Preface: Embracing the Era of Computing Power, Empowering the Smart Grid
Prologue: The Epochal Coordinates at the Crest of the Fourth Wave
We stand at an unprecedented historical confluence. The steam engine drove the First Industrial Revolution, electricity illuminated the brilliance of the Second, and computers and the internet ushered in the information age of the Third Industrial Revolution. Today, the wave of the Fourth Industrial Revolution, driven by artificial intelligence, big data, and cloud computing, is sweeping across the globe with unprecedented speed and breadth, reshaping production models, transforming lifestyles, and defining national core competitiveness.
In this magnificent transformation, if data is the "oil" of the new era, then computing power is undoubtedly the "engine" that drives this data field to continuously produce value. It is no longer merely a performance indicator in the field of information technology; like electricity a century ago, it is rapidly evolving into a fundamental, strategic societal resource — a key yardstick for measuring a nation's technological strength and innovation capability. From breakthroughs in basic science to the deepening of industrial applications, from macroeconomic regulation to the refinement of social governance, the penetration of computing power is everywhere, and its importance has been elevated to an unprecedented strategic height.
In particular, in recent years, generative AI technology represented by large language models (LLMs) has achieved disruptive breakthroughs, triggering a global "war of a hundred models." Behind this lies an even more intense "computing arms race." Model parameter scales have leaped from hundreds of millions to hundreds of billions or even trillions, and the volume of training data has climbed from TB to PB levels, all of which place continuously escalating demands on computing power. Computing power, especially intelligent computing capacity centered on GPUs, has become the "lifeblood" of AI development — the decisive factor determining whether a nation, an industry, or an enterprise can take a leading position in this intelligence competition.
It is against this grand backdrop that we examine our own energy industry, particularly the "national team" bearing the responsibility for the national economy, people's livelihoods, and national energy security — State Grid Corporation of China. The power grid is the artery of modern industrial civilization and the key infrastructure carrying the energy transition and the development of the digital economy. When this intelligent wave driven by computing power meets the power system, which bears the heavy responsibility of the "dual carbon" goals and energy security, a profound, systemic transformation has already begun. How to transform surging computing power into a powerful driving force for the high-quality development of the power grid? How to build a solid foundation capable of supporting the future intelligent needs of the grid? This is not only a technical issue but a major topic concerning the future of the enterprise, industry development, and national strategy.
Writing this book — Enterprise AI Computing Platform: Construction, Operation, and Ecosystem — is precisely to answer the questions of our era. We hope that by systematically organizing and summarizing State Grid's exploration and practice in AI computing platform construction and operations, we can provide fellow travelers with a roadmap worth referencing, offer decision-makers insightful references, and open a window for all readers concerned with the integrated development of energy and AI, allowing them to observe the evolution of the future smart grid. This is not only a technical guide but also a practical report on how large state-owned enterprises carry out self-innovation and strategic transformation amidst the waves of the digital age.
The Need of the Era — Why Must the Grid Build a Powerful AI Computing Foundation?
The intelligence of the power grid is not a new concept, but the new generation of AI technology, represented by large models, is redefining the meaning of "smart grid" with unprecedented depth and breadth. Traditional grid intelligence was more about scenario-specific automation and local optimization. The future smart grid, however, will be an "intelligent living entity" with global awareness, autonomous learning, intelligent decision-making, and collaborative control capabilities. To nurture and support such a complex living entity, a powerful, unified, and efficient AI computing foundation is an indispensable cornerstone.
The "Ballast Stone" of Energy Security: An Inevitable Choice for Meeting Extreme Challenges
Against the backdrop of intensifying global climate change and increasing geopolitical risks, ensuring the safe and stable operation of the power system faces unprecedented challenges. Extreme weather events occur frequently, posing serious threats to the physical infrastructure of the grid; cyber attacks are becoming increasingly sophisticated, placing higher demands on the grid's digital security defenses. Traditional emergency plans and defense systems often appear passive and lagging in the face of high uncertainty.
