Resource health diagnosis method and device, medium and computer equipment

Abstract

The invention relates to the technical field of resource diagnosis and the field of financial science and technology, and discloses a resource health diagnosis method and device, a medium and computer equipment, and the method comprises the steps: obtaining a resource scheme and a pre-constructed resource knowledge base; evaluating the scheme integrity dimension of the resource scheme based on a large language model according to the resource industry standard information; and evaluating the strategic fit dimension of the resource scheme based on a large language model according to historical resource strategic information, evaluating the value logic dimension of the resource scheme based on a large language model according to the industry scheme set; and determining a health diagnostic book corresponding to the resource scheme based on a large language model according to the evaluation results of the multiple dimensions and a pre-constructed resource knowledge base. According to the embodiment, the resource scheme is diagnosed on the basis of the resource knowledge base and the large language model, and the diagnostic book is provided on the basis of the diagnosis result, so that the overall resource planning and putting quality is improved, and the compliance response quality and efficiency are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of resource diagnosis technology and the field of financial technology, and in particular, to a resource health diagnosis method, device, medium and computer equipment. Background Art

[0002] With the continuous development of social economy, all walks of life are increasingly dependent on science and technology, especially in the field of financial technology. Enterprises' investment in scientific and technological resources has become a key factor in promoting business growth. In order to ensure that these resources can effectively promote the sustainable development of the economy and society, enterprises need to conduct a comprehensive diagnosis of the health of scientific and technological resources. The role of this diagnosis is reflected in two aspects: one is to reveal potential problems in resource use, and the other is to provide a scientific basis for the optimal allocation and continuous improvement of resources. In the interaction of complex projects and project sets, the use of objective judgment methods and intuitive experience methods can greatly improve the efficiency of resource allocation and help enterprises better achieve their goals in a highly competitive market environment.

[0003] However, in terms of related technologies, the diagnostic methods for resource monitoring mainly focus on the utilization evaluation of hardware resources and the underlying analysis of architecture and operation and maintenance. These methods usually rely on cloud computing or polling mechanisms and aim to improve the efficiency of hardware resource utilization. But the problem is that the solutions proposed by related technologies are weakly related to the business objectives of the enterprise, and insufficient attention is paid to the health of scientific and technological resources required for the overall development of the enterprise. In particular, there is a lack of consistent standards and methods when evaluating the value of scientific and technological resource investment. In addition, these technologies are insufficient in innovation, and the overall formulations are similar, making it difficult to meet the actual needs of enterprises in resource evaluation and optimal allocation. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a resource health diagnosis method, apparatus, medium and computer equipment, which diagnoses the resource solution itself based on a resource knowledge base and a large language model, and issues a diagnosis report based on the diagnosis results. This not only improves the quality of overall resource planning and deployment, but also enhances the ability and efficiency of compliance response, thereby better meeting the actual needs of enterprises in scientific and technological resource evaluation and optimal allocation.

[0005] According to one aspect of the present application, a method for resource health diagnosis is provided, the method comprising:

[0006] Acquire resource solutions and a pre-built resource knowledge base; wherein the pre-built resource knowledge base includes resource industry standard information, historical resource strategy information, and industry solution collection;

[0007] According to the resource industry standard information, the solution completeness dimension of the resource solution is evaluated based on the large language model; and, according to the historical resource strategy information, the strategic fit dimension of the resource solution is evaluated based on the large language model; and, according to the industry solution set, the value logic dimension of the resource solution is evaluated based on the large language model;

[0008] According to the evaluation results of multiple dimensions and the pre-built resource knowledge base, a health diagnosis certificate corresponding to the resource solution is determined based on the large language model.

[0009] According to another aspect of the present application, a resource health diagnosis device is provided, the device comprising:

[0010] An information acquisition module, used to acquire resource solutions and a pre-built resource knowledge base; wherein the pre-built resource knowledge base includes resource industry standard information, historical resource strategy information, and industry solution collection;

[0011] A solution evaluation module, for evaluating the solution integrity dimension of the resource solution based on the large language model according to the resource industry standard information; and, evaluating the strategic fit dimension of the resource solution based on the large language model according to the historical resource strategy information; and, evaluating the value logic dimension of the resource solution based on the large language model according to the industry solution set;

[0012] A diagnosis book generation module is used to determine a health diagnosis book corresponding to the resource solution based on the large language model according to the evaluation results of multiple dimensions and the pre-built resource knowledge base.

[0013] According to another aspect of the present application, an embodiment of the present disclosure provides a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, a resource health diagnosis method as described in any possible implementation manner described above is performed.

[0014] According to another aspect of the present application, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the resource health diagnosis method as described in any possible implementation manner described above.

[0015] Through the above technical scheme, the present application provides a resource health diagnosis method, device, medium and computer equipment to obtain resource solutions and a pre-built resource knowledge base; evaluate the complete dimension of the resource solution based on the big language model according to the resource industry standard information; and evaluate the strategic fit dimension of the resource solution based on the big language model according to the historical resource strategy information; and evaluate the value logic dimension of the resource solution based on the big language model according to the industry solution set; based on the evaluation results of multiple dimensions and the pre-built resource knowledge base, determine the health diagnosis certificate corresponding to the resource solution based on the big language model.

