, high-performance core intelligent computing centers, while routing low-priority or offline tasks to more cost-effective regional resource pools based on business priorities and service level agreements (SLAs), striking an optimal balance among performance, cost, and compliance.

1.3.4 Evolution of Intelligent Computing Services from Resource Supply to Task-Based Services

As the technological architecture shifts from centralization to cloud-edge-device synergy, the business and operational models of computing services are also undergoing profound changes. In the past, enterprises purchased "servers, bandwidth, and rack space," which later evolved into "GPU instances, storage capacity, and network traffic."

In the era of large models and agents, enterprises care more about "whether a specific intelligent task can be completed stably and predictably"—such as "how many tens of thousands of multi-modal Q&A sessions are processed daily," "how many channels of video inspection and risk detection are completed hourly," and "how many intelligent agent workflows are executed monthly." Consequently, intelligent computing services are transitioning from "resource supply" to "task-based services."

The core of task-based services is "using tasks as the unit of measurement" rather than relying solely on GPU-hours or bandwidth traffic. For the Lingyan Miaoyu platform, this means it can provide "scenario-based computing packages" tailored to clients across government affairs, finance, industry, education, and other sectors—such as "city governance intelligent inspection task packages," "financial risk control multi-agent collaboration task packages," and "smart education tutoring and companion learning task packages." The bottom layer is supported by distributed intelligent computing centers and cloud-edge-device collaborative architectures, while the upper layer bills and guarantees service based on metrics like task completion volume, service quality, and response latency.

This evolution also forces computing platforms to achieve more fine-grained task orchestration and monitoring capabilities from a technical perspective. The platform must identify which task type an invocation belongs to, which agent initiated it, what data and models are involved, and how much computing and network resources are consumed, mapping these details onto understandable business metrics. For enterprise users, they no longer need to worry about how many GPUs are specifically allocated; instead, they focus on "whether a given agent runs stably," "whether a business workflow completes as expected," and "whether overall AI investments align with business returns." In this model, Lingyan Miaoyu is not just a large model, but an intelligent computing service platform oriented toward tasks and scenarios, carrying the complete value chain from model inference to agent execution.

Huazhi Future: Full-Stack Capability System of Computing Power + Algorithm + Application

2.1 Group Positioning and Development Strategy

2.1.1 Introduction to Huazhi Industrial Development Group

Founded in 2017, Huazhi Industrial Development Group is one of the few domestic technology enterprises capable of simultaneously covering intelligent computing infrastructure construction, artificial intelligence large model research and development, industry agent application implementation, and green energy collaborative systems. During the critical period when large models transition from technological breakthroughs to industrial scale implementation, the group adheres to the development strategy of "computing power as the foundation, model as the core, agent as the carrier, and industry scenario as the traction," continuously promoting the deep integration of artificial intelligence in government affairs, city governance, public safety, finance, healthcare, education, and other fields. With the completion of the group's NASDAQ acquisition and listing in 2026, Huazhi Future has officially entered a stage of globalized development, forming a multi-dimensional industrial matrix covering intelligent computing centers, server supply chains, AI model R&D, industry agent platforms, and green energy collaboration, providing full-link support for enterprise-level AI transformation.

2.1.2 Full-Stack AI Capability Layout

Against the backdrop of enterprises' continuously increasing demands for large model implementation capabilities, Huazhi Future has built a full-stack AI capability system covering "computing power — scheduling — model — application — agent". This system takes the Xingchen Distributed Intelligent Computing Center as the computing power foundation, achieves cross-region and cross-center computing power aggregation and intelligent scheduling through the Tianyu Intelligent Computing Platform; uses the Lingyan Miaoyu large language model as the core algorithmic capability to provide foundational capacities such as multi-modal understanding, Chinese enhancement, and controllable semantic editing; takes the ALRO unified model access portal as the application layer entrance to achieve aggregation, distribution, and format conversion of domestic and foreign models; and transforms model capabilities into executable task capabilities through the industry agent system, allowing AI to truly be embedded into industry processes and forming a replicable and scalable intelligent upgrade path.

2.1.3 Development History and Ecosystem Expansion

The development history of Huazhi Future exhibits a continuous leap from technological research and development to industrial ecosystem. In its early stage, the group focused on core technology research such as machine learning, natural language processing, and computer vision, building a self-developed algorithm system and computing power scheduling capability. Subsequently, it entered the exploratory period of industry application, achieving multiple intelligent agent product implementations in scenarios such as city governance, intelligent manufacturing, and government services. Starting in 2024, the group accelerated ecosystem expansion by building edge intelligent computing centers, establishing postdoctoral workstations, co-building practice bases with multiple universities, and releasing multiple self-developed models such as the Lingyan Miaoyu large language model, the AI Smart Clothing large model, and the Youyutong large model. From 2025 to 2026, the group completed its NASDAQ listing, formed a green energy collaborative system, launched the Xingchen Distributed Intelligent Computing Center and the ALRO unified model access portal, and promoted the large-scale implementation of intelligent agents in fields such as government affairs, finance, and healthcare, further expanding its ecological layout.

2.1.4 Qualifications, Honors, and Research System

Huazhi Future has built a research system covering directions such as intelligent computing infrastructure, quantum security, AI large models, industry agents, and green energy collaboration, and has obtained multiple qualifications including national high-tech enterprise, municipal postdoctoral scientific research workstation, AI + Financial Technology Demonstration Base, and Digital RMB Innovative Application Cooperation Unit. The group holds multiple invention patents and software copyrights, and has established joint laboratories and practice bases with over a hundred domestic and foreign universities, forming an integrated "research — product — application" innovation system that provides long-term technical support for the continuous iteration of the Lingyan Miaoyu large language model and the large-scale implementation of industry agents.

2.2 Huazhi Future Full-Stack Technology Architecture

[Figure 2-1 | Huazhi Future Full-Stack Capability System]

As large models transition from technological breakthroughs to enterprise-grade scale implementation, the degree of coordination among the underlying computing power system, model architecture, scheduling platform, and industry agents determines whether enterprises can truly build stable, replicable, and sustainable intelligent capabilities. Relying on the group's deep layout in intelligent computing infrastructure, AI large model research and development, agent applications, and green energy collaboration, Huazhi Future has formed a full-stack technology architecture covering "computing power — scheduling — model — application — agent". This architecture not only supports the continuous iteration of the Lingyan Miaoyu large language model, but also supports the scaled implementation of industry agents in scenarios such as government affairs, city governance, public safety, finance, and healthcare, enabling artificial intelligence to truly be embedded into business processes and become the underlying capability for enterprise digital transformation.

2.2.1 Computing Power Layer: Xingchen Distributed Intelligent Computing Center

The Xingchen Distributed Intelligent Computing Center is a new generation of intelligent computing infrastructure built by Huazhi Future for large model training and inference. It adopts the design concept of "containerized deployment, modular expansion, and green energy collaboration," which is significantly different from the centralized architecture of traditional IDCs. As the parameter scale of large models continues to grow, inference concurrency continues to rise, and the demand for low latency and high reliability in industry scenarios becomes increasingly urgent, the limitations of traditional data centers in energy consumption, scalability, and deployment cycles are gradually emerging. Through the high integration of standardized computing power cabins, cooling source cabins, and power cabins, the Xingchen Intelligent Computing Center achieves rapid delivery within 90 days and supports an on-demand expandable "1+N" deployment mode, enabling enterprises to obtain scalable intelligent computing capabilities at a lower cost and within a shorter cycle.

In terms of energy efficiency, the Xingchen Intelligent Computing Center achieves a PUE as low as 1.15 through cold plate liquid cooling technology, intelligent temperature control systems, and green energy collaboration, with the proportion of green electricity reaching up to 80%, significantly reducing the long-term cost of large model training and inference. Meanwhile, the center supports high-density GPU clusters, with a single cabin capable of accommodating 32 B300 servers, meeting the needs of ten-thousand-card training tasks and high-concurrency inference tasks, providing a stable computing power foundation for the continuous iteration of the Lingyan Miaoyu large language model.

2.2.2 Scheduling Layer: Tianyu Intelligent Computing Platform

Against the backdrop of the coexistence of multi-center, multi-regional, and multi-type computing power resources, how to achieve unified aggregation, intelligent scheduling, and task orchestration of computing power has become a key challenge for enterprise-grade AI implementation. The Tianyu Intelligent Computing Platform is a third-party computing power scheduling and trading platform built precisely against this background. Through a unified computing power identification system, it aggregates the Xingchen Intelligent Computing Center, enterprise-self-built computing power, edge nodes, and third-party intelligent computing centers to form a nationwide computing power network.

The Tianyu platform can automatically allocate resources based on task types, achieving high-density cluster scheduling for training tasks, low-latency node scheduling for inference tasks, and multi-node collaborative scheduling for agent tasks, thereby increasing resource utilization to over 70%. In terms of computing power trading, the platform supports multiple models such as GPU leasing, Token billing, and intelligent agent task billing, enabling enterprises to use computing power resources in a more flexible and economical manner. Meanwhile, the platform adapts to wide-area RDMA high-speed networks to achieve low-latency scheduling across regions and centers, providing stable, efficient, and elastic computing power support for the Lingyan Miaoyu large model and industry agents.

2.2.3 Model Layer: Lingyan Miaoyu Large Language Model

The Lingyan Miaoyu large model is the core achievement of Huazhi Future in the field of large models. Built upon the self-developed LingYu Architecture, it possesses capabilities such as Chinese enhancement, multi-modal understanding, and controllable semantic editing. In terms of Chinese semantic understanding, through large-scale Chinese corpus training, Lingyan Miaoyu possesses robust long-text processing capabilities, structured output capabilities, and instruction-following capabilities, adapting to Chinese scenarios such as government affairs, finance, and healthcare. In terms of multi-modal capabilities, the model supports the unified processing of text, images, video, and audio, enabling it to achieve multi-modal understanding and task execution in scenarios such as city governance, public safety, and medical imaging.

The Lingyan Miaoyu model adopts the ScopeEdit controllable semantic editing system, achieving stable writing and cross-modal consistent generalization through the collaboration of local absorption branches and shared generalization branches, thereby avoiding the semantic pollution and over-generalization issues that traditional models exhibit in knowledge editing. The model provides a 9B parameter size, tailored respectively for edge inference and complex tasks, enabling enterprises to flexibly select model capabilities according to scenario requirements.

2.2.4 Application Layer: ALRO Unified Model Access Portal

During the enterprise-grade AI implementation process, the complexity of model invocation, format compatibility, and security often become key factors affecting efficiency. The ALRO Unified Model Access Portal is an application layer entrance built precisely to solve this problem. By aggregating over 200 mainstream models domestically and abroad, it achieves unified model invocation, unified formats, and unified permission management, enabling enterprises to utilize large model capabilities with a lower threshold and higher efficiency.

ALRO supports format conversion for models such as OpenAI, Claude, Gemini, DeepSeek, Qwen, and GLM, allowing enterprises to "call all models with one set of code". Meanwhile, the portal provides capabilities such as official direct connection, intelligent routing, and account pool management, ensuring that model capabilities are uncompromised and unwatered. In terms of enterprise-grade security, ALRO provides Key management, member permission assignment, consumption auditing, and independent encrypted channels, endowing the model invocation process with higher security and controllability.

2.2.5 Industry Agent System

In the process of large models transitioning from "capable of generation" to "capable of execution," agents have become key carriers connecting model capabilities to industry scenarios. Huazhi Future has built an agent system covering multiple industries such as government affairs, city governance, public safety, healthcare, finance, and education, enabling the Lingyan Miaoyu large model to undertake measurable task roles in specific scenarios.

In the government affairs sector, agents enable intelligent customer service, policy Q&A, service guidance, and document generation, making government services more efficient and convenient. In the city governance sector, agents can complete video inspection, illegal stall identification, illegal construction intelligent recognition, and comprehensive law enforcement assistance, making urban management more refined and intelligent. In the public safety sector, agents can achieve key area deployment, abnormal behavior identification, and pedestrian density monitoring, providing real-time safeguards for urban safety. In the medical sector, agents can accomplish clinical diagnosis assistance, intelligent triage, medication risk identification, and medical record structuring, making medical services more precise and efficient. In the financial sector, agents can undertake tasks such as intelligent traffic generation, intelligent auditing, and intelligent risk control, making financial operations safer and smarter. In the education sector, agents can achieve intelligent lesson preparation, intelligent companion learning, and personalized teaching, making educational experiences more intelligent and personalized.

The industry agent system enables Lingyan Miaoyu to transition from "model capability" to "executable task capability," serving as the core engine for enterprise intelligent upgrading.

2.3 Technology Advantage System

During the process of large models transitioning from pilots to scaled implementation, the advancement and stability of the underlying technology system determine whether enterprises can truly build long-term sustainable intelligent capabilities. Relying on the group's deep layout in green energy, quantum security, intelligent computing infrastructure, power systems, and scientific research systems, Huazhi Future has formed a future-oriented technology advantage system. These advantages not only support the continuous iteration of the Lingyan Miaoyu large model, but also provide high-reliability, high-security, and high-energy-efficiency intelligent foundations for industries such as government affairs, city governance, public safety, finance, and healthcare, enabling enterprises to acquire more stable, controllable, and competitive technical capabilities in the large model era.

2.3.1 Green Energy and ESG System

In the large model era, computing power costs and energy consumption costs have become key considerations for enterprises deploying AI. As model scales continue to expand and inference concurrency continuously rises, the pressures of traditional data centers in terms of energy consumption, cost, and sustainability are increasingly manifesting. By constructing a green energy collaborative system, Huazhi Future achieves two-way empowerment between computing power and energy, endowing the intelligent computing center with higher energy efficiency, lower costs, and stronger sustainability. Relying on the group's green energy subsidiary, the Xingchen Distributed Intelligent Computing Center achieves a green electricity proportion of up to 80% and a PUE as low as 1.15, significantly reducing the long-term costs of large model training and inference. Meanwhile, through the integrated collaboration of wind power, photovoltaics, and energy storage, the intelligent computing center can maintain stable power supply under different regions and climatic conditions, providing a solid guarantee for the continuous operation of large models. In terms of the ESG system, Huazhi Future has built a green system covering computing power, models, and applications. From liquid cooling technology, intelligent temperature control, to model inference energy consumption optimization, and further to energy consumption management of agent task scheduling, it enables enterprises to achieve green development goals while undergoing intelligent upgrading.

