Capability open service interface dynamic configuration and management method, system and device and storage medium

Abstract

The invention relates to the technical field of software engineering design, in particular to a capability open service interface dynamic configuration and management method, system and device and a storage medium. The configuration and management method comprises the following steps: analyzing a received service request from an external application to obtain a specific demand; according to the specific demand, performing optimal service link construction processing on the selected service component to generate a service link; continuously monitoring the running state of the service link, and generating service link configuration; using an integrated feedback loop mechanism to collect service quality data under the service link configuration, and generating a service configuration scheme; and synchronizing the service configuration scheme to a service resource pool in an incremental updating mode, and performing rapid construction processing of an efficient service link on a subsequent service request based on the optimized service configuration scheme to generate a service response. According to the technical scheme provided by the invention, the flexibility, reliability and performance of the service interface can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software engineering design, and in particular to a capability exposure service interface dynamic configuration and management method, system, device and storage medium. Background Art

[0002] In modern distributed systems and microservice architectures, the volume of service requests from external applications is large and diverse, placing extremely high demands on the system's response speed and stability. To meet these demands, the system needs to be able to quickly parse service requests, calculate service capabilities and configuration parameters, accurately identify specific needs, and dynamically select and build the optimal service link to provide efficient and stable service responses. Currently, many systems use static configuration and manual adjustment to manage and optimize service links. These systems usually predefine a set of fixed service components and configuration parameters. When receiving a service request, they select and combine service components according to predefined rules to generate a service link. In addition, some systems also use a simple monitoring mechanism to adjust the configuration of service components through manual intervention to deal with performance issues.

[0003] Existing static configuration methods cannot adapt to changes in service requests in real time, resulting in degraded system performance and prolonged response time under high load or burst traffic conditions; existing monitoring mechanisms can usually only provide basic performance indicators, lack the ability to predict potential performance bottlenecks, and are unable to take measures in advance to avoid performance problems; the process of manually adjusting service component configurations and optimizing service links is time-consuming and error-prone, making it difficult to achieve continuous optimization, causing the system to be unable to cope with complex and changing business needs. Summary of the invention

[0004] The present invention solves the problem of poor service component configuration effect in the prior art and proposes a method, system, device and storage medium for dynamic configuration and management of capability exposure service interface. The specific technical solution is as follows:

[0005] In a first aspect, the present invention provides a method for dynamically configuring and managing a capability exposure service interface, including the following process:

[0006] S100: parsing and processing the service request received from the external application, and obtaining the specific requirements by calculating the service capabilities and configuration parameters;

[0007] S200: According to the specific demand, a service component that best matches the demand is dynamically selected in the service resource pool using an intelligent matching algorithm, and an optimal service link construction process is performed on the selected service component to generate a service link;

[0008] S300: Utilize the real-time monitoring mechanism to continuously monitor the running status of the service link, use the machine learning model to predict the potential performance bottleneck of the monitoring data, and automatically trigger the dynamic adjustment of the service component configuration or the intelligent replacement of the service component when it is detected or predicted that the performance indicator may deviate from the preset range, and generate the service link configuration;

[0009] S400: using an integrated feedback loop mechanism to collect service quality data under the service link configuration, using a data analysis algorithm to perform in-depth analysis and processing on the collected data, identifying service optimization points, and using a reinforcement learning algorithm to continuously optimize the service component selection strategy and service link construction rules to generate a service configuration solution;

[0010] S500: Synchronize the service configuration scheme to the service resource pool through incremental update, quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme, and generate a service response.

[0011] In a second aspect, the present invention provides a capability exposure service interface dynamic configuration and management system, including:

[0012] The parsing module is used to parse and process the service requests received from external applications and obtain specific requirements by calculating service capabilities and configuration parameters;

[0013] A construction module is used to dynamically select the service component that best matches the demand in the service resource pool according to the specific demand using an intelligent matching algorithm, perform optimal service link construction processing on the selected service component, and generate a service link;

[0014] A monitoring module is used to continuously monitor the running status of the service link by implementing a real-time monitoring mechanism, and use a machine learning model to predict potential performance bottlenecks of the monitoring data. When it is detected or predicted that the performance indicator may deviate from a preset range, it automatically triggers the dynamic adjustment of the service component configuration or the intelligent replacement of the service component to generate a service link configuration;

[0015] A collection module is used to collect service quality data under the service link configuration by using an integrated feedback loop mechanism, perform in-depth analysis and processing on the collected data by using a data analysis algorithm, identify service optimization points, continuously optimize the service component selection strategy and service link construction rules by using a reinforcement learning algorithm, and generate a service configuration plan;

[0016] The construction module is used to synchronize the service configuration scheme to the service resource pool through incremental update, and to quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme to generate a service response.

[0017] In a third aspect, the present invention provides a computing device comprising: a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for dynamic configuration and management of a capability exposure service interface as described in the first aspect.

[0018] In a fourth aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for dynamic configuration and management of a capability exposure service interface as described in the first aspect.

[0019] The advantages of the present invention over the prior art are as follows:

[0020] The present invention parses and processes the service requests received from external applications, calculates the service capabilities and configuration parameters, and obtains specific requirements, so as to respond to user requirements quickly and accurately, and improve the response speed and quality of services; according to the specific requirements, the intelligent matching algorithm is used to dynamically select the service components that best match the requirements in the service resource pool, and the selected service components are subjected to the optimal service link construction process to generate service links, thereby realizing efficient resource utilization and reducing resource waste; the real-time monitoring mechanism is implemented to continuously monitor the running status of the service link, and the machine learning model is used to predict the potential performance bottlenecks of the monitoring data. When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment of the service component configuration or the intelligent replacement of the service component is automatically triggered to generate the service link configuration, thereby timely discovering and solving performance problems. , enhance the stability and reliability of the system; use the integrated feedback loop mechanism to collect service quality data under the service link configuration, use the data analysis algorithm to perform in-depth analysis and processing on the collected data, identify service optimization points, and use the reinforcement learning algorithm to continuously optimize the service component selection strategy and service link construction rules to generate a service configuration plan, thereby continuously improving service performance and enhancing user experience; synchronize the service configuration plan to the service resource pool through incremental updates, and quickly build and process efficient service links for subsequent service requests based on the optimized service configuration plan to generate service responses, so that the system can flexibly respond to changes in business needs and quickly adapt to new application scenarios; through the above measures, the present invention can significantly improve the flexibility, reliability and performance of the service interface, and ensure that the service capabilities provided to the outside world are always in the best state.