Artificial intelligence, especially large models trained on massive amounts of data, can provide entirely new approaches to grid security. Through in-depth analysis of meteorological data, equipment status data, and grid topology, AI can achieve accurate prediction and risk assessment of the impact of natural disasters, guiding disaster prevention and mitigation efforts in advance. In the field of network security, AI can monitor anomalous traffic in real time, identify novel attack patterns, and achieve a transformation from "passive defense" to "active immunity." For example, training a "grid digital twin" large model to simulate various extreme fault scenarios in virtual space and deduce optimal recovery strategies will greatly enhance the grid's resilience and impact resistance. All this complex computation and simulation rely on massive, high-performance computing power.
The "Accelerator" of the Dual Carbon Goals: The Core Mission of Building a New Power System
Achieving the goals of carbon peak and carbon neutrality is a broad and profound systemic transformation of the economy and society. Its main battlefield is in the energy sector, and its main front is in the power industry. Building a new power system with new energy as the mainstay is the core task of this transformation. However, renewable energy sources such as wind and solar are inherently random, volatile, and intermittent. Their large-scale, high-proportion integration into the grid poses disruptive challenges to real-time balancing, power flow control, and power quality.
How to "live by the weather" and achieve accurate prediction of new energy output? How to coordinate "source-grid-load-storage" to smooth out the fluctuations of new energy and ensure the dynamic stability of the grid? How to optimize dispatch so that every kilowatt-hour of green electricity can be efficiently consumed? The complexity of solving these problems has grown exponentially, far exceeding the limits of traditional computing methods. AI provides the key to breaking through. Through AI algorithms, we can build higher-precision new energy power prediction models; through technologies such as reinforcement learning, we can train "super dispatchers" capable of second-level and millisecond-level optimization and dispatch under complex working conditions; through analysis of massive user electricity consumption behavior, we can achieve precise demand-side response and aggregated regulation of virtual power plants. Behind these advanced intelligent applications lies the large-scale parallel training and real-time inference of massive multimodal data — meteorological, remote sensing, equipment, market, and more — the essence of which is a tremendous consumption of computing power. Without a powerful computing platform, building a new power system would be like "cooking without rice."
The "Multiplier" of Quality and Efficiency: The Internal Need for Driving Modernization of Enterprise Operations Management
As the world's largest public utility, State Grid possesses the world's largest transmission and distribution network, massive power equipment assets, and hundreds of millions of end users. Such a huge system has enormous optimization potential in its daily operations, asset management, customer service, and every other aspect.
In asset management, traditional periodic maintenance models are costly and inefficient. By introducing AI visual recognition technology, drones can autonomously inspect transmission lines and identify defects in real time; through deep learning of equipment operation data, a transition from "fault repair" to "predictive maintenance" can be achieved, greatly improving asset health and utilization efficiency. In customer service, intelligent customer service based on large language models can provide more natural and accurate service 24/7, handling various businesses from bill inquiries to fault reporting, with the potential to noticeably improve user satisfaction and operational efficiency. In areas such as infrastructure project management, supply chain optimization, and corporate business decision-making, AI can also empower more scientific and refined management through data insights. The widespread adoption and deepening of these applications will comprehensively improve the company's total factor productivity. All of this requires a unified computing platform capable of supporting multi-business, multi-scenario, and large-scale AI model training and inference.
The "Cradle" of Innovation-Driven Development: A Strategic Layout for Cultivating Future Core Competitiveness
In the digital economy era, an enterprise's core competitiveness is increasingly reflected in its ability to harness data and innovate technologically. The computing platform is not only a tool for supporting current businesses but also a "cradle" and "testing ground" for inspiring future innovation.
