Deep evaluation model and analysis method based on computing power application scenarios

Abstract

The application discloses a deep evaluation model and analysis method based on a computing power application scene, relates to the field of information technology and digital infrastructure planning, and specifically relates to a deep evaluation model and analysis method based on a computing power application scene. The model is constructed from ten dimensions of policy adaptability, regional industry integration, infrastructure support, construction operation mode, implementation strategy landing, operation and maintenance service, energy consumption control, cost return calculation, market supply and demand matching, and risk prevention and control system, taking actual landing requirements as the core. The importance of each item is graded and quantitatively scored, a feasibility determination standard is set, a full-closed-loop analysis system is formed, full-dimension and hierarchical deep analysis of the feasibility of computing power project construction and operation is realized, and support is provided for project decision-making.

Description

Technical Field

[0001] This invention relates to the field of information technology and digital infrastructure planning, specifically to a deep evaluation model and analysis method based on computing power application scenarios. Background Technology

[0002] In recent years, with the rapid penetration of technologies such as artificial intelligence, big data, cloud computing, and the industrial internet into various industries, computing power has become a key production factor driving economic and social development. Various regions are actively deploying computing centers, intelligent computing platforms, and edge computing nodes in an attempt to seize the high ground of the digital economy. However, the construction and operation of computing power projects involve a wide range of aspects, large-scale investments, and long cycles, and are highly dependent on multiple factors such as the policy environment, regional industrial structure, energy supply, and market demand. In practice, there are common problems such as a single evaluation dimension and insufficient analytical depth.

[0003] Current assessment methods often focus on technical indicators or single economic calculations, such as hardware performance, network bandwidth, or simple payback period calculations. They lack a comprehensive consideration of policy adaptation, industrial synergy, infrastructure maturity, compatibility of construction and operation models, energy consumption and green development requirements, and risk control. This leads to problems such as some projects becoming disconnected from regional industrial needs, inadequate policy support, difficulty in guaranteeing energy quotas, and excessively high operating costs after implementation. In some cases, projects are even left idle immediately after completion.

[0004] Furthermore, significant differences exist in industrial policies, industrial bases, and energy conditions across different regions. Using a uniform template for evaluation makes it difficult to accurately reflect the suitability and feasibility of projects in specific areas. Existing methods also rarely establish a closed-loop mechanism from evaluation to optimization recommendations. Evaluation results often remain at the level of scoring or rating, lacking actionable solutions that can directly guide improvements.

[0005] Therefore, there is a need for a deep evaluation model and analysis method that can address the actual needs of computing power application scenarios, take into account multiple factors such as policy, industry, technology, economy, energy, and security, and conduct comprehensive analysis in a quantifiable and hierarchical manner. This will improve the scientific nature of the preliminary demonstration of computing power projects and the accuracy of decision-making, and reduce construction and operation risks. Summary of the Invention

[0006] The purpose of this invention is to provide an in-depth evaluation model and analysis method based on computing power application scenarios. It conducts a comprehensive hierarchical analysis from ten dimensions, including policy adaptation and industrial integration, to accurately determine the feasibility of project construction and operation, provide support for scientific decision-making for computing power projects, and reduce implementation risks.

[0007] To achieve the above objectives, this invention provides the following technical solution: a deep evaluation model and analysis method based on computing power application scenarios. This method focuses on the actual implementation needs of computing power application scenarios, targeting key stages throughout the entire lifecycle of computing power projects, from planning and construction to long-term operation. It constructs a comprehensive evaluation model across ten dimensions: policy adaptability, regional industrial integration, infrastructure support, construction and operation mode, implementation strategy implementation, operation and maintenance services, energy consumption control, cost-return calculation, market supply and demand matching, and risk prevention and control system. Each dimension is broken down into specific, quantifiable evaluation items. Through hierarchical analysis logic including importance level classification, quantitative scoring, and feasibility threshold determination, a deep assessment of the necessity of computing power project construction, operational sustainability, and profitability feasibility is achieved, providing a scientific basis for project decision-making.

[0008] Furthermore, the assessment of the policy adaptability dimension includes four sub-items: the fit of regional industry guidance documents, the strength of government special policy support, the matching degree of targeted tracks in industrial economic circles such as the Greater Bay Area, and the implementation of special guidance requirements from various commissions and bureaus. Each sub-item is divided into four levels of importance: low, medium, high, and extremely high. Among them, the adaptability of core government guidance documents and the matching degree of targeted tracks in industrial economic circles are high / extremely high importance items. The quantitative scoring is based on the policy document release status (officially issued / directionally mentioned / no relevant policy), policy support type (funding subsidies / resource allocation / policy preferences / no support), and implementation supporting measures (with clear implementation details / no specific supporting measures / no implementation guarantee), with a scoring range of 0-10 points. The comprehensive score of the policy adaptability dimension is obtained by weighted summation of the individual item scores. A score ≥8 indicates excellent adaptability, 6-7 indicates good adaptability, and <6 indicates insufficient adaptability.

[0009] Furthermore, the assessment of the regional industrial integration dimension revolves around the degree of integration between computing power projects and the region's leading industries, digital transformation needs, and distinctive economic sectors. It focuses on analyzing the supporting capabilities of computing power for core industries such as regional manufacturing, transportation, low-altitude economy, and government services. The assessment process requires collecting basic information such as regional industrial scale data, digital development stage reports, existing total computing power supply, and computing power demand gaps in key industries. By constructing a "computing power supply-industry demand" matching matrix, it quantitatively analyzes the matching degree between computing power scale and industry demand, the fit between computing power service models and industry application scenarios, and the contribution of computing power empowerment to improving industry efficiency. A comprehensive score of ≥70 points on the matching matrix indicates good supply-demand matching, 50-69 points indicates basic matching, and <50 points indicates insufficient matching. Simultaneously, it proposes scenarios-based optimization directions for computing power to address matching shortcomings.