[0016] In this way, the disclosure can quickly generate a "diagnosis" and provide improvement suggestions by combining the industry's preset resource knowledge base and large language model to diagnose the health of resource solutions, reducing the manpower and time required for internal diagnosis, analysis and promotion. At the same time, this method abandons subjective judgment and improves the quality of resource planning and deployment. In addition, the disclosure can also provide objective and systematic tools for demand audits, project audits, and financial production value audits, ensuring compliance and improving the quality and efficiency of responses, thereby better meeting the needs of enterprises in the evaluation and optimization of scientific and technological resource allocation.

[0017] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required to be cited in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of an application environment of a resource health diagnosis method provided by an embodiment of the present disclosure is shown;

[0020] Figure 2 A flow chart of a resource health diagnosis method provided by an embodiment of the present disclosure is shown;

[0021] Figure 3 A flow chart showing a method for evaluating the value logic dimension in a resource health diagnosis method provided by an embodiment of the present disclosure is shown;

[0022] Figure 4A flow chart showing an evaluation result analysis method in a resource health diagnosis method provided in an embodiment of the present disclosure is shown;

[0023] Figure 5 A schematic diagram of the structure of a resource health diagnosis device provided by an embodiment of the present disclosure is shown;

[0024] Figure 6 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0026] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0027] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0028] To facilitate understanding of this embodiment, the execution subject of the resource health diagnosis method provided by the embodiment of the present disclosure is first introduced in detail. The resource health diagnosis method provided by the embodiment of the present invention can be applied to Figure 1In an application environment, the client communicates with the server through a network. The client can be a mobile device, a user terminal, a terminal, a handheld device, a computing device, etc. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms.

[0029] The resource health diagnosis method provided in the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. Figure 2 FIG. 2 is a flowchart of a resource health diagnosis method provided by an embodiment of the present disclosure, and the method includes the following S201 to S203:

[0030] S201, obtaining a resource solution and a pre-built resource knowledge base.

[0031] It is understandable that the resource solution is a project solution to be evaluated, which can include multiple specific project plans, detailed project implementation plans, and various resource allocation and deployment strategies necessary to implement these plans. The pre-built resource knowledge base is a comprehensive knowledge base that integrates various information, including resource industry standard information, historical resource strategy information, and industry solution collection. Among them, resource industry standard information provides a benchmark for evaluating the integrity and compliance of resource solutions. By referring to the industry's minimum standards, best practices, and relevant regulations, it ensures that the design and implementation of resource solutions comply with industry specifications and avoids potential risks and compliance issues; historical resource strategy information helps to determine the degree of fit between the current resource solution and the company's historical cases, and then evaluates its consistency and feasibility with the company's overall strategic goals; the industry solution collection brings together various industry cases related to resource solutions in the industry, providing rich reference materials for the evaluation of the solution value logic dimension.

[0032] For example, when evaluating the resource plan of the financial leasing industry, the resource industry standard information may include guidance materials on the business processes, project scope, input-output measurement, risk assessment, etc. of the financial leasing industry to help evaluate the feasibility and compliance of the resource plan; historical resource strategy information may include multiple historical strategy cases related to the resource plan to achieve consistency and feasibility of the resource plan and the overall strategic goals of the financial leasing company corresponding to the resource plan; the industry plan collection may include a series of industry cases related to the resource plan in the financial leasing industry.

[0033] In some other embodiments, the pre-built resource knowledge base may also include resource market dynamic information (including resource price fluctuation trends, market supply and demand conditions, resource acquisition channels and stability assessments, etc., which can be used to assess the cost-effectiveness of resource solutions, as well as flexibility and risk resistance in the face of market changes) and technology innovation and trend information (the latest technological advances and technological innovation solutions in the industry, which can be used to assess the technological advancement, sustainability and future competitiveness of resource solutions), etc., without specific limitations here.

[0034] S202, evaluating the complete dimension of the resource solution based on the big language model according to the resource industry standard information; and, evaluating the strategic fit dimension of the resource solution based on the big language model according to the historical resource strategy information; and, evaluating the value logic dimension of the resource solution based on the big language model according to the industry solution set.

[0035] Here, the large language model (such as the GPT series) is an artificial intelligence technology based on deep learning, which mainly understands and generates natural language by training massive text data. The model uses the Transformer architecture to capture the relationship between words in the context, thereby realizing tasks such as language generation, translation, summarization, and question and answer. In the process of health diagnosis of resource solutions, the present disclosure integrates the above-mentioned pre-built resource knowledge base as a RAG (Retrieve-Augmented Generation) plug-in knowledge base into the large language model to enhance the understanding and evaluation capabilities of the large language model. The RAG architecture introduces a retrieval mechanism so that the large language model can obtain relevant external information in real time during the generation process, effectively improving the model's understanding and processing capabilities of the data in the field where the resource solution is located, thereby enhancing the comprehensiveness and accuracy of the resource solution evaluation. In this way, the large language model can not only rely on the open source data that it can obtain, but also dynamically extract relevant information from the knowledge base for in-depth reasoning and analysis. This process greatly enhances the evaluation capability of the model, so that the evaluation of resource solutions is no longer limited to a single dimension, but can be diversified in a wider range of fields and multiple angles. Analysis.