An open, easy-to-use computing platform can greatly lower the barrier for internal employees, research institutions, and partners to engage in AI innovation. It is like a fertile "black soil" where various innovative "seeds" can take root and germinate. Business experts in the power grid can use the tools provided by the platform to quickly transform their domain knowledge into AI models; algorithm engineers can leverage powerful computing resources to explore more advanced model architectures and algorithms; front-line teams can develop small applications that solve real pain points based on the platform. This bottom-up innovation vitality is the fundamental driving force for an enterprise to maintain lasting vitality. Furthermore, by building an independently controllable computing foundation, we can effectively counter external technology blockade risks, keeping core technologies and data firmly in our own hands, laying a solid foundation for cultivating "power large models" and industry standards with independent intellectual property rights.
In summary, building a powerful AI computing foundation is not an "option" for State Grid but a "must-answer question" concerning survival and development. It is a strategic need for ensuring national energy security, a mission of the era for supporting the green transformation of energy, an internal driving force for promoting the modernization of enterprise management, and a long-term layout for cultivating future core competitiveness. This is the fundamental reason we have embarked on this journey.
The Path of Breakthrough — Facing the Three Major Challenges of Computing Construction and Operations
Having established the strategic necessity of building a computing platform, we must soberly recognize that this path is no smooth road. In practice, especially for a large-scale, complex traditional infrastructure enterprise like State Grid, there are "three mountains" standing between "owning computing power" and "using computing power well." The core content of this book revolves around how to "move these mountains."
The First Mountain: The Management "Black Box" — Lack of Full Life Cycle Scientific Operations
In the early stages of the project, many units often perceive computing power merely at the level of "hardware procurement." They think that investing heavily in the most advanced GPU servers completes the computing construction. However, this is merely the first step of a long march. We soon discovered that if computing resources lack scientific and refined operations management, their effectiveness will be greatly compromised, even resulting in enormous resource waste.
Mismatch between Supply and Demand: How should computing demands be submitted? Business departments often find it difficult to accurately quantify how much computing power and how much time an AI task actually requires. This leads to demand submissions that are either too conservative, failing to meet model training requirements, or too aggressive, applying for large amounts of resources that then sit idle. The management side on the supply end also lacks effective evaluation tools to judge the reasonableness of demands, leading to the phenomenon of "the crying baby gets the milk."
Lag in Allocation and Recovery: The allocation process for computing resources often relies on manual approval and manual configuration, resulting in lengthy and inefficient processes that cannot adapt to the needs of agile development. More critically, there is a lack of effective recovery mechanisms. After a training task ends or an inference service goes offline, the computing resources it occupies are often forgotten, becoming "zombie resources." This means computing power "sleeps" while new demands queue and wait.
"Unknowability" of Usage: Both managers and users find it difficult to understand the usage of computing resources in real time and clearly. Key indicators such as GPU utilization, memory usage, and network throughput remain a "black box." Users do not know why their programs are running slowly, managers do not know where the resource bottleneck is, and effective performance optimization and capacity planning cannot be carried out.
This extensive management model causes the return on investment (ROI) of the computing platform to fall far below expectations. Therefore, establishing a scientific operations system covering the full life cycle of demand submission, evaluation, allocation, monitoring, adjustment, and recovery is the first core challenge we must overcome.
The Second Mountain: The "High Wall" of Usage — The Technical Threshold of Intelligent Computing
Compared with traditional general-purpose computing (CPU computing), intelligent computing centered on GPUs has a significantly higher technical barrier. This "high wall" leaves many teams with business needs but lacking professional AI skills outside the door.
Complex Software and Hardware Environment: The operation of AI applications depends on a complex software and hardware stack, from the underlying GPU drivers and CUDA toolkit, to the intermediate containerized environment (such as Docker), to the upper-layer deep learning frameworks (such as TensorFlow and PyTorch) and various dependency libraries. Version compatibility, environment configuration, and dependency conflicts often frustrate non-specialist users.
Professional Performance Optimization Skills: To fully leverage the computing power of GPUs requires extensive professional knowledge. How to implement distributed training strategies such as data parallelism, model parallelism, and pipeline parallelism? How to choose the appropriate mixed-precision training strategy? How to optimize the I/O bottleneck of data loading? How to perform operator fusion and compilation optimization for specific models and hardware? These are almost like "a book from heaven" for ordinary business personnel.
