Resource production health assessment method and device, medium and equipment

Abstract

The invention relates to the technical field of resource commissioning and the field of financial science and technology, and provides a resource commissioning health assessment method and device, a medium and equipment, and the method comprises the steps: obtaining a resource commissioning scheme set and a preset resource knowledge base for a target business; for each resource commissioning scheme, obtaining the operation condition of the resource commissioning scheme through a preset data burying point, and evaluating the robustness of the resource commissioning scheme based on the large language model and the operation condition of the resource commissioning scheme to obtain a scheme robustness score; for each resource commissioning scheme, obtaining scheme information of the resource commissioning scheme, and determining a health degree score of the resource commissioning scheme based on the scheme information and the scheme robustness score of the resource commissioning scheme; and according to a preset resource knowledge base and the health degree score of each resource commissioning scheme, determining a resource commissioning health assessment result of the target business through a large language model. According to the embodiment, the high efficiency and sustainability of resource investment can be ensured, and the resource production efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of resource investment and production, as well as the field of fintech. Specifically, it relates to a method, device, medium, and equipment for resource investment and production health assessment. Background Art

[0002] With the continuous development of the national economy, when various industries conduct resource investment, there is an urgent need for a scientific evaluation system to measure the effect of resource investment and ensure the health and maximization of benefits of the investment. Especially in the field of fintech, resource investment and management involve multiple dimensions such as funds, technology, and human resources. How to accurately link resource investment with results has become the key to promoting business development.

[0003] Currently, the industry has not yet formed a perfect solution for the closed-loop control and optimization technology of resource investment and production health. Most of the existing technologies only collect and display resource-related data, fail to deeply explore the relationship between resource investment and operation management, and lack higher-level intelligent decision-making and deep links. At the same time, the existing solutions mostly focus on the functions of a single module and fail to establish an effective linkage mechanism among various links in the entire operation process. Therefore, it is impossible to achieve a full-cycle and all-dimensional evaluation of resource investment and production health. Summary of the Invention

[0004] Embodiments of the present disclosure at least provide a method, device, medium, and equipment for resource investment and production health assessment. By using data embedding technology to obtain various data in real time and combining large language models with external knowledge bases to conduct multi-dimensional evaluation of resource investment and production plans, it is not only possible to monitor the health status of investment and production in real time, but also ensure the efficiency and sustainability of resource investment to improve resource investment and production efficiency.

[0005] Embodiments of the present disclosure provide a method for resource investment and production health assessment, including:

[0006] Obtain a set of resource investment and production plans for a target business and a preset resource knowledge base; wherein, the set of resource investment and production plans includes at least one resource investment and production plan;

[0007] For each resource investment and production plan, obtain the operation situation corresponding to the resource investment and production plan through preset data embedding, and evaluate the robustness of the resource investment and production plan based on the large language model and the operation situation corresponding to the resource investment and production plan to obtain a plan robustness score;

[0008] For each resource investment and production plan, obtain the plan information corresponding to the resource investment and production plan, and determine the health score of the resource investment and production plan based on the plan information and the plan robustness score corresponding to the resource investment and production plan; wherein, the plan information includes plan change health, associated project investment and production compliance rate, and associated requirement investment and production compliance rate.

[0009] Based on the preset resource knowledge base and the health scores of each resource production plan, determine the resource production health assessment result corresponding to the target business through the large language model.

[0010] An embodiment of the present disclosure provides a resource production health assessment device, including:

[0011] A plan acquisition module, configured to acquire a set of resource production plans for a target business and a preset resource knowledge base; wherein, the set of resource production plans includes at least one resource production plan;

[0012] A robustness assessment module, configured to, for each resource production plan, obtain the operation situation corresponding to the resource production plan through preset data buried points, and evaluate the robustness of the resource production plan based on the large language model and the operation situation corresponding to the resource production plan to obtain a plan robustness score;

[0013] A health determination module, configured to, for each resource production plan, obtain the plan information corresponding to the resource production plan, and determine the health score of the resource production plan based on the plan information and the plan robustness score corresponding to the resource production plan; wherein, the plan information includes plan change health, associated project production compliance rate, and associated requirement production compliance rate;

[0014] An assessment result determination module, configured to determine the resource production health assessment result corresponding to the target business through the large language model according to the preset resource knowledge base and the health scores of each resource production plan.

[0015] An embodiment of the present disclosure provides a computer device, including: a processor, a memory, and a bus, the memory stores machine-readable instructions executable by the processor, when the computer device runs, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the resource production health assessment method described in any of the above possible implementation manners is executed.

[0016] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the resource production health assessment method described in any of the above possible implementation manners is implemented.