2.3.2 800VDC Forward-Looking Technology

Within intelligent computing infrastructure, the advancement of the power system directly determines the stability and energy efficiency of computing power. As large model training and inference place higher demands on power stability, energy efficiency ratios, and costs, traditional AC power supply systems gradually reveal bottlenecks in high-density cluster scenarios. Huazhi Future adopts 800VDC forward-looking technology in the Xingchen Distributed Intelligent Computing Center to build a new generation of efficient power supply systems, endowing the intelligent computing center with higher energy efficiency, lower loss, and stronger stability. High-voltage direct current power supply can significantly reduce transmission losses, increasing overall energy efficiency by 15% to 20%, and forms a synergy with cold plate liquid cooling technology to maintain stable power supply for high-density GPU clusters during large model training, preventing training interruptions or performance degradation caused by power fluctuations. Meanwhile, 800VDC technology adapts to new energy DC input, energy storage system DC output, and smart grid DC collaboration, providing forward-looking capabilities for future green intelligent computing centers and enabling enterprises to obtain higher energy efficiency and lower costs during intelligent upgrading.

2.3.3 Independent R&D and Post Doctoral Scientific Research System

Against the backdrop of the rapid evolution of large models and agents, sustained scientific research capability serves as the key for enterprises to maintain technological leadership. Huazhi Future has built a scientific research system covering algorithms, models, intelligent computing, energy, and industrial agents, and set up a municipal level post doctoral research workstation to form an integrated innovation chain of “research products applications”.

The post doctoral workstation focuses on directions including large model training and inference optimization, multimodal semantic fusion, agent task planning, quantum secure data systems, and green energy coordination. It enables Lingyan Miaoyu large language model to achieve continuous iteration in underlying architecture, semantic capability, and industry adaptability.

The Group possesses full link independent R&D capabilities ranging from intelligent computing centers and scheduling platforms to large model architectures and agent platforms. This allows stable technological evolution amid fast changing AI technology cycles. Meanwhile, Huazhi Future has established joint laboratories and practical bases with over one hundred domestic and overseas universities to build a global scientific research network, supporting continuous innovation of the Lingyan Miaoyu model and industrial agents across broader academic and industrial ecosystems.

2.4 Distributed Quantum Secure Data Cloud: The Sovereign Security Base for Enterprise Grade Agents

In highly sensitive industry scenarios such as government affairs, finance, and healthcare, data security represents not merely compliance requirements, but also a systematic capability boundary governing which data agents may access, which tools they may invoke, and which tasks they may execute. As multimodal data expands and agent task chains grow more complex, enterprises must simultaneously address long term data confidentiality, cross domain collaboration, identity authorization, and execution auditing.

Huazhi Future positions the Distributed Quantum Secure Data Cloud as the sovereign security base for enterprise grade agents. Drawing on the Group’s research layout in quantum computing, quantum perception, and quantum machine learning, it delivers data protection, controlled execution, and cross domain collaboration solutions tailored for the Lingyan Miaoyu large language model. Sovereign security emphasizes enterprise governance over data residency locations, access permissions, key lifecycles, and execution evidence. Actual functionalities shall be subject to project deployment inventories, test results, and acceptance scopes.

2.4.1 Quantum Secure Algorithms and Anti Quantum Attack Protection

The cryptographic system of the data cloud distinguishes the roles of Post Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Quantum Random Number Generation (QRNG). PQC refers to cryptographic algorithms designed against quantum computing threats; QKD is used for key distribution where corresponding equipment and links are available; QRNG acts as a random number source. The three must cooperate with identity authentication, key management, and endpoint protection. End to end anti quantum attack capability cannot be inferred merely from the name of “quantum related” technologies.

For data transmission, model invocation, agent task execution, and multimodal data storage, the data cloud solution implements controls including transmission and storage encryption, invocation identity authentication, task level key isolation, key rotation and revocation, as well as ciphertext and metadata integrity verification. For eligible projects, Post Quantum Cryptography migration and multi node QKD key coordination may be evaluated, with algorithm parameters, protocol implementations, device security, and fault handling mechanisms verified item by item.

Open quantum system theories and non Hermitian dissipative regulation can serve as relevant research directions, yet shall not be equated with validated anti quantum cryptographic algorithms. Conclusions on anti quantum protection must correlate with explicit algorithms, threat models, implementation versions, and test evidence. Key distribution security does not substitute for model content security or agent permission control.

2.4.2 Quantum Enhanced Perception and Agent Link Auditing

Auditability for agent task chains shall be built upon unified task identifiers, invocation identities, input output digests, tool authorizations, human approvals, and execution receipt records. Via integrity verification, trusted time stamp recording, and controlled log storage, it supports task tracing, anomaly localization, and post hoc review. Monitoring rules are configured for unauthorized invocation, link tampering, and abnormal collaborative behaviors.

Quantum enhanced imaging, quantum radar, and quantum recognition relate to specific physical objects and equipment. Their research outcomes cannot be directly extrapolated to software task link security capabilities. Task link perturbation detection, quantum feature fingerprints for agent behaviors, and multi agent quantum consistency verification may be explored within quantum enhanced perception research. Prior to product release, measurement objects, implementation mechanisms, false positive / false negative rates, and applicable boundaries must be clearly defined.

The auditing system pursues the construction objectives of “recordable, traceable, and verifiable”. For tamper resistance or anti forgery capabilities, protection mechanisms and attack assumptions shall be specified. The phrase “quantum grade security” or “unforgeable” shall not replace reproducible verification results.

2.4.3 Quantum Classical Hybrid Secure Computing and Industrial Inference

The data cloud takes classical secure computing and controlled inference environments as its foundation, and evaluates the application of quantum enhanced solvers in specific tasks. Data residency, access control, computational isolation, and output review each undertake security responsibilities. Whether quantum computing improves solution quality, inference stability, or efficiency shall be confirmed via comparative testing against classical baselines and shall not be deduced directly from computational architecture names.

In industrial applications, pilots for quantum classical hybrid computing may be carried out around financial risk control optimization, medical auxiliary analysis, government affairs knowledge retrieval, and multimodal feature processing. Sensitive data and intermediate results must comply with explicit data domain and authorization rules. Where external quantum computing resources are adopted, data transmission paths, task encoding processes, and result return paths shall first be reviewed; “data staying within domain” shall not be claimed outright.

Deployment conditions, algorithm mechanisms, and evaluation indicators shall be specified respectively for local quantum enhanced inference, quantum secure solving, and quantum secure feature encoding. Outputs from finance and medical scenarios still require review by business or specialized personnel. Computational enhancement does not substitute for risk control, clinical judgment, or project acceptance.

2.4.4 Distributed Architecture and Secure Controllable Execution Network

Organized around data domains, model service nodes, and agent execution nodes, the distributed architecture of the data cloud integrates the Xingchen Distributed Intelligent Computing Center and Tianyu Intelligent Computing Scheduling Platform. It supports the selection of execution locations according to data sensitivity levels, task permissions, computing resource status, and latency requirements. Near model inference and cross domain task scheduling shall jointly abide by principles of data residency and least privilege.

Requirements for “data staying within domain” must explicitly cover raw data, retrieval indexes, caches, logs, backups, and sensitive derived information. Cross domain collaboration requires verification of invoker identity, authorized purposes, and output scopes. Multi site disaster recovery shall restore data, task states, keys, and permission policies simultaneously. Recovery time objectives, recovery point objectives, and data consistency shall be verified through drills.

Where conditions permit, mechanisms such as Post Quantum Cryptography or QKD may be incorporated into communication and key protection schemes to conduct verification of cross domain quantum secure collaboration and disaster recovery adaptation. Security controls shall cover both the control plane and the data plane. Bypassing original protection through scheduling switching or node migration shall be avoided.

2.4.5 Industrial Security Compliance and Measurable Evaluation

Targeting finance, healthcare, and government affairs scenarios, the data cloud identifies applicable data security and cryptographic requirements based on specific businesses, data categories, system grades, and deployment modes. It forms control checklists, responsibility divisions, and verification evidence. Industrial adaptation must be implemented down to data classification and grading, identity authorization, encryption protection, log auditing, and emergency response. General architectural descriptions shall not substitute for compliance assessment.

The quantum security grade evaluation framework may serve as a direction for internal assessment system construction, used to record algorithm and key configurations, link protection coverage, audit coverage for tasks, anomaly identification effectiveness, and disaster recovery capabilities. The framework shall define indicator specifications, test methodologies, statistical calibers, and review procedures. Internal standards are not equivalent to national or industrial standards and do not constitute third party certification.

Exploratory capabilities such as quantum enhanced auditing shall be incorporated into projects only after special validation. When releasing conclusions such as “conforms to specifications”, “certified”, or “participates in standard setting”, concrete specification names & versions, applicable scopes, plus corresponding evaluation reports, certificates, or proof of standard participation must be provided simultaneously. Continuous testing and operational reviews shall foster measurable, verifiable, and continuously improving secure AI capabilities.

Lingyan Miaoyu Large Language Model: Technical System and Capability Description

As large models evolve from general purpose capabilities toward deep industrial applications, model architecture, data systems, training methodologies, and agent execution capabilities become critical factors determining model upper limits. Built upon Huazhi Future’s self developed LingYu Architecture, the Lingyan Miaoyu large language model forms systematic capabilities in multimodal understanding, Chinese semantic enhancement, controllable semantic editing, and agent task execution. This enables the model to undertake measurable task roles in government affairs, urban governance, public safety, finance, healthcare, education and other scenarios.

By constructing high quality data systems, sound human annotation systems, synthetic data generation systems, and dialogue context fusion systems, Lingyan Miaoyu establishes stable, replicable, and sustainable technical capabilities in semantic understanding, task execution, cross modal reasoning, and industrial knowledge fusion.

3.1 Fundamental Model Architecture (LingYu Architecture)

[Figure 3-1 | LingYu Architecture]

3.1.1 Overall Structure of LingYu Architecture

LingYu Architecture represents Huazhi Future’s core innovation in the underlying structure of large language models, addressing critical challenges including multimodal fusion, semantic consistency, stable knowledge editing, and agent task execution. The architecture comprises a multimodal input layer, a semantic fusion layer, a controllable knowledge editing layer, and an agent execution layer, forming a complete pipeline from input and reasoning through to task execution. At the multimodal input layer, a unified projector maps text, images, video, and audio into the same semantic space, enabling deep cross modal fusion in subsequent layers. At the semantic fusion layer, multi layer Transformer and FFN Key Value memory structures achieve deep semantic alignment, producing consistent semantic representations for textual and visual information. At the controllable knowledge editing layer, the ScopeEdit technology supports stable knowledge injection and consistent cross modal generalization, allowing knowledge updates without impairing pre existing model capabilities. At the agent execution layer, the model gains tool calling, task planning, and semantic scope control capabilities, equipping it to fulfill executable task oriented roles in industry specific scenarios.

3.1.2 LingYu Architecture Model Hierarchy

The LingYu Architecture model system consists of base models, enhanced models, and industry specific models, forming a multi level capability stack ranging from general purpose performance to domain specialized competencies.

Base models: Deliver general purpose capabilities for text comprehension, image recognition, video analysis, and audio processing.

Enhanced models: Achieve stronger reasoning performance via Chinese language semantic enhancement, multimodal fusion, and controllable semantic editing.

Industry specific models: Acquire professional competencies for government affairs, urban governance, public security, medical care, finance and other domains through domain specific data training and agent task reinforcement.

Lingyan Miaoyu provides a 9B parameter specification adaptable to edge side inference and complex task scenarios, enabling enterprises to flexibly select model capabilities aligned with scenario requirements. Thanks to the unified design of LingYu Architecture, the model family maintains consistent semantic performance and task execution performance across varying scales and use cases.

3.1.3 Multimodal Capability Expansion (Text / Image / Video / Audio)

In real world industry scenarios, single modality processing frequently cannot satisfy complex task requirements. Powered by LingYu Architecture, Lingyan Miaoyu processes text, images, video, and audio in a unified manner, supporting cross modal reasoning and task execution for multimodal workflows.

Text: Robust Chinese language comprehension, long document processing, and structured output capabilities tailored for government, finance, medical and other Chinese language scenarios.

Image: Scene recognition, OCR, object detection, and image semantic understanding for urban governance and public security workflows.

Video: Video frame analysis, temporal reasoning, and event recognition to support patrol oriented tasks in urban governance and public security settings.

Audio: Speech recognition, speech synthesis, and speech emotion analysis to enable natural language interaction in government service, educational, and medical scenarios.

3.1.4 Agentic Execution Layer

As large language models shift from content generation toward real world task execution, agents become the critical bridge connecting model capabilities with industry specific scenarios. The Agentic Execution Layer of Lingyan Miaoyu implements tool invocation, task planning, semantic boundary control, and multi agent collaboration, empowering the model to undertake measurable task oriented roles within industry contexts.

Tool invocation: Retrieval tools, computational utilities, and business oriented tools are invoked on demand to complete multi step complex task pipelines.

Task planning: Automatically generates procedural steps aligned with task objectives for stable execution of multi stage workflows.

Semantic scope control: Restricts semantic boundaries according to task contexts to preserve semantic consistency and operational safety during agent execution.

Multi agent collaboration: Cooperates with peer agents to accomplish composite assignments in urban governance, public security, medical care and other domains.

3.2 Data System and Training Methodology

3.2.1 High Quality Chinese Language Corpus Resources

For Chinese language scenarios, corpus quality directly determines model semantic comprehension and task execution performance. Lingyan Miaoyu has assembled a high quality Chinese language corpus repository covering government affairs, finance, medical care, education, urban governance and other sectors. Data cleansing, structuring, semantic enhancement, and multimodal fusion workflows equip the model with powerful Chinese language semantic capabilities. The corpus includes policy documents, laws and regulations, financial reports, clinical guidelines, urban governance specifications and other high value texts to build stable semantic understanding performance for industry specific applications.

3.2.2 In House Manual Annotation System (Point wise Scoring & Pair wise Comparison)

To boost dialogue quality, task execution performance, and semantic consistency, Huazhi Future has built an internal manual annotation system featuring point wise scoring, pair wise comparison, and multi dimensional evaluation.

Point wise scoring: Assesses answer accuracy, logical coherence, and safety.

Pair wise comparison: Evaluates relative quality between alternative outputs to solidify stable preference patterns during training.

Multi dimensional evaluation: Measures model performance across diverse scenarios to strengthen task execution competencies for industry specific workflows.

3.2.3 Synthetic Data Generation (Error Injection & Response Variation)

Synthetic data substantially improves model robustness and generalization capacity in large model training. Lingyan Miaoyu generates synthetic datasets via error injection, response variation, and semantic perturbation.

Error injection: Simulates erroneous user inputs to enhance fault tolerance in real world deployments.

Response variation: Produces stylistically and logically diverse outputs to strengthen model expressive capabilities.

Semantic perturbation: Simulates complex contextual conditions to sustain semantic consistency in multimodal contexts.