[0010] Furthermore, the assessment of the infrastructure support dimension includes four core sub-items: the level of computing power infrastructure deployment, the ability to build a computing power pool architecture, the feasibility of resource allocation and management technology implementation, and the ability to integrate ubiquitous fragmented resources. The assessment focuses on verifying whether the infrastructure matches the computing power scale requirements, resource scheduling efficiency requirements, and multi-scenario adaptability requirements of the application scenarios. The assessment criteria include the existing infrastructure construction foundation (data center size, existing equipment performance, network bandwidth), the feasibility of technology upgrades (hardware modification difficulty, software compatibility, upgrade cycle), and industry standard compatibility (whether it complies with relevant national and industry design specifications and technical standards). A 100-point scoring system is used, with the following weights: computing power infrastructure deployment level (30%), computing power pool architecture construction capability (25%), resource allocation and management technology implementation capability (25%), and ubiquitous fragmented resource integration capability (20%). A comprehensive score ≥80 indicates excellent support capability, 60-79 indicates satisfactory support capability, and <60 indicates insufficient support capability, requiring the development of a targeted infrastructure upgrade plan.

[0011] Furthermore, the assessment of the construction and operation model includes three core sub-items: policy alignment of the construction model, rationality of the investment and financing method selection, and adaptability of the cooperation structure model. The assessment of investment and financing methods covers various types, including independent investment and construction models, third-party investment cooperation models, construction and operation models based on special purpose vehicles (SPVs), ultra-long-term special treasury bond cooperation models, and social capital introduction models. During the assessment process, a multi-dimensional comparative analysis system needs to be constructed, taking into account factors such as the project's total funding needs, restrictions on fund usage, the complementarity of resources among partners (financial strength, technical capabilities, and channel resources), risk-sharing mechanisms, and policy adaptation requirements. By calculating the funding costs, investment recovery periods, and risk coefficients under different models, the optimal model and feasibility determination are completed. For the selected model, the funding path, the division of rights and responsibilities among the cooperating parties, and the profit distribution mechanism must be clearly defined to ensure the model's feasibility.

[0012] Furthermore, the assessment of the implementation strategy includes four core sub-items: construction method selection, implementation of technological innovation requirements, compliance with green and energy-saving standards, and market supply and demand forecasting. Construction methods are divided into two categories: new intelligent computing centers and upgrades of existing data centers. The assessment requires a comprehensive selection based on factors such as the existing infrastructure (data center structure, power supply, network conditions), renovation costs (hardware procurement costs, renovation construction costs, downtime losses), construction period, and technological compatibility. The assessment of technological innovation requirements focuses on the application of key technologies such as computing power scheduling technology, resource integration technology, and operation and maintenance management technology. Feasibility assessment; Green energy-saving standard compliance assessment is guided by the national "dual carbon" target, verifying PUE value control targets, energy-saving technology application schemes, and energy recycling measures; Market supply and demand relationship forecast focuses on analyzing the saturation of the regional computing power market, the scale of computing power demand from key customers, and the feasibility of computing power consumption channels (direct leasing, industrial cooperation, platform sharing, etc.). By surveying the operation data of similar projects in the same region and the results of potential customer demand surveys, the project's computing power utilization rate is predicted. A utilization rate of ≥70% is considered a good supply and demand match, 50-69% is a basic match, and <50% requires re-optimization of the implementation strategy.

[0013] Furthermore, the evaluation of the operation and maintenance service dimension includes five core sub-items: talent and technology reserve matching degree, large customer maintenance service capability, computing power marketing system construction, customized service capability, and rationality of operator selection. It also covers specific content such as information security technology application, localization adaptation, security assessment and compliance implementation, and security system construction. The talent and technology reserve evaluation focuses on the matching degree between the operation and maintenance team's personnel structure, technical qualifications, industry experience and project needs, and clarifies talent gaps and supplementation plans. The selection of operators requires evaluation of their industry qualifications, service cases, resource integration capabilities, and operation and maintenance response efficiency. The information security-related evaluation requires verification of the localization equipment adaptation ratio, security assessment and compliance filing progress, and the construction status of the security protection system (network security, data security, physical security). The evaluation adopts a combination of qualitative and quantitative methods. Quantitative indicators (such as talent matching rate and number of security compliance items) account for 60%, and qualitative indicators (such as service process standardization and emergency response capability) account for 40%. A comprehensive score of ≥75 indicates that the service capability meets the standards, and <75 indicates that optimization and improvement are needed.

[0014] Furthermore, the assessment of the energy consumption control dimension focuses on the PUE value and the compliance of energy indicators, while also covering auxiliary details such as energy supply stability, the effectiveness of energy-saving technology applications, and the utilization rate of green electricity resources. During the assessment, the total energy consumption (electricity, water resources, etc.) needs to be calculated based on industry energy consumption standards, taking into account the project's target computing power scale, and the supporting capacity needs to be verified by comparing it with the existing approved energy quotas. The optimization effect of green energy-saving technologies (such as distributed photovoltaics, waste heat recovery, and intelligent cooling) on ​​energy consumption should be analyzed, and the PUE value control target should be clarified (striving to reach an advanced level below 1.3, and not exceeding the industry average of 1.5). The acquisition channels and utilization rate of regional green electricity resources should be verified, and the feasibility of energy cost control should be assessed. If the calculated total energy consumption exceeds the approved quota, the PUE value cannot meet energy-saving requirements, or the energy cost is too high, the computing power scale plan needs to be adjusted or the energy-saving scheme optimized to ensure that the energy supply matches the long-term operational needs of the computing power project.

[0015] Furthermore, the cost-return assessment includes all cost elements such as hardware and software infrastructure costs, equipment procurement costs, energy consumption costs, operation and maintenance costs, labor costs, and marketing costs. A complete computing power profitability assessment system is also constructed. Cost assessment needs to consider factors such as market price fluctuation trends, equipment depreciation cycles (3-4 years), energy price policies, and personnel salary levels to accurately calculate the project's full lifecycle costs. The profitability assessment system needs to consider factors such as computing power leasing market prices (measured by per pet per year, per server per month, per GPU per hour, etc.), predicted computing power utilization rates, customer cooperation cycles, and value-added service revenue to calculate the project's annual operating revenue, investment return period, net profit margin, and cash flow stability. The system focuses on analyzing the impact of equipment cost fluctuations, market price changes, and changes in computing power utilization rates on investment returns, setting up optimistic, benchmark, and conservative scenarios for sensitivity analysis to ensure the rationality and reliability of profit forecasts.