[0017] The resource production health assessment method, device, medium, and equipment provided in the embodiments of the present disclosure obtain scenario data in real time through data embedding technology and perform real-time health diagnosis in combination with a large language model, eliminating the manual dependence in traditional production tracking and achieving accurate assessment and real-time monitoring in all dimensions of projects, requirements, and systems. At the same time, in combination with an external RAG knowledge base, the large language model is used to perform multi-dimensional assessment of the target business, which can not only monitor the production health status in real time, but also ensure the efficiency and sustainability of resource investment to improve the resource production efficiency.

[0018] To make the above objects, features, and advantages of the present disclosure more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 accompanying drawings are incorporated into the specification and constitute a part of the specification. These drawings show embodiments consistent with the present disclosure and, together with the specification, are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only show some embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 FIG. shows a schematic diagram of an application environment of a resource production health assessment method provided by an embodiment of the present disclosure;

[0021] Figure 2 FIG. shows a flowchart of a resource production health assessment method provided by an embodiment of the present disclosure;

[0022] Figure 3 FIG. shows a flowchart of a target business determination method provided by an embodiment of the present disclosure;

[0023] Figure 4 FIG. shows a flowchart of a method for determining the score of a target business provided by an embodiment of the present disclosure;

[0024] Figure 5 FIG. shows a flowchart of a resource production health assessment method for a target business provided by an embodiment of the present disclosure;

[0025] Figure 6 FIG. shows a schematic structural diagram of a resource production health assessment device provided by an embodiment of the present disclosure;

[0026] Figure 7 FIG. shows a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments. Components of the embodiments of the present disclosure described and illustrated in the accompanying 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 accompanying drawings is not intended to limit the scope of the claimed present disclosure, but merely represents selected embodiments of the present disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without creative efforts fall within the scope of protection of the present disclosure.

[0028] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0029] The term "and / or" in this article merely describes an association relationship and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0030] To facilitate the understanding of this embodiment, the execution subject of the resource commissioning health assessment method provided by the embodiments of the present disclosure will be introduced in detail first. The resource commissioning health assessment method provided by the embodiments of the present invention can be applied in an application environment such as Figure 1 where the client communicates with the server through a network. Among them, 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 providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms.

[0031] The following will describe in detail the resource commissioning health assessment method provided by the embodiments of the present application with reference to the accompanying drawings. Refer to Figure 2 As shown, it is a flowchart of a resource commissioning health assessment method provided by an embodiment of the present disclosure. The method includes the following S201 to S204:

[0032] S201, obtain a set of resource investment plans for the target business and a preset resource knowledge base.

[0033] It can be understood that the set of resource investment plans refers to multiple resource allocation and investment plans designed for different business requirements of the target business. Each plan may include different types of resources, allocation methods, investment scales, etc. Among them, the types of resources can include human resources, equipment, and funds, etc.; the allocation method refers to how to effectively allocate and arrange these resources, which may involve multiple strategies and plans, such as making efficient use of existing resources and phased investment; the investment scale refers to the specific investment volume of various resources, which can include the number of equipment, the number of personnel, the amount of funds, etc. Here, the set of resource investment plans includes at least one resource investment plan.

[0034] Exemplarily, in the financial field, the target business may involve credit business, investment banking business, asset management business, etc. The set of resource investment plans is a variety of resource allocation and investment plans designed for the different needs of these businesses. For example, increasing credit approval personnel, upgrading the trading system, expanding the scale of capital investment, etc. Each plan details the types of resources required (such as human resources, technical equipment, operating funds), the allocation method (such as concentrating resources to tackle a certain project, phased gradual investment), and the investment scale (such as the specific number of personnel, the number of equipment units, the amount of funds), etc. For example, if the target business is the credit business, for this business, a set of resource investment plans can be designed, including the following plans: Plan A: Increase credit approval personnel to improve approval efficiency and accuracy. This plan will allocate new human resources, specifically adding 10 credit approval experts, and gradually invest in phases to adapt to the growth needs of the business; Plan B: Upgrade the credit approval system and introduce an automated approval process. This plan involves the upgrade of technical equipment, including purchasing the latest approval software and hardware equipment, with an estimated investment of 5 million yuan, and is based on making efficient use of existing resources to achieve the automation of the approval process; Plan C: Expand the scale of credit capital investment to attract more customers. This plan will increase operating funds, specifically increasing the credit limit by 100 million yuan and gradually releasing it in phases to cope with market changes.

[0035] Specifically, the preset resource knowledge base contains a large amount of background information, historical data, and experience summaries related to the target business, which may include information such as historical score trends, industry peak and off-peak effects, score dispersion standards, and historical compliance warning situations. Among them, the historical score trend can reflect how the resource investment plan performs in different time periods, helping to analyze past investment effects and trends; the role of the industry peak and off-peak effect is that since some industries have different business demand changes in different seasons or time periods, the allocation of resources may need to be adjusted accordingly. Understanding these seasonal fluctuations helps to arrange resources more reasonably; in the process of evaluating the robustness of the investment plan, the score dispersion standard can be used to reflect the stability of the plan. The score dispersion refers to the score difference of the plan in different scoring dimensions. If the score difference is large, it may mean that the performance of the plan is not consistent or stable enough; the historical compliance warning situation refers to the warning of non-compliance caused by improper resource investment in historical data.