3.2.4 Dialogue Context Fusion (Critic Data Construction)

In dialogue oriented scenarios, contextual comprehension shapes task execution quality. Lingyan Miaoyu constructs critic datasets through dialogue context fusion workflows to strengthen contextual reasoning in conversational settings. Critic data resources assess logical consistency, coherence, and task execution quality in model dialogues, improving performance for government service, urban governance, and medical consultation agent workflows.

3.3 Instruction Tuning and Model Evaluation

As large language models progress from general purpose generation to industry specific task execution, instruction tuning and model evaluation constitute critical determinants of practical performance ceilings. Within its instruction tuning framework, Lingyan Miaoyu adopts pairwise preference tuning, RAG enhancement, knowledge base integration, and dialogue context strengthening to enhance stability, logical reasoning, and domain fit for complex task pipelines. For model evaluation, a multi dimensional assessment framework built upon RewardBench, industry specific task suites, and agent execution benchmarks evaluates generation quality, logical reasoning, task completion, safety, and controllability, ensuring stable performance in real world business environments.

3.3.1 Pairwise Preference Tuning

Pairwise preference tuning constitutes a key technique for improving dialogue quality and task execution performance. Annotators conduct point wise assessment of candidate outputs in terms of accuracy, logical rigor, expressive quality, and safety, guiding the model toward stable preference patterns and higher quality outputs aligned with user requirements. Pairwise preference tuning significantly elevates task execution performance in government services, medical consultation, financial risk control, and other complex context scenarios.

3.3.2 RAG Enhancement and Knowledge Base Integration

In industry specific scenarios, models frequently require access to external knowledge bases for task completion. Retrieval Augmented Generation (RAG) enables deep integration between Lingyan Miaoyu and knowledge repositories. The model first generates retrieval intent based on user queries; the retrieval module then accesses knowledge bases; retrieved materials are fused with model generation workflows to deliver accurate, domain specialized responses. RAG enhancement improves professional performance for policy inquiry, clinical guideline reference, financial regulation interpretation and comparable use cases, raising output quality for industry agent task execution.

3.3.3 RewardBench Evaluation System

Lingyan Miaoyu leverages RewardBench as its core evaluation framework, comprehensively assessing model performance across generation quality, logical reasoning, task execution, safety, and controllability. Multi dimensional task suites enable consistent performance measurement across varied scenarios. Within industry contexts, RewardBench quantifies performance for policy Q&A, medical consultation, financial risk assessment, urban governance workflows and other assignments, sustaining high quality outputs for real world business deployments.

3.3.4 Model Safety and Controllability

Safety and controllability represent top enterprise priorities for large model deployment. Lingyan Miaoyu implements controllable semantic editing workflows, task boundary enforcement, sensitive content filtering, and agent permission management to maintain secure, predictable agent behavior.

Controllable semantic editing: Supports knowledge updates without degrading foundational model capabilities for stable industry scenario operation.

Task boundary control: Restricts semantic scope in accordance with contextual requirements to guarantee agent safety and consistency during execution.

Permission management: Enforces role based access controls for agent task permissions, safeguarding data security and business integrity throughout AI transformation workflows.

3.4 Model Capability Matrix

Through systematic advances in Chinese language comprehension and generation, logical reasoning, long document processing, multimodal fusion, and vertical industry capabilities, Lingyan Miaoyu establishes a structured capability matrix for measurable task oriented agent deployment across government affairs, urban governance, public security, finance, medical care, education and other sectors.

3.4.1 Chinese Language Comprehension and Generation

Trained on extensive Chinese language corpora, Lingyan Miaoyu delivers robust Chinese language comprehension, long document processing, and structured output capabilities for policy interpretation, financial report analysis, clinical guideline review and analogous scenarios. For generation workloads, it produces policy explanations, medical recommendations, financial analyses and other artifacts to enable agents to fulfill actionable business task roles.

3.4.2 Logical Reasoning and Long Document Processing

Multi layer semantic fusion and controllable semantic editing deliver powerful logical reasoning capacity for stable multi step task performance. The model processes multi thousand character texts, supporting government document interpretation, medical chart analysis, financial report parsing and comparable workflows.

3.4.3 Multimodal Fusion Capabilities

Multimodal competencies form the foundation for task execution in urban governance, public security, medical imaging and other scenarios. Enabled by LingYu Architecture, Lingyan Miaoyu unifies processing for text, images, video, and audio to deliver cross modal reasoning and task execution:

Images: Object detection, scene recognition, and image semantic understanding.

Video: Temporal sequence analysis and event identification.

Audio: Speech recognition and speech synthesis to complete end to end capability chains for multimodal agents.

3.4.4 Vertical Industry Model Capabilities (Finance / Medical / Government Affairs, etc.)

Domain specific data training and agent task reinforcement equip Lingyan Miaoyu with specialized competencies for finance, medical care, government affairs, urban governance and other vertical sectors:

Finance: Risk control analysis, intelligent auditing, and intelligent campaign targeting.

Medical care: Clinical decision support, medication risk identification, and medical chart structuring.

Government affairs: Policy Q&A, service guidance workflows, and official document generation.

Urban governance: Video surveillance inspection, illegal occupancy detection, and unauthorized construction identification.

These competencies enable industry specific agents to deliver actionable task execution performance.

Computing Power Infrastructure: Xingchen Distributed Intelligent Computing Center

Large language models are transitioning from technical proof of concept toward enterprise scale mass deployment, making computing power infrastructure a decisive factor defining upper performance limits for enterprise grade AI. Traditional data centers face inherent constraints regarding energy consumption, deployment lead times, scalability, and total cost of ownership, and struggle to satisfy high density, high concurrency, low latency requirements for large model training, inference, and agent execution. Developed to address these challenges, the Xingchen Distributed Intelligent Computing Center adopts containerized deployment, modular expansion, and green energy coordination together with a unified scheduling platform, realizing the paradigm shift from legacy IDC deployments toward intelligent computing networks. It empowers enterprises to acquire scalable intelligent computing capabilities with reduced costs and compressed schedules, sustaining long term competitiveness in the large model era.

4.1 Product Positioning and Architectural Design

[Figure 4-1 | Xingchen Distributed Intelligent Computing Center Module Diagram]

4.1.1 Architecture: Unified Scheduling Platform plus Standardized Container Modules

The Xingchen Distributed Intelligent Computing Center adopts an architecture combining a Unified Scheduling Platform with standardized container modules. The Tianyu Intelligent Computing Platform delivers cross region, cross cluster computing power aggregation and intelligent scheduling. Standardized computing power containers, cooling containers, and power supply containers enable rapid deployment and flexible scaling. Under this architecture, computing resources transcend single data center boundaries: containerized units constitute fundamental resource blocks managed centrally by the scheduling platform. Enterprises may deploy computing nodes across geographies coordinated via unified scheduling, adapting flexibly to large model training, inference, and agent execution requirements across diverse scenarios.

4.1.2 2+3 / 1+N Standardized Module System

To accommodate computing power requirements of varying scale and scenario, the Xingchen Intelligent Computing Center implements a 2+3 / 1+N standardized module system built from highly integrated computing power containers, cooling containers, and power supply containers for modular deployment and on demand scaling.

2+3 configuration: Two core computing power containers paired with three auxiliary containers form complete intelligent computing units suited for medium to large scale training workloads.

1+N configuration: One core computing power container expands with multiple auxiliary containers, fitting edge inference and lightweight deployment scenarios.

Enterprises may construct intelligent computing facilities aligned with business requirements and scale capacity incrementally alongside business growth, establishing sustainable computing power evolution pathways.

4.1.3 Air Liquid Hybrid Cooling Containers

For edge inference scenarios requiring low latency, low energy consumption, and fast deployment, Air Liquid Hybrid Cooling Containers combine air cooling and liquid cooling mechanisms for stable operation across diverse environmental conditions with elevated energy efficiency. These containers support lightweight model and edge agent workloads, enabling local inference for urban governance, public security, manufacturing and other use cases, lowering network latency and improving task execution efficiency. They can be rapidly deployed within industrial parks, street level sites, industrial bases and analogous locations, supporting cost effective construction of edge intelligent computing networks.

4.1.4 Cold Plate Liquid Cooling Containers

High density GPU clusters for large model training demand robust heat dissipation and stable power supplies. Cold Plate Liquid Cooling Containers leverage cold plate liquid cooling technology to sustain reliable operation for high density training workloads while substantially reducing energy consumption. Each container accommodates 32 units of B300 class servers, supporting ten thousand GPU scale training assignments and enabling enterprises to complete large model training and iteration cycles on accelerated timelines. Integrated intelligent temperature control systems and green energy coordination deliver high energy efficiency and reduced operational costs, forming a solid computing power foundation for continuous iteration of the Lingyan Miaoyu large language model.

4.2 Five Core Advantages

4.2.1 90 Day Rapid Delivery

Within the large model era, computing power deployment lead times directly shape enterprise AI modernization timelines. Standardized container design and modular deployment workflows enable the Xingchen Intelligent Computing Center to complete construction and delivery within 90 days, drastically shortening enterprise access to scalable computing power timelines. In contrast with extended cycle legacy IDC construction patterns, it enables rapid initiation of large model training and agent deployment.

4.2.2 Dual Mode Asset Operation

Amid fluctuating computing power demand profiles, enterprises require flexible operational modalities for computing power assets. The Xingchen Intelligent Computing Center supports both in house utilization and rental modes:

In house utilization: Intelligent computing facilities function as internal resources to sustain large model training and agent execution.

Rental mode: Surplus computing power resources are made available to third party tenants via the Tianyu Intelligent Computing Platform for computing power transaction workflows, monetizing computing power asset value.

4.2.3 Ultra Low PUE of 1.15

Energy cost management constitutes a top enterprise concern for large model training and inference workloads. Cold plate liquid cooling hardware, intelligent temperature control systems, and green energy integration achieve a PUE as low as 1.15, materially lowering energy expenditures. Compared with high energy consumption legacy IDC deployments, it delivers superior total cost of ownership performance for large model training and inference workflows.

4.2.4 Physical Isolation and Regulatory Compliance

For high security requirement scenarios including government affairs, finance, and medical care, physical isolation and data security compliance represent critical intelligent computing center requirements. The Xingchen Intelligent Computing Center incorporates physical isolation design, independent power supply systems, and segregated networking architectures for enhanced security and regulatory conformance. It satisfies financial data security and medical data security specifications, enabling secure large model utilization while preserving data and business integrity during AI modernization initiatives.

4.2.5 Full Lifecycle Managed Services

Operations and maintenance overhead and administrative complexity frequently constitute major enterprise pain points for intelligent computing center deployments. The Xingchen Intelligent Computing Center delivers full lifecycle managed services spanning construction through ongoing operation: 7×24 hour O&M, network optimization, power supply assurance, equipment maintenance, and intelligent temperature control. Managed service offerings lower enterprise operational burdens, allowing organizations to redirect resources toward model R&D and agent application advancement for more efficient, sustainable AI modernization.

4.3 Green Computing Power System and ESG Value

As large model training scales expand and inference concurrency rises, computing power energy cost emerges as a decisive deployment consideration. Legacy data center limitations regarding heat dissipation, power supply stability, and energy mix restrict support for high density large model workloads. Integrating green energy coordination, liquid cooling technology stacks, SST HVDC power supply architecture, and ESG compliance frameworks, the Xingchen Distributed Intelligent Computing Center builds forward looking green computing power capabilities, delivering cost advantages, energy efficiency gains, and compliance related benefits for sustained enterprise competitiveness in the large model era.

4.3.1 Up to 80 Percent Renewable Energy Proportion

Electric power costs dominate overall expenditures for large model training and inference. Integrated with the group affiliated green energy subsidiary, the Xingchen Intelligent Computing Center combines wind power, photovoltaic, and energy storage assets to achieve a renewable energy share reaching as high as 80 %. For high density training workloads, renewable power materially reduces long term electricity expenses while advancing enterprise ESG performance metrics. Multi site deployments leverage region specific energy resource profiles to maintain stable power supplies for large model training and agent execution.

4.3.2 Liquid Cooling Technology Stack

For high density training assignments, heat dissipation performance governs computing power stability and energy efficiency. Cold plate liquid cooling technology sustains stable thermal conditions for high density GPU cluster operation while cutting overall energy consumption. Direct GPU chip cooling yields substantially superior heat removal performance relative to conventional air cooling. Intelligent temperature control systems execute real time thermal regulation, sustaining stable performance over extended training job durations and lowering total cost of ownership.

4.3.3 10 kV Medium Voltage AC plus Dual Busbar High Reliability Power Distribution Architecture

Power supply system sophistication determines intelligent computing center stability and energy efficiency. The Xingchen Intelligent Computing Center implements a 10 kV medium voltage AC and dual busbar high reliability power distribution architecture to enhance power supply continuity, lower failure risk, and boost overall energy efficiency. The 10 kV medium voltage system supports high precision power regulation to sustain high density training workloads amid power supply fluctuations. Dual busbar configurations improve power supply continuity during trial operation phases and deliver approximately 15 % 20 % aggregate energy efficiency gains. The power supply architecture natively supports renewable energy integration and energy storage system interconnection for long term competitiveness within future smart grid ecosystems.

4.3.4 ESG Compliance and Cost Advantages

ESG compliance has become a core deployment requirement for government, financial, medical and other high grade scenarios. Enabled by green energy systems, liquid cooling stacks, and HVDC power supply architectures, the Xingchen Intelligent Computing Center achieves robust ESG compliance profiles alongside tangible advantages for energy consumption, total cost of ownership, and carbon footprint management.

Cost dimension: Renewable energy and liquid cooling technology integration reduce long term electricity expenditures, delivering favorable five year horizon total cost of ownership outcomes.

Compliance dimension: Satisfies multiple ESG standards, generating differentiated competitive advantages during enterprise AI modernization.

4.4 Cost Modeling and Comparative Analysis

Within the large model era, computing power cost represents a decisive enterprise deployment consideration. Self built data centers, public cloud deployments, and Huazhi Future style architectures exhibit meaningful divergence regarding cost structure, energy efficiency performance, deployment timelines, and long term Total Cost of Ownership (TCO). Leveraging containerized deployment, green energy coordination, and liquid cooling infrastructure, the Huazhi Future solution delivers compelling five year horizon TCO advantages for sustained competitive strength.

4.4.1 Self Hosted versus Public Cloud versus Huazhi Future Model

Self built data centers: Incur heavy capital expenditures for infrastructure construction, equipment procurement, and ongoing O&M, and face inherent energy efficiency limitations.

Public cloud model: Offers operational flexibility yet accumulates substantial long run costs; high density training workloads risk resource contention and performance volatility.

Huazhi Future model: Containerized deployment, modular scaling, and green energy coordination enable enterprises to access scalable intelligent computing power while realizing material long term cost benefits.