[0036] Specifically, when evaluating the complete dimensions of the resource plan based on the big language model according to the resource industry standard information, first of all, for the plan composition, the big language model will check whether the resource plan contains all the necessary components according to the resource industry standard information, such as project background introduction, clear goal setting, clear implementation steps and reasonable resource allocation, etc., to ensure that the plan is structurally complete. Next, the big language model will evaluate the scope of the plan. By comparing the industry standard information with the plan description, the big language model can determine whether the scope of activities, services or products covered by the plan is appropriate and comprehensive, avoiding the scope of the plan being too narrow to omit key elements, or too broad to be effectively implemented. When it comes to the budget evaluation of resource plans, the big language model will analyze the cost estimates in the plan, including whether the allocation of human, material, financial and other resources is reasonable, and refer to the cost-benefit analysis in the industry standard to provide strong support for the financial feasibility of the plan. In addition, the assessment of solution risks is also an important part of the completeness assessment of resource solutions. The large language model will identify various types of risks that may be faced in the solution based on industry experience and historical data, such as market risks, technical risks, policy risks, etc., and evaluate the possibility and impact of these risks.

[0037] Exemplarily, after the completeness dimension evaluation of the resource solution is completed using the large language model in combination with resource industry standard information, a solution completeness evaluation result related to the solution and a solution completeness evaluation logic corresponding to the solution completeness evaluation result will be generated. The solution completeness evaluation logic is used to reflect the evaluation basis of the large language model for evaluating the solution completeness evaluation result.

[0038] Understandably, in order to further quantify the completeness and quality of the solution, the big language model will further refine the evaluation process according to the pre-set solution completeness scoring criteria. This scoring standard is usually based on industry best practices, lessons learned from past projects, and expert opinions, and is intended to ensure the objectivity and accuracy of the evaluation. In the scoring criteria, each evaluation aspect (such as solution composition, scope, budget, risk, etc.) will be assigned a corresponding weight to reflect their importance in the overall solution. Then the big language model will conduct a spot evaluation of the specific elements under each dimension based on the refined quantification of the information submitted for each aspect of the evaluation. That is, the big language model will check whether the solution covers these key elements and whether the description of these elements is detailed and specific enough. At the same time, based on the spot evaluation, the big language model will conduct a horizontal comparison and scoring of the coverage and refinement of project elements. This step involves comparing the current solution with industry standards, best practices, or other similar solutions to evaluate its coverage breadth and depth of key elements. Through comparison, the model can identify the strengths and weaknesses in the solution and provide guidance for subsequent improvements and optimizations.

[0039] Here, the big language model will comprehensively consider the overall performance of the solution when scoring, including the logical relationship between elements, the feasibility and operability of the solution, etc. In this way, the final score is a comprehensive evaluation of the quality and completeness of the solution.

[0040] For example, if an enterprise needs to evaluate a resource plan aimed at optimizing the portfolio management strategy, the large language model will first check whether the plan describes key elements such as investment objectives, asset allocation principles, and risk management measures; then, the model will evaluate whether the investment products and market scope covered by the plan are in line with the current financial market trends and industry standards; in terms of budget evaluation, the model will conduct an in-depth analysis of whether the allocation of funds is reasonable; finally, the model will also identify and evaluate the risk factors such as financial market fluctuations and credit risks that may be faced in the plan, and put forward corresponding risk management suggestions. Through this series of evaluations, the evaluation results of the plan in the dimension of plan completeness are determined, and then a quantitative plan completeness score can be obtained, which provides a basis for optimizing the portfolio management strategy.

[0041] In the disclosed embodiment, the integrity assessment of the resource solution is performed through a large language model based on resource industry standard information, which can provide comprehensive support for the optimization and improvement of the solution. In addition, the model also comprehensively evaluates the quality and feasibility of the solution through quantitative scoring criteria, combined with past project experience and best practices, and provides a basis for further optimization and decision-making of the solution, ensuring that the solution can be more effectively implemented in actual operations and achieve the expected goals.

[0042] It is understandable that when evaluating the strategic fit dimension of resource solutions based on historical resource strategy information based on the big language model, it includes: using the big language model to extract key historical strategy corpus tags based on historical resource strategy information. These tags represent the important directions and decision points of the company's past resource allocation and strategic planning; next, the model will match the resource solution with the previously extracted historical strategy corpus tags. At this time, the big language model will consider the similarity in the literal sense while taking into account the deep understanding and association at the semantic level, so as to more accurately judge the degree of fit between the resource solution or project and the company's historical strategy. Through this matching process, the strategic fit evaluation results and the corresponding strategic fit evaluation logic can be obtained. The strategic fit evaluation logic is used to reflect the evaluation basis of the big language model for evaluating the evaluation results.

[0043] In addition, in order to more intuitively display the degree of strategic fit, the strategic fit evaluation results can also be scored based on the large language model combined with the strategic fit scoring criteria to obtain a specific strategic fit score. Here, the strategic fit scoring criteria can include setting different weights for different strategic corpus tags in advance to reflect their importance and priority in the enterprise strategy. When a resource plan or project is highly matched with a certain tag, a corresponding score can be given according to the weight of the tag. By accumulating the scores of all matching tags, a specific strategic fit score can be obtained.