[0016] Furthermore, the method categorizes the sub-items across ten dimensions into importance levels (low, medium, high, and extremely high) and assigns quantitative scores (0-10 points per item). A weighted summation method is used to calculate the comprehensive score for each dimension (weights are set according to project type and industry characteristics). Clear feasibility criteria are established: Projects are deemed feasible when the average satisfaction rate (score ≥ 8 points is considered satisfactory) of high-importance items (sub-items with a weight ≥ 20%) is above 60%, and the average satisfaction rate of extremely high-importance items (sub-items with a weight ≥ 30%) is 100%. Projects are deemed feasible and risk-controllable when the average satisfaction rate of high-importance items is below 60%, but the average satisfaction rate of extremely high-importance items is 100%. Specific optimization measures are developed for high-importance items that do not meet the criteria. Projects are deemed temporarily infeasible when the average satisfaction rate of extremely high-importance items is below 80%. After the evaluation, a closed-loop evaluation and analysis report is required, clearly defining the project feasibility conclusions, a list of unmet criteria, targeted optimization strategies, and implementation suggestions, providing comprehensive support for project decision-making and subsequent implementation.

[0017] This invention provides a deep evaluation model and analysis method based on computing power application scenarios, which has the following beneficial effects: 1. Enhance the scientific rigor and accuracy of project decision-making. This model breaks through the limitations of traditional single-dimensional assessments, constructing a comprehensive analytical framework around ten core dimensions, including policy, industry, and infrastructure. It breaks down the abstract concept of "feasibility" into quantifiable and verifiable specific items (such as the implementation status of policy documents, industry demand gaps, and PUE values). Through hierarchical classification and scoring mechanisms, it can accurately identify project strengths and weaknesses—for example, the hard constraint of "100% satisfaction of extremely important items" in policy compatibility can prevent approval obstacles caused by policy incompatibility in advance; the analysis of "manufacturing and low-altitude economic support capabilities" in industrial integration can avoid the disconnect between computing power construction and actual regional needs. This in-depth analysis through hierarchical classification allows decision-makers to move beyond "gut feeling" and make more realistic choices based on data.

[0018] Strengthen the early prevention and control of policy and market risks. The model treats "policy adaptability" and "risk prevention and control system" as independent dimensions. The former is further refined into four aspects, including the regional document fit and the strength of special policy support. It is quantitatively scored through the document release status and the completion of supporting measures, which can predict in advance whether policy dividends can be implemented (e.g., if the matching degree of the targeted track in the Greater Bay Area is insufficient, the direction can be adjusted in time). The latter combines market supply and demand forecasts and customer cooperation cycles to identify risk points such as computing power market saturation and the feasibility of consumption channels. For example, if the calculation shows that the regional computing power saturation exceeds 80%, investment can be reduced in time or the focus can be shifted to a niche track to avoid idle resources due to supply and demand imbalance and reduce the probability of project "abandonment" from the source.

[0019] Promoting efficient synergy between computing resources and regional industries. The regional industry integration dimension focuses on the compatibility of computing power with local leading industries (manufacturing, transportation, etc.). By analyzing industry scale, digitalization stage, and computing power demand gaps, it clarifies the support path of computing power for industrial upgrading—for example, for the digital transformation needs of manufacturing, it can specifically assess whether computing power can meet the real-time computing requirements of intelligent production and supply chain optimization. This avoids the blind pursuit of building computing power "for the sake of building computing power," allowing computing power to truly become the "digital engine" of regional industries. This not only solves the pain point of "insufficient computing power" for enterprises but also enhances the competitiveness of regional industries, achieving a virtuous cycle of "computing power built locally, used locally, and empowering locally."

[0020] Promoting the implementation of green, low-carbon, and sustainable operations. Energy consumption control focuses on PUE (Power Usage Effectiveness) and energy indicators, combining computing power scale to calculate total energy consumption and existing capacity support, and also assessing the optimization effects of green and energy-saving technologies. For example, a project with an original PUE of 1.5 can reduce it to 1.2 through liquid cooling technology. The model can intuitively demonstrate the reduction in energy consumption and long-term operating cost savings, compelling projects to prioritize low-carbon solutions. Simultaneously, it verifies whether energy supply matches long-term demand, avoiding future shutdowns due to power shortages, contributing to the green development of the computing power industry under the "dual carbon" goals, and reducing the risk of profit compression for enterprises due to soaring energy costs.

[0021] Optimize resource allocation and full-cycle cost-return. The cost-return dimension covers all cost items, including hardware and software, energy consumption, and operation and maintenance, constructing a profitability calculation system. It combines rental prices, utilization rates, and customer lifecycles to calculate the payback period and net profit margin, and also analyzes the impact of equipment price fluctuations and market price changes. For example, if a project initially calculates a payback period of 5 years, but extends it to 7 years after considering chip price increases, the model can suggest that decision-makers adjust their procurement strategies (such as locking in long-term supply prices) or expand their high-margin customer base. This full-cost accounting makes input and output more transparent, helping investors rationally plan their funds and operators optimize pricing and services, ensuring that projects are "affordable to build, well-utilized, and profitable," thus improving resource allocation efficiency and business sustainability. Attached Figure Description

[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the overall framework of the deep evaluation model based on computing power application scenarios of this invention. Figure 2This is a flowchart for evaluating the policy adaptability dimension of this invention; Figure 3 This is a flowchart for the regional industrial integration dimension assessment of this invention; Figure 4 This is a flowchart illustrating the feasibility assessment criteria for this invention. Figure 5 This is a flowchart of the overall evaluation and analysis closed-loop system of this invention. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] How to use: I. Define the assessment objectives and scope First, identify the computing power project scenario to be evaluated (such as the computing power construction needs of a specific region or industry), focus on its core requirements for actual implementation, determine the project stage (feasibility assessment or operation optimization analysis) and key concerns to be covered by the evaluation, and define the boundaries for subsequent dimensional evaluations.