The "Dangerous Leap" from Training to Deployment: A model successfully trained in the lab does not mean it can stably provide online inference services. Model quantization, pruning, and distillation; inference engine selection and optimization; containerized packaging of services and elastic scaling deployment — all these require professional engineering capabilities.
This "high wall" means the computing platform can often only be used by a small number of "expert-level" users, unable to benefit the vast majority of business departments, making it difficult for AI technology to be widely and universally implemented within the enterprise. Therefore, how to knock down this "high wall," lower the barrier for users, and achieve universal access through platform-based services and professional technical support is the second core challenge we must overcome.
The Third Mountain: The "Silos" of Collaboration — Lack of Integrated Headquarters-Province Linkage
State Grid operates a multi-level management system of "headquarters-province-city." In the early stages of computing construction, various units conducted their own explorations to varying degrees, forming a number of "computing chimneys" and "data silos." This fragmented construction model has brought numerous problems.
Duplication of Construction and Resource Waste: Units independently procure hardware and build platforms, following different technical routes and varying standards and norms, resulting in serious duplication of investment. At the same time, due to the tidal effect of business, the computing resources of some units are stretched thin during peak periods and largely idle during off-peak periods, and they cannot be effectively transferred and shared within the company system.
Inability to Precipitate and Reuse Technology and Experience: The problems encountered and the experience accumulated by various units in model development and platform operations often remain confined internally and cannot be shared across the entire company. The "pitfalls" that one provincial company has fallen into may be encountered again by another. Excellent application cases and model algorithms are also difficult to quickly promote and reuse, preventing the formation of a company-wide technical synergy.
Inability to Support Ultra-Large-Scale Tasks: For national-level large model tasks that require the mobilization of network-wide data and ultra-large-scale training at the ten-thousand-card level, such as "Guangming Electric Power," the computing resources of any single provincial company are insufficient to support them independently. A "large computing power grid" that can uniformly dispatch and collaborate must be built to accomplish such major undertakings.
Therefore, how to break the "silos," following the integrated model of "headquarters + provincial," to build a computing operations network where resources can be shared, tasks can be divided, technology can be coordinated, and capabilities can be reused, leveraging the overall synergy advantage, is the third core challenge we must overcome.
Facing these three mountains head-on, State Grid's computing construction and operations path clearly defined its core tasks from the start: internally, build a scientific operations management system and a strong technical support system to achieve "cost reduction and efficiency improvement" and "lower the threshold"; externally, break down barriers, connect the two levels, and form an "integrated collaborative combat capability." The "Five-in-One" construction approach elaborated in this book is precisely the systematic answer centered on this core task.
The Method of Breaking Through — The "Five-in-One" General Program for Computing Platform Construction and Operations
Facing the above challenges, we deeply understand that piecemeal improvements and makeshift repairs are futile. A systematic, top-down transformation must be undertaken. Through continuous exploration, practice, and review, we gradually formed and perfected a set of "Five-in-One" general programs for computing platform construction and operations. It covers five interrelated and progressively deepened dimensions — top-level design, resource management, technical support, application promotion, and ecosystem construction — forming the core framework of this book.
The First Dimension: Strategic Layout — Setting Strategy and Rules through "Top-Level Design"
Preparedness ensures success; unpreparedness spells failure. Top-level design is the "general program" and "anchor" of computing platform construction. It answers the fundamental questions of "what kind of platform do we want to build" and "how should we organize ourselves to build and operate it?"
In terms of strategic positioning, we have clearly defined the integrated strategy of "headquarters coordination, provincial collaboration, resource sharing, service empowerment." The headquarters is not only a provider of computing power but also a maker of rules, an outputter of standards, and an enabler of capabilities. Provincial companies are users of computing power, innovators of scenarios, and implementers of applications. The two form a benign interaction, together constituting an organic whole.