[0036] S202. For each resource investment plan, obtain the operation situation corresponding to the resource investment plan through preset data logging, and evaluate the robustness of the resource investment plan based on the large language model and the operation situation corresponding to the resource investment plan to obtain the plan robustness score.

[0037] Here, in order to ensure that the performance of each resource investment plan in actual operation can be accurately tracked and evaluated, the present disclosure proposes to implement the data acquisition task through preset data logging. Data logging refers to a piece of code or instruction embedded in the target business system and / or platform to capture and record the occurrence of specific events or behaviors. Among them, in the application scenario of the resource investment plan, the data obtained by data logging can include information in multiple dimensions such as the input and output effects of resources, the efficiency of processes, and the interactive feedback of users. In the present disclosure, through data logging, these key information can be obtained in real time and accurately.

[0038] Specifically, the implementation methods of data logging can include the following (1) to (2):

[0039] (1) Determine the data logging corresponding to each resource investment plan in the target business;

[0040] (2) Deploy the data logging corresponding to each resource investment plan in the target business on the system and / or platform related to the target business to achieve real-time capture of the operation situation corresponding to each resource investment plan.

[0041] Here, when implementing the data logging tasks for each resource production plan, it is necessary to fully consider the characteristics of the plan, the behavior habits of the target audience, and the specific requirements of the business scenario. It is necessary to deeply analyze factors such as the processes involved in the plan, the usage of resources, and user feedback to ensure that the data can accurately reflect the operation of the plan. For example, if the core goal of the plan is to improve production efficiency, the relevant data logging may focus on the efficiency indicators of each link in the production process; if the plan focuses on user feedback, data logging related to user interaction may need to be set up. Then, methods such as A / B testing, event tracking, or user behavior path analysis can be used to determine the data logging for each resource production plan in combination with business requirements and goals, more effectively screen out valuable data, and avoid interference from redundant information on the data analysis results. At the same time, the selection of data logging types should also consider the diversity of business scenarios, which can include types such as real-time monitoring to achieve efficient and accurate collection of different types of data.

[0042] It can be understood that after determining the data logging, it needs to be deployed on the target business system and / or platform. During the deployment process, it is necessary to ensure the stability of the system and the reliability of the data, and at the same time, consider the real-time requirements for collecting operation data corresponding to the resource production plan. In addition, the deployed data logging should have flexible scalability to cope with changes in business requirements, so as to achieve rapid adjustment and optimization of data collection strategies when the business is adjusted or the resource production plan is optimized later, and ensure that the data logging plan is always consistent with the business goals.

[0043] Exemplarily, for Plans A, B, and C in step S201, their data logging situations can include: Plan A: Data logging includes the work efficiency of approval personnel, approval passing rate, customer feedback, etc. to monitor the input effect of human resources in real time; Plan B: Data logging covers system response time, automated approval ratio, error rate, etc. to evaluate the effect of technical equipment upgrades; Plan C: Data logging includes the scale of capital investment, customer application volume, loan default rate, etc. to monitor the investment effect of operating funds.

[0044] Specifically, after obtaining the operation status corresponding to each resource investment plan based on preset data logging, a large language model can be used to evaluate the robustness of the resource investment plan according to the operation status corresponding to each resource investment plan, and obtain a plan robustness score. Here, the large language model (such as the GPT series) is an artificial intelligence technology based on deep learning. It mainly understands and generates natural language by training a large amount of text data. The model uses the Transformer architecture and can capture the relationships between words in the context, so as to achieve tasks such as language generation, translation, summarization, and question answering. In the process of health diagnosis of the resource plan, the present disclosure integrates the above-mentioned preset resource knowledge base as a RAG (Retrieve-Augmented Generation) external knowledge base into the large language model to enhance the understanding and evaluation capabilities of the large language model. The RAG architecture enables the large language model to obtain relevant external information in real time during the generation process by introducing a retrieval mechanism, effectively improving the model's understanding and processing capabilities of data in the field of the target business, and further enhancing the comprehensiveness and accuracy of the evaluation of the resource investment plan for the target business. In this way, the large language model can not only rely on the open-source data it can obtain itself, but also dynamically extract relevant information from the knowledge base for in-depth reasoning and analysis. This process greatly enhances the evaluation ability of the model, making the evaluation of the resource investment plan of the target business no longer limited to a single dimension, but able to conduct diversified analysis in a wider range of fields and from multiple perspectives.