[0027] II. Conduct assessment and analysis across multiple dimensions The evaluation will proceed along ten dimensions, with each dimension further refined into specific evaluation points tailored to the project's actual situation. 1. Policy Adaptability: Verify the alignment of regional industry guidance documents, the strength of government special policy support, the matching degree of targeted tracks in industrial economic circles such as the Greater Bay Area, and the implementation of special guidance requirements from various commissions and bureaus. Based on the status of policy document release, support type, and supporting measures, a four-level quantitative score of low, medium, high, and very high is given.

[0028] Regional industrial integration: Focusing on the degree of integration between the project and the region's leading industries (such as manufacturing, transportation, and low-altitude economy), digital transformation needs, and distinctive economic tracks, and combining the region's industrial scale, digital development stage, and computing power demand gap, analyze the ability of computing power to support industries and the suitability of supply and demand.

[0029] Infrastructure support: Assess the level of computing power infrastructure deployment, computing power pooling architecture construction capabilities, resource allocation and management technology implementation, and ubiquitous fragmented resource integration capabilities. Verify whether it matches the computing power scale and resource scheduling efficiency requirements of the application scenario, and score based on the existing foundation, feasibility of technology upgrades, and standard compatibility.

[0030] Construction and operation model: Analyze the policy fit of the construction model, the rationality of the investment and financing method (independent investment, third-party investment, SPV model, government bond cooperation, etc.), and the adaptability of the cooperation structure model. Combine the project's funding needs, usage restrictions, and the complementarity of the partners' resources to select the most feasible model.

[0031] Implementation strategy: Evaluation of construction method (new construction / upgrading of existing facilities) selection (combining existing infrastructure, renovation costs, and cycle), implementation of technological innovation requirements, compliance with green and energy-saving standards, and market supply and demand forecasting (regional computing power saturation, key customer needs, and feasibility of consumption channels).

[0032] Operation and maintenance services: Verify the matching degree of talent and technology reserves, the ability to maintain and serve major customers, the construction of computing power marketing system, the ability to provide customized services, and the rationality of the choice of operator. At the same time, evaluate the application of information security technology, localization adaptation, implementation of confidentiality assessment and security level protection, and the construction of security system. Scoring is based on personnel structure, service process, and implementation of security standards.

[0033] Energy consumption control: Focusing on PUE value and energy indicator compliance, combined with computing power scale requirements, we calculate total energy consumption and the support capacity of existing approved energy quotas, analyze the optimization effect of green and energy-saving technologies on energy consumption, and verify the matching of energy supply with long-term operational needs.

[0034] Cost-return calculation: Calculate the costs of software and hardware infrastructure, equipment procurement, energy consumption, operation and maintenance, build a profit calculation system, and combine computing power leasing prices, utilization rates, and customer cooperation cycles to calculate the investment return cycle, net profit margin, cash flow stability, and analyze the impact of equipment costs and market price fluctuations.

[0035] Market supply and demand matching (implied in the implementation of strategies and market analysis): Combine the regional computing power market saturation, key customer needs, and the feasibility of computing power consumption channels to verify the matching degree between the project and market demand.

[0036] Risk control system (integrated into multi-dimensional assessment): Identify potential risks such as policy changes, deviations in industry demand, obstacles to technology implementation, and broken capital chains in the analysis of various dimensions, and incorporate them into the assessment considerations.

[0037] III. Comprehensive Judgment and Optimized Output For each dimension's sub-items, satisfaction levels are calculated according to importance levels (low, medium, high, and extremely high): if the satisfaction rate of high-importance items is >60% and the satisfaction rate of extremely high-importance items is 100%, the project is deemed feasible; if the satisfaction rate of high-importance items is ≤60% but the satisfaction rate of extremely high-importance items is 100%, the project is deemed feasible and the risk is controllable; if the satisfaction rate of extremely high-importance items is <80%, the project is deemed temporarily infeasible. For items that do not meet the standards, based on the problems identified in the evaluation of each dimension, targeted optimization strategies and implementation suggestions are proposed, forming a closed-loop evaluation result.

[0038] Example: Example 1: Evaluation of a Smart Manufacturing Computing Center Construction Project in a Coastal City Policy Adaptability Assessment: A review of the city's guidance document on intelligent manufacturing computing power support confirms that the project highly aligns with the document's direction of "computing power empowering discrete manufacturing." A review of specific government policies reveals that the local government prioritizes land use and provides tax deferral support for computing center construction, indicating a high level of support. In conjunction with the Greater Bay Area's industrial economic circle's targeted tracks, the project matches the requirements of the "advanced manufacturing + computing power collaboration" track. Among the specific guidance from various commissions and bureaus, the Industry and Information Technology Department requires that computing power be aligned with the digital transformation needs of local home appliance and equipment manufacturing enterprises; this has been verified as part of the project's planning and is included in the alignment list, indicating a high level of implementation. All four sub-items, after being quantitatively scored according to their release status and support type, are rated as high or above.

[0039] Regional Industrial Integration Assessment: The city's leading industries are home appliances and equipment manufacturing, and it is currently promoting digital transformation in its production processes, resulting in a gap in computing power demand for real-time process simulation and intelligent supply chain scheduling. The project plan provides collaborative services of edge computing power and cloud computing power, which can support data modeling of the entire product lifecycle for home appliance companies and computing power scheduling for flexible production lines for equipment manufacturing companies. Analysis shows that the supply of computing power is well-matched with industry demand in terms of scenario coverage and response time.

[0040] Infrastructure Support Assessment: The project adopts a computing power infrastructure deployment, planning to build a computing power pooling architecture to achieve multi-tenant resource isolation. The resource allocation and management technology will introduce a dynamic priority scheduling mechanism, while also integrating fragmented resources from idle industrial data centers within the region. The existing infrastructure consists of two locally built backbone network links. Technical upgrades can be achieved through modular transformation. The project will be standardized to meet the computing power interface specifications of the intelligent manufacturing field, verifying that the infrastructure matches the requirements for computing power scale and scheduling efficiency.