[0021] A real-time monitoring mechanism is implemented to continuously monitor the operating status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report; based on the performance monitoring report, a pre-trained machine learning model is used to analyze the performance of the service link, predict performance bottlenecks that may occur in the future, and generate a performance prediction report; when it is detected or predicted that the performance indicator may deviate from the preset range, according to the performance prediction report, the dynamic adjustment processing of the service component configuration is automatically triggered, and the performance is optimized by modifying the configuration parameters of the existing service component, and preliminary optimization suggestions are generated; according to the preliminary optimization suggestions, the system performs intelligent replacement processing, selects a more suitable service component from the service resource pool to replace the current component, and generates a final optimization plan; based on the final optimization plan, the selection and configuration adjustment of the service component are completed, and the service link configuration is generated. Through real-time monitoring and performance prediction, potential performance bottlenecks can be discovered and resolved in a timely manner to ensure the stability and reliability of system operation; service components can be dynamically adjusted and intelligently replaced to ensure efficient use of resources, reduce resource waste, and improve system performance; optimization solutions can be quickly generated and applied to shorten service response time and improve user experience; through feedback loop mechanisms and reinforcement learning algorithms, service component selection strategies and service link construction rules can be continuously optimized to continuously improve service quality and performance; the system can flexibly respond to changes in business needs, quickly adjust and optimize service links, and adapt to new application scenarios.

[0022] By using the integrated feedback loop mechanism, the service quality data under the service link configuration is collected to generate a service quality data set; based on the service quality data set, the collected data is deeply analyzed and processed using the data analysis algorithm to identify the key factors and potential problems affecting the service quality, and a service optimization point report is generated; based on the service optimization point report, the service component selection strategy and service link construction rules are continuously optimized by the reinforcement learning algorithm to generate selection strategies and construction rules; the service configuration scheme is generated by using the selection strategy and construction rules. By collecting and analyzing the service quality data, the key factors and potential problems affecting the service quality are identified, and a service optimization point report is generated, so as to improve the service quality in a targeted manner; based on the service optimization point report, the service component selection strategy and service link construction rules are continuously optimized by the reinforcement learning algorithm to generate selection strategies and construction rules, and the performance and efficiency of the system are continuously improved; through in-depth analysis and continuous optimization, the occurrence of potential problems is reduced, and the reliability and stability of the system are improved; the generated service configuration scheme can better adapt to different business needs and scenarios, and improve the flexibility and adaptability of the system; by optimizing the service component selection strategy and service link construction rules, the efficient use of resources is ensured, resource waste is reduced, and the resource utilization rate of the overall system is improved.

[0023] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings in the following description without creative work.

[0025] Figure 1 A flowchart of a method for dynamically configuring and managing a capability exposure service interface provided by the present invention.

[0026] Figure 2 A schematic diagram of the structure of a capability exposure service interface dynamic configuration and management system provided by the present invention.

[0027] Figure 3 A schematic diagram of the structure of a computing device provided by the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0029] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The sequence numbers of the operations, such as S100, S200, etc., are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0030] The following will be combined with the accompanying drawings of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, a method for dynamically configuring and managing a capability exposure service interface includes the following processes:

[0032] S100: parsing and processing the service request received from the external application, and obtaining the specific requirements by calculating the service capabilities and configuration parameters;

[0033] In this step, service request refers to the request from external applications, including information such as the type and parameters of the required service. Service capability refers to the functions and performance indicators of each service provided by the platform. Configuration parameters refer to the specific parameters used to configure the service, such as resource allocation, performance requirements, etc. Specific requirements refer to the actual requirements calculated based on the service request and the platform's service capabilities, including the required service type, performance indicators, resource requirements, etc.

[0034] Brief explanation of the solution process: The platform receives a service request from an external application. It parses the service request and extracts information such as the service type and parameters in the request. Based on the platform's service capabilities and configuration parameters, it calculates specific requirements and identifies the required resources and service features.

[0035] S200 uses an intelligent matching algorithm to dynamically select a service component that best matches the demand in a service resource pool according to the specific demand, performs optimal service link construction processing on the selected service component, and generates a service link;

[0036] In this step, the intelligent matching algorithm refers to the algorithm used to select the service component that best meets specific needs in the service resource pool. The service resource pool refers to a resource collection containing multiple different types of service components. The service link refers to a service path formed by connecting multiple service components in a specific order to complete specific business logic.

[0037] Brief explanation of the solution process: Based on the specific needs calculated in S100, the intelligent matching algorithm is used to select the most matching service component in the service resource pool. The selected service component is processed to build the optimal service link to ensure the efficiency and reliability of the service link. The final service link is generated and is ready to provide services to external applications.

[0038] S300: Utilize the real-time monitoring mechanism to continuously monitor the running status of the service link, use the machine learning model to predict the potential performance bottleneck of the monitoring data, and automatically trigger the dynamic adjustment of the service component configuration or the intelligent replacement of the service component when it is detected or predicted that the performance indicator may deviate from the preset range, and generate the service link configuration;

[0039] In this step, real-time monitoring mechanism: technical means to continuously monitor the operating status of the service link, such as log analysis, performance monitoring, etc. Machine learning model: a model used to predict potential performance bottlenecks of the service link, trained with historical data. Performance indicators: indicators that measure the operating status of the service link, such as response time, throughput, etc. Service link configuration: the configuration after dynamically adjusting or replacing the service component configuration based on the performance bottleneck prediction results.

[0040] Brief explanation of the solution process: Use the real-time monitoring mechanism to continuously monitor the operating status of the service link and collect performance indicator data. Use machine learning models to analyze the collected monitoring data and predict potential performance bottlenecks. When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment or intelligent replacement of the service component configuration is automatically triggered. Generate a new service link configuration to ensure the stability and high performance of the service link.

[0041] The S300 includes the following specific processes:

[0042] S310: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report;

[0043] S320: Based on the performance monitoring report, use a pre-trained machine learning model to analyze the performance of the service link, predict performance bottlenecks that may occur in the future, and generate a performance prediction report;

[0044] S330: When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment process of the service component configuration is automatically triggered according to the performance prediction report, and the performance is optimized by modifying the configuration parameters of the existing service component to generate preliminary optimization suggestions;

[0045] S340: According to the preliminary optimization suggestion, the system performs intelligent replacement processing, selects a more suitable service component from the service resource pool to replace the current component, and generates a final optimization solution;

[0046] S350: Based on the final optimization solution, complete the selection and configuration adjustment of service components and generate a service link configuration.

[0047] Suppose there is an online video platform whose service chain consists of multiple microservices, including video streaming, user authentication, content recommendation and other service components. The system needs to ensure stable performance and fast response during peak hours such as large live events. The real-time monitoring mechanism continuously collects performance indicator data of each service component, such as the bandwidth utilization rate of the video streaming service, the response time of the user authentication service, and the memory usage of the content recommendation service, and generates a performance monitoring report. The pre-trained machine learning model is used to analyze the data in the performance monitoring report to predict future performance bottlenecks. For example, the model predicts that during large live events, the bandwidth utilization rate of the video streaming service may exceed 90%, resulting in video freezes. When the bandwidth utilization rate of the video streaming service is detected to be close to 90%, the system automatically triggers the dynamic adjustment of the service component configuration according to the performance prediction report, and generates preliminary optimization suggestions by increasing the bandwidth resource allocation of the video streaming service. According to the preliminary optimization suggestions, the performance bottlenecks and service component deficiencies pointed out in the preliminary optimization suggestions are analyzed, and more suitable service components are selected from the service resource pool to replace the current components, and a list of candidate service components is generated. For example, a video streaming service component that supports higher bandwidth transmission is selected; based on the candidate service component list, the optimal service component is selected for replacement by comprehensively considering the performance indicators such as bandwidth capacity, compatibility, and resource utilization such as bandwidth utilization of the candidate service components, and a final optimization plan is generated; based on the final optimization plan, the service component selection and configuration adjustment are completed to generate a new service link configuration. The new configuration ensures that during large-scale live events, the video streaming service can run efficiently and the bandwidth utilization rate remains within a reasonable range, thereby improving the user experience.