4.4.2 Five Year TCO Comparison

Self built data center TCO over five years: Includes infrastructure construction capital outlays, equipment procurement and refresh cycles, electricity consumption, and O&M expenses; equipment residual value at end of life should also be factored.

Public cloud mode TCO: Encompasses computing power rental or model invocation fees plus storage, network, and auxiliary service charges; overall expense levels are sensitive to business scale, resource utilization duration, and billing regimes.

Huazhi Future mode: Liquid cooling hardware, green energy coordination, and HVDC power supply architecture optimize energy utilization efficiency. Modular deployment and consolidated O&M control construction and operational expense profiles, delivering potential full lifecycle cost reduction benefits.

Comparative five year horizon TCO assessments across the three approaches must adopt consistent business workload, model configuration, task effect, and service level agreement baselines. Cost reduction percentages are calculated by the formula:

(TCO of comparison scheme − TCO of Huazhi Future scheme) ÷ TCO of comparison scheme × 100 %.

Exact outcomes depend on project configuration, formal quotations, and real world operational metrics; no uniform magnitude cost reduction commitment is given.

4.4.3 Dual Dimensional Cost Reduction via Electricity Price and Energy Efficiency Improvements

Electricity price levels and energy efficiency ratios dominate TCO outcomes for large model training and inference workloads. The Xingchen Intelligent Computing Center implements a dual pronged cost reduction system built upon green energy resources and liquid cooling technology:

Green energy systems materially lower per kWh electricity costs across geographically distributed deployments.

Liquid cooling technology stacks raise aggregate energy efficiency ratios, cutting total energy consumption to complete training job assignments.

Combined mechanisms deliver meaningful long run cost reduction for enterprise AI modernization workflows.

Computing Power Scheduling and Model Services: Tianyu Intelligent Computing Platform plus ALRO

As large model deployments advance from single site installation toward multi site collaboration, and from isolated model invocation toward agent driven execution, computing power scheduling and model service systems emerge as foundational enterprise AI deployment infrastructure. Legacy paradigms relying upon “single data center computing power plus isolated model invocation” cannot satisfy modern era requirements for high concurrency inference, cross geography collaboration, heterogeneous model ecosystems, and complex agent task pipelines. The Tianyu Intelligent Computing Platform transforms static computing power resources into dynamic network accessible capacity through unified computing power identification schemas, intelligent task scheduling, and asset light aggregation and operation methodologies. The ALRO Unified Model Access Portal enables multi model collaboration via aggregation, distribution, and format translation capabilities. Together they constitute Huazhi Future’s “computing power – model – agent” service ecosystem, empowering enterprises with superior efficiency, reduced costs, and enhanced long run sustainability in the large model era.

5.1 Tianyu Intelligent Computing: Computing Power Scheduling Platform

5.1.1 Unified Computing Power Identification Schema

In environments with multi site, multi geography, heterogeneous computing power assets, unified computing power identification constitutes a prerequisite for cross domain scheduling. The Tianyu Intelligent Computing Platform establishes a unified computing power identification schema to standardize descriptions for Xingchen Intelligent Computing Center resources, enterprise self hosted capacity, edge site nodes, and third party intelligent computing assets. Physical hardware resources are abstracted into orchestratable logical resources. Computing power may be scheduled analogously to network traffic, permitting training jobs, inference jobs, and agent tasks to migrate geographically, forming a nationwide intelligent computing power network.

5.1.2 Intelligent Task Scheduling

Divergent task types impose complex and distinct scheduling strategy requirements. Through task profiling and intelligent scheduling algorithms, the Tianyu Intelligent Computing Platform:

Routes training workloads automatically toward high density clusters;

Directs inference workloads toward low latency nodes;

Orchestrates multi node collaborative execution for agent oriented assignments.

Intelligent scheduling lifts resource utilization ratios above 70 %, enabling enterprises to realize superior computing power efficiency at lowered costs while sustaining stable performance during business peak load intervals.

5.1.3 Elastic Scaling and Resource Utilization Ratio Optimization

Computing power demand profiles for large model inference and agent execution workloads are frequently volatile. Elastic scaling functionality automatically expands or contracts resource allocations aligned with task demand fluctuations: capacity expands for business peak load periods and contracts during low activity intervals to control expenditures. Resource utilization optimization algorithms enable heterogeneous task co execution within shared clusters, elevating hardware utilization density and reducing long term enterprise computing power costs.

5.1.4 Asset Light Aggregation and Operation Mode

Legacy heavy capital self build approaches suffer high capital expense, extended deployment cycles, and substantial risk. The asset light aggregation and operation paradigm implemented by the Tianyu Intelligent Computing Platform empowers enterprises to access scalable intelligent computing power with reduced entry barriers. Aggregated multi site computing power resources support on demand capacity leasing. Multiple billing modalities including GPU rental, Token based billing, and agent task oriented billing are available. Computing power transitions from physical asset status toward service delivery status to unlock enhanced economic value.

5.2 ALRO: AI Model Unified Access Portal

5.2.1 Product Positioning: Aggregation + Distribution + Format Translation

Complex model invocation workflows, format incompatibility, and security concerns frequently degrade enterprise AI deployment efficiency. The ALRO AI Model Unified Access Portal delivers aggregation, distribution, and format translation capabilities, lowering barriers and raising efficiency for enterprise consumption of domestic and international large model capabilities. It aggregates more than 205 distinct model offerings. Official direct connection channels plus intelligent routing enable high quality model distribution. Format translation functionality permits enterprises to “invoke all supported models using one unified codebase”, materially cutting model integration overhead.

5.2.2 Matrix of 205+ Supported Models

As the AI model ecosystem expands rapidly, enterprises frequently must coordinate multiple models to complete complex task pipelines. The ALRO gateway aggregates domestic model resources spanning text, image, video, and audio modalities, including DeepSeek, Tongyi Qianwen, GLM, Wenxin Yiyan and other domestic model families. Unified access and coordinated invocation support enterprise construction of diverse scenario intelligent applications, advancing deep integration between domestic model capabilities and industry business workflows.

5.2.3 Official Direct Connection and Transparent Billing

Capability degradation via proxy intermediaries, invocation latency penalties, and opaque billing constitute major enterprise pain points for model consumption. ALRO’s official direct connection architecture preserves full model capabilities without functional degradation. A transparent billing framework delivers full visibility regarding per invocation costs, enhancing controllability and transparency for model invocation workflows.

5.2.4 Domestic Direct Connection and Renminbi Denominated Payment

For Chinese enterprises invoking overseas hosted models, network latency and payment modalities present substantial adoption barriers. ALRO’s domestic direct connection architecture enables stable overseas model invocation within China based network environments. Renminbi denominated payment support lowers adoption barriers for leveraging global model resources in convenient, regulatory compliant fashion.

5.2.5 Enterprise Grade Dedicated Channels and Data Isolation

Data security and invocation isolation represent paramount enterprise concerns for government affairs, finance, medical care and other high security grade scenarios. ALRO implements enterprise grade dedicated channels and data isolation architectures to strengthen security and compliance posture, safeguarding data and business integrity during enterprise AI modernization.

5.3 Model Service System

5.3.1 Text Model Services

Text processing capabilities constitute core task execution foundations for government affairs, finance, medical care and analogous scenarios. ALRO delivers text comprehension, text generation, structured output, and long document processing services, supporting stable performance for policy interpretation, financial analysis, medical consultation and comparable workflows. Text model services interoperate with agent systems to sustain robust performance across multi step task pipelines.

5.3.2 Image Generation Services

Image generation capabilities materially improve task execution efficiency within urban governance, public security, manufacturing and other scenarios. ALRO aggregates image generation models including Midjourney, Flux, and Stable Diffusion, enabling complete capability chains for image generation, image editing, and image enhancement workflows. Agents sustain high quality output for visual task assignments.

5.3.3 Video Generation Services

Driven by rapid advances in video generation technology, enterprise demand for video creation and editing rises across marketing, education, urban governance and other use cases. ALRO aggregates video models including Sora, Veo, and Runway, delivering complete capability chains for video generation, video editing, and video enhancement workflows. Agents sustain stable performance for multimodal task assignments.

5.3.4 Multimodal and Speech Services

Within multimodal scenarios, systems must simultaneously process text, image, video, and audio information. ALRO delivers multimodal fusion services to achieve consistent semantic performance for cross modal assignments. For speech oriented scenarios, it provides speech recognition, speech synthesis, and speech emotion analysis capabilities to enable natural language interaction for agents within government service, educational, and medical contexts.

AI+ Industry Application Scenarios (Government Affairs / Urban Governance / Public Security / Medical Care / Education / Transportation / Industrial Parks / Manufacturing / Mining Safety)

[Figure 6-1 | AI + Industry Application Scenario Landscape]

As large language models evolve beyond general purpose competencies toward deep industry adoption, AI value materializes across urban governance, government service delivery, public security, healthcare, education, traffic management, industrial park operations, manufacturing, and smart mining use cases. Enabled by multimodal understanding, agent execution, knowledge base fusion, and task pipeline planning, Lingyan Miaoyu empowers AI agents to undertake measurable task oriented roles within industry contexts, transitioning from auxiliary tool status toward bona fide business executors. Leveraging the Xingchen Intelligent Computing Center, Tianyu Intelligent Computing Platform, and ALRO Model Portal, Huazhi Future builds comprehensive industry agent systems for stable, controllable, replicable intelligent upgrade workflows within real world business processes.

6.1 AI + Urban Governance

6.1.1 Intelligent Video Patrol and Early Warning

Video patrol constitutes a foundational core capability for urban governance workflows. Expanding city scales, rising camera counts, and increasing event complexity render legacy manual patrol paradigms insufficient regarding real time response and coverage requirements. Multimodal capabilities and agent execution architectures allow Lingyan Miaoyu to transform video patrol workflows from manual review toward automated intelligent identification. The model analyzes live video streams in real time, detecting illegal street vending, unauthorized parking, refuse accumulation, road surface damage and other events. Agent task pipelines auto generate early warning notices, enabling municipal administrative departments to accelerate incident response and disposition. Supported by the Tianyu Intelligent Computing Platform, patrol tasks execute collaboratively across distributed nodes to boost real time performance and coverage for urban governance.

6.1.2 AI + 5G + UAV Based Unauthorized Construction Identification

Unauthorized construction supervision via legacy patrol methods suffers limited coverage, low efficiency, and identification difficulties. The AI + 5G + UAV collaborative system transforms unauthorized construction detection from manual inspection toward intelligent cruise surveillance. UAV platforms stream video feeds in real time over 5G networks. The model performs on flight image recognition, structural analysis, and anomaly detection for unauthorized construction identification with improved accuracy and timeliness. Agents auto compile inspection reports upon detection, accelerating disposition cycles for law enforcement agencies. Multiple site pilot deployments have demonstrated materially enhanced unauthorized construction governance efficiency, representing a key direction for urban governance intelligent modernization.

6.1.3 AI Assistant for Comprehensive Law Enforcement

Law enforcement personnel must process large volumes of policy documents, statutory provisions, and complex business procedures, creating high task complexity and information burden. Powered by government affairs oriented semantic enhancement and task pipeline planning, Lingyan Miaoyu delivers an AI assistant for law enforcement practitioners. Based on field circumstances, the model auto generates law enforcement records, cites policy justifications, and produces disposition recommendations for more standardized and efficient law enforcement proceedings. Agents integrate with work order systems to automate task routing, forming complete end to end pipelines spanning incident identification, disposition, and archiving to raise overall urban governance intelligence levels.

6.1.4 Urban Operation Situation Analysis

Situation analysis capabilities constitute a cornerstone for intelligent urban management. Through multimodal fusion and data analytics, Lingyan Miaoyu analyzes multi dimensional datasets including traffic flow metrics, public security telemetry, environmental monitoring readings, and government service statistics to construct urban operation situation graphs. Agents auto trigger early warning alerts in response to situation shifts, empowering urban administrative departments to accelerate decision making. Supported by the Xingchen Intelligent Computing Center and Tianyu Intelligent Computing Platform, situation analysis tasks execute collaboratively across distributed nodes for enhanced real time performance and urban management intelligence.

6.2 AI + Government Service Delivery

6.2.1 AI Intelligent Customer Service

Intelligent customer service represents one of the earliest AI capabilities deployed within government service scenarios. Chinese language semantic enhancement and policy knowledge base fusion equip Lingyan Miaoyu agents to deliver accurate, standardized, traceable responses for government consultation workflows. Agents auto generate policy interpretations, service procedural guides, and process explanations to streamline and improve access to government services. Supported by the ALRO Model Portal, intelligent customer service capabilities invoke models consistently across diverse channels for enhanced uniformity and reliability.

6.2.2 Multi Turn Dialogue and Service Guidance Workflows

Citizens frequently require multi turn dialogue interactions to complete government service transactions. Dialogue context fusion and task pipeline planning enable Lingyan Miaoyu to preserve semantic consistency and task continuity across multi turn conversations. Agents auto derive step by step service procedures aligned with citizen requirements to deliver guided service navigation within dialogue sessions. This capability delivers material value for government service halls, government service mobile applications, and hotline service channels, advancing government services from mere information provision toward end to end task completion.

6.2.3 Knowledge Graph and RAG Fusion Workflows

Policy clauses are complex and subject to frequent updates; models must access up to date knowledge resources to deliver correct responses for policy consultation use cases. Lingyan Miaoyu combines knowledge graph technology with Retrieval Augmented Generation (RAG) to reference latest policy materials during response generation, improving answer accuracy and regulatory compliance. Agents auto refresh knowledge bases upon policy changes to sustain robust stability and operational continuity for government service workflows.

6.2.4 Work Order System and Business Process Integration

Work order systems constitute core business routing infrastructure within government service contexts. Lingyan Miaoyu agent execution architectures support deep integration with work order systems. Agents auto generate work orders derived from citizen requirements to automate business process routing for consultation, service guidance, transaction processing, and archiving work streams, elevating overall government service intelligence levels.

6.3 AI + Public Security

Within public security systems, expanding urban scales, growing incident diversity, and accelerated risk propagation render legacy manual patrol plus post incident response paradigms insufficient for modern urban security requirements. Leveraging multimodal understanding, agent execution, video analytics, and behavior recognition, Lingyan Miaoyu advances public security from passive response toward proactive early warning, and from manual patrol toward intelligent preventive monitoring. Supported by high density inference capacity within the Xingchen Intelligent Computing Center and cross domain scheduling capabilities of the Tianyu Intelligent Computing Platform, public security agents accelerate incident detection, risk alerting, and task disposition to enhance real time performance and intelligence for urban security systems.