[0041] Assessment from the perspective of construction and operation model: The construction model is in line with the local policy orientation of "government guidance + market participation"; the investment and financing adopts the SPV model, which is jointly funded by local urban investment, leading intelligent manufacturing enterprises and computing power service providers. Combined with the funding needs of the project to be implemented quickly and to diversify risks, this model can integrate the complementary advantages of the resources of all parties; the three parties are divided into "construction-operation-application" in the cooperation structure, which is suitable for the cross-entity collaboration needs of the project.

[0042] Implementation strategy assessment: New construction is chosen because there are no readily available computing facilities locally and the cost of renovation is higher than that of new construction; technological innovation requires the implementation of computing power and industrial software compatibility technologies, and plans are underway to collaborate with universities to tackle these challenges; green and energy-saving standards align with national data center energy efficiency requirements, and liquid cooling technology is proposed; market supply and demand forecasts show that the annual growth rate of computing power demand from local smart manufacturing enterprises is significant, key customers have expressed their intention to cooperate, and computing power can be absorbed through direct supply to enterprises and access to regional computing power service platforms.

[0043] Operation and maintenance service assessment: The operations team is equipped with industrial software engineers and computing power scheduling specialists, and its technical reserves match the needs of intelligent manufacturing scenarios; it has developed customized service plans for key home appliance companies and possesses the ability to maintain large clients; it has planned computing power marketing channels for manufacturing enterprises and can provide customized process simulation computing power packages; the selection of a local state-owned enterprise with both industrial service experience and computing power operation qualifications as the operator is highly reasonable. Simultaneously, a domestic computing power chip adaptation solution is planned, and the requirements of confidentiality assessment and Level 3 information security protection are implemented to build an industrial data security protection system.

[0044] Energy consumption control assessment: With PUE value and energy indicators as the core, the project's computing power scale needs stable power support, and the existing approved energy quota can cover the initial needs; the plan is to introduce waste heat recovery for park heating, combined with high-efficiency power supply modules to optimize energy consumption; and to verify that the energy supply can match the computing power expansion needs in long-term operation.

[0045] Cost-return assessment dimensions: This includes calculating the costs of hardware and software infrastructure, equipment procurement, energy consumption, and operation and maintenance to construct a profitability calculation system. By combining local computing power leasing market price levels, expected utilization rates, and long-term cooperation cycles with key clients, the investment return cycle and cash flow stability are calculated. The impact of equipment cost fluctuations and market price changes on returns is analyzed to form a risk assessment.

[0046] Overall assessment: The satisfaction rate of highly important items is higher than 60% and the satisfaction rate of extremely important items is 100%, so the item is deemed feasible.

[0047] Example 2: Evaluation of a Computing Power Service Station Project at a Transportation Hub in an Inland Province Policy Adaptability Assessment: The province's plan to build a strong transportation sector explicitly supports the deployment of computing power facilities at hub nodes, and the project aligns with the "smart transportation computing power support" requirement in the plan; government special policies provide special subsidies for computing power projects in the transportation sector, with a high level of support; it fits the province's targeted track for integrating into the Yangtze River Economic Belt's digital transportation industry circle; the Department of Transportation's special guidance requires that computing power serve business such as road network monitoring and traffic flow prediction, and the project plan has already aligned with the needs of the traffic operation monitoring center, with a high level of implementation. All four sub-items received high or higher quantitative scores.

[0048] Regional industrial integration assessment: The province's leading industries include modern logistics and cross-border transportation, and it is promoting the digital transformation of transportation hubs. There is a gap in computing power demand for road network congestion early warning and intelligent scheduling of multimodal transport. The project provides low-latency computing power services, which can support real-time analysis of data from highway network cameras and optimization of railway and highway intermodal transport routes. Analysis shows that the computing power is well-suited to the needs of logistics cost reduction and efficiency improvement, and transportation safety enhancement.

[0049] Infrastructure Support Assessment: Computing power will be deployed in the hub park using infrastructure-based methods, establishing a pooled computing power architecture to meet peak computing power demands from transportation operations. Resource allocation and management will utilize traffic-aware scheduling, integrating redundant computing power resources from existing communication base stations within the hub. The existing infrastructure includes a dual-circuit power supply and a dedicated fiber optic network. Technical upgrades can be achieved through software-defined computing resources, adhering to real-time data processing standards specific to the transportation industry, and verifying the matching of computing power scale and low-latency scheduling requirements.

[0050] Assessment from the perspective of construction and operation model: The construction model is in line with the policy orientation of "public welfare attribute + market-oriented operation" in the transportation sector; the investment and financing adopts the national debt cooperation model, applying for special national debt for transportation and combining it with social capital matching, which meets the funding needs of the project to guarantee the public service attribute, and this model is in line with the restrictions on the use of funds; the cooperation structure is a joint effort of transportation departments, state-owned investment companies and computing power enterprises, and the complementarity of resources is reflected in the combination of policy support, funding guarantee and technical services.

[0051] Implementation strategy assessment: The chosen approach is new construction, as the existing facilities of the hub are mainly for transportation business systems and lack suitable computing infrastructure; technological innovation requires the implementation of cross-modal fusion computing technology for transportation data, and plans to collaborate with research institutes for research and development; green and energy-saving standards align with the low-carbon requirements of the transportation industry, and natural cooling technology is proposed; market supply and demand forecasts show that the demand for computing power in provincial transportation hubs will increase with the construction of smart transportation, with key customers being transportation operation companies and logistics companies, and computing power will be absorbed through embedding into transportation management systems and logistics platforms.

[0052] Operation and maintenance service assessment: The operations team is equipped with traffic data analysts and computing power maintenance engineers, with technical reserves matching road network monitoring and traffic flow prediction scenarios; computing power service plans have been developed for large logistics companies, demonstrating the ability to maintain large clients; a computing power marketing system has been planned for the transportation industry, providing customized traffic flow simulation computing power services; the selection of a local state-owned transportation information technology company as the operator is highly reasonable as it is familiar with business needs. Simultaneously, the adaptation of domestic computing power is implemented, traffic data security assessment and compliance requirements are enforced, and a secure encrypted transmission system for traffic data is constructed.

