Performance data management methods and systems based on mini-programs

Abstract

This invention relates to the field of performance data management technology, specifically disclosing a performance data management method and system based on a mini-program. The method includes: when a high-concurrency scenario is identified, real-time monitoring and analysis of data interaction parameters between the mini-program and the server are performed to determine whether the high-frequency data interaction process is stable; the mini-program receives raw performance data returned by the server; if the interaction process is unstable, the raw data is preprocessed and the interaction process is adjusted synchronously; if the interaction process is stable, preliminary processing is performed directly to improve processing efficiency and obtain the final performance data; a performance data form is generated based on the final performance data, and the form generation process parameters are parsed and obtained to determine whether data corruption has occurred. This method enables dynamic adjustment of the interaction process in a high-concurrency environment, ensuring data processing stability, improving the generation efficiency and accuracy of performance data forms, and enhancing the performance and reliability of the mini-program in complex scenarios.

Description

Technical Field

[0001] This invention relates to the field of performance data management technology, and in particular to a performance data management method and system based on mini-programs. Background Technology

[0002] In existing technologies, front-end development often utilizes the native WeChat Mini Program framework for deep integration with the WeChat ecosystem, or cross-platform frameworks such as Taro and Uniapp to improve development efficiency. Back-end development typically leverages the Spring Boot framework to build efficient and stable Mini Programs, and sometimes utilizes WeChat Cloud Development to reduce development and maintenance costs. For data storage, MySQL databases are widely used due to their high reliability, while WeChat Cloud Database is suitable for small projects or scenarios with high development efficiency requirements. Data collection and processing combine automatic collection and manual entry; some Mini Programs also support real-time data processing and multi-dimensional analysis.

[0003] For example, Chinese invention patent CN109493109B discloses a marketing management method, device, computer equipment, and storage medium based on a mini-program. The method includes: obtaining salesperson performance information; determining the salesperson's performance ranking in the performance leaderboard according to preset rules; and displaying the performance ranking to the salesperson. By obtaining the corresponding performance information of each salesperson and ranking them according to the specific content of the performance information based on the amount of performance completed, a real-time ranking based on the actual business performance of each salesperson is generated.

[0004] For example, Chinese invention patent CN119228305A discloses a method, mini-program, and related equipment for managing safety codes for personnel in large-scale engineering projects. The method includes providing a backend configuration for the safety code mini-program to quantify and evaluate the safety codes; receiving registration applications from participating units, allocating enterprise accounts and uploading project personnel information; generating and distributing personal safety codes to each project worker. Utilizing the mini-program and the quantitative model and algorithm technology of the safety credit code, it achieves quantitative and intelligent evaluation of the personal safety credit and performance of workers in large-scale construction projects. Based on the comprehensive evaluation results, it implements access area control, on-site behavior supervision, and safety rewards and punishments, and uses artificial intelligence algorithms for data analysis and risk prediction.

[0005] The aforementioned technology has at least the following technical problems: In high-concurrency scenarios, high-frequency communication anomalies, i.e., unstable interactions between the mini-program and the backend, can interfere with the timing and integrity of data transmission. This can lead to disordered response order for requests such as performance data form generation and field updates, interruption or duplicate transmission of key data fields. Such transmission anomalies can cause inconsistencies between the performance form data cached locally by the mini-program and the records in the backend database. At the same time, after users perceive the delay in operation feedback, they repeatedly trigger operations such as saving and modifying, which exacerbates the conflict between multiple versions of data. Ultimately, this can lead to disordered phenomena such as disordered field values, duplicate submission records, or loss of key information in the performance form data. Summary of the Invention

[0006] To address the technical problem of performance form data corruption caused by frequent communication anomalies in existing technologies, this invention provides a performance data management method and system based on a mini-program. The technical solution is as follows:

[0007] On the one hand, a performance data management method based on mini-programs is provided. This method includes: Step 1: When a high-concurrency scenario is identified, the mini-program and the server frequently interact with each other. The parameters of the data interaction process between the mini-program and the server are monitored and analyzed to determine whether the high-frequency data interaction process between the mini-program and the server is in a stable state. The data interaction process parameters reflect the operating status of the mini-program in a high-concurrency environment. Step 2: The mini-program receives the raw performance data returned by the server. If the high-frequency data interaction process is in an unstable state, the received raw performance data is preprocessed to obtain the final performance data. At the same time, the data interaction process between the mini-program and the server is adjusted and optimized. If the high-frequency data interaction process is in a stable state, the received raw performance data is initially processed to obtain the final performance data. Step 3: A performance data form is generated based on the final performance data. The parameters of the performance data form generation process are parsed and obtained to determine whether the performance data form has data disorder, thereby realizing the performance data management of the mini-program. The performance data form generation process parameters are used to assist in judging the performance data form.

[0008] On the other hand, a performance data management system based on mini-programs is provided, which includes:

[0009] The interaction monitoring module monitors and analyzes the parameters of the frequent data interactions between the mini-program and the server when high-concurrency scenarios are detected. This allows the mini-program to determine whether the high-frequency data interaction process is stable, and the parameters reflect the mini-program's operating status under high-concurrency conditions. The interaction adjustment module receives raw performance data from the server. If the high-frequency data interaction process is unstable, it preprocesses the received raw performance data to obtain the final performance data and optimizes the data interaction process between the mini-program and the server. If the high-frequency data interaction process is stable, it performs preliminary processing on the received raw performance data to obtain the final performance data. The disorder monitoring module generates performance data forms based on the final performance data, parses and obtains the parameters of the performance data form generation process, and determines whether data disorder has occurred in the performance data form. This enables performance data management for the mini-program. The performance data form generation process parameters assist in judging the performance data form.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0011] (1) This invention provides a performance data management method and system based on mini-programs, covering the entire process from high-frequency data interaction identification and raw performance data differentiation processing to performance data form generation and stability determination. This solution achieves simultaneous improvement in data processing accuracy and response efficiency of mini-programs under large-scale concurrent requests by constructing a multi-parameter fusion interaction stability analysis mechanism, introducing a data processing path selection model, and establishing a disorder index evaluation system oriented towards the generation process. This method effectively avoids performance degradation caused by mini-program overload, ensures performance stability and data integrity under conditions of simultaneous execution of multiple tasks, and fundamentally improves the adaptability and intelligent response level of mini-programs to complex and high-pressure operating environments.

[0012] (2) By judging the stability of the high-frequency data interaction process between the mini-program and the server in real time, the entire process from high-frequency data interaction identification and raw performance data differentiation processing to performance data form generation and stability judgment is analyzed. This solution achieves simultaneous improvement in data processing accuracy and response efficiency of the mini-program under large-scale concurrent requests by constructing a multi-parameter fusion interaction stability analysis mechanism, introducing a data processing path selection model, and establishing a disorder index evaluation system oriented towards the generation process. This method effectively avoids performance degradation caused by mini-program overload, ensures performance stability and data integrity under the condition of simultaneous execution of multiple tasks, and fundamentally improves the adaptability and intelligent response level of the mini-program to complex and high-pressure operating environments.