6.3.1 AI Police Robots

AI police robots represent important carriers for public security intelligent modernization. Enabled by speech recognition, video analytics, and task planning, Lingyan Miaoyu powered police robots undertake measurable task oriented roles for street patrols, venue security surveillance, and traffic node duty assignments. Robots detect abnormal behaviors, assist citizen inquiries, perform identity verification, and auto generate disposition recommendations upon incident occurrence, advancing policing from manual patrol toward human AI collaborative operation. Supported by Lingyan Miaoyu agent architectures, police robots sustain stable performance across diverse scenarios.

6.3.2 Perimeter Monitoring and Behavior Recognition

Layered perimeter monitoring constitutes a key proactive early warning capability for high priority site security defense. Leveraging video analytics and behavior recognition, the system identifies anomalous activities including loitering, running, crowd gathering, and verbal altercations across security perimeters, auto generating early warning notices aligned with risk severity levels. Agents auto adjust monitoring strategies in response to scenario shifts for enhanced flexibility and intelligence within public security systems. Supported by the Tianyu Intelligent Computing Platform, monitoring tasks execute collaboratively across distributed nodes to improve coverage and real time responsiveness for urban security operations.

6.3.3 Intelligent Patrol for Key Premises

Intelligent patrol operations for high importance sites including commercial districts, campuses, hospitals, and transport hubs form a vital component of public security work streams. Enabled by multimodal fusion, agents identify abnormal behaviors, detect hazardous articles, and analyze crowd density during patrol runs, auto generating disposition recommendations upon incident detection. Patrol workflows integrate with work order systems to form complete end to end pipelines spanning incident identification, alerting, and archiving for higher overall public security intelligence levels.

6.4 AI + Smart Healthcare

Healthcare scenarios feature complex data types, cumbersome work flows, and high professional entry barriers, creating efficiency and quality challenges for legacy health service delivery models. Enabled by medical semantic enhancement, knowledge graph fusion, and agent task execution, Lingyan Miaoyu agents undertake measurable task oriented roles for patient triage, pre consultation screening, medical chart structuring, prescription review, and medical knowledge management work streams. Supported by the ALRO Model Portal and Tianyu Intelligent Computing Platform, healthcare agents collaborate across departments and use cases to elevate health service intelligence levels.

6.4.1 Intelligent Triage and Pre Consultation Screening

Patient triage and pre consultation screening strongly influence patient experience within hospital settings. Medical semantic enhancement enables Lingyan Miaoyu to recognize symptom descriptions, analyze potential etiologies, and produce initial guidance during dialogue interactions, delivering professional pre visit guidance for patients. Agents auto recommend target clinical departments derived from patient descriptions to improve triage efficiency and accuracy. Supported by Lingyan Miaoyu medical domain models, pre consultation screening sustains stable performance across multi department contexts.

6.4.2 Medical Chart Structuring

Medical chart structuring represents a key capability for advancing health information technology efficiency. Leveraging long document processing and medical semantic understanding, Lingyan Miaoyu transforms unstructured medical chart content into structured datasets to reduce physician medical chart entry burdens. Agents extract medical history details, symptom profiles, examination findings, and diagnostic conclusions to deliver improved accuracy and consistency for medical chart structuring while establishing data foundations for subsequent diagnostic support and medical analytics work streams.

6.4.3 Intelligent Prescription Review and Medication Recommendation

Prescription review and medication recommendation are critical for guaranteeing medical safety within pharmaceutical work streams. Enabled by medical knowledge graph and RAG technology, Lingyan Miaoyu identifies medication risks, analyzes drug interaction profiles, and generates medication suggestions during prescription review to standardize and strengthen safety for prescription processing work flows. Agents auto formulate medication regimens derived from patient medical histories to raise health service intelligence levels.

6.4.4 Medical Knowledge Graph

Knowledge graph technology forms foundational medical knowledge management infrastructure for intelligent medical work streams. Lingyan Miaoyu builds medical knowledge graph resources covering diseases, symptoms, pharmaceutical agents, diagnostic tests, and treatment regimens. The model queries up to date medical knowledge during response generation to enhance answer accuracy and professional quality. Knowledge graph resources enable agents to maintain stable performance for diagnostic assistance, prescription review, and medical consultation use cases.

6.5 AI + Urban Safety and Emergency Management

Within urban operation systems, safety management and emergency response represent high complexity, high real time requirement, system critical core functions. Expanding urban scales, rising population densities, and diversifying risk typologies render legacy paradigms relying upon manual patrol, post incident response, and fragmented management insufficient for modern urban safety demands. Enabled by multimodal understanding, agent execution, situation analytics, and task pipeline planning, Lingyan Miaoyu advances urban safety from static management toward dynamic monitoring, and from passive disposition toward proactive early warning to build full domain intelligent urban safety systems. Supported by the Xingchen Intelligent Computing Center and Tianyu Intelligent Computing Platform, urban safety agents execute collaboratively across geographic regions to enhance urban resilience and emergency response capabilities.

6.5.1 Unified Urban Safety Monitoring Platform

Unified urban safety monitoring constitutes foundational full domain collaboration infrastructure for urban safety management. Enabled by multimodal data fusion, Lingyan Miaoyu powered urban safety systems integrate video surveillance feeds, IoT device telemetry, environmental monitoring readings, traffic flow statistics, and public security datasets to construct real time urban operation situation graphs. Agents auto detect risk events across diverse scenarios to accelerate disposition work streams for urban administrative departments. Supported by the Tianyu Intelligent Computing Platform, the unified monitoring system operates collaboratively across multiple data centers to enhance real time responsiveness and coverage for urban safety operations.

6.5.2 Risk Early Warning and Collaborative Disposition

Early warning capabilities determine urban emergency response velocity within risk management work streams. Enabled by behavior recognition, event detection, and semantic analysis, the system identifies pre incident anomalous signals including crowd aggregation, equipment malfunction, and environmental variation and auto generates early warning notices. Agents auto orchestrate relevant department resources aligned with risk severity levels to advance disposition work streams from manual coordination toward intelligent collaboration. Within multi department coordination scenarios, agents auto generate task pipelines to improve efficiency and standardization for emergency response operations.

6.5.3 Digital Twin and Full Domain Monitoring

Digital twin technology represents an important capability for full domain monitoring within urban operation management. Enabled by multimodal fusion, Lingyan Miaoyu supports urban digital twin construction that maps real world urban operational states into virtual space. Administrative departments conduct simulation analysis and early warning assessment within virtual environments. Agents auto generate risk prediction outputs derived from digital twin models to enhance foresight and intelligence for urban management work streams.

6.5.4 “One Map + One Brain” Emergency Command System

Within emergency command scenarios, the “One Map + One Brain” architecture delivers key capabilities for accelerated decision making. Enabled by situation analytics and task planning, the emergency command system presents full domain information within a unified interface and operates as a command brain via agent execution capabilities. Upon incident occurrence, the system auto generates disposition plans to streamline and intelligentize command processes. Supported by high density inference capacity within the Xingchen Intelligent Computing Center, the “One Map + One Brain” system sustains stable operation for complex scenarios.

6.6 AI + Smart Transportation

Within urban transport systems, operational efficiency, route network coordination, and passenger flow management strongly influence urban quality of life. Expanding transport network footprints, shifting passenger flow profiles, and volatile transport demand patterns render legacy paradigms relying upon manual statistics and static planning insufficient for real time and complex modern traffic management requirements. Enabled by multimodal analytics, agent execution, and transport knowledge graph technology, Lingyan Miaoyu advances traffic management from static planning toward dynamic optimization and from post hoc analysis toward real time scheduling to build end to end smart transport systems.

6.6.1 Operational Performance Evaluation and Analysis

Operational performance evaluation constitutes foundational infrastructure for transport optimization work streams. Enabled by multimodal data analytics, the system performs real time analysis of traffic flow volumes, road congestion metrics, public transit on time rates, and rail transit operational efficiency to construct transport operational performance evaluation systems. Agents auto generate optimization recommendations derived from evaluation outcomes to raise transport management intelligence levels. Supported by Lingyan Miaoyu transport domain models, operational performance evaluation sustains stable performance across diverse traffic scenarios.

6.6.2 Route Network Collaborative Optimization

Route network collaborative optimization improves aggregate system efficiency within transport planning work streams. Enabled by transport knowledge graph technology and task planning, the system executes cross route collaborative analysis to identify bottleneck road segments, optimize transfer node layouts, and produce route network adjustment proposals. Agents auto generate scheduling strategies aligned with passenger flow variations to enhance flexibility and intelligence for transport systems.

6.6.3 Anomaly Detection and Passenger Flow Analysis

Anomaly detection and passenger flow analysis are critical for safeguarding transport safety and operational efficiency during transport operation work streams. Enabled by video analytics and behavior recognition, the system identifies anomalous activities including congestion events, pedestrian loitering, and wrong way travel at transport nodes and auto generates early warning notices. Agents auto generate scheduling strategies derived from passenger flow variations to sustain stable transport system operation during peak traffic intervals.

6.7 AI + Smart Industrial Parks

Data silos, fragmented work flows, and fragmented management represent long standing structural pain points for smart industrial park construction initiatives. Expanding park scales, growing enterprise populations, and increasing operational scenario complexity render legacy paradigms relying upon manual statistics, off line inspection runs, and fragmented systems insufficient for modern park efficiency, safety, and collaboration requirements. Enabled by multimodal fusion, agent execution, and task pipeline planning, Lingyan Miaoyu advances park operations from fragmented management toward full domain collaboration and from manual decision making toward AI assisted decision making to build smart industrial park systems covering people, vehicles, physical assets, environmental conditions, and business work streams.

6.7.1 Full Factor Data Fusion

Park operational data sources are diverse, format heterogeneous, and subject to frequent updates, posing challenges for legacy system unification efforts. Enabled by multimodal fusion and semantic understanding, Lingyan Miaoyu integrates video surveillance feeds, access control system telemetry, vehicle management records, energy consumption monitoring readings, environmental sensor data, and enterprise business datasets to construct real time park operation situation graphs. Agents auto detect anomalous events across diverse scenarios to improve real time responsiveness and intelligence for park management work streams. Supported by the Tianyu Intelligent Computing Platform, data fusion tasks execute collaboratively across distributed nodes.

6.7.2 Collaborative Office and Project Management

Collaborative office work streams and project management represent key efficiency enhancement capabilities for enterprise operations within industrial parks. Enabled by task planning and semantic understanding, agents undertake measurable task oriented roles within project management work streams: meeting minute generation, task decomposition, progress tracking, and risk alerting to accelerate completion of collaborative work items. Agents integrate with park management systems to streamline communication between enterprises and park administrative bodies and raise overall park operational intelligence levels.

6.7.3 Safety Monitoring and Decision Support

Video patrol inspection, access control management, and environmental monitoring constitute core capabilities for park safety management work streams. Enabled by multimodal analytics, the system identifies anomalous behaviors, detects hazardous articles, and analyzes crowd density within park premises and auto generates disposition recommendations upon incident detection. Agents auto produce decision support information aligned with risk severity levels to accelerate disposition cycles for park administrative departments and strengthen park safety intelligence levels.

6.8 AI + Manufacturing Industry

Within manufacturing scenarios, complex equipment assets, cumbersome work flows, and stringent quality requirements render legacy paradigms relying upon manual inspection and experience based judgment insufficient for modern manufacturing efficiency, quality and safety demands. Enabled by machine vision, semantic understanding, and agent execution, Lingyan Miaoyu advances manufacturing work streams from experience driven toward data driven operation and from manual inspection toward intelligent diagnosis to build smart manufacturing systems covering production, quality inspection, scheduling, and management work streams.

6.8.1 Equipment Fault Diagnosis

Equipment fault diagnosis is critical for guaranteeing stable production operations within equipment management work streams. Enabled by multimodal analytics, the system identifies anomalous signals including vibration abnormalities, temperature deviations, and acoustic noise variations during equipment operation and auto generates fault diagnosis reports. Agents auto produce maintenance recommendations derived from diagnostic findings to raise equipment management intelligence levels. Supported by Lingyan Miaoyu manufacturing domain models, fault diagnosis sustains stable performance across multi equipment deployments.

6.8.2 Machine Vision Based Quality Inspection

Machine vision technology improves quality inspection efficiency within quality management work streams. Enabled by image recognition and semantic analysis, the system detects product defects, dimension deviations, and surface quality anomalies on production lines to streamline and improve accuracy for quality inspection work streams. Agents auto generate quality reports derived from inspection findings to raise production management intelligence levels.

6.8.3 MES System Collaborative Scheduling

Manufacturing Execution System (MES) platforms form foundational collaboration infrastructure for production scheduling work streams. Enabled by task planning and semantic understanding, agents support deep integration with MES systems to automate business process routing for production tasks. Agents auto generate scheduling recommendations derived from production progress metrics to raise production management intelligence levels.

6.9 AI + Mining Safety Work Streams

Within mining safety management contexts, complex operational environments, high inherent risk levels, and diverse scenario characteristics render legacy paradigms relying upon manual patrol and post incident disposition insufficient for modern mine safety efficiency and safety requirements. Enabled by multimodal fusion, agent execution, and risk early warning capabilities, Lingyan Miaoyu advances mine safety work streams from manual patrol toward intelligent early warning and from fragmented management toward full domain collaboration to build smart mine systems covering personnel, equipment, environmental conditions, and operational procedures.

6.9.1 AI System for Anti Violation of Safety Regulations Early Warning

Safety regulation violations represent major contributing factors for mine operational incidents within mine safety management work streams. Enabled by behavior recognition and semantic analysis, the system detects unsafe operational acts, unsafe command practices, and labor discipline violations at work sites and auto generates early warning notices to raise mine safety management intelligence levels. Agents auto produce disposition recommendations aligned with risk severity levels to standardize and improve efficiency for safety management work streams.

6.9.2 Multi Sensor Fusion Identification

Within mine environments, single sensor sources frequently cannot satisfy identification requirements for complex scenarios. Enabled by multi sensor fusion technology, Lingyan Miaoyu integrates video feeds, temperature readings, gas concentration measurements, vibration telemetry, and position data for unified analysis to improve accuracy and real time responsiveness for risk identification work streams. Agents auto generate risk assessment reports for enhanced mine safety intelligence levels.

6.9.3 Second Level Alarm Triggering and Closed Loop Management

Alarm trigger velocity determines disposition effectiveness within mine safety management work streams. Enabled by agent execution architectures, the system delivers second level alarm triggering upon risk detection and auto generates disposition pipelines to advance mine safety work streams from manual response toward intelligent closed loop operation. Agents integrate with work order systems to form complete end to end pipelines spanning risk identification, alerting, and archiving for higher mine safety intelligence levels.