In terms of organizational assurance, we have promoted the construction of a two-level "headquarters + provincial" intelligent computing operations team. This team serves as the "nerve center" and "executive terminals" of the computing operations system, ensuring that the strategic intent of the headquarters can be precisely transmitted and the business needs of the provinces can be quickly responded to. We have clearly defined the work interfaces, job responsibilities, and collaboration mechanisms for this team, so that "professionals do professional work."
In terms of institutional construction, we have focused on building a complete set of standards and specifications — the key to transforming computing operations from "rule by man" to "rule by law." This system covers everything from "Computing Usage Evaluation Standards" to "Computing Operations Personnel Management Methods," from "Technical Support Management Processes" to "Vendor Service Quality Assessment Methods," providing institutional safeguards for the orderly, efficient, and fair operation of the entire computing platform.
The Second Dimension: Intensive Cultivation — Full Life Cycle Closed Loop Centered on "Resource Management"
If top-level design is the "blueprint," then resource management is the "construction." Drawing on the lean production philosophy of modern industry, we meticulously manage the full life cycle of computing resources, aiming to make every computing resource fulfill its potential and maximize its value.
From "Guesstimation" to "Precise Calculation": We established a scientific computing demand estimation mechanism, developing standardized estimation models and tools to guide business departments in accurately quantifying their needs from dimensions such as model parameters and data scale, achieving a shift from "approximate calculation" to "precise calculation."
From "Manual Blocking" to "Process Flow": We built standardized computing application, allocation, and recovery processes, and solidified them through the computing management platform, achieving automated and online operations and greatly improving resource turnover efficiency.
From "Static Allocation" to "Dynamic Adjustment": We established a "dual-threshold" monitoring and early warning system and a two-level collaborative expansion process, achieving minute-level elastic supply for inference computing. At the same time, for training tasks, we established an emergency demand rapid response channel and dynamic scheduling mechanism to ensure the timely completion of key tasks.
From "Black Box" to "Transparency": We pioneered the "computing resource display" mechanism, regularly publishing resource levels, utilization rates, economic indicators, and other metrics to all users in monthly and annual reports, enhancing resource transparency. Through data analysis, we provide early warnings and evaluations, driving users to optimize their resource usage behavior.
The Third Dimension: Escorting — Lowering the Usage Threshold through "Technical Support"
The technical support system is the "bridge" connecting "powerful computing" with "broad application." Our goal is to allow users to "use the AI platform well even without understanding AI technology," upgrading from providing "unfurnished" IaaS resources to providing "fully furnished" or "move-in ready" PaaS and MaaS (Model as a Service) services.
Comprehensive Consulting Services: We provide end-to-end consulting services, from data center planning and design and computing cluster architecture selection to resource configuration optimization for specific business scenarios, helping users "build well and configure accurately."
Systematic Training Assurance: We provide full-process assurance services covering pre-training (cluster health check), mid-training (fast fault recovery), and post-training (performance tuning, model adaptation), ensuring that users' models "can run, run fast, and run stably."
Fine-Grained Inference Optimization: We focus on the performance bottlenecks of the inference phase, providing a series of services from deployment optimization and resource scheduling to distributed parallelism and system architecture optimization, committed to improving application response speed, throughput, and user experience, so that models "are used well and used economically."
Active Operations Assurance: We have established a comprehensive "computing-storage-network" indicator monitoring system, health assessment mechanism, and high-reliability assurance plan (redundancy, stress testing, drills), achieving a transformation from "passive response" to "active prevention" in the operations model.
The Fourth Dimension: Value-Driven — Promoting Value Transformation through "Application Promotion"
The ultimate value of technology and platforms is reflected in the effectiveness of business applications. We have always adhered to the principle of "promoting construction through use," making application promotion and value realization the starting point and ultimate goal of all work.
Make Services "Findable": We have compiled and published the "Intelligent Computing Operations Service Catalog," promoting it through various online and offline channels, so that potential users can clearly understand what the platform can provide and how to access the services.