[0045] Specifically, the large language model will comprehensively consider multiple dimensions such as the implementation effect of the plan, user feedback, resource utilization efficiency, and adaptability and recovery ability in the face of various internal and external changes, quantify the impact of these factors on the plan robustness, and finally generate a plan robustness score to reflect the stability performance and potential risk resistance ability of the resource investment plan.

[0046] Exemplarily, for the plans A, B, and C in step S201, the specific process of the large language model analyzing the operation status corresponding to the plans obtained through the above data logging can include: For plan A: The large language model can give relatively high robustness scores (such as 90, 95 points) based on factors such as the improvement of the work efficiency of approval personnel, the increase in the approval passing rate, and positive customer feedback. For plan B: Although the system response time is shortened and the proportion of automated approvals is increased, the error rate has also increased. The large language model gives medium robustness scores (such as 80, 85 points) considering these factors comprehensively. For plan C: The expansion of the capital investment scale has brought an increase in customer application volume, but the loan default rate has also increased slightly. The large language model gives relatively low robustness scores (such as 60, 70 points) after evaluation.

[0047] In some possible embodiments, referring to Figure 3 as shown, to improve the stability and efficiency of the target business operation, the present disclosure also proposes to determine whether the robustness score of the target business is greater than a preset value, aiming to ensure the healthy state of the business operation through quantitative analysis. Specifically, it may include the following steps S301 to S303:

[0048] S301, determine the target robustness score of the target business based on the scenario robustness scores corresponding to each resource investment scenario, and determine whether the target robustness score is greater than a preset value.

[0049] It can be understood that the target robustness score of the target business is comprehensively calculated based on the robustness scores corresponding to each resource investment scenario. Here, weighted average, fuzzy comprehensive evaluation or other mathematical models can be used for calculation to obtain the target robustness score of the target business. Then, it is determined whether the target robustness score exceeds the preset value set in advance. This preset value is a threshold set based on historical data, industry standards or business-specific requirements, aiming to distinguish the healthy and risky states of business operation.

[0050] S302, in the case where the target robustness score is greater than the preset value, determine the target business as a qualified task.

[0051] Specifically, when the target robustness score is higher than the preset value, it means that the target business shows good robustness and sustainable development potential under the support of the current resource investment scenario. Therefore, the target business can be marked as a qualified task.

[0052] S303, in the case where the target robustness score is not greater than the preset value, determine the target business as an unqualified task.

[0053] Specifically, if the target robustness score fails to reach the preset value, it means that there are some hidden dangers or deficiencies in the business operation, which may be caused by improper resource allocation, ineffective cost control or inadequate risk management measures. At this time, the target business will be marked as an unqualified task by the system.

[0054] At this time, in order to promptly identify and respond to businesses with long-term poor operations, a monitoring mechanism can be added: continuously track and record the number of times a target business is consecutively marked as a non-compliant task. When the number of times the target business is consecutively marked as a non-compliant task reaches a preset warning threshold, a series of response measures can be automatically triggered. This includes but is not limited to implementing temporary or permanent blocking operations on the target business to prevent the risk from further expanding; at the same time, activating the alarm mechanism to send emergency notifications to relevant personnel, requiring them to intervene in the investigation and take necessary corrective measures. In this way, through proactive risk management and timely intervention, the overall stability and efficiency of business operations can be effectively guaranteed, and potential losses can be avoided.

[0055] S203. For each resource investment plan, obtain the plan information corresponding to the resource investment plan, and determine the health score of the resource investment plan based on the plan information and the plan robustness score corresponding to the resource investment plan.

[0056] It can be understood that after obtaining the plan robustness scores for each resource investment plan, the health scores of each resource investment plan can be calculated based on the plan information (such as the health of plan changes, the compliance rate of associated project investments, and the compliance rate of associated demand investments). Among them, the health of plan changes mainly refers to whether the plan has undergone multiple adjustments or changes during the implementation process, and whether these changes are conducive to resource optimization; for example, frequent adjustments may indicate a lack of stability in the implementation stage or insufficient preliminary planning of the plan, while appropriate adjustments may be to meet new requirements or optimize resource allocation. Based on this, the health score of plan changes can be calculated by quantifying the health of plan changes. If the change brings about resource optimization or improvement in implementation effects, the health score of plan changes will be higher; conversely, frequent and ineffective changes will result in a lower score.

[0057] Secondly, the compliance rate of associated project investments refers to whether other projects associated with the resource investment plan have reached the preset goals or standards, which can reflect the effectiveness of resource allocation and whether the implementation of the resource investment plan meets expectations. If the compliance rate of associated project investments is high (i.e., the compliance score of associated project investments is high), it means that the resource allocation is reasonable and the implementation is smooth, thereby increasing the health score of the resource investment plan; by quantifying the compliance rate of associated project investments, it is possible to more accurately evaluate whether the resource investment plan reasonably allocates resources among multiple projects and promotes the achievement of goals.