Representative Case Studies: Industry Benchmark Practices

As large language models advance beyond technical proof of concept toward deep industry adoption, enterprises increasingly demand replicable, scalable, measurable intelligent modernization use cases. Leveraging the Xingchen Intelligent Computing Center, Tianyu Intelligent Computing Platform, and ALRO Model Portal, Lingyan Miaoyu has produced benchmark case studies across finance, marketing, government affairs, urban governance, healthcare, manufacturing and other sectors. These deployments validate large model practical feasibility within real world business contexts and illustrate core value delivered by agent systems for task execution, process collaboration, and business growth, providing actionable intelligent upgrade pathways for industry participants.

7.1 Financial Industry Case Studies

Within the financial sector, risk control, audit review, marketing campaigns, and customer service represent high data density, high process complexity, high risk sensitivity core business workflows. Enhanced with financial domain semantics, knowledge base fusion, and agent task pipelines, Lingyan Miaoyu enables AI agents to take on measurable task oriented roles in risk control analysis, intelligent auditing, intelligent campaign targeting, and customer service scenarios, realizing the upgrade path from auxiliary analysis to intelligent execution.

7.1.1 AI Intelligent Risk Control System

Traditional workflows relying on rule engines and manual auditing struggle to meet requirements of high concurrency business and complex risk structures in risk control scenarios. Equipped with financial knowledge graph and multimodal analysis capabilities, the AI intelligent risk control system identifies abnormal behaviors, analyzes risk characteristics and outputs risk ratings during transactions, shifting risk control paradigms from rule driven operation toward intelligent recognition. Agents automatically generate audit suggestions according to risk levels, improving efficiency and accuracy of risk control workflows. Supported by Lingyan Miaoyu financial domain models, the risk control system maintains stable performance across diverse business scenarios.

7.1.2 AI Multi Agent Collaboration System for Finance

Risk control, auditing, marketing and customer service in financial business often require cross departmental coordination. Lingyan Miaoyu’s multi agent collaboration system allows distinct agents to cooperatively execute tasks within one business pipeline, transforming financial operations from fragmented processing toward coordinated execution. In loan review scenarios, risk control agents complete risk identification; audit agents produce review comments; customer service agents handle user communications, bringing higher efficiency and consistency to the whole workflow. In marketing scenarios, agents automatically formulate marketing strategies based on user portraits, lifting the intelligence level of financial businesses.

7.2 Marketing Industry Case Studies

Within the marketing industry, diverse user demands, complicated channels and frequently updated content make traditional manual planning and static delivery modes inadequate for modern day requirements for efficiency, precision and personalization. Leveraging user portrait analysis, content generation and agent collaboration, Lingyan Miaoyu transforms marketing workflows from manual planning to intelligent generation, and from static delivery to dynamic optimization, building a smart marketing system covering content, channels and end users.

7.2.1 AI Smart Marketing System

Content creation and strategy formulation represent highly time consuming links in marketing planning. With multimodal generation and semantic understanding capabilities, the AI smart marketing system automatically produces marketing copy, poster concepts, short video scripts and delivery strategies, enabling marketing teams to finish planning within shorter cycles. Agents optimize content dynamically according to user feedback and improve the dynamism and intelligence of marketing work. Supported by the ALRO Model Portal, marketing systems can invoke model capabilities uniformly across different channels.

7.2.2 User Portrait and Precise Targeting

Precise audience engagement constitutes a key capability to improve marketing outcomes in user operation workstreams. Through user behavior analysis and semantic understanding, the system identifies interest preferences, consumption habits and behavioral characteristics within user portraits, and automatically generates engagement strategies. Agents produce personalized content based on user portraits to improve marketing precision and conversion rates. In multi channel scenarios, agents automatically select optimal engagement channels to boost efficiency and intelligence of marketing activities.

7.3 Urban Governance Case Studies

Video patrol, law enforcement assistance and unauthorized construction identification are core real time, complex and collaborative scenarios within urban governance systems. As cities expand, governance objects multiply and incident categories diversify, legacy manual inspection and post incident disposition paradigms can no longer satisfy modern day requirements for efficiency, coverage and regulatory standardization. Powered by multimodal analysis, agent execution and task pipeline planning, Lingyan Miaoyu large language model enables urban governance workflows to shift from manual patrol to intelligent identification, and from fragmented law enforcement toward collaborative disposition, forming benchmark practical cases.

7.3.1 Video Patrol and Law Enforcement Assistance

Video patrol serves as a fundamental capability for real time urban management work. With image recognition, behavior analysis and semantic understanding capabilities, the system automatically detects events such as illegal street vending, illegal parking, garbage accumulation and road surface damage within video streams, and outputs law enforcement suggestions automatically upon detection, helping law enforcement personnel complete disposition faster. Agents generate work orders automatically according to incident types, shifting workflows from manual record keeping to intelligent circulation. Supported by Lingyan Miaoyu urban governance models, patrol tasks are executed collaboratively across multiple regions, improving real time responsiveness and coverage for urban governance.

7.3.2 UAV Powered Unauthorized Construction Identification

Legacy patrol patterns suffer insufficient coverage, low efficiency and identification difficulties for unauthorized construction governance. The AI + 5G + UAV collaborative system transforms unauthorized construction identification from manual inspection to intelligent cruise surveillance. UAVs stream real time video during flights; the model recognizes building structures, analyzes architectural forms and produces identification reports, enabling law enforcement departments to complete disposition within shorter cycles. Agents automatically generate cruise inspection routes based on identification outcomes, lifting the intelligence level of unauthorized construction governance.

7.4 Smart Industrial Park Case Studies

Data silos, fragmented workflows and decentralized management are long standing structural pain points for smart park construction. As park scale expands, enterprise count rises and operational scenarios grow more complex, legacy modes built on manual statistics, offline inspections and fragmented systems fail to satisfy modern day requirements for efficiency, safety and collaboration. Enabled by multimodal fusion, agent execution and task pipeline planning, Lingyan Miaoyu large language model pushes park operation workflows from decentralized management toward full domain collaboration, delivering benchmark practical cases for smart industrial parks.

7.4.1 Park Big Data Platform

Park operation data comes from diverse sources with heterogeneous formats and frequent updates, creating obstacles for unification within legacy systems. Leveraging multimodal fusion and semantic understanding, Lingyan Miaoyu integrates video surveillance feeds, access control system telemetry, vehicle management records, energy consumption monitoring, environmental sensor data and enterprise business data, constructing real time park operation situation graphs. Agents automatically detect anomalous events across different scenarios and improve real time responsiveness and intelligence for park management. Supported by the Tianyu Intelligent Computing Platform, big data platforms execute collaborative computation across multi nodes and expand park coverage capabilities.

7.4.2 Intelligent Operation System

Collaborative office workstreams and project management, safety monitoring constitute important links to improve efficiency within park operations. With task planning and semantic understanding capabilities, agents undertake measurable task oriented roles in project management work: generating meeting minutes, decomposing tasks, tracking progress and issuing risk alerts, helping enterprises finish collaborative work more rapidly. Agents integrate with park management systems to streamline communication between enterprises and park administrations and raise overall operational intelligence of industrial parks.

7.5 Smart Transportation Case Studies

Operational efficiency, route network coordination and passenger flow management are key factors shaping the quality of urban transport systems. As transport networks keep expanding, passenger flow structures keep changing and transport demands fluctuate, legacy paradigms built on manual statistics and static planning can no longer meet real time and complex requirements of modern day traffic management. Powered by multimodal analysis, agent execution and transport knowledge graph, Lingyan Miaoyu large language model advances traffic management workflows from static planning toward dynamic optimization, and from post hoc analysis toward real time scheduling, forming benchmark practical cases for smart transportation.

7.5.1 Operational Index Analysis

Operational indexes serve as important metrics measuring overall efficiency of traffic systems within traffic management workstreams. By analyzing multi dimensional data such as traffic flow, road congestion rates, public transit on time ratios and rail transit operational efficiency in real time, the system automatically generates traffic operation indexes and triggers early warning notifications upon index changes. Agents produce optimization suggestions automatically according to index variations and improve intelligence levels of traffic management. Supported by Lingyan Miaoyu transportation domain models, operational index analysis maintains stable performance across diverse traffic scenarios.

7.5.2 Route Network Optimization and Passenger Flow Prediction

Within transport planning workstreams, route network coordination represents an important capability for improving overall efficiency. Leveraging transport knowledge graph and task planning, the system conducts collaborative analysis across different routes, identifies bottleneck road segments, optimizes transfer nodes and produces route network adjustment proposals. Agents automatically generate scheduling strategies responding to passenger flow changes and improve flexibility and intelligence of transport systems. In passenger flow prediction scenarios, the model generates passenger flow forecasts based on historical and real time data, enabling traffic management departments to formulate scheduling schemes in advance and maintain stable system performance during peak hour traffic.

7.6 Mining Safety Case Studies

Mining safety management features complex operating environments, high risk exposure and diverse scenarios; legacy paradigms relying on manual patrol and post incident disposition cannot satisfy modern day mine safety requirements for safety and efficiency. Enabled by multimodal fusion, agent execution and risk early warning capabilities, Lingyan Miaoyu large language model transforms mine safety workflows from manual inspection toward intelligent early warning, and from fragmented management toward full domain collaboration, delivering benchmark practical cases for smart mines.

7.6.1 AI Anti Violation of Safety Regulations System

Safety regulation violation behaviors constitute major contributors to operational incidents in mine safety management workstreams. Leveraging behavior recognition and semantic analysis, the system detects unsafe operations, unsafe command and labor discipline violations at job sites and automatically generates early warning notifications to lift intelligence levels of mine safety management. Agents produce disposition recommendations aligned with risk levels to make safety management workflows more standardized and efficient. Supported by Lingyan Miaoyu mine domain models, the anti violation of safety regulations system maintains stable performance across multiple scenarios.

7.6.2 Risk Early Warning and Closed Loop Management

Risk early warning and closed loop management serve as important guarantees for safety within mine safety management workstreams. Through multimodal fusion, Lingyan Miaoyu identifies pre incident abnormal signals upon risk emergence and automatically generates early warning reports. Agents build disposition pipelines automatically according to risk levels and push mine safety workflows from manual response to intelligent closed loop operation. Integrated with work order systems, the system forms complete pipelines covering risk identification, early warning and archiving and improves intelligence levels of mine safety.

Enterprise AI Transformation Methodology: Three Stage Implementation System

[Figure 8-1 | Enterprise AI Transformation Three-Stage Roadmap]

Enterprise adoption of large language models and agents is not merely point technology upgrading, but a systematic project covering strategic planning, data governance, scenario selection, organizational collaboration and long term operation. Practical deployments of Lingyan Miaoyu across multiple industries demonstrate that enterprise AI transformation generally follows the three stage path: Planning & Preparation → Model Deployment → Iterative Optimization. The quality of work in the first stage directly determines efficiency and outcomes of subsequent implementation. This chapter constructs replicable, executable and measurable planning preparation methodology around core tasks in early phase AI transformation, providing structured pathways for enterprise intelligent upgrading.

8.1 Stage 1: Planning & Preparation

At the initial phase of AI transformation, enterprises need to complete foundational work including strategy clarification, data inventory, scenario screening and team building, laying clear directions and executable pathways for subsequent pilot deployment. The core objective of the planning preparation stage is to shift enterprises from “wanting to implement AI” toward “knowing what AI to build, how to build it and who will build it”, and form measurable transformation blueprints.

8.1.1 Strategic Driven and Business Driven Approaches

Strategy driven and business driven logics constitute dual cores for enterprise AI planning. Strategy driven: focuses on long term objectives in digital transformation, intelligent upgrading and industrial competition, e.g. improving operational efficiency, building intelligent products and establishing differentiated competitive advantages. Business driven: centers on concrete business pain points such as heavy customer service burdens, low risk control efficiency, high inspection costs and unsatisfactory quality inspection accuracy.

Practical experience from Lingyan Miaoyu projects shows that successful AI transformation usually combines strategic guidance with concrete business deliverables, forming closed loops between macro level objectives and micro level execution. Relying on enterprise AI strategic planning, enterprises define transformation goals and business priority levels in the planning phase.

8.1.2 Data Asset Inventory

In large model implementation, data represents a core resource determining model outcomes and agent execution capabilities. Enterprises need to systematically inventory structured data, unstructured data, multimodal data and business knowledge, clarifying data quality, data distribution, data permissions and data availability. Project practices of Lingyan Miaoyu show that data asset inventory is not merely technical work, but also a business sorting process that helps enterprises reach unified understanding on the data dimension. Through data inventory, enterprises clarify which scenarios possess adequate data foundations, which scenarios require supplementary data and which scenarios need knowledge base construction, and lay solid foundations for follow up pilot implementation.

8.1.3 Scenario Screening and Priority Planning

At the early stage of AI transformation, scenario selection directly shapes pilot outcomes. Enterprises screen scenarios from dimensions of business value, data foundation, technical feasibility, organizational collaboration and implementation cycles, and formulate priority plans. Summarized from industry deployments, Lingyan Miaoyu puts forward scenario selection principles: high business value, high frequency, measurable outcomes and replicability. Scenarios such as intelligent customer service, video patrol, intelligent risk control, medical chart structuring and intelligent quality inspection are typical high value and highly replicable pilot directions for enterprise AI transformation.

8.1.4 Cross Departmental Team Building

Organizational collaboration represents an important factor determining implementation efficiency in AI transformation. Enterprises need to build cross departmental teams composed of business departments, technical departments, data departments and management departments, so that AI transformation evolves from single department projects into enterprise grade engineering. Practical cases show cross departmental teams can cooperate in requirement definition, data preparation, model fine tuning and business implementation, granting pilot projects stronger execution power and implementation efficiency. Supported by agent collaboration systems, enterprises build sustainable intelligent capabilities at the organizational level.

8.2 Stage 2: Model Deployment

In the second stage of enterprise AI transformation, core tasks shift from blueprint planning to real world model roll out. The key objective of this stage is to embed models into real business workflows and form verifiable, measurable and iterable intelligent capabilities. Model deployment is both technical and business engineering, covering model selection, effect evaluation, fine tuning system construction, computing power preparation, platform building and application launch. Lingyan Miaoyu project experience indicates that quality of the model deployment phase determines pilot outcomes and acts as a critical watershed for enterprises to advance toward large scale phases.

8.2.1 Model Selection and Outcome Evaluation

At the early phase of model deployment, enterprises need to select suitable model capabilities aligned with business scenarios. Model selection is conducted along three dimensions: capability matching, cost structure and compliance requirements. Capability matching: adaptability in text comprehension, image recognition, video analysis, speech processing and domain specific knowledge. Cost structure: inference costs, invocation costs and computing power costs. Compliance requirements: focus on data security, privacy protection and industry specific specifications.

Lingyan Miaoyu has built a model evaluation system, conducting comprehensive assessment on models with indicators including accuracy, recall ratio, task completion rate, response latency and stability. It clarifies capability boundaries of models before deployment. Supported by the model evaluation system, enterprises obtain quantifiable model selection grounds in pilot phases.