[0013] (3) Based on the stability of data interaction, a differentiated raw data processing path is adopted to significantly improve processing efficiency while ensuring data quality. If the high-frequency data interaction process is detected to be in an unstable state, the mini-program will perform optimization operations, including increasing the thread pool capacity, adding load balancing nodes, and expanding data verification preloading rules, to further improve the mini-program's processing capability and adaptability under high concurrency impact. This differentiated processing mechanism ensures that the mini-program can maintain efficient acquisition and stable parsing of raw performance data even in high-concurrency scenarios, providing high-quality, low-latency data support for the subsequent form generation process, and effectively reducing the risk of data loss, delay, or deviation caused by concurrency impact.

[0014] (4) After high-concurrency data processing is completed, a performance data form is generated based on the final performance data, and a performance data form disorder index is constructed to determine whether there is a risk of data disorder. If the disorder index is lower than the set threshold, the mini-program will trigger an early warning mechanism and automatically execute optimization processing strategies, such as adjusting the parallel processing granularity, increasing the capacity of the asynchronous task queue, and optimizing task priorities, thereby ensuring the stability and correctness of the form generation. This mechanism not only improves the mini-program's data processing throughput in high-concurrency scenarios, but also enhances the mini-program's rapid response and fault tolerance to abnormal situations, ensuring that the performance form results are reliable and complete. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a performance data management method based on a mini-program provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the structure of the performance data management system based on a mini-program provided in an embodiment of the present invention;

[0018] Figure 3 This is a flowchart of a method for determining the stable state of high-frequency data interaction provided in an embodiment of the present invention;

[0019] Figure 4 This is a flowchart of a method for determining whether to issue an early warning for high-frequency data interaction processes between a mini-program and a server, provided in an embodiment of the present invention.

[0020] Figure 5 This is a flowchart of a method for determining whether a performance data form is disordered, provided in an embodiment of the present invention.

[0021] Figure 6 This is a screenshot of the homepage interface of the mini-program provided in this embodiment of the invention;

[0022] Figure 7 This is a screenshot of the performance management interface of the mini-program provided in this embodiment of the invention;

[0023] Figure 8 This is a diagram of the report statistics interface of the mini-program provided in this embodiment of the invention. Detailed Implementation

[0024] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0025] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0026] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0027] This invention provides a performance data management method based on a mini-program, such as... Figure 1 The flowchart shown below illustrates a performance data management method based on a mini-program. The processing flow of this method may include the following steps:

[0028] Step 1: When a high-concurrency scenario is identified, the mini-program and the server engage in frequent data interactions. Specifically, the mini-program uses front-end components to convert user actions into HTTP / HTTPS requests in real time, sending them to the server via RESTful API or GraphQL interface protocols. Upon receiving the requests, the backend uses its logic processing layer to call resources such as the database, caching middleware, and message queue to return corresponding response data, which is then encapsulated in JSON format. The mini-program front-end receives and processes this data, monitoring and analyzing the parameters of the data interaction process between the mini-program and the server to determine if the high-frequency data interaction is stable. These parameters reflect the mini-program's operational status in a high-concurrency environment. Identifying a high-concurrency scenario includes, but is not limited to, detecting that the number of requests issued by the mini-program per unit time exceeds the maximum allowed number of requests, or that the number of users increases rapidly in a short period and the growth rate exceeds the maximum allowed growth rate. In such cases, it is determined that the mini-program has entered a high-concurrency scenario.

[0029] Step 2: The mini-program receives the raw performance data returned by the server. If the high-frequency data interaction process is in an unstable state, the received raw performance data is preprocessed to obtain the final performance data. At the same time, the data interaction process between the mini-program and the server is adjusted and optimized. If the high-frequency data interaction process is in a stable state, the received raw performance data is initially processed to obtain the final performance data.

[0030] The aforementioned preprocessing of the received raw performance data refers to enhanced preprocessing of the received raw performance data, including data format standardization, field validation, anomaly removal, and content repair. The data is divided into multiple sub-tasks and executed synchronously through a parallel processing mechanism, which significantly reduces processing latency. At the same time, the asynchronous task queue is used to handle time-consuming tasks such as completion, reconstruction, or inference, which helps to alleviate the pressure on the main thread and avoid blocking. This ensures that the mini-program can maintain good responsiveness and stability in complex scenarios, thereby providing a reliable data foundation for subsequent performance analysis.

[0031] The aforementioned preliminary processing of the received raw performance data refers to performing lightweight preliminary processing on the raw performance data to reduce the resource consumption of the mini-program. This includes field mapping, fast validation, and data integration. It also utilizes parallel processing and asynchronous task queue mechanisms to effectively ensure the processing priority of the main process, achieve an efficient processing path under light load, and improve the overall performance and data throughput of the mini-program.

[0032] Step 3: Generate a performance data form based on the final performance data, parse and obtain the parameters of the performance data form generation process, and thus determine whether the performance data form has data disorder, thereby realizing the performance data management of the mini program. The parameters of the performance data form generation process are used to assist in judging the performance data form.

[0033] Specifically, to determine whether the high-frequency data interaction process between the mini-program and the server is in a stable state, the specific analysis process is as follows: By analyzing the data interaction process parameters between the mini-program and the server, a high-frequency data interaction stability index is obtained. The high-frequency data interaction stability index is compared with the first-class and second-class threshold values ​​of interaction stability. The first-class threshold value is a value preset in the management database to distinguish between the high-frequency data interaction process between the mini-program and the server being in an ideal state and a warning state. The second-class threshold value is a value preset in the management database to distinguish between the high-frequency data interaction process between the mini-program and the server being in a warning state and a fault state.

[0034] When the high-frequency data interaction stability index is not lower than the first-class threshold value of interaction stability, the high-frequency data interaction process between the mini-program and the server is judged to be in a stable state and marked as an ideal state. At the same time, the cache hit rate and database request frequency during the high-frequency data interaction process are monitored to maintain the continuity of the ideal state. Monitoring the cache hit rate during the high-frequency data interaction process means that during the data interaction process between the mini-program and the server, the mini-program can integrate a cache access log monitoring module to count the total number of cache read requests and the number of cache hits in real time. The cache hit rate is obtained by dividing the number of cache hits by the total number of cache requests. When the cache hit rate begins to decline but has not yet fallen below the preset ideal state threshold, the mini-program can automatically trigger a cache update mechanism, such as increasing the frequency of hot data preloading, extending the cache time-to-live (TTL), and increasing the cache layer capacity, thereby enhancing the cache's resilience and maintaining a high hit rate. Monitoring the database request frequency during high-frequency data interaction refers to using a database access proxy or middleware to record the number of query, update, and other operation requests initiated by the mini-program to the database per unit time. This serves as a request frequency indicator. If the database request frequency continues to rise but no warning is triggered, the mini-program can trigger request merging strategies (such as batch processing of read requests in a short period of time), index optimization suggestions, read-only replica traffic distribution, and other measures to reduce the pressure on the main database and avoid degradation to a warning or fault state due to frequent access.