[0048] Through the above process, the system can monitor and optimize the performance of the service link in real time to ensure stable and efficient services during peak hours.

[0049] The S340 includes the following specific processes:

[0050] S341: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report;

[0051] S342: Based on the performance monitoring report, use a pre-trained machine learning model to analyze the performance of the service link, predict future performance bottlenecks, and generate a performance prediction report;

[0052] S343: When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment process of the service component configuration is automatically triggered according to the performance prediction report, and the performance is optimized by modifying the configuration parameters of the existing service component, and a preliminary optimization suggestion is generated;

[0053] S344: Analyze the performance bottlenecks and deficiencies of the service components pointed out in the preliminary optimization suggestions according to the preliminary optimization suggestions, select more suitable service components from the service resource pool to replace the current components, and generate a list of candidate service components;

[0054] S345: Based on the candidate service component list, the performance indicators, compatibility and resource utilization of the candidate service components are comprehensively considered, and the optimal service component is selected for replacement to generate a final optimization solution.

[0055] Suppose there is an online medical service platform whose service chain consists of multiple microservices, including user registration, appointment registration, doctor consultation, electronic medical record management and other service components. The system needs to ensure stable performance and fast response during high-traffic periods such as the flu season; the system continuously collects key performance indicator data of each service component, such as the response time of the user registration service, the throughput of the appointment registration service, the error rate of the doctor consultation service, and the memory usage of the electronic medical record management service; the system regularly generates performance monitoring reports to record the performance of each service component, including indicators such as response time, throughput, and error rate; the system uses a pre-trained machine learning model to analyze the data in the performance monitoring report and predict future performance bottlenecks. For example, the model predicts that during the flu season, the response time of the doctor consultation service may increase significantly, resulting in a decrease in user experience; the system generates a performance prediction report, which records in detail the predicted performance indicator change trend and potential performance bottlenecks, such as the response time of the doctor consultation service may exceed 10 seconds; when it is detected that the response time of the doctor consultation service is close to 10 seconds, the system automatically triggers the dynamic adjustment of the service component configuration according to the performance prediction report; the system optimizes performance by modifying the configuration parameters of the existing doctor consultation service and generates preliminary optimization suggestions. For example, increase the concurrent processing capability of the doctor consultation service and improve the memory allocation of the server; based on the performance bottlenecks and deficiencies of the service components pointed out in the preliminary optimization suggestions, the system selects more suitable service components from the service resource pool to replace the current components and generates a list of candidate service components. For example, select a doctor consultation service component that supports higher concurrent processing capabilities; based on the list of candidate service components, the system comprehensively considers performance indicators such as concurrent processing capabilities, compatibility, and resource utilization such as memory usage of the candidate service components, selects the best service component for replacement, and generates the final optimization plan; based on the final optimization plan, the system completes the selection and configuration adjustment of the service components and generates a new service link configuration. The new configuration ensures that during the flu season, the doctor consultation service can run efficiently and the response time remains within a reasonable range, thereby improving the user experience.

[0056] Through the above process, the system is able to monitor and optimize the performance of the service link in real time, ensuring stable and efficient services during high-traffic periods, especially during high-demand periods such as flu season, ensuring that users can obtain medical services quickly and smoothly.

[0057] The S330 includes the following specific processes:

[0058] S331: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report;

[0059] S332: According to the performance bottlenecks and their potential causes indicated in the performance prediction report, the system analyzes the service components and their configuration parameters that need to be adjusted, and determines an adjustment strategy;

[0060] S333: By executing the adjustment strategy, the configuration parameters of the selected service component are modified to achieve the purpose of optimizing performance and generate preliminary optimization suggestions.

[0061] Suppose there is an online e-commerce platform whose service chain consists of multiple microservices, including order processing, inventory management, payment processing and other service components. The system needs to ensure stable performance and fast response during high-traffic periods such as the Double 11 Shopping Festival; the real-time monitoring mechanism continuously collects performance indicator data of each service component, such as the CPU usage of the order processing service, the memory usage of the inventory management service, the network latency of the payment processing service, etc., and generates a performance monitoring report; the pre-trained machine learning model is used to analyze the data in the performance monitoring report to predict possible performance bottlenecks in the future. For example, the model predicts that during the Double 11 Shopping Festival, the CPU usage of the order processing service may exceed 80%, resulting in a longer response time; when the CPU usage of the order processing service is detected to be close to 80%, the system automatically triggers the dynamic adjustment of the service component configuration according to the performance prediction report; according to the performance bottlenecks and potential causes such as insufficient CPU resources pointed out in the performance prediction report, the system analyzes the service components such as the order processing service that need to be adjusted and their configuration parameters such as CPU resource allocation, and determines the adjustment strategy such as increasing CPU resources; by executing the adjustment strategy, the CPU resources of the order processing service are adjusted to generate preliminary optimization suggestions; according to the preliminary optimization suggestions, the performance bottlenecks and deficiencies of the service components pointed out in the preliminary optimization suggestions are analyzed, and a more suitable service component is selected from the service resource pool to replace the current component to generate a list of candidate service components. For example, an order processing service component that supports multi-threaded processing is selected; based on the list of candidate service components, the performance indicators such as the multi-threaded processing capability of the candidate service components, compatibility and resource utilization such as CPU utilization are comprehensively considered, and the optimal service component is selected for replacement to generate a final optimization plan; based on the final optimization plan, the selection and configuration adjustment of the service component are completed to generate a new service link configuration. The new configuration ensures that during the Double 11 Shopping Festival, the order processing service can run efficiently and the CPU usage remains within a reasonable range, thereby improving the user experience.

[0062] Through the above process, the system can monitor and optimize the performance of the service link in real time to ensure stable and efficient services during high traffic periods.

[0063] The present invention takes into account that in modern distributed systems and microservice architectures, performance optimization of service links is an important topic. In order to ensure that the system can run stably under high load and complex environments, it is necessary to monitor the performance of service links in real time and predict future performance bottlenecks. Traditional static configuration and manual adjustment methods can no longer meet this demand. Therefore, it is crucial to use machine learning models and mathematical formulas to automate performance prediction and optimization.