8.2.2 Fine Tuning System (Prompt / RAG / SFT)

During model deployment, fine tuning systems constitute key links to improve model performance. Summarized from industry practices, Lingyan Miaoyu forms a three tier fine tuning system consisting of Prompt, RAG and SFT, allowing enterprises to select fine tuning paths flexibly according to business demands. 1. Prompt tuning: lightweight optimization that improves output quality, logicality and task execution performance via instruction design. 2. RAG tuning: integrates knowledge bases so that models can reference up to date business knowledge during generation and greatly enhance accuracy for professional scenarios. 3. SFT tuning: leverages domain specific data for training, strengthening semantic capabilities and task execution performance for specific scenarios; it delivers long term value.

The three approaches form a fine tuning spectrum from lightweight to heavy weight. Supported by the RAG knowledge base system, enterprises rapidly improve model performance in pilot phases.

8.2.3 Computing Power Preparation and Platform Construction

Computing power preparation and platform building serve as foundational engineering to guarantee stable model operation during model deployment. Enterprises select matching computing power resources according to model scale, inference concurrency and business scenarios. They leverage the Tianyu Intelligent Computing Platform for computing power scheduling, elastic scaling and resource optimization so models maintain stable performance across different scenarios. Platform building covers model access, permission management, invocation monitoring, log auditing and security isolation, creating controllable and manageable runtime environments for models inside enterprises. Supported by the Tianyu Intelligent Computing Platform and ALRO Model Portal, enterprises complete model deployment rapidly in pilot phases.

8.2.4 Application Launch and Performance Testing

Application launch and performance testing constitute critical steps guaranteeing pilot success in the final link of model deployment. Application launch embeds model capabilities into business workflows so agents can undertake task oriented roles in real world scenarios. Performance testing validates model performance under high concurrency conditions via stress testing, stability testing, latency testing and security testing. Lingyan Miaoyu practical experience demonstrates that performance testing is a vital guarantee for pilot success, helping enterprises discover potential risks before launch and maintain stable operation post launch. Supported by the agent execution system, enterprises form replicable application launch pathways in pilot phases.

8.3 Stage 3: Iterative Optimization

After enterprises finish model deployment and enter pilot business phases, intelligent capabilities will not improve spontaneously. Continuous iterative optimization systems are required to drive steady improvements in model performance, agent capabilities and business value. Numerous industry practices of Lingyan Miaoyu reveal that true value of enterprise AI transformation does not come from one off model launch, but derives from long term evolution cycles: agent oriented upgrading → horizontal scenario expansion → in depth task refinement → data closed loop optimization. The core objective of the iterative optimization stage is to push AI from “usable” toward “high quality”, shifting it from auxiliary tools toward core business performers and building enterprise grade intelligent growth curves.

8.3.1 Agent Oriented Upgrading

After model deployment, enterprises frequently find simple model invocation insufficient for supporting complex business workflows. At this point, agent oriented upgrading becomes the first step for iterative optimization. Through task planning, tool invocation, semantic boundary control and multi agent collaboration, agents transform models from “generating answers” to “executing tasks”. Lingyan Miaoyu summarizes three tier upgrade pathways for agents: from single agent to multi agent collaboration; from static tasks to dynamic task pipelines; from manual triggering to automatic triggering.

In this way, enterprises build measurable intelligent execution capabilities within business workflows. Supported by the agent execution system, enterprises construct sustainable agent capabilities in iterative phases.

8.3.2 Horizontal Scenario Expansion

After validation of pilot scenario effectiveness, enterprises need to extend intelligent capabilities from single scenarios to multiple scenarios, enabling AI to deliver value across broader business chains. Horizontal expansion generally follows the priority sequence: same type scenarios → intra department scenarios → cross department scenarios, expanding intelligent capabilities under low risk conditions.

Examples: Intelligent customer service may expand to hotlines, APPs, official WeChat accounts and offline service halls. Video patrol may expand to transportation, industrial parks, public security and urban governance. Intelligent risk control may expand to auditing, marketing and customer service.

Supported by scenario expansion methodology, enterprises achieve large scale intelligent coverage in iterative optimization phases.

8.3.3 Vertical In Depth Task Refinement

After completing horizontal expansion, enterprises deepen complex tasks vertically, enabling agents to take on higher difficulty and higher value business roles. Vertical deepening evolves from single step tasks to multi step tasks, from static tasks to dynamic tasks, and from auxiliary execution toward leading execution.

Examples: Government affairs: agents evolve from policy Q&A to service guidance, then further to work order generation and automatic circulation. Finance: agents evolve from risk identification to audit suggestion output, then further to automatic auditing. Manufacturing: agents evolve from defect identification to quality analysis, then further to production scheduling.

Supported by complex task pipeline systems, enterprises create higher intelligent value in vertical deepening.

8.3.4 Data Closed Loop and Continuous Optimization

In iterative optimization phases, the data closed loop serves as the key factor determining whether intelligent capabilities can keep growing. Enterprises need to build closed loop systems covering data collection, data annotation, model feedback, effect evaluation and model retraining, so models keep improving performance when running real world business scenarios. Lingyan Miaoyu project experience shows that data closed loops not only lift model performance, but also substantially improve agent execution capabilities, forming positive cycles of “data → model → agent → business” for long term intelligent growth. Supported by data closed loop systems, enterprises build sustainable intelligent growth curves in iterative optimization phases.

Future Outlook: Roadmap for Chinese Enterprise AI in the Agent Era

With continuous improvement of model capabilities, maturation of agent systems and rapid evolution of computing power infrastructure, Chinese enterprises are arriving at a critical inflection point shifting from digitalization to intelligentization. Future enterprise competition depends not merely on products, channels and organizational efficiency, but on agents’ capabilities for business link execution, collaboration and self learning. Practical deployments of Lingyan Miaoyu in government affairs, urban governance, finance, medical care, manufacturing and other industries show that the AI roadmap for enterprises in the agent era follows an evolutionary path: enhanced model capabilities → agent popularization → business intelligentization → organizational restructuring. This chapter sorts out future trends of model capabilities and provides long term outlooks for Chinese enterprise intelligent transformation.

9.1 Trend of Sustained Model Capability Enhancement

Over the next three to five years, large model capabilities will improve along multiple dimensions, pushing enterprises from simple model invocation toward agent collaboration and shifting AI from auxiliary tools toward core business performers. This trend originates not only from expanding model scale, but also from breakthroughs in architectural innovation, data system upgrading and agent execution capabilities.

First, foundational model capabilities will keep strengthening. Driven by architectural optimization, upgraded training methodologies and mature multimodal fusion technologies, models will achieve higher stability and consistency in Chinese language comprehension, long document processing, logical reasoning, cross modal reasoning and structured output, enabling them to undertake more complex task oriented roles in government affairs, finance, medical care, manufacturing and other scenarios. Supported by LingYu Architecture, models gain stronger semantic consistency and task execution capabilities.

Second, multimodal capabilities will become core competitiveness of future models. With integration of image, video, audio and sensor data, models will build cross modal task pipelines in urban governance, public security, manufacturing and smart mining scenarios, pushing agents from “virtual assistants” toward “real world executors”.

Third, agent execution capabilities will act as a key driver for enterprise intelligentization. Future models must not only generate content, but also plan tasks, invoke tools, execute workflows and cooperate with other agents, enabling enterprises to build measurable intelligent execution systems covering full business chains. Supported by agent collaboration systems, enterprises can construct agent networks covering complete business links and realize systematic upgrading from point breakthroughs.

Finally, domain specific model capabilities will keep deepening. As industry data systems, knowledge base systems and SFT fine tuning systems mature, models will gain stronger professional performance in financial risk control, medical diagnosis, government service, manufacturing quality inspection and urban governance scenarios. Domain specific capabilities improve agent performance in industry scenarios and help enterprises form differentiated advantages in intelligent competition.

9.2 Agents Becoming Mainstream Enterprise Paradigm

In future enterprise intelligent systems, agents will evolve from innovative pilots to mainstream forms and become core components of enterprise organizational structures, business workflows and production modes. As large model capabilities keep improving, tool invocation systems mature and task pipeline planning stabilizes, agents will gradually replace legacy “process automation + manual auditing” patterns and serve as enterprise digital labor, business executors and knowledge carriers. Practical experience from Lingyan Miaoyu across industries shows that enterprises in the agent era will demonstrate evolutionary trends of role specialization, collaboration orientation, autonomy and large scale deployment, pushing agents from auxiliary point functions toward systematic collaboration and finally to mainstream productive force forms.

Role specialization: future enterprises will no longer rely on a single model, but multiple agents with well defined responsibilities: customer service agents, risk control agents, inspection agents, quality inspection agents, auditing agents, marketing agents etc. Each agent has independent task boundaries, tool permissions and knowledge bases to fulfill measurable roles within business chains. Supported by agent role systems, enterprises build agent networks covering full business links. Collaboration orientation: enterprises shift from single agent task execution toward multi agent collaborative task completion. In financial scenarios, risk control agents, auditing agents and customer service agents cooperate to finish loan reviews; in government affairs scenarios, policy agents, service guidance agents and work order agents cooperate for service processing; in manufacturing scenarios, quality inspection agents, scheduling agents and equipment agents cooperate to complete production tasks. Collaboration improves efficiency and strengthens stability for complex tasks. Autonomy: future agents can not only execute tasks, but also automatically adjust strategies, update knowledge bases and optimize task pipelines according to business changes, possessing self learning, self adaptation and self optimization capabilities. Autonomy enables agents to maintain stable intelligent growth curves during long term operation and helps enterprises sustain long term advantages in intelligent competition. Supported by agent autonomy systems, enterprises build self evolving agent networks. Large scale deployment: with popularization of agents inside enterprises, enterprises expand from single scenario pilots to multi scenario coverage, and from single department applications toward enterprise wide collaboration, forming agent systems covering customer service, marketing, risk control, auditing, inspection, quality inspection, scheduling and management. Large scale deployment not only lifts enterprise efficiency, but also promotes enterprise organizational transformation from department based structures toward agent oriented structures, creating new enterprise organizational forms centered on agents.

9.3 Enterprise Intelligentization: From Auxiliary Tools to Core Business Performers

In the evolutionary path of enterprises within the agent era, the most decisive step is AI’s transition from auxiliary tools to core business performers. This shift means not merely improved model capabilities, but fundamental restructuring of enterprise organizations, business workflows and production modes. Practical deployments of Lingyan Miaoyu in multiple industries show that core trends of enterprise intelligentization are accelerating from tool orientation toward main participant orientation. AI evolves from efficiency improving means into key drivers for business growth, risk control and operational stability.

First, AI shifts from tool mode to workflow mode. In legacy modes, AI mostly undertakes auxiliary analysis, content generation and information retrieval work at the tool layer. In the agent era, AI embeds deeply into business workflows and takes core responsibilities for task execution, workflow circulation and result output. For instance, in government affairs scenarios, agents advance from policy Q&A to service guidance, and further to automatic generation and circulation of work orders; in financial scenarios, agents advance from risk identification to audit suggestions and further to automatic auditing; in manufacturing scenarios, agents advance from defect identification to quality analysis and further to production scheduling. Supported by agent execution systems, AI becomes main executors of business workflows.

Second, AI shifts from auxiliary mode to leading mode. In the agent era, AI no longer merely assists human beings to finish tasks, but can take leading roles within task pipelines. For example, in customer service scenarios, agents automatically finish consultation, service guidance and work order generation, shifting human customer service personnel from main performers to supervisors; in marketing scenarios, agents automatically generate content, formulate strategies and execute delivery, shifting marketing teams from executors to strategists; in urban governance scenarios, agents automatically identify incidents, produce disposition suggestions and push work order circulation, shifting law enforcement personnel from inspectors to decision makers. This trend pushes enterprises forward from human AI collaboration toward agent led operation.

Third, AI shifts from partial coverage toward full domain coverage. In early intelligent transformation phases, AI is usually deployed in isolated scenarios such as customer service, inspection or risk control. In the agent era, AI covers full enterprise business chains from front office to middle platform and back office, spanning operations, management and decision making to build full domain intelligent systems. For smart industrial parks, agents cover security protection, energy consumption, operations, collaboration and management; for smart cities, agents cover governance, transportation, security, emergency response and services; for smart finance, agents cover risk control, auditing, marketing, customer service and compliance. Supported by enterprise intelligent systems, AI becomes full domain productive force of enterprises.

Fourth, AI shifts from static mode to dynamic mode. In legacy modes, AI capabilities rely on fixed models and static rules. In the agent era, AI possesses self learning, self adaptation and self optimization capabilities. Agents can adjust strategies automatically, update knowledge bases and optimize task pipelines responding to business changes. For risk control scenarios, agents update rules dynamically according to risk variations; for inspection scenarios, agents adjust inspection routes according to incident distribution; for marketing scenarios, agents optimize content and strategies according to user feedback. Supported by agent autonomy systems, AI becomes dynamic intelligent engines for enterprises.

9.4 Computing Power and Energy Becoming Core Competitiveness

In the competitive landscape of the agent era, computing power and energy will elevate from basic resources to core productive forces, and become key factors determining enterprise intelligent levels, business scale and innovation speed. As model scale keeps expanding, inference concurrency rises and agent collaboration grows complex, enterprise demands for computing power grow exponentially. Meanwhile, the energy system behind computing power becomes a decisive factor affecting costs, stability and sustainability. Practical experience from Lingyan Miaoyu across industries shows that future enterprise competition compares not merely model capabilities and agent systems, but also computing power scheduling capabilities, energy efficiency levels and energy mix, forming new competitive logic: computing power as productive force, energy as cost competitiveness.

Computing power becomes strategic enterprise resource. Future enterprises will no longer rely on single computing power centers, but cross regional, multi center schedulable computing power networks to guarantee stable agent performance across diverse scenarios. The Tianyu Intelligent Computing Platform leverages unified computing power identification, intelligent scheduling and elastic scaling to grant enterprises higher computing power capacity during business peaks and lower costs during business troughs, transforming computing power from fixed assets to dynamic resources. Supported by the Tianyu Intelligent Computing Platform, enterprises build sustainable computing power competitiveness in the agent era. Energy efficiency becomes enterprise cost foundation. As model inference costs keep rising, energy efficiency acts as the key factor determining enterprise intelligent costs. Liquid cooling technologies, SST + HVDC power supply architecture and high density clusters will become standard configurations for future computing power infrastructure, enabling enterprises to acquire higher computing power under lower energy consumption. The Xingchen Intelligent Computing Center leverages liquid cooling systems and HVDC architecture to greatly improve energy efficiency, bringing enterprises 30 % 50 % cost advantages within five year cycles. Supported by green computing power systems, enterprises obtain lower cost and higher efficiency competitiveness in the agent era. Energy mix becomes enterprise ESG competitiveness. As global requirements for carbon emission and energy mix keep rising, enterprises must satisfy both energy efficiency and ESG compliance in intelligent upgrading processes. With up to 80 % renewable energy proportion, the Xingchen Intelligent Computing Center grants enterprises higher ESG value for agent execution with lower carbon emission and stronger sustainability. Future enterprise competition evaluates not merely intelligent capabilities, but also energy mix, making green computing power a long term competitive advantage for enterprises. Computing power scheduling and energy collaboration become system level enterprise capabilities. Future enterprises need to build complete systems covering computing power scheduling, energy efficiency management, energy collaboration and agent execution, so agents obtain stable computing power and low cost energy in diverse scenarios. Lingyan Miaoyu practical cases demonstrate that computing power scheduling and energy collaboration determine agent execution efficiency, helping enterprises build systematic competitiveness in the agent era.