Make Experience "Visible and Learnable": We established a normalized results compilation and publicity mechanism, regularly summarizing and distilling the advanced practices and excellent cases of various units, forming the "Computing Operations Results Case Collection," setting benchmarks, and driving the rapid replication and promotion of experience through exemplary cases.
Make Knowledge "Precipitated and Flowing": We attach great importance to knowledge management, building a computing operations knowledge base, systematically collecting, organizing, and sharing various knowledge assets — from implementation plans and technical documents to user manuals and troubleshooting experience — transforming them into the core capability of the organization.
The Fifth Dimension: Open and Win-Win — Gathering Internal and External Forces through "Ecosystem Construction"
Alone, you go fast; together, you go far. In the face of the rapidly evolving AI technology landscape, no closed system can maintain long-term vitality. We are committed to building an open, cooperative, and win-win computing ecosystem.
Bring in "External Brains": We actively build and manage a computing expert team, gathering top wisdom from academia and industry to provide decision support for our technology path and strategic planning.
Make "Friends": We have built a computing ecosystem partner system, establishing deep cooperation with leading domestic and international hardware manufacturers, software companies, algorithm enterprises, and research institutions, jointly conducting technology R&D, product integration, and market promotion.
Build "Standards": We have designed an ecosystem construction effectiveness evaluation system, scientifically assessing the results of ecological cooperation from multiple dimensions such as technological innovation, resource introduction, and business value, and based on this, continuously optimizing our operations strategy.
Set "Samples": By selecting pilot units, we practice the results of ecosystem cooperation first, forming replicable promotion plans and gradually expanding the ecological influence.
This "Five-in-One" construction and operations general program, like five structural pillars of a precision building, together support the magnificent edifice of State Grid's AI computing platform. It is an organic, continuously iterative system, ensuring that our computing construction not only "can be built" but also "can be managed clearly, used smoothly, demonstrates value, and thrives as an ecosystem."
Future Outlook — Toward a "Computing Network" and the Grid's "Intelligent Living Entity"
Standing at today's vantage point and looking back, we are gratified by the phased achievements we have made. But we are even more aware that this is merely the beginning of a long journey. The development of AI and computing technology advances at a tremendous pace, and the form of the future smart grid will continue to evolve. We must think about and lay out the future with a more open mind and a more forward-looking vision.
From "Computing Centers" to "Computing Networks"
The current "headquarters + provincial" integrated system solves the problem of two-level collaboration, but it is essentially still a collection of multiple computing centers. The ultimate form in the future will be the "Computing Network." This concept draws on the operating model of the power grid, aiming to make computing power, like electricity, achieve unified scheduling, on-demand allocation, and immediate availability across "cloud-edge-device."
One Network Connecting All Regions: We will strive to break down geographic and administrative divisions, using advanced networking technologies (such as RDMA and DPU) and cloud-native scheduling platforms to truly "connect" computing resources from all over the country into a logical "computing network." At that time, a task deployed in East China can seamlessly call upon idle computing power in North China, achieving ultimate optimization of resource utilization.
Deep Coupling of Computing and Electricity: As a grid enterprise, we possess unique advantages. In the future, the computing network will be deeply integrated with the power network, achieving "computing follows green electricity." We can intelligently schedule high-energy-consumption training tasks to data centers in areas rich in wind and solar resources with low electricity prices, truly realizing green computing and supporting the "East Data West Computing" strategy and the "dual carbon" goals.
Integrated Cloud-Edge-Device Collaboration: Many AI application scenarios occur at the edge of the grid, such as substations, transmission towers, and distribution rooms. The future computing network will cover the full "cloud-edge-device" scenario: the cloud handles large-scale model training and global decision-making, the edge handles low-latency real-time inference and local autonomy, and devices handle data collection and simple processing, forming an efficient and collaborative distributed intelligent system.
From "AI-Enabled" to "AI-Native"
Currently, we are more about using AI technology to "enable" existing grid business processes. In the future, as technology matures and applications deepen, we will move toward an "AI-Native" era. This means that business processes, organizational structures, and even business models will be restructured and designed around AI and data.