[0035] For example, the mini-program calculates the high-frequency data interaction stability index in real time through its set monitoring logic. The current index is 0.87, which is higher than the preset stability threshold of 0.75. Therefore, it is judged that the current high-frequency data interaction process is in an ideal state. The mini-program monitoring found that the current Redis cache hit rate is 93%, which is slightly lower than the previous period (96%). The mini-program automatically executes the cache refresh strategy, preloads the performance check-in data that has been frequently accessed in the last 15 minutes into the cache, and extends the cache expiration time of this type of data to 10 minutes to prevent repeated requests from falling into the database in a short period of time. The mini-program detects that the current database processes 1200 requests per minute, which is higher than the average level (about 900 times). It triggers the request merging strategy to aggregate multiple report requests from the same account or the same department in a short period of time. In the case of cache miss, these requests are merged into a single database query. The result is cached and quickly responds to multiple front-end requests, significantly reducing the database duplicate query rate and improving the overall response efficiency.

[0036] When the high-frequency data interaction stability index is between the first and second category thresholds for interaction stability, the high-frequency data interaction process between the mini-program and the server is determined to be in an unstable state and marked as a warning state. At the same time, the high-frequency data interaction process between the mini-program and the server is optimized. When the high-frequency data interaction stability index is below the second category threshold for interaction stability, the high-frequency data interaction process between the mini-program and the server is determined to be in an unstable state and marked as a fault state. At the same time, the high-frequency data interaction process between the mini-program and the server is adjusted. The high-frequency data interaction stability index is reacquired and marked as the high-frequency data interaction stability reassessment index. It is then determined whether to issue a warning for the high-frequency data interaction process between the mini-program and the server.

[0037] Specifically, the high-frequency data interaction process between the mini-program and the server is optimized. The optimization process is as follows: based on the high-frequency data interaction stability index and the first-class boundary value of interaction stability, a first-class deviation value is obtained. Based on the first-class deviation value, a load balancing node increase coefficient is matched to increase the number of nodes for backend business processing services. At the same time, the algorithm of the critical path in the high-frequency data interaction between the mini-program and the server is optimized, and database read and write separation operation is added.

[0038] Obtaining a first-class deviation value refers to subtracting the high-frequency data interaction stability index from the first-class stability threshold value. Based on this first-class deviation value, a load balancing node increase coefficient is matched. The specific matching process is as follows: the management database presets load balancing node increase coefficients corresponding to each first-class deviation value range. The obtained first-class deviation values ​​are input into the database, and the management database can match the corresponding load balancing node increase coefficient for each first-class deviation value range. The obtained load balancing node increase coefficient is multiplied by the original load balancing node, and the result is the load balancing node that needs to be adjusted. A load balancing node increase coefficient greater than 1 indicates that the number of nodes for backend business processing services needs to be increased by a multiple. Rapid expansion can alleviate processing bottlenecks in certain service modules, prevent task backlog and response delays, and avoid problems such as link congestion and service timeouts caused by a surge in instantaneous requests, ensuring the consistency and stability of the user access experience.

[0039] In this invention, to achieve intelligent control of the high-concurrency data interaction process between the mini-program and the server, a mapping rule system is configured based on historical operational data and experimental stress test results. This system integrates the correspondence between multi-dimensional indicators (such as concurrency intensity, response latency, load change rate, etc.) and multi-level adjustment parameters. Through data fitting and normalization, a segmented and scalable parameter mapping table is established. This system not only supports static configuration but can also be dynamically updated according to the mini-program's operating status, ensuring the real-time adaptability and controllability of the control strategy. The system includes various mapping relationships, such as the mapping between type II deviation values ​​and the secondary increase coefficient of the load balancing node, and the mapping between the database lock contention rate ratio coefficient and its corresponding effect coefficient. These are all preset in the management database and can be automatically invoked. All mapping relationships involved in this invention can be obtained through this mapping rule system.

[0040] Algorithm optimization for critical paths in high-frequency data interactions between mini-programs and servers includes, but is not limited to, request sequence simplification, cache shifting strategies, and asynchronous decoupling calls. Request sequence simplification refers to removing redundant intermediate calls (such as duplicate validation logic); cache shifting strategies involve caching the return results of functions whose computational complexity exceeds the preset maximum value in the management database at edge nodes; and asynchronous decoupling calls include, for example, changing data logging and event tracking to asynchronous queue pushes. By optimizing key algorithms and task flows in high-frequency interaction paths between mini-programs and servers, resource consumption and processing time in a single data interaction can be effectively reduced. For example, caching some calculation results, asynchronousizing call logic, and simplifying redundant steps can significantly reduce blocking operations and improve the overall response speed of the mini-program. This not only improves the processing efficiency of the mini-program in high-concurrency environments but also reduces unnecessary data transmission between servers, thereby extending the runtime of the mini-program under ideal conditions and improving its sustainability and efficiency.

[0041] Database read / write separation refers to the automatic routing of all read-only queries to the read-only replica database in a master-slave architecture, while write requests are still sent to the master database. Data synchronization channels are used to maintain consistency between master and slave databases, and slave database latency is dynamically monitored. If the latency is too high, the mini-program automatically switches read requests to low-latency nodes. By distributing read-only requests to the read-only replica database, performance degradation caused by a large backlog of read operations on the master database during peak hours is avoided, and the mini-program's throughput capacity when handling high-concurrency queries is improved. Especially in business scenarios such as performance data, where read requests are far more frequent than writes, optimizing the database load structure through a separation architecture helps reduce query latency, improve mini-program stability, and reduce interface response delays or failures caused by database blocking, effectively extending the duration of high-performance operation.

[0042] Furthermore, the high-frequency data interaction process between the mini-program and the server is adjusted. Specifically, the adjustment process is as follows: based on the high-frequency data interaction stability index and the second-class boundary value of interaction stability, the second-class deviation value is obtained. Based on the second-class deviation value, the maximum thread increase coefficient is matched to increase the maximum number of threads in the thread pool used for high-frequency data interaction in the backend business processing service. By dynamically expanding the maximum number of threads in the thread pool, the mini-program can handle more concurrent tasks in a short time, effectively preventing request queuing, timeouts and loss caused by thread saturation. This helps to improve the mini-program's recovery capability and task throughput under heavy load and reduce the duration of the fault state.