9.5 Organizational Restructuring: Agent Driven New Type Enterprise Structure

In the evolutionary path of the agent era, organizational restructuring constitutes one of the most profound and disruptive changes. Enterprise organizational upgrading over the past two decades centered on digitalization: from workflow informatization, business online operation to data driven decision making. In the coming decade, agents will become core productive forces of enterprises, pushing organizations from workflow driven toward agent driven, shifting from post based systems toward agent based systems and forming brand new enterprise structural patterns. Practical experience from Lingyan Miaoyu across industries shows that organizational restructuring in the agent era presents systematic trends: virtualized posts, intelligent workflows, network style collaboration and autonomous management, bringing enterprises long term advantages in intelligent competition.

First, posts will be virtualized. Legacy posts rely on human execution; in the agent era, agents undertake core tasks and human personnel shift from executors to supervisors and strategists. For example, in customer service posts, agents handle consultation, service guidance and work order generation while human staff deal with complex problems and emotion management; in risk control posts, agents complete risk identification and produce audit suggestions while human personnel make high risk decisions; in inspection posts, agents finish video identification and early warning while human staff conduct on site disposition. Post virtualization enables enterprises to gain higher efficiency at lower cost. Supported by agent role systems, enterprises build agent networks covering full posts.

Second, workflows will be intelligentized. Legacy workflows rely on manual circulation, manual judgment and manual recording. In the agent era, workflows execute automatically by agents, shifting from human driven toward agent driven. In government affairs workflows, agents automatically generate work orders, circulate tasks and complete archiving; in financial workflows, agents automatically finish audit pipelines and produce compliance records; in manufacturing workflows, agents automatically complete quality inspection, scheduling and production optimization. Workflow intelligentization improves efficiency and greatly enhances standardization and consistency. Supported by agent execution systems, enterprises build measurable intelligent workflow systems.

Third, collaboration will be network oriented. Legacy organizational collaboration follows department based mechanisms. In the agent era, collaboration relies on agent networks, shifting enterprises from inter departmental cooperation toward inter agent cooperation. In financial scenarios, risk control agents, auditing agents and customer service agents cooperate for loan review; in urban governance scenarios, inspection agents, law enforcement agents and work order agents cooperate for incident disposition; in manufacturing scenarios, quality inspection agents, scheduling agents and equipment agents cooperate for production tasks. Agent network style collaboration brings higher execution efficiency and stronger organizational resilience for enterprises. Supported by multi agent collaboration systems, enterprises build cross departmental intelligent collaboration networks.

Fourth, management will become autonomous. Legacy management depends on manual supervision, manual assessment and manual optimization. In the agent era, management leverages agent autonomy systems. Agents automatically adjust strategies, update knowledge bases and optimize task pipelines responding to business changes. In risk control management, agents update rules dynamically responding to risk fluctuations; in inspection management, agents adjust inspection routes according to incident distribution; in marketing management, agents optimize content and strategies according to user feedback. Autonomous management brings self learning, self adaptation and self optimization intelligent growth curves for enterprises. Supported by agent autonomy systems, enterprises build new type organizational structures with self evolution capabilities.

9.6 Long Term Roadmap for Chinese Enterprise AI Transformation

In the macro evolution of the agent era, Chinese enterprise AI transformation will follow a long term roadmap with Chinese characteristics, shaped by global technology trends, Chinese enterprise organizational structures, industrial chain collaboration, digital foundation and policy environment. Deep practical experience of Lingyan Miaoyu in government affairs, finance, manufacturing, urban governance, medical care and industrial park scenarios shows that Chinese enterprise AI transformation will advance through five phases: foundation consolidation → agent oriented transformation → business restructuring → organizational upgrading → ecosystem collaboration, building intelligent systems with global level competitiveness.

1. Foundation Consolidation Phase: Core missions are to complete data governance, workflow digitalization, system integration and computing power foundation and lay underlying capabilities for AI implementation. Chinese enterprises possess prominent advantages in digital foundations, especially within government affairs, finance and manufacturing sectors, with large scale data assets and highly standardized workflows. Enterprises can complete foundational AI preparation within relatively short cycles, laying solid groundwork for the agent era. 2. Agent Oriented Transformation Phase: Enterprises shift from simple model invocation toward agent execution, pushing AI from auxiliary tools to core business performers. Agents are deployed across customer service, inspection, risk control, auditing, quality inspection, scheduling and management scenarios, forming measurable intelligent execution capabilities within business chains. Benefiting from standardized workflows and organizational collaboration advantages of Chinese enterprises, agents can achieve large scale application within shorter cycles. Supported by agent collaboration systems, enterprises build agent networks covering full business chains. 3. Business Restructuring Phase: Enterprises restructure business workflows around agents. Workflows shift from human driven toward agent driven, from linear circulation toward dynamic task pipelines and from department based systems toward agent based systems. For example, audit pipelines in finance will execute automatically by agents; business guidance and work order circulation in government affairs will finish via agents; quality inspection and scheduling in manufacturing will be completed by agent collaboration. Business restructuring greatly improves enterprise efficiency and creates structural advantages in intelligent competition. 4. Organizational Upgrading Phase: Enterprises restructure organizational systems around agents. Virtualized posts, intelligent workflows, network style collaboration and autonomous management become core organizational features. Agents serve as enterprise digital labor, pushing organizations from post based systems toward agent based systems and forming brand new organizational patterns. Supported by agent autonomy systems, enterprises gain self learning, self adaptation and self optimization and form long term intelligent growth curves. 5. Ecosystem Collaboration Phase: Enterprises build cross enterprise, cross industry and cross region intelligent ecosystems centered on agents. Agents achieve collaboration within supply chains, industrial chains and value chains. For manufacturing industry, agents cooperate across enterprises for production scheduling; for urban governance, agents cooperate across departments for incident disposition; for finance industry, agents cooperate across institutions for risk control and auditing. Ecosystem collaboration will become a long term competitive advantage for Chinese enterprises in global competition and build industry ecosystems with global influence.

Appendix: Technical Indicators / Architecture Diagrams / Capability Matrices

The appendix provides measurable, reusable and referential technical indicator systems, architectural references and capability matrices for enterprises, government institutions and industry partners, enabling this white paper to deliver not merely strategic guidance but also engineering implementation reference materials. The appendix covers model capability indicators, agent execution architectures, computing power system structures, data governance frameworks and industry capability matrices and offers systematic references for planning, deployment and operation in the agent era.

10.1 Technical Indicator System (Model & Agent Metrics)

In the agent era, technical indicator systems are used not only to evaluate model capabilities, but also to assess agent execution performance, system stability and business value. Summarized from industry practices, Lingyan Miaoyu establishes a four tier indicator system: model indicators + agent indicators + system indicators + business indicators, helping enterprises build measurable intelligent capabilities in different phases.

Model indicators: semantic comprehension, logical reasoning, long document processing, multimodal recognition, structured output and other dimensions, serving as core metrics for evaluating foundational model performance. Agent indicators: task completion rate, tool invocation success rate, task pipeline stability and collaboration efficiency, acting as key metrics for evaluating agent execution capabilities. System indicators: response latency, concurrency capacity, stability and energy efficiency ratio, representing important metrics for evaluating system engineering performance. Business indicators: efficiency improvement, cost reduction, risk mitigation and user experience enhancement, serving as ultimate metrics for evaluating intelligent transformation value.

Supported by model evaluation systems, enterprises build quantifiable technical indicator systems across different phases.

10.2 Architecture Diagrams (Model / Agent / Compute Architecture)

In the agent era, architectural design constitutes core engineering determining enterprise intelligent capabilities. Summarized from multiple industry practices, Lingyan Miaoyu adopts a five tier architecture: model layer — agent layer — tool and knowledge layer — computing power layer — security layer, delivering stable intelligent capabilities for diverse scenarios.

Model layer: provides semantic comprehension, content generation, multimodal recognition and logical reasoning capabilities, serving as foundational capability source for agents. Agent layer: takes charge of task planning, tool invocation, pipeline execution and collaboration management, representing core execution layer of the agent era. Tool and knowledge layer: provides retrieval, knowledge bases, workflow engines, work order systems and business APIs as capability extensions for agent task execution. Computing power layer: realizes computing power scheduling, elastic scaling and energy efficiency optimization via the Tianyu Intelligent Computing Platform, serving as foundation for stable agent operation. Security layer: guarantees AI security via permission management, data isolation, audit logs and compliance systems, acting as bottom line engineering for enterprise grade intelligentization.

Supported by LingYu Architecture and the Tianyu Intelligent Computing Platform, enterprises build agent architecture systems featuring high stability, high energy efficiency and high security.

10.3 Industry Capability Matrix

[Figure 10-1 | Industry Capability Matrix]

The capability matrix displays capability coverage of Lingyan Miaoyu across diverse industries and scenarios, helping enterprises rapidly identify implementable scenarios and expandable paths in planning phases. The capability matrix unfolds along industry dimension, scenario dimension and capability dimension and builds referential and reusable intelligent capability maps.

Finance industry: covers risk control, auditing, marketing, customer service and compliance scenarios, enabling agents to deliver stable performance within high risk, high concurrency and highly professional scenarios. Government affairs industry: covers policy Q&A, service guidance, work order circulation and urban governance, enabling agents to build systematic capabilities within high standard, workflow intensive and collaboration oriented scenarios. Manufacturing industry: covers quality inspection, scheduling, equipment diagnosis and production optimization, enabling agents to deliver engineering grade capabilities within high complexity, high precision and real time demanding scenarios. Supported by the industry capability matrix system, enterprises build scalable intelligent upgrade paths in the agent era.

10.4 Data Governance Framework

[Figure 10-2 | Data Governance Closed Loop]

In the agent era, data governance constitutes core engineering determining model effects, agent execution capabilities and system security. Summarized from Lingyan Miaoyu industry practices, a five tier governance framework is formed: data collection → data cleansing → data annotation → knowledge construction → data security, helping enterprises build stable data foundations in intelligent upgrading processes.

1. Data collection: covers structured data, unstructured data and multimodal data to lay data foundations for diverse scenarios. 2. Data cleansing: improves data quality through de noising, error correction and standardization. 3. Data annotation: improves model training outcomes through manual annotation and semi automatic annotation. 4. Knowledge construction: improves professional model performance through RAG knowledge bases and industry knowledge graphs. 5. Data security: safeguards intelligent system security via permission management, data isolation and compliance systems.

Supported by the data closed loop system, enterprises build sustainable data governance capabilities in the agent era.

Concluding Remarks

Facing the agent era, enterprises should regard large models, computing power, data, tools and organizational capabilities as an integrated system. Leveraging controllable model capabilities, orchestratable agent execution and sustainable computing power base, Lingyan Miaoyu and its full stack supporting system support enterprises advancing from isolated pilot tests toward large scale application, while balancing business value, operational stability and security compliance.

Huazhi Future (Chongqing) Technology Co., Ltd.

Computing Power × Algorithm × Application · Building Enterprise Grade Intelligent Future

FAQ: Huazhi Future Lingyan Miaoyu White Paper

What is the Lingyan Miaoyu large language model?

Lingyan Miaoyu is the large language model developed by Huazhi Future (Chongqing) Technology Co., Ltd. Built on the self-developed LingYu Architecture, it provides Chinese language enhancement, multimodal understanding covering text, image, video, and audio, controllable semantic editing via the ScopeEdit system, and agent task execution. It is offered in a 9B parameter specification adaptable to edge-side inference and complex task scenarios.

What is the LingYu Architecture?

LingYu Architecture is Huazhi Future's underlying large model architecture. It comprises a multimodal input layer, a semantic fusion layer, a controllable knowledge editing layer, and an agent execution layer, forming a complete pipeline from input and reasoning through task execution. Its model system consists of base models, enhanced models, and industry-specific models spanning general-purpose performance to domain-specialized capabilities.

How fast can the Xingchen Distributed Intelligent Computing Center be deployed, and how energy-efficient is it?

According to the white paper, the Xingchen Distributed Intelligent Computing Center achieves standardized rapid delivery within 90 days through standardized computing power cabins, cooling source cabins, and power cabins. It reaches a PUE as low as 1.15 with a green electricity proportion of up to 80%, and its 800VDC power architecture increases overall energy efficiency by 15% to 20%. A single cabin accommodates 32 B300 servers for ten-thousand-card training tasks.

What does the ALRO Unified Model Access Portal do?

The ALRO Unified Model Access Portal aggregates more than 205 mainstream models domestically and abroad, achieving unified model invocation, unified formats, and unified permission management. It supports format conversion for models such as OpenAI, Claude, Gemini, DeepSeek, Qwen, and GLM, enabling enterprises to "call all models with one set of code", with official direct connection, intelligent routing, and enterprise-grade security including Key management, permission assignment, consumption auditing, and independent encrypted channels.

What is the recommended path for enterprise AI transformation?

The white paper proposes a three-stage implementation methodology: Planning & Preparation, Model Deployment, and Iterative Optimization. Enterprises are advised to cut in from high-value, quantifiable scenarios following the selection principles of high business value, high frequency, measurable outcomes, and replicability, ultimately forming an agent-driven business closed loop.

Related Reading

• MAAS Technology Advancement
• MAAS Newsroom
• Maase Inc. Official Site

Important Note

This white paper is structurally edited and visually designed based on original materials provided by the company. Unless otherwise specified, the product parameters, construction cycles, energy efficiency metrics, number of models, case results, and development judgments involved in the text are as stated in the materials, aiming to be used for technical exchanges and solution explanations, and do not constitute commitments, guarantees, or investment advice.

© 2026 Huazhi Future (Chongqing) Technology Co., Ltd. All rights reserved.

Huazhi Future (Chongqing) Technology Co., Ltd.
Computing Power × Algorithm × Application · Building Enterprise-Grade Intelligent Future