[0058] At the same time, the production compliance rate of associated requirements refers to whether the requirements related to the resource production plan meet the preset standards. This indicator evaluates the matching degree between resource allocation and associated requirements. A high compliance rate (i.e., a high production compliance score for associated requirements) usually indicates that the resource production plan can effectively meet the target requirements, promote the realization of requirements, and thus increase the health score of the plan. If the requirement compliance rate is low (i.e., a low production compliance score for associated requirements), it may indicate that the resources are not effectively allocated or there are certain deviations in the resource allocation process.

[0059] Among them, the comprehensive plan robustness score, the plan change health score, the associated project production compliance score, and the associated requirement production compliance score jointly affect the final health score of the resource production plan. By calculating these scores with weights, a comprehensive health score of the resource production plan can be finally obtained, so as to comprehensively evaluate the overall performance of each resource production plan. In this way, the health score can not only reflect the implementation effect of the resource production plan in each dimension, but also provide a basis for decision-makers to help them identify and optimize those resource production plans that are not healthy enough or have potential risks.

[0060] In this way, through this multi-dimensional evaluation method, a comprehensive view of whether the resource production plan is healthy can be obtained, and it can help professionals make more scientific decisions to achieve more efficient resource allocation, reduce unnecessary risks, and improve the overall efficiency of resource production.

[0061] In some possible embodiments, with reference to Figure 4 as shown, after determining the health score of each resource production plan, the following steps S401 - S402 can be included:

[0062] S401, determining a set of target scores for the target business based on the health score of each resource production plan, and determining the target health score of the target business based on the set of target scores.

[0063] Specifically, after obtaining the scores of each resource production plan, for the plan robustness score, the plan change health score, the associated project production compliance score, and the associated requirement production compliance score, a set of target scores for the target business in these four dimensions can be determined respectively, that is, the target plan robustness score, the target plan change health score, the target associated project production compliance score, and the target associated requirement production compliance score. Further, based on the set of target scores, by weighting, normalizing or other mathematical processing of the scores in each dimension, the target health score of the target business can be determined.

[0064] S402. Determine the score dispersion degree corresponding to the target business based on the target solution robustness score, the target solution change health score, the target associated project production compliance score, and the target associated requirement production compliance score.

[0065] Here, the score dispersion degree of the target business reflects the differences between the scores of the target business in different dimensions, revealing the performance of the target business in different dimensions. If the score dispersion degree is large, it may mean that the performance in some dimensions is not stable, or there is a large uncertainty in the health of the target business.

[0066] S204. Determine the resource production health assessment result corresponding to the target business through the large language model according to the preset resource knowledge base and the health score of each resource production plan.

[0067] Specifically, based on the preset resource knowledge base and the health score of each resource production plan, the large language model comprehensively considers factors such as the historical score trend of the target business, the industry off-peak and peak season effects, the score dispersion degree standard, and the historical compliance warning situation, etc., to determine the resource production health assessment result corresponding to the target business. The resource production health assessment result can not only provide an intuitive and quantitative assessment basis for decision-makers, but also help identify potential problem areas, guide subsequent resource optimization and strategy adjustment, and ensure the sustainable and healthy development of the target business.

[0068] In some possible embodiments, after obtaining the target health score of the target business and the score dispersion degree corresponding to the target business, the resource production health assessment result corresponding to the target business can also be determined through the large language model according to the preset resource knowledge base, the target health score of the target business, and the score dispersion degree corresponding to the target business. Refer to Figure 5 As shown, when evaluating the resource production health of the target business, the following steps S501 - S503 can be included:

[0069] S501. Determine the abnormality degree of the production health state of the target business based on the large language model, the score dispersion degree standard, and the score dispersion degree corresponding to the target business.

[0070] It can be understood that based on the large language model, the score dispersion degree standard, and the score dispersion degree of the target business, the abnormality degree of the production health state of the target business can be determined. The score dispersion degree, as an important indicator for evaluating the score fluctuation of the target business, reflects the performance volatility of the business within a certain period of time. If the score distribution of the target business is relatively dispersed, it may mean that there are instabilities or inefficiencies in its resource allocation. Through the analysis of the large language model, the system can identify and quantify this abnormality degree, thus providing a basis for subsequent evaluation and optimization.

[0071] S502. Analyze the changing trend of the production launch health status of the target business based on the large language model, the historical score trend, the industry off-peak and peak season effects, and the target health score of the target business.

[0072] Here, by combining the large language model with the historical score trend, the industry off-peak and peak season effects, and the target health score of the target business, the changing trend of the production launch health status of the target business can be further analyzed. Among them, the review of historical data can help capture long-term and short-term change patterns, and the industry off-peak and peak season effects can provide the impact of industry cyclical fluctuations on production launch health. Through the analysis of these factors by the large language model, the health status changes of the target business at different time nodes can be identified, and thus reasonable predictions and plans can be provided for future resource production launches.