[0064] The prediction formula in S342 is as follows:

[0065]

[0066] The performance stability evaluation formula is as follows:

[0067]

[0068] P(t) represents the service link performance indicator predicted at time t; w i represents the weight of the i-th service component, obtained by training the machine learning model; a i0 ,a i1 ,a i2 ,a i3 ,a i4 represents the coefficients of polynomial regression, exponential smoothing and sine wave, used to describe the performance indicator x of the i-th service component i The changing trend over time t is obtained through the training process; x i represents the performance indicator of the i-th service component, which is directly obtained from the performance monitoring report; α i represents the smoothing factor of exponential smoothing, which is used to describe the decay rate of the performance indicator of the i-th service component over time and is obtained through the training process; β i Represents the frequency of the sine wave, which is used to describe periodic performance fluctuations and is obtained through the training process; γ i represents the phase of the sine wave, which is used to describe the initial position of the periodic fluctuation and is obtained through the training process; n represents the number of service components in the service link, which is determined by the actual structure of the service link; b represents the bias term, which is used to adjust the overall level of the predicted value and is usually used in the training process together with the weight w i Optimize together to get; S i (t) represents the performance stability index of the i-th service component at time t, which is used to evaluate the performance fluctuation of the service component; T represents the time window length, which is used to calculate the performance stability; x i (τ) represents the performance indicator of the i-th service component at time τ; represents the average performance index of the i-th service component in the time window [tT, t]; λ represents the smoothing parameter, which is used to balance the fluctuation and change rate of the performance index and is obtained through the training process;

[0069] By using the prediction formula P(t) and the performance stability evaluation formula S i (t), combining the performance indicators of each service component obtained from the performance monitoring report, calculate the change trend and performance stability of future performance indicators, and generate a detailed performance prediction report.

[0070] This method aims to predict the performance bottlenecks that may occur in the future by comprehensively considering the performance indicators of each service component in the service chain and using pre-trained machine learning models. In this way, potential problems can be identified in advance and timely intervention can be made to improve the overall performance and stability of the system.

[0071] In the prediction formula P(t), the polynomial regression term Used to capture the linear and nonlinear trends of service component performance indicators. Exponential smoothing term It is used to describe the attenuation trend of performance indicators over time and is applicable to situations where performance indicators gradually decrease. i4 sin(β i t+γ i ): used to describe periodic performance fluctuations, and is applicable to situations where performance indicators change periodically. i : represents the weight of the i-th service component, obtained by training the machine learning model, reflecting the impact of the service component on the overall performance. Bias term b: used to adjust the overall level of the predicted value, usually in the training process with the weight w i —Optimized together.

[0072] In the performance stability evaluation formula S i (t), the variance term It is used to evaluate the performance fluctuation of service components within the time window [tT, t]. Used to evaluate the rate of change of performance indicators and reflect the instantaneous changes of performance indicators. Smoothing parameter λ: used to balance the fluctuation and rate of change of performance indicators, obtained through the training process.

[0073] Among them, w i ,a i0 ,a i1 ,a i2 ,a i3 ,a i4 ,α i ,β i ,γ i ,b,λ: These parameters are usually obtained by training on a large amount of historical performance monitoring data through machine learning algorithms such as gradient descent or random forest. The training process aims to minimize the prediction error so that the model can predict future performance indicators as accurately as possible. i :These performance indicators can be directly obtained from the performance monitoring report, including but not limited to response time, throughput, CPU utilization, memory usage, etc. T: The length of the time window, which can be adjusted according to the actual application scenario and requirements. Usually a reasonable value is selected to balance the calculation complexity and prediction accuracy.

[0074] Suppose there is an online e-commerce platform whose service chain consists of multiple microservices, including order processing, inventory management, payment processing and other service components. The system needs to ensure stable performance and fast response during high-traffic periods (such as the Double 11 Shopping Festival); the real-time monitoring mechanism continuously collects performance indicator data of each service component, such as the CPU usage of the order processing service, the memory usage of the inventory management service, the network latency of the payment processing service, etc., and generates a performance monitoring report; the pre-trained machine learning model is used to analyze the data in the performance monitoring report, and the prediction formula P(t) is used to calculate the performance bottlenecks that may occur in the future; for example, the model predicts that during the Double 11 Shopping Festival, the CPU usage of the order processing service may exceed 80%, resulting in a longer response time; the performance stability evaluation formula S i (t) Evaluate the performance stability of each service component; for example, calculate the performance stability index of the order processing service and evaluate its fluctuation under high load; combine the prediction formula P(t) and the performance stability evaluation formula S i (t), generate a detailed performance forecast report, which includes the trend of future performance indicators and the results of performance stability assessment; when it is detected that the CPU usage of the order processing service is close to 80%, the system automatically triggers the dynamic adjustment of the service component configuration according to the performance forecast report; according to the performance bottlenecks and potential causes such as insufficient CPU resources pointed out in the performance forecast report, the system analyzes the service components such as the order processing service that need to be adjusted and their configuration parameters such as CPU resource allocation, and determines the adjustment strategy such as increasing CPU resources; by executing the adjustment strategy, the CPU resources of the order processing service are adjusted to generate preliminary optimization suggestions; according to the preliminary optimization suggestions, the performance bottlenecks and deficiencies of the service components pointed out in the preliminary optimization suggestions are analyzed, and a more suitable service component is selected from the service resource pool to replace the current component, and a list of candidate service components is generated. For example, an order processing service component that supports multi-threaded processing is selected; based on the list of candidate service components, the performance indicators such as the multi-threaded processing capability of the candidate service components, compatibility and resource utilization such as CPU utilization are comprehensively considered, and the optimal service component is selected for replacement to generate a final optimization plan; based on the final optimization plan, the selection and configuration adjustment of the service component are completed to generate a new service link configuration. The new configuration ensures that during the Double 11 Shopping Festival, the order processing service can run efficiently and the CPU usage remains within a reasonable range, thereby improving the user experience.

[0075] Through the above process, the system can monitor and optimize the performance of the service link in real time to ensure stable and efficient services during high traffic periods.

[0076] S400: using an integrated feedback loop mechanism to collect service quality data under the service link configuration, using a data analysis algorithm to perform in-depth analysis and processing on the collected data, identifying service optimization points, and using a reinforcement learning algorithm to continuously optimize the service component selection strategy and service link construction rules to generate a service configuration solution;

[0077] In this step, the feedback loop mechanism is integrated: the service quality data under the service link configuration is collected to continuously optimize the service configuration. Data analysis algorithm: an algorithm used to deeply analyze the collected data and identify service optimization points. Reinforcement learning algorithm: an algorithm that gradually optimizes the service component selection strategy and service link construction rules through continuous trial and error and feedback. Service configuration plan: the optimized service component selection strategy and service link construction rules.

[0078] Brief explanation of the solution process: Use the integrated feedback loop mechanism to collect service quality data under the service link configuration. Use data analysis algorithms to conduct in-depth analysis of the collected data and identify service optimization points. Use reinforcement learning algorithms to continuously optimize service component selection strategies and service link construction rules. Generate an optimized service configuration solution to improve service quality and efficiency.