[0043] Obtaining the Type II deviation value involves subtracting the high-frequency data interaction stability index from the Type II boundary value for interaction stability; based on the Type II deviation value, the maximum thread increase coefficient is matched, specifically: T is the maximum thread increase coefficient, D is the type II deviation value, and k is the linear coefficient. The specific values ​​are determined by those skilled in the art based on the actual situation. Substituting the obtained type II deviation value into this formula will yield the maximum thread increase coefficient. Multiplying the obtained maximum thread increase coefficient by the original maximum number of threads in the thread pool will give the result as the maximum number of threads in the thread pool that needs to be adjusted.

[0044] Based on the two types of deviation values, a secondary increase coefficient is determined for the load balancing nodes, thereby further increasing the number of nodes for backend business processing services. By strengthening the processing resource pool of backend services, the load pressure on a single node is significantly reduced, thus decreasing issues such as request packet loss, response blocking, and task failure. Simultaneously, multiple new nodes share the burden of high-concurrency requests under the load balancing mechanism, effectively improving the overall stability and availability of the mini-program. Especially in fault conditions, the rapid scaling mechanism helps the mini-program self-repair and gradually recover to a warning or ideal state, ensuring uninterrupted critical business operations.

[0045] The above-mentioned secondary increase coefficient for load balancing nodes is determined based on the two types of deviation values. The specific matching process is as follows: The management database presets the secondary increase coefficients for load balancing nodes corresponding to each range of two types of deviation values. The obtained two types of deviation values ​​are input into the database, and the management database can match the secondary increase coefficients for load balancing nodes corresponding to the range of two types of deviation values. The obtained secondary increase coefficient is multiplied by the original load balancing node, and the result is the load balancing node to be adjusted. The above-mentioned secondary increase coefficient for load balancing nodes is greater than 1, which indicates that the number of nodes for backend business processing services needs to be increased by a multiple. It should be noted that the secondary increase coefficient for load balancing nodes is greater than the load balancing node increase coefficient.

[0046] like Figure 3The flowchart of the method for determining the stable state of high-frequency data interaction provided in this embodiment of the invention shows that the current high-frequency data interaction stability index is calculated, and the high-frequency data interaction stability index is compared with the preset first-class and second-class threshold values ​​of interaction stability to determine whether the current state is ideal, warning, or faulty. When the high-frequency data interaction stability index is greater than or equal to the first-class threshold value of interaction stability, it indicates that the current mini-program is in an ideal stable state. At this time, the mini-program will perform preliminary processing on the raw performance data returned by the server and monitor key operating indicators such as cache hit rate and database request frequency in real time to provide basic data support for the subsequent generation of performance data forms. At the same time, the performance data forms are generated, and the performance data form disorder index is calculated. When the high-frequency data interaction stability index is between the first-class and second-class threshold values ​​of interaction stability, the mini-program is in a warning state. During this stage, while performing preprocessing on the raw performance data, the mini-program will dynamically increase the number of load balancing nodes based on the first-class deviation value to alleviate the pressure of hot requests, optimize the data processing algorithm in the critical path, reduce task processing latency, and implement a database read-write separation strategy to improve database operation efficiency. After the above optimization measures are completed, the mini-program will re-acquire the high-frequency data interaction stability review index and conduct another status assessment. If the high-frequency data interaction stability index is lower than the Type II threshold for interaction stability, the mini-program is in a fault state. In this state, the mini-program will perform the same preprocessing on the original performance data and make more significant adjustments based on the Type II deviation value, including increasing the maximum number of threads in the thread pool and further expanding the number of load balancing nodes, to enhance the mini-program's processing capabilities. After the above adjustments are completed, the high-frequency data interaction stability review index must also be re-acquired.

[0047] Specifically, the process for determining whether to issue an alert for high-frequency data interaction between the mini-program and the server is as follows: the high-frequency data interaction stability review index is compared with the first-class threshold value for interaction stability; when the high-frequency data interaction stability review index is not lower than the first-class threshold value for interaction stability, it is determined that no alert will be issued for the high-frequency data interaction between the mini-program and the server, while monitoring the cache hit rate and database request frequency during the high-frequency data interaction process to maintain the continuity of the ideal state; the above monitoring of the cache hit rate and database request frequency during the high-frequency data interaction process is consistent with the monitoring process described above.

[0048] When the high-frequency data interaction stability review index falls below the first-class threshold for interaction stability, an early warning is issued for the high-frequency data interaction process between the mini-program and the server. Simultaneously, adaptive flow control and rate limiting are triggered on both the mini-program and server sides to alleviate interaction pressure and improve stability. Issuing an early warning for the high-frequency data interaction process between the mini-program and the server refers to sending an early warning signal to both the mini-program front-end module and the server back-end module. The warning information includes indicator parameters describing the abnormal characteristics of the current data interaction (such as the frequency of abnormal database requests).

[0049] The adaptive flow control and rate limiting mechanisms on the mini-program side mainly include dynamic request frequency limiting, API request degradation, and client-side caching enhancement. Specifically, the mini-program dynamically limits the minimum call interval of certain high-frequency APIs, caches user operations in a queue to process requests in batches, thereby reducing the impact of instantaneous concurrency. At the same time, the front-end extends the caching period of static configurations and local data, prioritizes displaying locally cached content, and only asynchronously refreshes data in the background. These measures can effectively control the frequency of client access to the server, alleviate sudden load surges, and improve the stability and fault tolerance of user operations.

[0050] The server-side adaptive flow control and rate limiting mechanisms include adopting interface frequency limiting strategies based on token bucket or leaky bucket algorithms to dynamically control the access frequency of a single IP or user; at the same time, converting some high-latency requests into asynchronous tasks and placing them in a background queue to smooth out the pressure through peak shaving and valley filling; for complex or slow-processing interfaces, the server can temporarily simplify the response content, change to segmented return, or respond to requests in a processing status. These mechanisms can significantly reduce the risk of load fluctuations in the main service and improve the overall robustness and service stability of the mini-program.

[0051] Furthermore, the data interaction parameters between the mini-program and the server are monitored and analyzed. Specifically, the data interaction parameters include the database lock contention rate ratio, thread scheduling delay ratio, and hardware clock drift ratio during the data interaction process. By pre-setting the effect coefficients of each ratio in the management database, their weighted contribution to the high-frequency data interaction stability index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the high-frequency data interaction stability index. The high-frequency data interaction stability index represents the stability of high-frequency data interaction between the mini-program and the server. The database lock contention rate ratio represents the ratio of the database lock contention rate to the defined database lock contention rate during the data interaction process. The thread scheduling delay ratio represents the ratio of the thread scheduling delay duration to the defined thread scheduling delay duration during the data interaction process. The hardware clock drift ratio represents the ratio of the hardware clock drift amount to the defined hardware clock drift amount during the data interaction process.