[0053] Energy consumption control assessment: With PUE value and energy indicators as the core, the project's computing power needs to ensure uninterrupted operation of transportation services, and the existing approved energy quota is calculated to meet the initial needs; the plan adopts photovoltaic complementary power supply and intelligent power consumption control to optimize energy consumption; and it verifies that the energy supply can match the computing power requirements of newly added monitoring equipment in long-term operation.

[0054] Cost-return assessment dimensions: This includes calculating the costs of hardware and software infrastructure, equipment procurement, energy consumption, and operation and maintenance to construct a profitability calculation system. By combining pricing practices for computing power services in the transportation industry, projected utilization rates, and long-term cooperation cycles with transportation departments, the investment return cycle and cash flow stability are calculated. The impact of equipment cost fluctuations and market price changes on returns is analyzed to form a risk assessment.

[0055] Overall assessment: The satisfaction rate of highly important items is higher than 60% and the satisfaction rate of extremely important items is 100%, so the item is deemed feasible.

[0056] Example 3: Evaluation of a Low-Altitude Economic Computing Power Test Site Project in a Lingang New Area Policy Adaptability Assessment: The Lingang New Area Low-Altitude Economic Development Plan lists computing power as a key support, and the project aligns with the plan's focus on "low-altitude flight computing power support." Government policies provide high-level support by authorizing the use of airspace data for low-altitude economic computing power projects. It also aligns with the targeted development path of the Greater Bay Area's low-altitude economic industry cluster. Special guidance from the Civil Aviation Administration of China requires computing power to meet the accuracy requirements of flight safety monitoring and route planning; the project plan has been incorporated into the airspace data sharing mechanism, demonstrating high implementation capability. All four sub-items received high or higher quantitative scores.

[0057] Regional Industrial Integration Assessment: The New Area's leading industries are low-altitude logistics and drone inspection. It is currently undergoing digital transformation in the low-altitude domain, and there is a gap in computing power demand for real-time flight attitude calculation and airspace conflict early warning. The project provides high-precision spatiotemporal computing power services, which can support dynamic optimization of drone logistics paths and real-time analysis of inspection images. Analysis shows that the computing power is well-suited to the needs of low-altitude safety assurance and operational efficiency improvement.

[0058] Infrastructure Support Assessment: Computing power will be deployed in a infrastructure-based manner around the air traffic control center in the new area, establishing a computing power pool architecture to isolate computing power for low-altitude operations. Resource allocation and management technology will employ time- and space-priority scheduling, integrating existing redundant communication and navigation computing resources in the new area. The existing infrastructure includes a dedicated low-altitude communication network already established in the new area. Technical upgrades can be achieved through the deployment of edge computing nodes, adapting to low-altitude flight data interaction specifications, and verifying the matching of computing power scale and high-precision scheduling requirements.

[0059] Assessment from the perspective of construction and operation model: The construction model is in line with the policy orientation of "pilot first + diversified participation" for the low-altitude economy; the investment and financing adopts a third-party investment model, with leading enterprises in the low-altitude economy jointly investing with computing power companies. Combined with the funding needs of the project to quickly verify the technology, this model can introduce technical experience from the industry side; the cooperation structure is a joint effort of enterprises, research institutions and the management committee of the new area, and the complementarity of resources is reflected in the combination of industrial scenarios, technology research and development and policy support.

[0060] Implementation strategy assessment: New construction is chosen because the existing facilities in the new area lack dedicated low-altitude computing infrastructure; technological innovation requires the integration of low-altitude data and computing power, and plans to co-build laboratories with enterprises; green and energy-saving standards align with the low-carbon requirements of the low-altitude field, and efficient heat dissipation technology is proposed; market supply and demand forecasts show that the demand for computing power from low-altitude economic enterprises in the new area will increase with the expansion of the pilot program, with key customers being drone operators and logistics companies, and computing power will be absorbed by embedding into the low-altitude management platform and enterprise operating systems.

[0061] Operation and maintenance service assessment: The operations team is equipped with low-altitude data engineers and computing power security specialists, with technical reserves matching flight monitoring and route planning scenarios; customized service plans are developed for leading drone companies, demonstrating the ability to maintain large clients; a computing power marketing channel targeting the low-altitude economy has been planned, providing customized airspace conflict early warning computing power services; the operator is a local low-altitude industry service platform company, familiar with scenario requirements, making the selection highly reasonable. Simultaneously, domestic computing power adaptation is implemented, low-altitude data security assessment and compliance requirements are enforced, and a secure flight data transmission system is constructed.

[0062] Energy consumption control assessment: With PUE value and energy indicators as the core, the project's computing power needs to ensure the safety of low-altitude operations, and the existing approved energy quota is calculated to cover the initial demand; the plan adopts energy storage complementary power supply and computing power load balancing to optimize energy consumption; and it verifies that the energy supply can match the computing power demand of newly added aircraft in long-term operation.

[0063] Cost-return assessment dimensions: This includes calculating the costs of hardware and software infrastructure, equipment procurement, energy consumption, and operation and maintenance to construct a profitability calculation system. By combining expected pricing for low-altitude economic computing power services, projected utilization rates, and the medium- to long-term cooperation cycle with clients, the investment return cycle and cash flow stability are calculated. The impact of equipment cost fluctuations and market price changes on returns is analyzed to form a risk assessment.

[0064] Overall assessment: The satisfaction rate of highly important items is less than 60%, while the satisfaction rate of extremely important items is 100%. Therefore, the project is deemed feasible and the risks are controllable.

[0065] Example 4: Evaluation of a Computing Power-Enabled Agriculture Project at the Edge of an Ecological Reserve Policy Adaptability Assessment: The digital rural planning of the province where the ecological protection zone is located mentions edge computing power services for agriculture, but there are no specific guiding documents, so the project's alignment with the planning is at a medium level; government special policies provide low support for agricultural computing power projects, with no explicit subsidies or authorizations; the matching degree with the Greater Bay Area's agricultural digitalization track is at a medium level; the special guidance from the Department of Agriculture and Rural Affairs requires computing power to serve agricultural product traceability, but the project plan only partially incorporates this requirement, so its implementation is at a medium level. The quantitative scores for most of the four sub-items are at a medium or lower level.