[0079] The S400 includes the following specific processes:

[0080] S410: using an integrated feedback loop mechanism, collecting service quality data under the service link configuration to generate a service quality data set;

[0081] S420: Based on the service quality data set, using a data analysis algorithm to perform in-depth analysis and processing on the collected data, identify key factors and potential problems that affect service quality, and generate a service optimization point report;

[0082] S430: Based on the service optimization point report, the service component selection strategy and service link construction rule are continuously optimized by a reinforcement learning algorithm to generate a selection strategy and construction rule;

[0083] S440: Generate a service configuration solution using the selection strategy and construction rules.

[0084] Suppose there is an online video platform whose service chain consists of multiple microservices, including video streaming, user authentication, content recommendation and other service components. The system needs to ensure stable performance and fast response during high-traffic periods such as large live events; using the integrated feedback loop mechanism, continuously collect service quality data of each service component, such as the bandwidth utilization rate of the video streaming service, the response time of the user authentication service, the memory usage of the content recommendation service, etc., to generate a service quality data set; based on the service quality data set, use the data analysis algorithm to deeply analyze and process the collected data to identify the key factors and potential problems that affect the service quality. For example, the analysis found that the bandwidth utilization rate of the video streaming service during high-traffic periods was too high, resulting in video freezes; generate a service optimization point report, which records in detail the bandwidth utilization problem of the video streaming service and its potential causes; based on the service optimization point report, the service component selection strategy and service chain construction rules are continuously optimized through the reinforcement learning algorithm. For example, optimize the bandwidth allocation strategy of video streaming services, select service components that support higher bandwidth transmission; generate selection strategies and construction rules, the selection strategy includes giving priority to service components with high bandwidth transmission, and the construction rules include the order and method of optimizing bandwidth allocation; use the selection strategy and construction rules to generate a service configuration plan. The plan includes the specific configuration of optimized service component selection and link construction, ensuring that during large-scale live events, the video streaming service can run efficiently and the bandwidth utilization rate remains within a reasonable range, thereby improving the user experience.

[0085] Through the above process, the system can collect and analyze service quality data in real time, identify service optimization points, continuously optimize service component selection strategies and service link construction rules through reinforcement learning algorithms, generate optimized service configuration plans, and ensure that the system provides stable and efficient services during high-traffic periods.

[0086] The S410 includes the following specific processes:

[0087] S411: Based on the service optimization point report, extract key factors and potential problems affecting service quality, and form optimization goals and constraints;

[0088] S412: using a reinforcement learning algorithm, continuously optimizing the service component selection strategy according to the optimization goal and constraint conditions, simulating different selection strategies, evaluating their impact on service quality, gradually adjusting the parameters of the selection strategy, and generating a service component selection strategy;

[0089] S413: Based on the service component selection strategy, the service link construction rule is optimized, different link construction schemes are simulated, the impact of the different link construction schemes on the overall performance is evaluated, the parameters of the link construction rule are adjusted, and the service link construction rule is generated;

[0090] S414: Generate a service quality data set using the service component selection strategy and the service link construction rule.

[0091] Suppose there is an online video platform whose service chain consists of multiple microservices, including video streaming, user authentication, content recommendation and other service components. The system needs to ensure stable performance and fast response during high-traffic periods such as large live events; using the integrated feedback loop mechanism, continuously collect service quality data of each service component, such as the bandwidth utilization rate of the video streaming service, the response time of the user authentication service, the memory usage of the content recommendation service, etc., to generate a service quality data set; based on the service quality data set, use the data analysis algorithm to perform in-depth analysis and processing on the collected data to identify key factors and potential problems that affect service quality. For example, the analysis found that the bandwidth utilization rate of the video streaming service during high-traffic periods was too high, resulting in video freezes; generate a service optimization point report, which records in detail the bandwidth utilization rate problem of the video streaming service and its potential causes; based on the service optimization point report, extract key factors such as bandwidth utilization rate and potential problems such as video freezes that affect service quality, and form optimization goals such as reducing bandwidth utilization rate and improving video fluency, as well as constraints such as hardware resource limitations and software compatibility requirements; use the reinforcement learning algorithm to continuously optimize the service component selection strategy according to the optimization goals and constraints. By simulating different selection strategies, evaluating their impact on service quality, gradually adjusting the parameters of the selection strategy, and generating a service component selection strategy. For example, select a video streaming service component that supports higher bandwidth transmission; based on the service component selection strategy, optimize the service link construction rules. By simulating different link construction schemes, evaluating the impact of the different link construction schemes on the overall performance, adjusting the parameters of the link construction rules, and generating service link construction rules. For example, optimize the order and method of bandwidth allocation to ensure that the bandwidth utilization rate of the video streaming service during high traffic periods remains within a reasonable range; use the service component selection strategy and the service link construction rules to generate a new service quality data set. The new data set includes the specific configuration of the optimized service component selection and link construction, providing data support for subsequent optimization processing.

[0092] Through the above process, the system can collect and analyze service quality data in real time, identify service optimization points, continuously optimize service component selection strategies and service link construction rules through reinforcement learning algorithms, generate optimized service configuration plans, and ensure that the system provides stable and efficient services during high-traffic periods.

[0093] The present invention takes into account that in modern distributed systems and microservice architectures, performance optimization of service links is a complex task. In order to ensure that the system can run stably under high load and complex environments, it is necessary to conduct in-depth analysis of service quality data to identify key factors and potential problems that affect service quality. Traditional static analysis methods can no longer meet this demand, so it is crucial to use mathematical formulas and machine learning models to automatically evaluate and optimize service performance.

[0094] In S420, the score of each service optimization point is more accurately evaluated by the following formula:

[0095]

[0096] Among them, O i is the score of the i-th service optimization point; n is the number of factors affecting service quality; w j is the weight of the jth factor; x ij is the value of the jth factor of the i-th service optimization point; θ j is the parameter of the jth factor; h(x ij ,θ j ) is the analysis function for the jth factor; λ is the regularization parameter; η is the sensitivity parameter; z i is the comprehensive score of the i-th service optimization point; β is the weight of the time decay factor; γ is the parameter of the time decay factor; t i is the timestamp of the i-th service optimization point; t0 is the benchmark timestamp; ∈ is the error term, which represents the random fluctuations that the model cannot capture;

[0097] The analytical function h(x ij ,θ j ) The specific expression is as follows:

[0098]

[0099] Where: x ij is the value of the jth factor of the i-th service optimization point; θ j1 is the linear weight of the jth factor; θ j2 is the power of the jth factor; θ j3 is the exponential weight of the jth factor; θ j4 is the exponential coefficient of the jth factor; θ j5is the sine weight of the jth factor; θ j6 is the sine coefficient of the jth factor; θ j7 is the constant term of the jth factor;

[0100] A comprehensive score is given to each service optimization point to identify key factors and potential problems affecting service quality and generate a service optimization point report.