[0044] For example, a financial institution is evaluating whether a new investment project is in line with its current corporate strategy. First, the institution uses a large language model to extract key strategic corpus tags from historical investment strategy information, such as "financial technology investment" and "diversified investment". Then, the relevant information of the new investment project is input into the large language model. The model will match this information with the strategic corpus tags to determine the degree of fit between the new investment project and the company's historical strategy. If the new investment project is highly consistent with the company's historical strategy in terms of financial technology investment, and is also reflected in diversified investment, the model will give a higher strategic fit assessment result and score. This will help decision makers of financial institutions to be more confident that the investment project is in line with the company's strategic direction, so as to make more informed investment decisions.

[0045] In this way, in the disclosed embodiment, through the in-depth analysis and evaluation of historical resource strategy information by the large language model, it is possible to effectively judge the degree of fit between the resource plan and the company's historical strategy, which can not only provide decision makers with a quantitative evaluation basis, but also help optimize resource allocation and strategic decision-making, thereby improving the success rate and efficiency of the company's strategy implementation.

[0046] Specifically, refer to Figure 3 As shown, when evaluating the value logic dimension of the resource solution based on the industry solution set based on the large language model, the following S301 to S304 may be included:

[0047] S301, obtaining industry similar solutions corresponding to the resource solution from the industry solution set, and evaluating the industry competitiveness corresponding to the resource solution according to the industry similar solutions based on the large language model to obtain a first evaluation result.

[0048] It is understandable that industry similar solutions refer to other solutions in the same industry with similar characteristics, goals or operating methods. Industry similar solutions provide an industry background reference for resource solutions to help evaluate their competitiveness in the market. The goal of industry competitiveness evaluation is to determine whether the resource solution can occupy a favorable position in the industry, whether it has sustainable competitive advantages, and its potential in the industry ecosystem. Here, the big language model analyzes these industry similar solutions, combines market environment, technology trends, resource input and other dimensions, evaluates the industry competitiveness of resource solutions, and obtains the first evaluation result.

[0049] S302, obtaining a historical resource solution corresponding to the resource solution from the industry solution set, and evaluating the solution improvement degree corresponding to the resource solution according to the historical resource solution based on the large language model to obtain a second evaluation result.

[0050] It is understandable that the historical resource solution refers to a historical resource solution that shares the same project goal as the current resource solution, and the historical resource solution includes multiple historical resource version solutions that have an improvement relationship with the resource solution. In this step, the large language model compares the historical resource solution, analyzes its development context and improvement process, evaluates the degree of improvement of the current resource solution compared to the historical solution, and obtains the second evaluation result. Here, when evaluating the resource solution compared to the historical resource solution, the evaluation indicators may include efficiency improvement, cost control, function expansion, etc. The second evaluation result can help determine whether the resource solution draws on past experience to further improve performance and operability, or whether there are repeated problems that have not been effectively solved.

[0051] S303, obtaining a comparative similar solution corresponding to the resource solution from the industry solution set, and evaluating the solution market information corresponding to the resource solution according to the comparative similar solution based on the large language model to obtain a third evaluation result.

[0052] Among them, the comparative similar solutions include multiple comparative similar solutions that belong to the same industry as the resource solution; the market information of these comparative similar solutions is analyzed through the large language model, and the adaptability of the resource solution in the current market and its potential market demand are evaluated to obtain the third evaluation result, which provides a reference for the positioning of the resource solution in the market and helps to better adjust the direction of products or services. Here, the focus of the evaluation is whether the resource solution can meet the current market demand trend and whether it can achieve market expansion or occupy a specific market segment.

[0053] S304, determining the value logic evaluation result and the value logic evaluation logic corresponding to the value logic evaluation result based on the first evaluation result, the second evaluation result and the third evaluation result, so as to realize the evaluation of the value logic dimension of the resource plan; and scoring the value logic evaluation result based on the large language model combined with the value logic scoring standard to obtain a value logic score.

[0054] Specifically, after obtaining the first evaluation result, the second evaluation result, and the third evaluation result, the large language model will combine the first evaluation result (industry competitiveness evaluation), the second evaluation result (solution improvement evaluation), and the third evaluation result (market information evaluation) to comprehensively judge the value logic of the resource solution. By determining the weight of each evaluation result, the model will derive an overall value logic evaluation result and clarify the value logic evaluation logic corresponding to the evaluation result. Finally, combined with the value logic scoring criteria, the model scores the overall value logic of the resource solution and obtains a specific value logic score as the comprehensive performance of the solution in the value logic dimension during the resource evaluation process.

[0055] For example, taking the resource case in the financial field as an example, for a new investment and financial product, the specific process of using the large language model to conduct a comprehensive evaluation of its value logic dimension may include: first, select other investment and financial products similar to the financial product from the financial industry solution set, and use the large language model to analyze the market performance, customer feedback, investment strategy and risk control measures of these similar products to evaluate their industry competitiveness. Next, find the historical version of the financial product or the historical resource solution of similar products, and evaluate the degree of improvement of the new financial product compared with the historical solution by comparing the development trajectory, improvement points and existing problems of these historical solutions. Then, select other investment and financial products belonging to the same category as the product as the comparison similar solution, analyze the market information of these comparison solutions, and evaluate the adaptability of the new financial product in the current market and its potential market demand. Finally, combining these three evaluation results (industry competitiveness, solution improvement, market information), the large language model will comprehensively judge the value logic of the financial product, and score it based on the value logic scoring standard to obtain a specific value logic score as the comprehensive performance of the financial product in the value logic dimension in the resource evaluation process.