[0066] Regional industrial integration assessment: The dominant industry around the protected area is ecological agriculture, and its digital transformation is in its initial stage. There is a demand for computing power to monitor the growth environment of agricultural products, but the demand gap is small and scattered. The project provides edge computing power for real-time processing of field sensor data, which can help growers adjust irrigation strategies. Analysis shows that there is a gap between the computing power and the industry's needs in terms of large-scale application.

[0067] Infrastructure Support Assessment: A simplified computing power pooling architecture will be built in the concentrated area of ​​agricultural cooperatives, with static allocation and management of resources to integrate idle office computing resources. The existing infrastructure suffers from weak regional network coverage; technology upgrades require significant improvements to communication facilities; standard adaptation to agricultural data collection specifications is challenging; and the existing infrastructure is insufficient to meet the scale of computing power and real-time scheduling requirements.

[0068] Assessment from the perspective of construction and operation model: The construction model has low compatibility with the local policy of "protection first, moderate development"; the investment and financing chooses an independent investment model, with agricultural technology companies providing funding. Given the small funding needs of the project but uncertain returns, the financial pressure of this model is concentrated on a single entity; the cooperation structure is simple and the resource complementarity is insufficient.

[0069] Implementation strategy assessment: The chosen approach is to upgrade existing facilities and renovate the cooperative's idle computer room. However, the renovation cost is higher than expected due to network shortcomings, and the construction period is long. Technological innovation requires the implementation of lightweight agricultural data processing technology, which is relatively easy to implement. Green and energy-saving standards are easy to meet. Market supply and demand forecasts show that regional agricultural enterprises have insufficient understanding of computing power, key customer needs are unclear, and the consumption channels rely on government promotion, making the feasibility low.

[0070] Operation and maintenance service assessment: The operations team lacks technical personnel in the agricultural field, resulting in low matching technical reserves; there is no experience in maintaining mature large clients; a computing power marketing system has not been established; customized service capabilities are weak; the choice of an agricultural technology company as the operator lacks experience in computing power operation, which is not reasonable. Information security technology applications only meet basic requirements; localization adaptation, security assessment, and compliance with information security standards are inadequate, and the security system is incomplete.

[0071] Energy consumption control assessment: Based on PUE value and energy indicators, the project has a small computing power scale, which can be covered by the existing approved energy quota, but the room for optimization of green and energy-saving technologies is limited, and the long-term matching of energy supply is questionable due to the impact of network expansion.

[0072] Cost-return assessment dimensions: The cost of hardware and software infrastructure, equipment procurement, energy consumption and operation and maintenance is calculated. Due to low utilization, the profitability calculation system shows that the investment return cycle is long and the cash flow stability is poor. Fluctuations in equipment costs and market price changes have a significant impact on returns.

[0073] Overall assessment: The satisfaction rate for the "extremely important" criteria is below 80%, therefore the project is deemed temporarily infeasible. Regarding insufficient policy adaptability, it is recommended to seek support from provincial-level digital rural development policies; regarding weak infrastructure, it is recommended to collaborate with telecommunications companies to improve the network; regarding unclear market supply and demand, it is recommended to first conduct small-scale pilot projects to cultivate demand.

[0074] Example 5: Evaluation of a Medical Computing Power Sharing Platform Project in a Provincial Capital City Policy Adaptability Assessment: The provincial capital's Healthy City Construction Plan explicitly supports the sharing of medical computing power, and the project highly aligns with the "integration of medical data computing power" direction in the plan; government special policies provide special support for data security guarantees for medical computing power projects, with a high level of support; it fits the targeted track of the regional medical and health industry cluster; the Health Commission's special guidance requires that computing power meet the needs of patient privacy protection and cross-institutional data access, and the project plan has passed the privacy computing technology solution review, with a high level of implementation. All four sub-items received a high or higher quantitative score.

[0075] Regional industrial integration assessment: The city's leading industries include biomedicine and high-end medical care, and it is promoting the digital transformation of medical institutions, resulting in a demand gap in areas such as AI-based medical image diagnosis and shared computing power for scientific research data. The project provides a dedicated medical computing power pool, which can support parallel computing of image data from tertiary hospitals and on-demand access to scientific research computing power from small and medium-sized hospitals. Analysis shows that the computing power is well-suited to the needs of balancing medical resources and improving diagnostic and treatment efficiency.

[0076] Infrastructure Support Assessment: The system will utilize a computing power infrastructure deployment within a medical data center cluster, establishing a medical computing power pooling architecture to achieve data isolation across multiple institutions. Resource allocation and management will employ medical task priority scheduling, integrating existing idle computing resources from hospitals. The existing infrastructure consists of a dedicated medical network already in place in the city. Technical upgrades can be achieved through the deployment of a medical computing power gateway, ensuring standard compatibility with medical data interaction specifications and verifying the matching of computing power scale and security scheduling requirements.

[0077] Assessment of the construction and operation model: The construction model is in line with the policy orientation of "public welfare-led + market supplement" in the medical field; the investment and financing adopts the SPV model, which is jointly funded by public hospital groups, medical information technology companies and computing power service providers. Combined with the project's need to balance the funding requirements of public welfare and sustainable operation, this model can integrate medical resources and technological advantages; the cooperation structure is divided into "construction-operation-service", which is suitable for cross-institutional collaboration needs.

[0078] Implementation strategy assessment: A combination of upgrading existing facilities and constructing new ones will be chosen, upgrading the hospital's existing computing power facilities and building new regional shared nodes, with controllable renovation costs and a reasonable construction period; technological innovation requires the implementation of medical data anonymization and computing power collaboration technologies, with plans for joint research with medical institutions; green and energy-saving standards will align with medical industry requirements, and high-efficiency cooling technology will be adopted; market supply and demand forecasts show that medical institutions have an urgent need for shared computing power, with key customers being hospitals at all levels and medical research institutions, and computing power will be consumed through direct supply from the platform and collaborative research projects.