[0101] This method aims to comprehensively consider multiple factors that affect service quality, use pre-trained models to score each service optimization point, identify key factors and potential problems, and generate a service optimization point report. In this way, specific optimization suggestions can be provided to service providers to improve service quality.

[0102] In the service optimization point scoring formula O i In the factor weighting The influence of each factor on the service optimization point is comprehensively considered and calculated by weighted summation. Regularization term λ·log(1+exp(-η·z i )): Used to prevent model overfitting, which is controlled by introducing regularization parameter λ and sensitivity parameter η. Time decay term Used to consider the impact of time on service optimization points. As time goes by, the importance of some optimization points may weaken. Error term ∈: represents random fluctuations that the model cannot capture, used to deal with uncertainty and noise.

[0103] In the factor analysis function h(x ij ,θ j ), the polynomial term Used to capture the nonlinear change trend of factors. Sine wave term θ j5 ·sin(θ j6 ·x ij ): used to describe the periodic trend of change. Constant term θ j7 : Used to adjust the overall level of the analysis function.

[0104] Among them, w j ,θ j ,λ,η,β,γ: These parameters are usually obtained by training on a large amount of service quality data through machine learning algorithms such as gradient descent or random forest. The training process aims to minimize the prediction error so that the model can evaluate the score of the service optimization point as accurately as possible. ij :The values ​​of these factors can be directly obtained from the service quality dataset, including but not limited to response time, user satisfaction, failure rate, etc. i : The comprehensive score of the ith service optimization point, which can be obtained through preliminary analysis or expert evaluation. i,t0: Timestamp, representing the time of the i-th service optimization point and the benchmark time, respectively, used to calculate the time decay term. ∈: Error term, representing the random fluctuations that the model cannot capture, usually estimated by residual analysis during training.

[0105] Suppose there is an online video platform whose service chain consists of multiple microservices, including video streaming, user authentication, content recommendation and other service components. The system needs to ensure stable performance and fast response during high-traffic periods such as large-scale live events; use an integrated feedback loop mechanism to continuously collect service quality data for each service component, such as bandwidth utilization of the video streaming service, response time of the user authentication service, memory usage of the content recommendation service, etc., to generate a service quality data set; based on the service quality data set, use data analysis algorithms to conduct in-depth analysis and processing of the collected data to identify key factors and potential problems that affect service quality. For example, the analysis found that the bandwidth utilization of the video streaming service during high-traffic periods was too high, resulting in video freezes; a service optimization point report was generated, which detailed the bandwidth utilization issues of the video streaming service and their potential causes;

[0106] Give each service optimization point a comprehensive score using the formula:

[0107]

[0108] For example, for the bandwidth usage problem of video streaming service, the comprehensive score is calculated. i , where n is the number of factors that affect the quality of service, such as response time, bandwidth usage, memory usage, etc.; w j is the weight of each factor, obtained by training the machine learning model; x ij is the value of the jth factor of the i-th service optimization point, obtained from the service quality dataset; θ j is the parameter of each factor, obtained through the training process; λ is the regularization parameter, obtained through the training process; η is the sensitivity parameter, obtained through the training process; z i is the comprehensive score of the ith service optimization point, calculated by the comprehensive score formula; β is the time decay factor weight, obtained through the training process; γ is the time decay factor parameter, obtained through the training process; t i is the timestamp of the i-th service optimization point, obtained from the service quality dataset; t0 is the benchmark timestamp, set according to the specific application scenario; ∈ is the error term, obtained through residual analysis during model training; according to the comprehensive score O i, generate a service optimization point report, which records in detail the key factors and potential problems that affect service quality, as well as the score of each service optimization point; for example, the report points out the bandwidth utilization problem of the video streaming service, with a score of 8.5, and recommends increasing bandwidth resource allocation; based on the service optimization point report, the service component selection strategy and service link construction rules are continuously optimized through the reinforcement learning algorithm to generate an optimized service configuration plan; for example, select video streaming service components that support higher bandwidth transmission, optimize the order and method of bandwidth allocation, and ensure that the bandwidth utilization of the video streaming service during high traffic periods remains within a reasonable range.

[0109] Through the above process, the system can collect and analyze service quality data in real time, identify service optimization points, evaluate the importance of each service optimization point through a precise scoring formula, generate a service optimization point report, and provide specific guidance for subsequent optimization processing to ensure that the system provides stable and efficient services during high traffic periods.

[0110] S500: Synchronize the service configuration scheme to the service resource pool through incremental update, quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme, and generate a service response.

[0111] In this step, incremental update: the optimized service configuration scheme is synchronized to the service resource pool in an incremental manner to reduce the impact on existing services. Service response: according to the optimized service configuration scheme, an efficient service link is quickly built to respond to subsequent service requests.

[0112] Brief explanation of the solution process: The optimized service configuration solution generated in S400 is synchronized to the service resource pool through incremental update. Based on the optimized service configuration solution, efficient service links are quickly constructed for subsequent service requests. The final service response is generated to ensure that external applications can obtain high-quality services.

[0113] The S500 includes the following specific processes:

[0114] S510: Synchronize the service configuration scheme to the service resource pool by using an incremental update method, only update the changed parts, reduce synchronization time and resource consumption, and obtain the service resource pool configuration;

[0115] S520: According to the service resource pool configuration, the service components in the service resource pool are updated to generate a service resource pool;

[0116] S530: Based on the service resource pool, the received subsequent service request is parsed and processed, and specific requirements are obtained by calculating service capabilities and configuration parameters;

[0117] S540: According to the specific demand, a service component that best matches the demand is dynamically selected from the updated service resource pool using an intelligent matching algorithm, and an optimal service link construction process is performed on the selected service component to generate a service link;

[0118] S550: Utilize the service link to quickly construct and process subsequent service requests and generate a service response.

[0119] Assume that there is an online e-commerce platform whose service chain consists of multiple microservices, including service components such as order processing, inventory management, and payment processing. The system needs to ensure that it can provide stable performance and fast response during high-traffic periods such as the Double 11 Shopping Festival; synchronize the optimized service configuration scheme to the service resource pool using the incremental update method. For example, the optimized configuration scheme increases the CPU resource allocation of the order processing service and reduces the memory usage of the inventory management service; only update the changed parts to reduce the synchronization time and resource consumption, and obtain the service resource pool configuration; according to the service resource pool configuration, update the service components in the service resource pool. For example, update the CPU resource allocation of the order processing service, adjust the memory usage of the inventory management service, and generate a new service resource pool; based on the service resource pool, parse and process the subsequent service requests received. For example, parse the order processing service request, extract the product information, user information, etc. in the request, calculate the service capacity and configuration parameters, and obtain the specific requirements; according to the specific requirements, use the intelligent matching algorithm to dynamically select the service component that best matches the requirements in the updated service resource pool. For example, select an order processing service component that supports multi-threaded processing, and select a high-performance inventory management service component; perform optimal service link construction processing on the selected service components to generate a service link. For example, build an efficient service link to ensure that the order processing service and the inventory management service can work together to provide a fast response; use the service link to quickly build and process subsequent service requests to generate a service response. For example, process an order request and return order confirmation information to ensure that the service request is processed quickly and efficiently.