[0056] In the disclosed embodiment, the resource solution is evaluated at multiple levels, such as industry competitiveness, historical improvements, and market information, through a large language model, to achieve a logical evaluation of the value of the solution, which not only improves the accuracy of the evaluation, but also fully reflects the performance of the resource solution in different aspects. By quantifying the logical value score, it can help decision makers gain an in-depth understanding of the potential advantages and disadvantages of the solution, so as to formulate more targeted improvement measures, optimize resource allocation, enhance the market adaptability and long-term competitiveness of the solution, and ultimately promote the successful implementation of the project and the realization of value.

[0057] Exemplarily, when conducting a health diagnosis of a resource solution, it is also possible to include an assessment of the demand maturity of the resource solution to understand the feasibility, effectiveness and fit of the solution with the target demand in actual application. The large language model can evaluate each dimension of the solution in combination with the resource solution itself and the corresponding solution maturity data. For example, for solution design, the large language model can analyze whether the structure of the solution meets actual needs and whether it is executable; for target logic, the model can evaluate whether the solution effectively points to clear needs and has a path to achieve these goals; in the evaluation of solution effects, the model can predict its possible effects based on historical data and the execution results of similar solutions; for solution support, the model can evaluate whether the solution has sufficient resources, technical support and organizational guarantees, that is, whether the solution has a certain degree of endorsement. According to the evaluation results of solution design, target logic, solution effect and solution support, the demand maturity evaluation results and the demand maturity evaluation logic corresponding to the demand maturity evaluation results can be obtained. In addition, by combining the demand maturity scoring criteria and quantifying the demand maturity assessment results based on the capabilities of the large language model, a specific demand maturity score can be obtained. This score will reflect the readiness of the resource plan for implementation and is an important starting mark for the subsequent actual deployment of resources.

[0058] In the disclosed embodiment, by performing a demand maturity assessment on a resource solution, it can be determined whether the solution has a certain degree of maturity, thereby improving the feasibility of implementing the resource solution.

[0059] S203, according to the evaluation results of multiple dimensions and the pre-built resource knowledge base, determine the health diagnosis certificate corresponding to the resource solution based on the large language model.

[0060] It is understandable that the pre-built resource knowledge base also includes a resource solution optimization suggestion collection, which is a comprehensive knowledge system. Through in-depth analysis of the success cases, failure lessons and industry best practices of historical resource solutions in the industry, generally applicable optimization strategies and specific improvement measures are extracted. These strategies and measures are systematically organized and classified to form a resource solution optimization suggestion library. It can provide comprehensive optimization guidance for resource solutions based on different evaluation dimensions, such as solution completeness, strategic fit, demand maturity and value logic.

[0061] Specifically, refer to Figure 4 As shown, when analyzing the evaluation results using the large language model, the following S401 to S404 may be included:

[0062] S401, using the large language model to analyze the solution completeness assessment results, strategic fit assessment results, demand maturity assessment results and value logic assessment results, to determine the overall evaluation analysis results corresponding to the resource solution.

[0063] It is understandable that the big language model will first parse the specific content of each evaluation dimension to understand the meaning they represent and the logical connection behind them; then, the big language model will consider this information, identify the strengths and weaknesses of the resource solution in each dimension, as well as the interactions and influences between them, and finally form a comprehensive and in-depth overall evaluation analysis result, which reflects the overall advantages and disadvantages of the resource solution and provides a basis for subsequent optimization suggestions.

[0064] S402: for each evaluation result, determine an analysis result corresponding to the evaluation result.

[0065] Then, for each evaluation dimension, the large language model will further refine the analysis and obtain the analysis results corresponding to each evaluation result. These analysis results include not only the evaluation logic, that is, why the dimension obtains such an evaluation result, but also the specific evaluation analysis results, that is, in which aspects the dimension performs well and which aspects need improvement.

[0066] S403: Determine an optimization suggestion for the resource solution by using the large language model in combination with the resource solution optimization suggestion set according to the overall evaluation analysis result and the analysis result corresponding to each evaluation result.

[0067] It is understandable that after obtaining the analysis results corresponding to each evaluation result, the large language model will combine the resource solution optimization suggestion set to provide specific optimization suggestions for the resource solution. These suggestions cover the solution adjustment direction, that is, in which aspects of the resource solution need to be adjusted to better meet the needs; key improvement points, that is, the specific links in the resource solution that need to be improved; and resource allocation suggestions, that is, how to allocate resources more reasonably to improve the execution efficiency and effectiveness of the solution.

[0068] S404: Based on the overall evaluation analysis result, the analysis result corresponding to each evaluation result, and the optimization suggestion, the large language model is used to generate a health diagnosis certificate corresponding to the resource solution.

[0069] Specifically, based on the overall evaluation analysis results, the analysis results corresponding to each evaluation result, and the optimization suggestions, the large language model will generate a health diagnosis book corresponding to the resource solution. The health diagnosis book will present the comprehensive overall evaluation analysis results, summarize the overall health status of the resource solution, and point out its strengths and weaknesses in terms of resource utilization, strategic fit, demand satisfaction, and value creation. Then, for the analysis results of each evaluation dimension, the diagnosis book will provide in-depth analysis and interpretation, revealing the specific performance of the resource solution in various key areas, as well as the root causes of these performances. In addition, the health diagnosis book will also include specific action guidelines for the improvement of the resource solution in combination with optimization suggestions. These suggestions not only include optimization and improvement strategies for resource utilization, aiming to help improve the overall resource health, but also quickly identify shortcomings and bottlenecks in resource utilization. These suggestions use resource packages as logical units, taking into account the correlation between resources and ensuring the practicality and operability of the suggestions, so as to truly improve the quality and utilization efficiency of resources.