[0052] The aforementioned database lock contention rate measures the degree of contention for database lock resources among multiple transactions or threads within the same time period. It is obtained by comparing the number of database lock waits per unit time with the total number of lock requests. The aforementioned thread scheduling delay refers to the delay time caused by the mini-program scheduling mechanism (such as preemption) under high concurrency. It is obtained by measuring the time difference between when a thread is requested to execute and when it actually begins execution. The aforementioned hardware clock drift refers to the difference in local hardware clocks between different devices (such as nodes in a server cluster). It is obtained by comparing the offset between the local mini-program time and the standard server time.

[0053] The above definitions of database lock contention rate represent the maximum value of the database lock contention rate within a specified range; the definition of thread scheduling delay time represents the maximum value of the thread scheduling delay time within a specified range; and the definition of hardware clock drift amount represents the maximum value of the hardware clock drift amount within a specified range.

[0054] The specific evaluation method for the high-frequency data interaction stability index is as follows:

[0055] ;

[0056] In the formula, HDISI is the high-frequency data interaction stability index, LCRF is the database lock contention rate ratio coefficient during the data interaction process between the mini-program and the server, TSDF is the thread scheduling delay ratio coefficient during the data interaction process between the mini-program and the server, HCDF is the hardware clock drift ratio coefficient during the data interaction process between the mini-program and the server, gl is the effect coefficient corresponding to the preset database lock contention rate ratio coefficient in the management database, gt is the effect coefficient corresponding to the preset thread scheduling delay ratio coefficient in the management database, and gh is the effect coefficient corresponding to the preset hardware clock drift ratio coefficient in the management database.

[0057] The higher the ratio of the database lock contention rate to its defined database lock contention rate, the more severe the contention for database resources under high-frequency requests. This results in a larger database lock contention rate coefficient, causing data access latency, further exacerbating thread waiting, and thus increasing the thread scheduling latency coefficient. An increased thread scheduling latency coefficient triggers dynamic adjustments to the mini-program's time scheduling mechanism, potentially leading to a larger offset between the mini-program's local clock and standard time, resulting in a higher hardware clock drift coefficient. These three factors create a synergistic effect in high-concurrency scenarios, mutually amplifying each other's adverse effects and ultimately causing a decline in the stability index of high-frequency data interaction.

[0058] The effect coefficients corresponding to the database lock contention rate ratio coefficients mentioned above indicate that when the database lock contention rate ratio coefficient changes by a unit magnitude, the high-frequency data interaction stability index will change accordingly. Similarly, the effect coefficients corresponding to the thread scheduling latency ratio coefficients mentioned above indicate that when the thread scheduling latency ratio coefficient changes by a unit magnitude, the high-frequency data interaction stability index will change accordingly. Finally, the effect coefficients corresponding to the hardware clock drift ratio coefficients mentioned above indicate that when the hardware clock drift ratio coefficient changes by a unit magnitude, the high-frequency data interaction stability index will change accordingly.

[0059] The management database stores the mapping relationships between database lock contention rate ratio coefficients and their corresponding effect coefficients, thread scheduling delay ratio coefficients and their corresponding effect coefficients, and hardware clock drift ratio coefficients and their corresponding effect coefficients. In this embodiment, the mapping relationship is a mapping table. For example, when the database lock contention rate ratio coefficient, thread scheduling delay ratio coefficient, and hardware clock drift ratio coefficient are input into the management database, the management database can match the corresponding effect coefficients of the database lock contention rate ratio coefficient, thread scheduling delay ratio coefficient, and hardware clock drift ratio coefficient based on the preset mapping relationship table. The numerical range of each effect coefficient is strictly controlled between 0 and 1.

[0060] like Figure 4 The flowchart of the method for determining whether to issue an early warning for the high-frequency data interaction process between the mini-program and the server provided in this embodiment of the invention shows that if the high-frequency data interaction stability review index rises above the first-class threshold value of interaction stability, it is considered that the interaction process has recovered to an ideal state, and the mini-program will enter the performance data form generation stage; otherwise, it is considered to be continuously unstable, and the mini-program will issue an early warning for the high-frequency data interaction process and automatically trigger an adaptive flow control mechanism and rate limiting strategy to suppress the spread of interaction disorder and ensure the quality of basic data.

[0061] Specifically, the parameters for the performance data form generation process are analyzed and obtained. The analysis process includes: the mini-program load fluctuation ratio coefficient, the data mutation frequency ratio coefficient, and the cross-validation failure rate ratio coefficient. Simultaneously, the stable final value of high-frequency data interaction is obtained. This stable final value is a real-time updated high-frequency data interaction stability index or a high-frequency data interaction stability review index. By pre-setting the effect coefficients of each ratio coefficient and the stable final value of high-frequency data interaction in the management database, their weighted contribution to the performance data form disorder index is quantified. Finally, a weighted average fusion algorithm is used to synthesize the performance data form disorder index. The performance data form disorder index represents the degree of disorder in the performance data form generation process. The mini-program load fluctuation ratio coefficient represents the ratio of the mini-program load fluctuation value to the defined mini-program load fluctuation value during the performance data form generation process. The data mutation frequency ratio coefficient represents the ratio of the data mutation frequency to the defined data mutation frequency during the performance data form generation process. The cross-validation failure rate ratio coefficient represents the ratio of the cross-validation failure rate to the defined cross-validation failure rate during the performance data form generation process.

[0062] The aforementioned mini-program load fluctuation value represents the fluctuation range of the utilization rate of resources such as CPU, memory, and I / O in the mini-program within a unit of time. Based on a set sampling time window, continuous time-series data of CPU utilization, memory usage, and disk I / O usage are periodically collected from the server's performance monitoring interface for mini-program operations. The standard deviation of each indicator within the time window is calculated, and the standard deviations are weighted and summed according to preset indicator weighting coefficients to obtain the mini-program load fluctuation value. The aforementioned data mutation frequency represents the frequency at which the value change in the original performance data exceeds a set threshold within a continuous time period during the form generation process. The raw performance data generated during the form generation process is statistically analyzed using a sliding window to identify whether the data change amplitude within any consecutive time slice exceeds a preset mutation threshold. The cumulative number of mutation events within a unit of time is also counted to determine the data mutation frequency. The aforementioned cross-validation failure rate represents the proportion of inconsistent validation between multiple data sources (such as user input) during the performance data form generation process. During form generation, consistency validation is performed on the same data field from multiple data sources. The number of failures due to data inconsistency is counted in the total number of validations, and the ratio of the number of failures to the total number of validations is calculated to obtain the cross-validation failure rate.

[0063] The above definition of mini-program load fluctuation value represents the maximum value of mini-program load fluctuation value within the specified range; the definition of data mutation frequency represents the maximum value of data mutation frequency within the specified range; and the definition of cross-validation failure rate represents the maximum value of cross-validation failure rate within the specified range.