[0120] Through the above process, the system can efficiently synchronize the optimized service configuration plan to ensure the latest configuration of the service resource pool. Based on the updated service resource pool, the system can quickly parse service requests, dynamically select the most matching service components, build the optimal service link, generate service responses, and ensure that service requests are processed quickly and efficiently.

[0121] like Figure 2 As shown, a capability exposure service interface dynamic configuration and management system includes:

[0122] The parsing module 21 is used to parse the service request received from the external application and obtain the specific demand by calculating the service capability and configuration parameters;

[0123] A construction module 22 is used to dynamically select the service component that best matches the demand in the service resource pool according to the specific demand using an intelligent matching algorithm, perform optimal service link construction processing on the selected service component, and generate a service link;

[0124] A monitoring module 23 is used to continuously monitor the running status of the service link by implementing a real-time monitoring mechanism, and use a machine learning model to predict potential performance bottlenecks of the monitoring data. When it is detected or predicted that the performance indicator may deviate from a preset range, the dynamic adjustment of the service component configuration or the intelligent replacement of the service component is automatically triggered to generate a service link configuration;

[0125] A collection module 24 is used to collect service quality data under the service link configuration by using an integrated feedback loop mechanism, perform in-depth analysis and processing on the collected data by using a data analysis algorithm, identify service optimization points, and continuously optimize the service component selection strategy and service link construction rules by using a reinforcement learning algorithm to generate a service configuration solution;

[0126] The construction module 25 is used to synchronize the service configuration scheme to the service resource pool through incremental update, and to quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme to generate a service response.

[0127] Figure 2 The capability exposure service interface dynamic configuration and management system can be executed Figure 1 The implementation principle and technical effect of the method for dynamically configuring and managing a capability exposure service interface are not described in detail. The specific manner in which each module and unit performs operations in the above embodiment of the method has been described in detail in the embodiment of the method, and will not be described in detail here.

[0128] In one possible design, Figure 2 A capability exposure service interface dynamic configuration and management system system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0129] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0130] The processing component 32 is used to: use an intelligent matching algorithm to dynamically select the service component that best matches the demand in the service resource pool, perform optimal service link construction processing on the selected service component, and generate a service link; use a real-time monitoring mechanism to continuously monitor the operating status of the service link, use a machine learning model to predict potential performance bottlenecks on the monitoring data, and when it is detected or predicted that the performance indicator may deviate from the preset range, automatically trigger the dynamic adjustment of the service component configuration or the intelligent replacement of the service component to generate a service link configuration; use an integrated feedback loop mechanism to collect service quality data under the service link configuration, use a data analysis algorithm to perform in-depth analysis and processing on the collected data, identify service optimization points, and continuously optimize the service component selection strategy and service link construction rules through a reinforcement learning algorithm to generate a service configuration plan; synchronize the service configuration plan to the service resource pool through an incremental update method, and quickly construct efficient service links for subsequent service requests based on the optimized service configuration plan to generate a service response.

[0131] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits ASIC, digital signal processors DSP, digital signal processing devices DSPD, programmable logic devices PLD, field programmable gate arrays FPGA, controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0132] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory SRAM, electrically erasable programmable read-only memory EEPROM, erasable programmable read-only memory EPROM, programmable read-only memory PROM, read-only memory ROM, magnetic memory, flash memory, magnetic disk or optical disk.

[0133] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0134] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0135] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0136] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0137] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A capability exposure service interface dynamic configuration and management system method is shown.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] The device embodiments described above are merely illustrative, wherein 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 they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device, which can be a personal computer, a server, or a network device, etc., to execute the methods described in each embodiment or some parts of the embodiments.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically configuring and managing a capability exposure service interface, characterized in that: The process includes the following: S100: parsing and processing the service request received from the external application, and obtaining the specific requirements by calculating the service capabilities and configuration parameters; S200: According to the specific demand, a service component that best matches the demand is dynamically selected in the service resource pool using an intelligent matching algorithm, and an optimal service link construction process is performed on the selected service component to generate a service link; S300: Implement a real-time monitoring mechanism to continuously monitor the running status of the service link, use a machine learning model to predict potential performance bottlenecks on the monitoring data, and automatically trigger dynamic adjustment of the service component configuration or intelligent replacement of the service component when it is detected or predicted that the performance indicator may deviate from the preset range, and generate a service link configuration; S400: using an integrated feedback loop mechanism to collect service quality data under the service link configuration, using a data analysis algorithm to perform in-depth analysis and processing on the collected data, identifying service optimization points, and using a reinforcement learning algorithm to continuously optimize the service component selection strategy and service link construction rules to generate a service configuration solution; S5 00: Synchronize the service configuration scheme to the service resource pool through incremental update, and quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme to generate a service response.

2. A method for dynamic configuration and management of capability exposure service interface according to claim 1, characterized in that: The S300 includes the following process: S310: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report; S320: Based on the performance monitoring report, a pre-trained machine learning model is used to analyze the performance of the service link, predict the performance bottlenecks that may occur in the future, and generate a performance prediction report; prediction formula: Performance stability evaluation formula: P(t) represents the service link performance indicator predicted at time t; w i represents the weight of the i-th service component, obtained by training the machine learning model; a i0 ,a i1 ,a i2 ,a i3 ,a i4 represents the coefficients of polynomial regression, exponential smoothing and sine wave, used to describe the performance indicator x of the i-th service component i The changing trend over time t is obtained through the training process; x i represents the performance indicator of the i-th service component, which is directly obtained from the performance monitoring report; α i represents the smoothing factor of exponential smoothing, which is used to describe the decay rate of the performance indicator of the i-th service component over time and is obtained through the training process; β i Represents the frequency of the sine wave, which is used to describe periodic performance fluctuations and is obtained through the training process; γ i It represents the phase of the sine wave, which is used to describe the initial position of the periodic fluctuation and is obtained through the training process. n represents the number of service components in the service link and is determined by the actual structure of the service link. b represents the bias term, which is used to adjust the overall level of the predicted value and is usually used with the weight w during the training process. i Optimize together; S i (t) represents the performance stability index of the i-th service component at time t, which is used to evaluate the performance fluctuation of the service component; T represents the time window length, which is used to calculate the performance stability; x i (τ) represents the performance indicator of the i-th service component at time τ; represents the average performance index of the i-th service component in the time window [tT, t]; λ represents the smoothing parameter, which is used to balance the fluctuation and change rate of the performance index and is obtained through the training process; By using the prediction formula P(t) and the performance stability evaluation formula S i (t), combining the performance indicators of each service component obtained from the performance monitoring report, calculating the change trend and performance stability of future performance indicators, and generating a detailed performance prediction report; S330: When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment process of the service component configuration is automatically triggered according to the performance prediction report, and the performance is optimized by modifying the configuration parameters of the existing service component to generate preliminary optimization suggestions; S340: According to the preliminary optimization suggestion, the system performs intelligent replacement processing, selects a more suitable service component from the service resource pool to replace the current component, and generates a final optimization solution; S350: Based on the final optimization solution, complete the selection and configuration adjustment of service components and generate a service link configuration.