[0073] S503. Combine the abnormality degree of the production launch health status of the target business, the changing trend of the production launch health status of the target business, and the historical compliance warning situation, and generate a resource production launch health assessment result corresponding to the target business through the large language model.

[0074] It can be understood that by combining the abnormality degree, the changing trend, and the historical compliance warning situation of the production launch health status of the target business, a specific resource production launch health assessment result for the target business can be generated through the large language model.

[0075] To facilitate the understanding of this solution, the following takes the resource production launch health assessment of a commercial bank's credit business as an example for illustration. Specifically, assume that the bank hopes to evaluate the resource production launch health status of its personal consumption credit business to ensure the sustainable and healthy development of this business. According to the preset resource knowledge base, the bank has detailed data on different credit products, customer groups, market strategies, etc. At the same time, for each resource production launch plan, the bank has calculated the corresponding health score.

[0076] Next, the bank will use the large language model to comprehensively consider the historical score trend of this credit business to identify long-term and short-term performance changes; consider the industry off-peak and peak season effects to analyze the impact of seasonal fluctuations on business health; combine the score dispersion standard to evaluate the volatility of business scores and reflect the stability and efficiency of resource allocation; and refer to the historical compliance warning situation to identify potential problems and hidden dangers in the past. Finally, by combining the abnormality degree, the changing trend, and the historical compliance warning situation of the production launch health status, the large language model generates a detailed resource production launch health assessment report, providing an intuitive and quantitative assessment basis for decision-makers to ensure the sustainable and healthy development of the credit business.

[0077] In some possible embodiments, after obtaining the resource production health assessment results corresponding to the target business, a result display window corresponding to the target business can also be set to display the assessment results to relevant personnel in real time. Here, the result display window can not only present the assessment results, but also provide corresponding actionable suggestions to ensure that managers can make fast and accurate decisions based on real-time data.

[0078] The resource production health assessment method, device, medium and equipment provided in the embodiments of the present disclosure obtain solution data in real time through data embedding technology, and perform real-time health diagnosis in combination with a large language model, eliminating the manual dependence in traditional production tracking and achieving accurate assessment and real-time monitoring of all dimensions of projects, requirements and systems. At the same time, combined with the external RAG knowledge base, the large language model is used to conduct multi-dimensional assessment of the target business, which can not only monitor the production health status in real time, but also ensure the efficiency and sustainability of resource investment to improve resource production efficiency.

[0079] 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.

[0080] Based on the same inventive concept, a resource production health assessment device corresponding to the resource production health assessment 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 production health assessment 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.

[0081] Reference Figure 6 FIG. 6 is a schematic diagram of a resource production health assessment device 600 provided in an embodiment of the present disclosure, wherein the device comprises:

[0082] The solution acquisition module 601 is used to acquire a resource production solution set and a preset resource knowledge base for a target business; wherein the resource production solution set includes at least one resource production solution;

[0083] The robustness evaluation module 602 is used to obtain the operation status corresponding to each resource production plan through preset data points, and evaluate the robustness of the resource production plan based on the large language model and the operation status corresponding to the resource production plan to obtain a plan robustness score;

[0084] A health determination module 603, configured to, for each resource production plan, obtain the plan information corresponding to the resource production plan, and determine the health score of the resource production plan based on the plan information and the plan robustness score corresponding to the resource production plan; wherein, the plan information includes plan change health, associated project production compliance rate, and associated requirement production compliance rate;

[0085] An evaluation result determination module 604, configured to determine a resource production health evaluation result corresponding to the target business through the large language model according to the preset resource knowledge base and the health score of each resource production plan.

[0086] In some possible embodiments, the robustness evaluation module 602 is further configured to:

[0087] Determine data buried points corresponding to each resource production plan in the target business;

[0088] Deploy the data buried points corresponding to each resource production plan in the target business on the systems and / or platforms related to the target business to achieve real-time capture of the running conditions corresponding to each resource production plan.

[0089] In some possible embodiments, the robustness evaluation module 602 is further configured to:

[0090] Determine the target robustness score of the target business based on the plan robustness scores corresponding to each resource production plan, and determine whether the target robustness score is greater than a preset value;

[0091] In the case where the target robustness score is greater than the preset value, determine the target business as a qualified task;

[0092] In the case where the target robustness score is not greater than the preset value, determine the target business as an unqualified task;

[0093] The robustness evaluation module 602 is further configured to:

[0094] Monitor and record whether the number of times the target business is continuously marked as an unqualified task reaches a warning threshold;

[0095] In the case where the number of times the target business is continuously marked as an unqualified task reaches the warning threshold, perform a blocking operation on the target business and trigger an alarm mechanism.