[0064] The larger the ratio of the mini-program load fluctuation value to the defined mini-program load fluctuation value, the larger the mini-program load fluctuation ratio coefficient, indicating that the mini-program's operating status is more unstable, leading to data processing delays and resource contention conflicts. The larger the ratio of the data mutation frequency to the defined data mutation frequency, the larger the data mutation frequency ratio coefficient, indicating that there are sudden abnormal behaviors during the data generation process, affecting the continuity and verifiability of the data. The larger the ratio of the cross-validation failure rate to the defined cross-validation failure rate, the larger the cross-validation failure rate ratio coefficient, indicating that there are more consistency or integrity anomalies in the performance data forms. The three proportional coefficients mentioned above and the stable final value of high-frequency data interaction influence each other. Fluctuations in the mini-program's load may lead to an increase in the frequency of data mutations, while high-frequency mutations may also increase the mini-program's load and the cross-validation failure rate, thus causing the stable final value of high-frequency data interaction to decrease. As an important external constraint variable in the performance data form generation process, the decrease in the stable final value of high-frequency data interaction will further exacerbate the disorder in the performance data form generation process, ultimately leading to a significant increase in the performance data form disorder index. The stable final value of high-frequency data interaction is an important exogenous variable for measuring whether the overall performance data form generation process is in a stable state, and it has a direct regulatory significance on the structural integrity of performance data and the orderliness of the generation process.

[0065] The specific assessment method for the disorder index of performance data forms is as follows:

[0066] ;

[0067] In the formula, PDFDI is the performance data form disorder index, HDISI_z is the stable final value of high-frequency data interaction, SLVF is the mini-program load fluctuation ratio coefficient in the performance data form generation process, DMFF is the data mutation frequency ratio coefficient in the performance data form generation process, CCFRF is the cross-validation failure rate ratio coefficient in the performance data form generation process, fs is the effect coefficient corresponding to the mini-program load fluctuation ratio coefficient preset in the management database, fd is the effect coefficient corresponding to the data mutation frequency ratio coefficient preset in the management database, fc is the effect coefficient corresponding to the cross-validation failure rate ratio coefficient preset in the management database, and fh is the effect coefficient corresponding to the stable final value of high-frequency data interaction preset in the management database.

[0068] The effect coefficients corresponding to the aforementioned mini-program load fluctuation ratio coefficients indicate that when the mini-program load fluctuation ratio coefficient changes by a unit magnitude, the performance data form disorder index will change accordingly. Similarly, the effect coefficients corresponding to the aforementioned data mutation frequency ratio coefficients indicate that when the data mutation frequency ratio coefficient changes by a unit magnitude, the performance data form disorder index will change accordingly. The effect coefficients corresponding to the aforementioned cross-validation failure rate ratio coefficients indicate that when the cross-validation failure rate ratio coefficient changes by a unit magnitude, the performance data form disorder index will change accordingly. Finally, the effect coefficients corresponding to the aforementioned high-frequency data interaction stable final value indicate that when the high-frequency data interaction stable final value changes by a unit magnitude, the performance data form disorder index will change accordingly.

[0069] The management database stores the mapping relationships between the mini-program load fluctuation ratio coefficient and its corresponding effect coefficient, the data mutation frequency ratio coefficient and its corresponding effect coefficient, the cross-validation failure rate ratio coefficient and its corresponding effect coefficient, and the stable final value of high-frequency data interaction and its corresponding effect coefficient. In this embodiment, the mapping relationship is a mapping table. For example, the mini-program load fluctuation ratio coefficient, data mutation frequency ratio coefficient, cross-validation failure rate ratio coefficient, and stable final value of high-frequency data interaction are input into the management database. Based on the preset mapping relationship table, the management database can match the corresponding effect coefficients of the mini-program load fluctuation ratio coefficient, the data mutation frequency ratio coefficient, the cross-validation failure rate ratio coefficient, and the stable final value of high-frequency data interaction. The numerical range of each effect coefficient is strictly controlled between 0 and 1.

[0070] like Figure 5 The flowchart of the method for determining whether a performance data form is disordered, provided in this embodiment of the invention, shows that if the disorder of the performance data form is below the disorder threshold, it is determined that the current form generation process is not seriously disordered. The mini-program will automatically decouple the form generation process logic and break it down into several sub-steps to improve the independence and processing flexibility between modules, and then the process ends.

[0071] If the disorder index of the performance data form is not lower than the disorder threshold, the form generation process is considered to have a certain degree of disorder. The mini-program will perform the following adjustments based on the current disorder deviation: firstly, increase the number of pre-loaded rules used for data validation to improve data structure standardization; secondly, extend the length of the verification code attached during cross-node data transmission to enhance data consistency. After these adjustments, the mini-program will obtain the final disorder value of the performance data form and use it to determine whether it has reached the threshold. If the final disorder value is lower than the disorder threshold, the mini-program will perform decoupling processing of the form generation process, and the process will end normally. Otherwise, a form generation process warning will be triggered, further optimizing the parallel processing strategy and asynchronous task queue execution mechanism to improve processing efficiency and stability, ultimately ending the process.

[0072] Specifically, the process for determining whether a performance data form is disordered involves comparing the disorder index with the disorder threshold, which is the maximum value of the pre-defined disorder index within a specified range in the management database. If the disorder index is not lower than the disorder threshold, the performance data form is considered to be free of disorder. Simultaneously, the generation process is decoupled, breaking down the process of generating the performance data form.

[0073] The aforementioned decoupling of the generation steps refers to the process of breaking down the generation of performance data forms into four relatively independent steps: data access, format conversion, content filling, and style rendering. A buffer queue is introduced between each step to cache the output of the previous step and the input data of the next. Specifically, the data access step receives raw performance data from high-frequency data interactions; the format conversion step standardizes the raw data according to a preset template format; the content filling step fills in the actual performance information and indicator values ​​based on the converted format; and the style rendering step styles and visually renders the filled data form structure to form the final displayable form. The buffer queue acts as a bridging mechanism between steps, and its capacity is dynamically adjusted automatically based on the current amount of data received and the processing load. This ensures that data output from previous steps can be temporarily stored if it cannot be processed by subsequent steps in a timely manner, thus preventing previous steps from being blocked or subsequent steps from running idle due to sudden data interruptions. By decoupling the generation steps and setting up a buffer queue, the concurrent execution and decoupled operation of each step are achieved, which improves the overall data processing smoothness and generation efficiency, and effectively reduces the impact of sudden load changes on the stability of data form generation.

[0074] When the disorder index of the performance data form is not lower than the disorder threshold of the performance data form, it is determined that the performance data form is disordered. Based on the disorder index and the disorder threshold of the performance data form, the disorder deviation value is obtained. Based on the disorder deviation value, the increase in the number of data verification preload rules is matched, thereby increasing the number of data verification preload rules. Based on the disorder deviation value, the check code length increase coefficient is matched, thereby increasing the check code length transmitted across nodes.