3. A method for dynamic configuration and management of capability exposure service interface according to claim 2, characterized in that: The S340 includes the following specific processes: S341: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report; S342: Based on the performance monitoring report, use a pre-trained machine learning model to analyze the performance of the service link, predict future performance bottlenecks, and generate a performance prediction report; S343: When it is detected or predicted that the performance indicator may deviate from the preset range, the dynamic adjustment process of the service component configuration is automatically triggered according to the performance prediction report, and the performance is optimized by modifying the configuration parameters of the existing service component, and a preliminary optimization suggestion is generated; S344: Analyze the performance bottlenecks and deficiencies of the service components pointed out in the preliminary optimization suggestions according to the preliminary optimization suggestions, select more suitable service components from the service resource pool to replace the current components, and generate a list of candidate service components; S345: Based on the candidate service component list, the performance indicators, compatibility and resource utilization of the candidate service components are comprehensively considered, and the optimal service component is selected for replacement to generate a final optimization solution.

4. A method for dynamic configuration and management of capability exposure service interface according to claim 2, characterized in that: The S330 includes the following process: S331: Utilize the real-time monitoring mechanism to continuously monitor the operation status of the constructed service link, collect key performance indicator data of each service component, and generate a performance monitoring report; S332: According to the performance bottlenecks and their potential causes indicated in the performance prediction report, the system analyzes the service components and their configuration parameters that need to be adjusted, and determines an adjustment strategy; S333: By executing the adjustment strategy, the configuration parameters of the selected service component are modified to achieve the purpose of optimizing performance and generate preliminary optimization suggestions.

5. A method for dynamically configuring and managing capability exposure service interfaces according to claim 1, characterized in that: The S400 includes the following process: S410: using an integrated feedback loop mechanism, collecting service quality data under the service link configuration to generate a service quality data set; S420: Based on the service quality data set, using a data analysis algorithm to perform in-depth analysis and processing on the collected data, identify key factors and potential problems that affect service quality, and generate a service optimization point report; The score of each service optimization point is more accurately evaluated by the following formula: Among them, O i is the score of the i-th service optimization point; n is the number of factors affecting service quality; w j is the weight of the jth factor; x ij is the value of the jth factor of the i-th service optimization point; θ j is the parameter of the jth factor; h(x ij ,θ j ) is the analysis function for the jth factor; λ is the regularization parameter; η is the sensitivity parameter; z i is the comprehensive score of the i-th service optimization point; β is the weight of the time decay factor; γ is the parameter of the time decay factor; t i is the timestamp of the i-th service optimization point; t0 is the benchmark timestamp; ∈ is the error term, which represents the random fluctuations that the model cannot capture; The analytical function h(x ij ,θ j )The specific expression is as follows: Where: x ij is the value of the jth factor of the i-th service optimization point; θ j1 is the linear weight of the jth factor; θ j2 is the power of the jth factor; θ j3 is the exponential weight of the jth factor; θ j4 is the exponential coefficient of the jth factor; θ j5 is the sinusoidal weight of the jth factor; θ j6 is the sine coefficient of the jth factor; θ j7 is the constant term of the jth factor; Comprehensively score each service optimization point, identify key factors and potential problems affecting service quality, and generate a service optimization point report; S430: Based on the service optimization point report, the service component selection strategy and service link construction rule are continuously optimized by a reinforcement learning algorithm to generate a selection strategy and construction rule; S440: Generate a service configuration solution using the selection strategy and construction rules.

6. A method for dynamic configuration and management of capability exposure service interface according to claim 5, characterized in that: The S410 includes the following process: S411: Based on the service optimization point report, extract key factors and potential problems affecting service quality, and form optimization goals and constraints; S412: using a reinforcement learning algorithm, continuously optimizing the service component selection strategy according to the optimization goal and constraint conditions, simulating different selection strategies, evaluating their impact on service quality, gradually adjusting the parameters of the selection strategy, and generating a service component selection strategy; S413: Based on the service component selection strategy, the service link construction rule is optimized, different link construction schemes are simulated, the impact of the different link construction schemes on the overall performance is evaluated, the parameters of the link construction rule are adjusted, and the service link construction rule is generated; S414: Generate a service quality data set using the service component selection strategy and the service link construction rule.

7. A method for dynamically configuring and managing capability exposure service interfaces according to claim 1, characterized in that: The S500 includes the following process: S510: Synchronize the service configuration scheme to the service resource pool by using an incremental update method, only update the changed parts, reduce synchronization time and resource consumption, and obtain the service resource pool configuration; S520: According to the service resource pool configuration, the service components in the service resource pool are updated to generate a service resource pool; S530: Based on the service resource pool, the received subsequent service request is parsed and processed, and specific requirements are obtained by calculating service capabilities and configuration parameters; S540: According to the specific demand, a service component that best matches the demand is dynamically selected from the updated service resource pool using an intelligent matching algorithm, and an optimal service link construction process is performed on the selected service component to generate a service link; S550: Utilize the service link to quickly construct and process subsequent service requests and generate a service response.

8. A capability exposure service interface dynamic configuration and management system, characterized in that: include: The parsing module is used to parse and process the service requests received from external applications and obtain specific requirements by calculating service capabilities and configuration parameters; A construction module is used to dynamically select the service component that best matches the demand in the service resource pool according to the specific demand using an intelligent matching algorithm, perform optimal service link construction processing on the selected service component, and generate a service link; A monitoring module is used to continuously monitor the running status of the service link by implementing a real-time monitoring mechanism, and use a machine learning model to predict potential performance bottlenecks of the monitoring data. When it is detected or predicted that the performance indicator may deviate from a preset range, it automatically triggers the dynamic adjustment of the service component configuration or the intelligent replacement of the service component to generate a service link configuration; A collection module is used to collect service quality data under the service link configuration by using an integrated feedback loop mechanism, perform in-depth analysis and processing on the collected data by using a data analysis algorithm, identify service optimization points, continuously optimize the service component selection strategy and service link construction rules by using a reinforcement learning algorithm, and generate a service configuration plan; The construction module is used to synchronize the service configuration scheme to the service resource pool through incremental update, and to quickly construct and process efficient service links for subsequent service requests based on the optimized service configuration scheme to generate a service response.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a capability exposure service interface dynamic configuration and management method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for dynamically configuring and managing a capability exposure service interface as claimed in any one of claims 1 to 7 is implemented.

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