[0096] In some possible embodiments, the health determination module 603 is specifically configured to:

[0097] Determine the program change health score of the resource production plan based on the program change health corresponding to the resource production plan; and, determine the associated project production compliance score of the resource production plan based on the associated project production compliance rate corresponding to the resource production plan; and, determine the associated demand production compliance score of the resource production plan based on the associated demand production compliance rate corresponding to the resource production plan;

[0098] Determine the health score of the resource production plan based on the program robustness score, the program change health score, the associated project production compliance score, and the associated demand production compliance score corresponding to the resource production plan.

[0099] In some possible embodiments, the health determination module 603 is further configured to:

[0100] Determine the target score set of the target service based on the health score of each resource production plan, and determine the target health score of the target service based on the target score set; wherein, the target score set includes a target program robustness score, a target program change health score, a target associated project production compliance score, and a target associated demand production compliance score;

[0101] Determine the score dispersion degree corresponding to the target service based on the target program robustness score, the target program change health score, the target associated project production compliance score, and the target associated demand production compliance score;

[0102] The evaluation result determination module 604 is specifically configured to:

[0103] According to the preset resource knowledge base, the target health score of the target service, and the score dispersion degree corresponding to the target service, determine the resource production health evaluation result corresponding to the target service through the large language model.

[0104] In some possible embodiments, the preset resource knowledge base includes historical score trends, industry off-peak and peak season effects, score dispersion degree standards, and historical compliance warning situations; the evaluation result determination module 604 is specifically configured to:

[0105] Determine the abnormal degree of the production health status of the target service based on the large language model, the score dispersion degree standard, and the score dispersion degree corresponding to the target service;

[0106] Analyze the change trend of the production health status of the target service based on the large language model, the historical score trend, the industry off-peak and peak season effects, and the target health score of the target service;

[0107] Combined with the degree of abnormality of the production health status of the target service, the change trend of the production health status of the target service, and the historical compliance warning situation, the large language model is used to generate a resource production health assessment result corresponding to the target service.

[0108] In some possible embodiments, the evaluation result determination module 604 is further configured to:

[0109] Set a result display window corresponding to the target service, and display the resource production health assessment result corresponding to the target service in real time in the result display window.

[0110] Based on the same technical concept, an embodiment of the present disclosure also provides a computer device. Refer to Figure 7 As shown, it is a schematic structural diagram of a computer device 700 provided by an embodiment of the present disclosure, including a processor 701, a memory 702, and a bus 703. Among them, the memory 702 is used to store execution instructions, including an internal memory 7021 and an external memory 7022; here, the internal memory 7021 is also called the main memory, which is used to temporarily store the operation data in the processor 701 and the data exchanged with the external memory 7022 such as a hard disk, and the processor 701 exchanges data with the external memory 7022 through the internal memory 7021.

[0111] In the embodiment of the present application, the memory 702 is specifically used to store the application program code for implementing the solution of the present application, and is controlled by the processor 701 to execute. That is, when the computer device 700 runs, the processor 701 communicates with the memory 702 through the bus 703, so that the processor 701 executes the application program code stored in the memory 702, and further executes the method described in any of the foregoing embodiments.

[0112] Among them, the memory 702 may be, but is not limited to, a random access memory (Random Access Memory, RAM), a read-only memory (Read Only Memory, ROM), a programmable read-only memory (Programmable Read-Only Memory, PROM), an erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable programmable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0113] The processor 701 may be an integrated circuit chip with the ability to process signals. 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 gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0114] It can be understood that the structure schematically shown in the embodiments of the present application does not constitute a specific limitation on the computer device 700. In other embodiments of the present application, the computer device 700 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure can be implemented in hardware, software, or a combination of software and hardware.

[0115] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the resource commissioning health assessment method described in the above method embodiments. Among them, the storage medium may be a volatile or non-volatile computer-readable storage medium.

[0116] The embodiments of the present disclosure also provide a computer program product, which carries program code. The instructions included in the program code can be used to execute the steps of the resource commissioning health assessment method described in the above method embodiments. For specific details, please refer to the above method embodiments and will not be elaborated here.

[0117] Among them, the above computer program product can be specifically implemented in a manner of hardware, software, or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0118] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. In several embodiments provided in the present disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, in each embodiment of the present disclosure, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0121] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present disclosure. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0122] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than limiting them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present disclosure can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; 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 all be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A resource production health assessment method, characterized in that: include: Acquire a resource production plan set and a preset resource knowledge base for the target business; wherein the resource production plan set includes at least one resource production plan; For each resource production plan, the operation status corresponding to the resource production plan is obtained through preset data points, and the robustness of the resource production plan is evaluated based on the large language model and the operation status corresponding to the resource production plan to obtain a plan robustness score; For each resource production plan, obtain the plan information corresponding to the resource production plan, and determine the health score of the resource production plan based on the plan information and the plan robustness score corresponding to the resource production plan; wherein the plan information includes the health of the plan change, the production compliance rate of the associated project, and the production compliance rate of the associated demand; According to the preset resource knowledge base and the health score of each resource production plan, the resource production health assessment result corresponding to the target business is determined through the large language model.