[0075] Obtaining the disorder deviation value refers to subtracting the disorder index of the performance data form from the disorder threshold value. The above-mentioned increase in the number of data validation preloading rules based on the disorder deviation value is achieved through the following matching process: The management database stores the increase in the number of data validation preloading rules corresponding to each disorder deviation value interval. The obtained disorder deviation value is input into the management database, which then matches the corresponding increase in the number of data validation preloading rules. Adding this increase to the original number of data validation preloading rules yields the required number of data validation preloading rules. By dynamically increasing the number of data validation preloading rules based on the disorder deviation value, more dimensions of data consistency checks can be performed in advance during the performance data form generation process. This effectively improves the structural integrity verification capability of data in the access, transformation, and filling stages, thereby reducing the form construction failure rate caused by structural errors or format abnormalities and enhancing the robustness of the mini-program in handling complex data flow scenarios.

[0076] The above-mentioned process of matching the checksum length amplification factor based on the disorder deviation value is as follows: The current disorder deviation value is input into the processing module and compared with multiple preset deviation level segments in the management database for interval judgment. Different checksum length adjustment strategies are set for each deviation level segment. The mini-program selects the checksum length amplification factor corresponding to the determined segment level, and multiplies the obtained checksum length amplification factor by the original checksum length. The result is the checksum length that needs to be adjusted. The above checksum length amplification factor is greater than 1, which indicates that the checksum length of cross-node transmission needs to be increased by a certain factor. By appropriately increasing the data checksum length of cross-node transmission according to the disorder deviation value, the integrity verification accuracy of data packets transmitted between distributed nodes is enhanced. It can more efficiently identify abnormal transmission behaviors such as misalignment, omission, or tampering, significantly reduce the probability of form data corruption caused by data damage, and ensure the accuracy and security of performance data in the multi-node collaborative processing process.

[0077] Reacquire the disorder index of the performance data form, mark it as the final value of the disorder in the performance data form, and determine whether to issue an early warning for the generation process of the performance data form.

[0078] like Figure 6The homepage interface of the mini-program provided in this embodiment of the invention is shown in the figure. This interface is an overview of the system's main functions and is divided into two main functional modules: daily management and statistical management. The daily management module includes seven functions: task posting, task management, shift management, scheduling management, member management, application records, and blacklist management. These functions support refined management of tasks, manpower, shifts, and other matters in daily operations. The statistical management module includes four functions: attendance management, card sales management, performance management, and report export. It provides comprehensive statistical support for employee attendance, card sales, performance scores, and overall data reports. All functions are presented in the form of icons and text, arranged neatly for easy and quick access.

[0079] Furthermore, the system determines whether to issue an alert for the performance data form generation process. Specifically, it compares the final disorder value of the performance data form with the disorder threshold value. If the final disorder value is not lower than the disorder threshold value, no alert is issued for the performance data form generation process. If the final disorder value is lower than the disorder threshold value, an alert is issued for the performance data form generation process, and the parallel processing and asynchronous task queue of the performance data form generation process are optimized based on the final disorder value.

[0080] The aforementioned optimization of the parallel processing and asynchronous task queues in the performance data form generation process based on the disordered final value of the performance data form refers to optimizing the number of parallel processing tasks and the capacity of the asynchronous task queue during the form generation process. Specifically, based on the disordered final value of the performance data form and combined with the mapping rule system configured in the management database, this system presets a combination mapping relationship between the number of parallel processing tasks and the capacity of the asynchronous task queue corresponding to different disordered final value intervals. The number of parallel processing tasks and the capacity of the asynchronous task queue are then increased as needed. For example, when the disordered final value of the performance data form is below the critical limit, the mapping rule system can increase the number of parallel processing tasks and the capacity of the asynchronous task queue to their corresponding maximum configuration values ​​to ensure minimum response performance of the form generation process under extreme disorder conditions. The aforementioned critical limit refers to the lowest safe boundary value of the performance data form disorder index within the tolerable range of the mini-program.

[0081] like Figure 7The performance management interface of the mini-program provided in this embodiment of the invention is shown in the figure. This interface displays the performance management function, supporting the viewing of employee performance score reward and deduction records by time period. The top of the interface is the time period selection area, currently selected as April 1, 2025 to April 8, 2025. Below, the performance details within this time period are listed in sequence, including records such as "Deduction -50" and "Reward +120", which are represented by green and red numbers to indicate score changes and their direction, respectively. The specific date is displayed on the right. There is an add button at the bottom of the interface, which users can click to add performance events, such as entering new rewards and penalties, to achieve flexible maintenance of performance data.

[0082] Figure 8 shows the report statistics interface of the mini-program provided in this embodiment of the invention. This interface is the report statistics function page, mainly used for summarizing and exporting various types of data in the system. A time period selection control is also provided at the top; the current time range is from April 1, 2025 to April 8, 2025. The middle of the page displays the prompt: "Click Export to export the attendance, performance, and card sales commission data of employees during this period," indicating that this function supports merging and summarizing multi-dimensional data. A prominent blue export button is located at the bottom of the interface; users can click it to export all statistical reports for the corresponding time period with one click, facilitating external analysis, archiving, or reporting.

[0083] A second aspect of this invention provides a performance data management system based on a mini-program, such as... Figure 2 The diagram shows the structure of a performance data management system based on a mini-program. The system includes: an interactive monitoring module, an interactive adjustment module, a disorder monitoring module, and a management database.

[0084] The interactive monitoring module is connected to the interactive adjustment module, and the interactive adjustment module is connected to the disorder monitoring module. The interactive monitoring module, the interactive adjustment module, and the disorder monitoring module are all connected to the management database, which is used to store various parameters involved in the performance data management mini-program.

[0085] The interaction monitoring module monitors and analyzes the parameters of the frequent data interactions between the mini-program and the server when high-concurrency scenarios are detected. This allows the mini-program to determine whether the high-frequency data interaction process is stable, and the parameters reflect the mini-program's operating status under high-concurrency conditions. The interaction adjustment module receives raw performance data from the server. If the high-frequency data interaction process is unstable, it preprocesses the received raw performance data to obtain the final performance data and optimizes the data interaction process between the mini-program and the server. If the high-frequency data interaction process is stable, it performs preliminary processing on the received raw performance data to obtain the final performance data. The disorder monitoring module generates performance data forms based on the final performance data, parses and obtains the parameters of the performance data form generation process, and determines whether data disorder has occurred in the performance data form. This enables performance data management for the mini-program. The performance data form generation process parameters assist in judging the performance data form.