[0070] It is understandable that in order to make the evaluation results of the health diagnosis dimension of the resource solution more intuitive and comprehensive, the present disclosure proposes a visualization method, that is, integrating the solution completeness score, strategic fit score, demand maturity score and value logic score to generate a four-dimensional radar chart. Here, the four-dimensional radar chart is a graphical tool based on a two-dimensional plane to display multidimensional data. It extends outward from the center point through multiple axes (in this case, four axes, representing the above four evaluation dimensions), and marks the corresponding score points on each axis, and then connects these points with line segments to form a closed polygonal figure. The shape, area and angle of each side of this polygon with the coordinate axis can intuitively reflect the performance of the resource solution in each evaluation dimension: the closer the polygon is to the ideal circumscribed circle (that is, all dimension scores are high and balanced), the better the overall performance of the solution; on the contrary, if one side is shorter or the overall shape is irregular, it means that the solution may be insufficient or need improvement in the corresponding dimension. At the same time, the four-dimensional radar chart can also be flexibly embedded in the health diagnosis book as an important part of the report. In the health diagnosis book, you can attach a detailed interpretation of the scores of each dimension, the basis for scoring, improvement suggestions, etc. around the radar chart to form a complete and systematic health diagnosis report of the resource plan. Such a report can not only help decision makers quickly grasp the overall status of the plan, but also provide them with targeted optimization directions, promote the continuous improvement and optimization of the resource plan, and ultimately improve the efficiency of resource allocation.

[0071] The resource health diagnosis method, device, medium and computer equipment provided in the embodiments of the present disclosure can quickly generate a "diagnosis" and provide improvement suggestions by combining the industry's preset resource knowledge base and large language model to perform health diagnosis on resource solutions, reducing the manpower and time investment required for internal diagnosis, analysis and promotion. At the same time, this method abandons subjective judgment and improves the quality of resource planning and deployment. In addition, the present disclosure can also provide objective and systematic tools for demand audits, project audits, and financial production value audits, ensuring compliance and improving the quality and efficiency of responses, thereby better meeting the needs of enterprises in the evaluation and optimization of scientific and technological resources.

[0072] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0073] Based on the same inventive concept, a resource health diagnosis device corresponding to the resource health diagnosis method is also provided in the embodiment of the present disclosure. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned resource health diagnosis method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0074] Reference Figure 5 FIG. 5 is a schematic diagram of a resource health diagnosis device 500 provided in an embodiment of the present disclosure, wherein the device comprises:

[0075] The information acquisition module 501 is used to acquire resource solutions and a pre-built resource knowledge base; wherein the pre-built resource knowledge base includes resource industry standard information, historical resource strategy information, and industry solution collection;

[0076] A solution evaluation module 502 is used to evaluate the solution integrity dimension of the resource solution based on the large language model according to the resource industry standard information; and to evaluate the strategic fit dimension of the resource solution based on the large language model according to the historical resource strategy information; and to evaluate the value logic dimension of the resource solution based on the large language model according to the industry solution set;

[0077] The diagnosis book generating module 503 is used to determine the health diagnosis book corresponding to the resource solution based on the large language model according to the evaluation results of multiple dimensions and the pre-built resource knowledge base.

[0078] In some possible embodiments, the solution evaluation module 502 is specifically used to:

[0079] Based on the large language model, the solution composition, solution scope, solution budget and solution risk corresponding to the resource solution are evaluated respectively according to the resource industry standard information, and a solution completeness evaluation result and a solution completeness evaluation logic corresponding to the solution completeness evaluation result are obtained to realize the evaluation of the solution completeness dimension of the resource solution; and the solution completeness evaluation result is scored based on the large language model combined with the solution completeness scoring standard to obtain a solution completeness score.

[0080] In some possible embodiments, the solution evaluation module 502 is specifically used to:

[0081] Based on the large language model, historical strategic corpus labels are extracted according to the historical resource strategic information, and the matching degree between the resource plan and the historical strategic corpus labels is evaluated based on the large language model to obtain a strategic fit evaluation result and a strategic fit evaluation logic corresponding to the strategic fit evaluation result, so as to realize the evaluation of the strategic fit dimension of the resource plan; and the strategic fit evaluation result is scored based on the large language model combined with the strategic fit scoring standard to obtain a strategic fit score.