2. The method according to claim 1, characterized in that Before obtaining the operation status corresponding to the resource production plan through the preset data tracking point, it includes: Determine the data points corresponding to each resource production plan in the target business; The data points corresponding to each resource production plan in the target business are deployed on the system and / or platform related to the target business to realize real-time capture of the operation status corresponding to each resource production plan.

3. The method according to claim 1, characterized in that After evaluating the robustness of the resource production plan based on the large language model and the operation status corresponding to the resource production plan to obtain the plan robustness score, the method further includes: Determine a target robustness score of the target business based on the solution robustness score corresponding to each resource commissioning solution, and determine whether the target robustness score is greater than a preset value; When the target robustness score is greater than a preset value, determining the target business as a target-reaching task; If the target robustness score is not greater than a preset value, the target business is determined as a non-compliant task; After determining the target business as a non-standard task, the method further includes: Monitor and record whether the number of times the target business is continuously marked as a non-compliant task reaches a warning threshold; When the number of times that the target business is continuously marked as a non-compliant task reaches a warning threshold, the target business is blocked and an alarm mechanism is triggered.

4. The method according to claim 3, characterized in that Determining the health score of the resource production plan includes: Determine the scheme change healthiness score of the resource production scheme based on the scheme change healthiness corresponding to the resource production scheme; and determine the associated project production compliance score of the resource production scheme based on the associated project production compliance rate corresponding to the resource production scheme; and determine the associated demand production compliance score of the resource production scheme based on the associated demand production compliance rate corresponding to the resource production scheme; The health score of the resource production plan is determined based on the plan robustness score corresponding to the resource production plan, the plan change health score, the associated project production compliance score, and the associated demand production compliance score.

5. The method according to claim 4, characterized in that After determining the health score of the resource production plan based on the plan robustness score corresponding to the resource production plan, the plan change health score, the associated project production compliance score, and the associated demand production compliance score, the method further includes: Determine a target score set for the target business based on the health score of each resource production plan, and determine a target health score for the target business based on the target score set; wherein the target score set includes a target plan robustness score, a target plan change health score, a target-related project production compliance score, and a target-related demand production compliance score; Determine the score dispersion corresponding to the target business based on the target solution robustness score, the target solution change health score, the target-related project production compliance score, and the target-related demand production compliance score; The determining, according to the preset resource knowledge base and the health score of each resource production plan, a resource production health assessment result corresponding to the target business through the large language model includes: According to the preset resource knowledge base, the target health score of the target business and the score dispersion corresponding to the target business, the resource production health assessment result corresponding to the target business is determined through the large language model.

6. The method according to claim 5, characterized in that The preset resource knowledge base includes historical score trends, industry off-season and peak-season effects, score dispersion standards, and historical compliance alarm conditions; the resource production health assessment result corresponding to the target business is determined through the large language model based on the preset resource knowledge base, the target health score of the target business, and the score dispersion corresponding to the target business, including: Determining the abnormality of the production health status of the target business based on the large language model, the score dispersion standard, and the score dispersion degree corresponding to the target business; Analyze the change trend of the production health status of the target business based on the large language model, the historical score trend, the industry off-season effect and the target health score of the target business; In combination with the abnormal degree of the production health status of the target business, the changing trend of the production health status of the target business and the historical compliance alarm situation, the large language model is used to generate a resource production health assessment result corresponding to the target business.

7. The method according to claim 6, characterized in that After the resource production health assessment result corresponding to the target business is generated by the large language model, the method includes: A result display window corresponding to the target business is set, and the resource production health assessment result corresponding to the target business is displayed in real time in the result display window.

8. A resource production health assessment device, characterized in that: include: A solution acquisition module, used to acquire a resource production solution set and a preset resource knowledge base for a target business; wherein the resource production solution set includes at least one resource production solution; A robustness evaluation module is used to obtain the operation status corresponding to each resource production plan through preset data points, and evaluate the robustness of the resource production plan based on the large language model and the operation status corresponding to the resource production plan to obtain a plan robustness score; A health determination module is used to obtain, for each resource commissioning plan, plan information corresponding to the resource commissioning plan, and determine the health score of the resource commissioning plan based on the plan information and the plan robustness score corresponding to the resource commissioning plan; wherein the plan information includes the health of the plan change, the commissioning compliance rate of the associated project, and the commissioning compliance rate of the associated demand; An assessment result determination module is used to determine a resource production health assessment result corresponding to the target business through the large language model according to the preset resource knowledge base and the health score of each resource production plan.

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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