Deep Evolution of the "Power Large Model": The "Guangming Electric Power" large model we are training will evolve from current language and visual capabilities toward deeper multimodal fusion, scientific computing, and decision control capabilities. It will not be merely a Q&A tool or an analysis assistant but will truly become the "digital brain" of the smart grid, capable of understanding complex grid physical laws, making long-term trend predictions, and performing short-cycle real-time control.
The Rise of AI for Science: We will promote the extension of AI technology from empowering engineering applications to empowering basic scientific research. Using AI and high-performance computing, we will accelerate research breakthroughs in areas such as new materials (such as room-temperature superconductivity), meteorological physics, and electrical theory, solving at the source the bottleneck problems restricting the development of the power industry.
"Driverless" Power Grid: Drawing on the development path of autonomous driving technology, the future power grid will also gradually achieve higher levels of autonomous operation capability. From L1-level auxiliary operation, to L2 and L3-level partial autonomous regulation, ultimately moving toward L4 and even L5-level "unmanned power grid." In this ultimate form, the grid will become an "intelligent living entity" capable of self-perception, self-diagnosis, self-healing, and self-optimization.
From "Technology Leader" to "Ecosystem Leader"
As our computing platform and technical capabilities continue to mature, State Grid will not only be a technology adopter but also have the opportunity to become a leader in industry standards and ecosystems.
Build the "AI Infrastructure System" for the Energy Industry: We will strive to build the computing platform, power large model, and a series of industry applications into an open, standardized "Energy AI Infrastructure System." Through API interfaces, SDK toolkits, and other forms, we will open our AI capabilities to the entire industry and even society at large, empowering upstream and downstream partners to jointly build a thriving energy intelligent application ecosystem.
Lead Industry Standard Setting: We will actively participate in and even lead the formulation of standards in areas such as AI applications, data governance, and computing networks within the power industry, elevating our practical experience into industry norms and promoting the healthy and orderly development of the entire industry.
Conclusion: An Invitation, A Beginning
At the methodological level, this book is not merely a technical manual. The construction and governance of intelligent computing centers is, in essence, the process of converging "computing power" from batches of isolated hardware procurement into a multi-scale resource order spanning headquarters and provincial units, technology stacks, and business scenarios — a perspective in direct continuity with the RC theoretical system (Process Realism: Observational Convergence and the Generation of Certainty): the "integration" of a computing platform corresponds to observers at different levels forming a shareable convergent consensus over resource distribution; "resource transparency" and "usage evaluation" are operationalized attempts to move assessment power from a black box toward a public arena and avert evaluation alienation; while "elastic expansion," "appropriate forward positioning," and "dynamic adjustment" are the infrastructure-scale landing of that system's practice theory — "keeping options in play" and "hedging uncertainty with redundancy and fault tolerance." The chapters on operations, technical support, and ecosystem building that follow can all be traced back along this thread to its order theory and practice theory. Readers interested in the philosophical foundations may follow this entry point back to the source.
This book is both a summary and a new starting point. It systematically records State Grid's thinking and practice in the field of AI computing power. We have unreservedly shared the detours we took, the experience we summarized, and our visions for the future. We deeply understand that on this unprecedented path of exploration, we are still "elementary school students," with countless unknowns waiting to be explored.
Therefore, this book is also a sincere "invitation." We invite every reader — whether colleagues from the energy industry, experts from the field of information technology, or friends from all walks of life concerned with the intelligent development of national infrastructure — to engage in a dialogue of ideas with us through this book. We look forward to your criticism and corrections, and we thirst for your wisdom and insights.
The future of the smart grid is magnificent, vast, and bright. We firmly believe that, driven by surging computing power, this grid, which carries the light and hope of 1.4 billion people, will become stronger, smarter, and greener. Let us join hands and together embrace this great era of computing power, empowering a better future with wisdom and hard work.
This is the preface.