[0082] In some possible embodiments, the solution evaluation module 502 is specifically used to:

[0083] Acquire an industry similar solution corresponding to the resource solution from the industry solution set, and evaluate the industry competitiveness corresponding to the resource solution according to the industry similar solution based on the large language model to obtain a first evaluation result; wherein the industry similar solution includes multiple similar solutions belonging to the same industry as the resource solution;

[0084] Acquire a historical resource solution corresponding to the resource solution from the industry solution set, and evaluate the solution improvement degree corresponding to the resource solution according to the historical resource solution based on the large language model to obtain a second evaluation result; wherein the historical resource solution and the resource solution share the same project goal, and the historical resource solution includes multiple historical resource version solutions that have an improvement relationship with the resource solution;

[0085] Obtaining a comparative similar solution corresponding to the resource solution from the industry solution set, and evaluating the solution market information corresponding to the resource solution according to the comparative similar solution based on the large language model to obtain a third evaluation result; wherein the comparative similar solution includes multiple comparative similar solutions belonging to the same industry as the resource solution;

[0086] Determine the value logic evaluation result and the value logic evaluation logic corresponding to the value logic evaluation result based on the first evaluation result, the second evaluation result and the third evaluation result to realize the evaluation of the value logic dimension of the resource plan; and score the value logic evaluation result based on the large language model combined with the value logic scoring standard to obtain a value logic score.

[0087] In some possible embodiments, the solution evaluation module 502 is further configured to:

[0088] Acquire solution maturity information corresponding to the resource solution, and evaluate the solution design, target logic, solution effect and solution support corresponding to the resource solution based on the large language model according to the resource solution and the solution maturity information, and obtain a demand maturity evaluation result and a demand maturity evaluation logic corresponding to the demand maturity evaluation result, so as to realize the evaluation of the demand maturity dimension of the resource solution; and, score the demand maturity evaluation result based on the large language model in combination with the demand maturity scoring standard to obtain a demand maturity score.

[0089] In some possible embodiments, the pre-built resource knowledge base also includes a resource solution optimization suggestion set, the evaluation results include solution completeness evaluation results, strategic fit evaluation results, demand maturity evaluation results, and value logic evaluation results, and the diagnosis book generation module 503 is specifically used to:

[0090] Analyze the solution completeness evaluation results, strategic fit evaluation results, demand maturity evaluation results, and value logic evaluation results using the large language model to determine the overall evaluation analysis results corresponding to the resource solution;

[0091] For each evaluation result, determining an analysis result corresponding to each evaluation result; wherein the analysis result includes the evaluation logic and the evaluation analysis result corresponding to each evaluation result;

[0092] According to the overall evaluation analysis result and the analysis result corresponding to each evaluation result, the optimization suggestion of the resource solution is determined by using the large language model in combination with the resource solution optimization suggestion set; wherein the optimization suggestion includes the solution adjustment direction, key improvement points, and resource allocation suggestions;

[0093] Based on the overall evaluation analysis result, the analysis result corresponding to each evaluation result and the optimization suggestion, the large language model is used to generate a health diagnosis certificate corresponding to the resource solution.

[0094] In some possible embodiments, the solution evaluation module 502 is further configured to:

[0095] Generate a four-dimensional radar chart based on the solution completeness score, the strategic fit score, the demand maturity score, and the value logic score;

[0096] The diagnosis book generating module 503 is also used for:

[0097] Based on the overall evaluation analysis report, the analysis results corresponding to each evaluation result, the optimization suggestions and the four-dimensional radar chart, the large language model is used to generate a health diagnosis certificate corresponding to the resource plan.

[0098] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 6 , which is a schematic diagram of the structure of a computer device 600 provided in an embodiment of the present disclosure, including a processor 601, a memory 602, and a bus 603. The memory 602 is used to store execution instructions, including a memory 6021 and an external memory 6022; the memory 6021 is also called an internal memory, which is used to temporarily store the operation data in the processor 601 and the data exchanged with the external memory 6022 such as a hard disk. The processor 601 exchanges data with the external memory 6022 through the memory 6021.

[0099] In the embodiment of the present application, the memory 602 is specifically used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 601. That is, when the computer device 600 is running, the processor 601 communicates with the memory 602 through the bus 603, so that the processor 601 executes the application code stored in the memory 602, and then executes the method described in any of the above embodiments.

[0100] Among them, the memory 602 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0101] Processor 601 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0102] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the computer device 600. In other embodiments of the present application, the computer device 600 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0103] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the resource health diagnosis method described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0104] The presently disclosed embodiment also provides a computer program product, which carries a program code. The program code includes instructions that can be used to execute the steps of the resource health diagnosis method described in the above method embodiment. Please refer to the above method embodiment for details, which will not be repeated here.

[0105] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0106] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0109] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0110] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A resource health diagnosis method, characterized in that: include: Acquire resource solutions and a pre-built resource knowledge base; wherein the pre-built resource knowledge base includes resource industry standard information, historical resource strategy information, and industry solution collection; According to the resource industry standard information, the solution completeness dimension of the resource solution is evaluated based on the large language model; and, according to the historical resource strategy information, the strategic fit dimension of the resource solution is evaluated based on the large language model; and, according to the industry solution set, the value logic dimension of the resource solution is evaluated based on the large language model; According to the evaluation results of multiple dimensions and the pre-built resource knowledge base, a health diagnosis certificate corresponding to the resource solution is determined based on the large language model.

2. The method according to claim 1, characterized in that The step of evaluating the complete dimension of the resource solution based on the large language model according to the resource industry standard information includes: Based on the large language model, the solution composition, solution scope, solution budget and solution risk corresponding to the resource solution are evaluated respectively according to the resource industry standard information, and a solution completeness evaluation result and a solution completeness evaluation logic corresponding to the solution completeness evaluation result are obtained to realize the evaluation of the solution completeness dimension of the resource solution; and the solution completeness evaluation result is scored based on the large language model combined with the solution completeness scoring standard to obtain a solution completeness score.