[0079] Operation and maintenance service assessment: The operations team comprises medical information engineers and computing power security experts, with technical reserves matching the scenarios of image diagnosis and scientific research computing; dedicated service agreements have been developed for large hospitals, demonstrating the ability to maintain large clients; a computing power marketing system has been planned for the medical industry, providing customized AI diagnostic computing power packages; the selection of a local institution with both medical background and computing power operation experience is highly reasonable. Simultaneously, domestic computing power adaptation is implemented, medical data confidentiality assessment and Level 3 information security requirements are enforced, and a patient privacy data security system is constructed.

[0080] Energy consumption control assessment: With PUE value and energy indicators as the core, the project's computing power needs to ensure the continuity of medical services, and the existing approved energy quota can cover the initial needs; the plan adopts intelligent temperature control and computing power peak scheduling to optimize energy consumption; and it verifies that the energy supply can match the computing power needs of newly added medical institutions in long-term operation.

[0081] Cost-return assessment dimensions: This includes calculating the costs of hardware and software infrastructure, equipment procurement, energy consumption, and operation and maintenance to construct a profitability calculation system. Combining the non-profit pricing principle of medical computing services, expected utilization rates, and long-term cooperation cycles with institutions, the investment return cycle and cash flow stability are calculated. The impact of equipment cost fluctuations and market price changes on returns is analyzed to form a risk assessment.

[0082] Overall assessment: The satisfaction rate of highly important items is higher than 60% and the satisfaction rate of extremely important items is 100%, so the item is deemed feasible.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A deep evaluation model and analysis method based on computing power application scenarios, characterized in that, The method focuses on the actual implementation needs of computing power application scenarios, and constructs an evaluation model from ten dimensions: policy adaptability, regional industrial integration, infrastructure support, construction and operation mode, implementation strategy implementation, operation and maintenance services, energy consumption control, cost return calculation, market supply and demand matching, and risk prevention and control system. This enables a comprehensive and hierarchical in-depth analysis of the feasibility of computing power project construction and operation.

2. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the policy adaptability dimension includes four sub-items: the degree of fit of regional industry guidance documents, the strength of government special policy support, the degree of matching of targeted tracks in industrial economic circles such as the Greater Bay Area, and the implementation of special guidance requirements from various commissions and bureaus. Each sub-item is divided into four levels of importance: low, medium, high, and very high. Quantitative scoring is completed based on the policy document release status, support type, and supporting implementation measures.

3. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the regional industrial integration dimension revolves around the degree of integration between computing power projects and the region's leading industries, digital transformation needs, and distinctive economic sectors. It focuses on analyzing the supporting capabilities of computing power for regional manufacturing, transportation, and low-altitude economy industries, and completes the supply and demand matching analysis by combining the regional industrial scale, digital development stage, and computing power demand gap.

4. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the infrastructure support dimensions includes the level of computing power infrastructure deployment, the ability to build computing power pool architecture, the feasibility of resource allocation and management technology implementation, and the ability to integrate ubiquitous fragmented resources. The focus is on verifying whether the infrastructure matches the computing power scale requirements and resource scheduling efficiency requirements of the application scenario. The assessment is completed based on the existing infrastructure foundation, the feasibility of technology upgrades, and the standard compatibility.

5. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the construction and operation model includes the policy fit of the construction model, the rationality of the investment and financing method selection, and the adaptability of the cooperation structure model. The assessment of investment and financing methods covers types such as independent investment, third-party investment, SPV model, and government bond cooperation. The model selection and feasibility analysis are completed by combining the project's funding needs, funding restrictions, and the complementarity of the partners' resources.

6. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the implementation strategy includes the selection of construction methods, the implementation of technological innovation requirements, the compliance with green and energy-saving standards, and the prediction of market supply and demand. The construction methods are divided into two categories: new construction and upgrading of existing facilities. The selection is completed by combining the existing infrastructure of the facilities, the cost of renovation, and the construction period. The market supply and demand prediction focuses on analyzing the saturation of the regional computing power market, the needs of key customers, and the feasibility of computing power consumption channels.

7. The in-depth evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the operation and maintenance service dimensions includes the matching degree of talent and technology reserves, the ability to maintain services for major customers, the construction of computing power marketing system, the ability to provide customized services, and the rationality of the choice of operator. It also covers the application of information security technology, localization adaptation, implementation of security assessment and information security compliance, and the construction of security system. The assessment is completed based on personnel structure, service process, and the implementation of security standards.

8. The deep evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The assessment of the energy consumption control dimension focuses on the PUE value and the compliance of energy indicators. It combines the project's computing power scale requirements to calculate the total energy consumption, the support capacity of the existing approved energy quota, analyze the optimization effect of green and energy-saving technologies on energy consumption, and verify whether the energy supply matches the long-term operation needs of the computing power project.

9. The deep evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The cost-return assessment dimensions include hardware and software infrastructure costs, equipment procurement costs, energy consumption costs, and operation and maintenance costs. At the same time, a computing power profitability assessment system is constructed, which combines computing power leasing market prices, computing power utilization rates, and customer cooperation cycles to calculate the project investment return cycle, net profit margin, and cash flow stability, and analyze the impact of equipment cost fluctuations and market price changes on returns.

10. The deep evaluation model and analysis method based on computing power application scenarios according to claim 1, characterized in that, The method classifies and quantifies the importance of each item in the ten dimensions, and sets a feasibility judgment standard: when the satisfaction rate of high importance items is higher than 60% and the satisfaction rate of extremely high importance items is 100%, the item is deemed feasible. When the satisfaction rate of highly important items is less than 60% and the satisfaction rate of extremely important items is 100%, the project is deemed feasible and the risk is controllable. When the satisfaction rate of extremely important items is less than 80%, the project is deemed temporarily infeasible. At the same time, targeted optimization strategies and implementation suggestions are proposed for the unmet items, forming a closed-loop evaluation and analysis system.

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