3. The method according to claim 2, characterized in that The evaluating the strategic fit dimension of the resource solution based on the large language model according to the historical resource strategy information includes: Based on the large language model, historical strategic corpus labels are extracted according to the historical resource strategic information, and the matching degree between the resource plan and the historical strategic corpus labels is evaluated based on the large language model to obtain a strategic fit evaluation result and a strategic fit evaluation logic corresponding to the strategic fit evaluation result, so as to realize the evaluation of the strategic fit dimension of the resource plan; and the strategic fit evaluation result is scored based on the large language model combined with the strategic fit scoring standard to obtain a strategic fit score.

4. The method according to claim 3, characterized in that The evaluating the value logic dimension of the resource solution according to the industry solution set based on the large language model also includes: Acquire an industry similar solution corresponding to the resource solution from the industry solution set, and evaluate the industry competitiveness corresponding to the resource solution according to the industry similar solution based on the large language model to obtain a first evaluation result; wherein the industry similar solution includes multiple similar solutions belonging to the same industry as the resource solution; Acquire a historical resource solution corresponding to the resource solution from the industry solution set, and evaluate the solution improvement degree corresponding to the resource solution according to the historical resource solution based on the large language model to obtain a second evaluation result; wherein the historical resource solution and the resource solution share the same project goal, and the historical resource solution includes multiple historical resource version solutions that have an improvement relationship with the resource solution; Obtaining a comparative similar solution corresponding to the resource solution from the industry solution set, and evaluating the solution market information corresponding to the resource solution according to the comparative similar solution based on the large language model to obtain a third evaluation result; wherein the comparative similar solution includes multiple comparative similar solutions belonging to the same industry as the resource solution; Determine the value logic evaluation result and the value logic evaluation logic corresponding to the value logic evaluation result based on the first evaluation result, the second evaluation result and the third evaluation result to realize the evaluation of the value logic dimension of the resource plan; and score the value logic evaluation result based on the large language model combined with the value logic scoring standard to obtain a value logic score.

5. The method according to claim 4, characterized in that The method further comprises: Acquire solution maturity information corresponding to the resource solution, and evaluate the solution design, target logic, solution effect and solution support corresponding to the resource solution based on the large language model according to the resource solution and the solution maturity information, and obtain a demand maturity evaluation result and a demand maturity evaluation logic corresponding to the demand maturity evaluation result, so as to realize the evaluation of the demand maturity dimension of the resource solution; and, score the demand maturity evaluation result based on the large language model in combination with the demand maturity scoring standard to obtain a demand maturity score.

6. The method according to claim 5, characterized in that The pre-built resource knowledge base also includes a resource solution optimization suggestion set, the evaluation results include solution completeness evaluation results, strategic fit evaluation results, demand maturity evaluation results, and value logic evaluation results, and the health diagnosis corresponding to the resource solution is determined based on the large language model according to the evaluation results of multiple dimensions and the pre-built resource knowledge base, including: Analyze the solution completeness evaluation results, strategic fit evaluation results, demand maturity evaluation results, and value logic evaluation results using the large language model to determine the overall evaluation analysis results corresponding to the resource solution; For each evaluation result, determining an analysis result corresponding to each evaluation result; wherein the analysis result includes the evaluation logic and the evaluation analysis result corresponding to each evaluation result; According to the overall evaluation analysis result and the analysis result corresponding to each evaluation result, the optimization suggestion of the resource solution is determined by using the large language model in combination with the resource solution optimization suggestion set; wherein the optimization suggestion includes the solution adjustment direction, key improvement points, and resource allocation suggestions; Based on the overall evaluation analysis result, the analysis result corresponding to each evaluation result and the optimization suggestion, the large language model is used to generate a health diagnosis certificate corresponding to the resource solution.

7. The method according to claim 6, characterized in that After evaluating the multiple dimensions of the resource solution based on the pre-built resource knowledge base and the large language model, the method further includes: Generate a four-dimensional radar chart based on the solution completeness score, the strategic fit score, the demand maturity score, and the value logic score; The generating of a health diagnosis certificate corresponding to the resource solution using the large language model based on the overall evaluation analysis result, the analysis result corresponding to each evaluation result, and the optimization suggestion also includes: Based on the overall evaluation analysis report, the analysis results corresponding to each evaluation result, the optimization suggestions and the four-dimensional radar chart, the large language model is used to generate a health diagnosis certificate corresponding to the resource plan.

8. A resource health diagnosis device, characterized in that: include: An information acquisition module, used to acquire resource solutions and a pre-built resource knowledge base; wherein the pre-built resource knowledge base includes resource industry standard information, historical resource strategy information, and industry solution collection; A solution evaluation module, for evaluating the solution integrity dimension of the resource solution based on the large language model according to the resource industry standard information; and, evaluating the strategic fit dimension of the resource solution based on the large language model according to the historical resource strategy information; and, evaluating the value logic dimension of the resource solution based on the large language model according to the industry solution set; A diagnosis book generation module is used to determine a health diagnosis book corresponding to the resource solution based on the large language model according to the evaluation results of multiple dimensions and the pre-built resource knowledge base.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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