[0086] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0087] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A performance data management method based on mini-programs, characterized in that, The method includes: Step 1: When a high-concurrency scenario is identified, the mini-program and the server engage in frequent data interactions. The parameters of this data interaction process are monitored and analyzed to determine whether the high-frequency data interaction between the mini-program and the server is in a stable state. These data interaction parameters reflect the operating status of the mini-program in a high-concurrency environment. The specific analysis process for monitoring and analyzing these parameters includes the database lock contention rate ratio, thread scheduling latency ratio, and hardware clock drift ratio during the mini-program-server data interaction process. The effect coefficients of each ratio are preset in the management database to quantify their... The weighted contribution values ​​of the high-frequency data interaction stability index are finally synthesized using a weighted average fusion algorithm. The high-frequency data interaction stability index represents the stability of high-frequency data interaction between the mini-program and the server. The database lock contention rate ratio coefficient represents the ratio of the database lock contention rate to the defined database lock contention rate during the data interaction between the mini-program and the server. The thread scheduling delay ratio coefficient represents the ratio of the thread scheduling delay duration to the defined thread scheduling delay duration during the data interaction between the mini-program and the server. The hardware clock drift ratio coefficient represents the ratio of the hardware clock drift amount to the defined hardware clock drift amount during the data interaction between the mini-program and the server. Step 2: The mini-program receives the raw performance data returned by the server. If the high-frequency data interaction process is in an unstable state, the received raw performance data is preprocessed to obtain the final performance data. At the same time, the data interaction process between the mini-program and the server is adjusted and optimized. If the high-frequency data interaction process is in a stable state, the received raw performance data is initially processed to obtain the final performance data. Step 3: Generate a performance data form based on the final performance data, parse and obtain the parameters of the performance data form generation process, and thus determine whether data disorder has occurred in the performance data form, thereby realizing the performance data management of the mini-program. The specific analysis process of parsing and obtaining the performance data form generation process parameters is as follows: The performance data form generation process parameters include the mini-program load fluctuation ratio coefficient, the data mutation frequency ratio coefficient, and the cross-validation failure rate ratio coefficient during the performance data form generation process. At the same time, the stable final value of high-frequency data interaction is obtained. By preset the effect coefficients of each ratio coefficient and the stable final value of high-frequency data interaction in the management database, the impact on performance is quantified. The weighted contribution values ​​of the performance data form disorder index are used to synthesize the performance data form disorder index using a weighted average fusion algorithm. The performance data form disorder index represents the degree of disorder in the performance data form during its generation process. The mini-program load fluctuation ratio coefficient represents the ratio of the mini-program load fluctuation value to the defined mini-program load fluctuation value during the performance data form generation process. The data mutation frequency ratio coefficient represents the ratio of the data mutation frequency to the defined data mutation frequency during the performance data form generation process. The cross-validation failure rate ratio coefficient represents the ratio of the cross-validation failure rate to the defined cross-validation failure rate during the performance data form generation process.

2. The performance data management method based on a mini-program according to claim 1, characterized in that, The specific analysis process for determining whether the high-frequency data interaction process between the mini-program and the server is in a stable state is as follows: By analyzing the data interaction process parameters between the mini-program and the server, a high-frequency data interaction stability index is obtained. The high-frequency data interaction stability index is compared with the first-class and second-class threshold values ​​of interaction stability. The first-class threshold value of interaction stability is used to distinguish between the ideal state and the warning state of the high-frequency data interaction process between the mini-program and the server. The second-class threshold value of interaction stability is used to distinguish between the warning state and the fault state of the high-frequency data interaction process between the mini-program and the server. When the high-frequency data interaction stability index is not lower than the first-class threshold value of interaction stability, it is determined that the high-frequency data interaction process between the mini-program and the server is in a stable state and marked as an ideal state. At the same time, the cache hit rate and database request frequency in the high-frequency data interaction process are monitored to maintain the continuity of the ideal state. When the high-frequency data interaction stability index is between the first-class and second-class thresholds of interaction stability, the high-frequency data interaction process between the mini-program and the server is judged to be in an unstable state and marked as a warning state. At the same time, the high-frequency data interaction process between the mini-program and the server is optimized. When the high-frequency data interaction stability index is lower than the second-class threshold value of interaction stability, the high-frequency data interaction process between the mini-program and the server is judged to be in an unstable state and marked as a fault state. At the same time, the high-frequency data interaction process between the mini-program and the server is adjusted. Reacquire the high-frequency data interaction stability index, mark it as the high-frequency data interaction stability reassessment index, and determine whether to issue an early warning for the high-frequency data interaction process between the mini-program and the server.

3. The performance data management method based on mini-programs according to claim 2, characterized in that, The optimization process for the high-frequency data interaction between the mini-program and the server is as follows: Based on the high-frequency data interaction stability index and the first-class boundary value of interaction stability, a first-class deviation value is obtained. Based on the first-class deviation value, a load balancing node increase coefficient is matched to increase the number of nodes for backend business processing services. At the same time, the algorithm is optimized for the critical path in the high-frequency data interaction between the mini-program and the server, and database read-write separation operation is added.

4. The performance data management method based on mini-programs according to claim 2, characterized in that, The adjustment process for the high-frequency data interaction between the mini-program and the server is as follows: Based on the high-frequency data interaction stability index and the interaction stability binary boundary value, the binary deviation value is obtained, and the maximum thread increase coefficient is matched based on the binary deviation value, thereby increasing the maximum number of threads in the thread pool used for high-frequency data interaction in the backend business processing service. Based on the two types of deviation values, a secondary increase coefficient is determined for the load balancing nodes, thereby further increasing the number of nodes for backend business processing services.

5. The performance data management method based on mini-programs according to claim 2, characterized in that, The specific process for determining whether to issue an alert for high-frequency data interaction between the mini-program and the server is as follows: Compare the high-frequency data interaction stability re-evaluation index with the first-class boundary value of interaction stability; When the high-frequency data interaction stability review index is not lower than the first-class threshold value of interaction stability, it is determined not to issue an early warning for the high-frequency data interaction process between the mini-program and the server, while monitoring the cache hit rate and database request frequency during the high-frequency data interaction process, so as to maintain the continuity of the ideal state. When the high-frequency data interaction stability review index is lower than the first-class threshold value of interaction stability, it is determined to issue an early warning for the high-frequency data interaction process between the mini-program and the server, and at the same time trigger adaptive flow control and rate limiting on both the mini-program and the server to alleviate interaction pressure and improve stability.

6. The performance data management method based on a mini-program according to claim 1, characterized in that, The specific process for determining whether the performance data form is disordered is as follows: The disorder index of the performance data form is compared with the disorder limit value of the performance data form. The disorder limit value of the performance data form refers to the maximum value of the disorder index of the performance data form in the management database within a specified range. When the disorder index of the performance data form is lower than the disorder threshold of the performance data form, it is determined that the performance data form has not been disordered. At the same time, the generation steps are decoupled and the generation process of the performance data form is broken down. When the disorder index of the performance data form is not lower than the disorder threshold of the performance data form, it is determined that the performance data form is disordered. Based on the disorder index and the disorder threshold of the performance data form, the disorder deviation value is obtained. Based on the disorder deviation value, the increase in the number of data verification preload rules is matched, thereby increasing the number of data verification preload rules. Based on the disorder deviation value, the check code length increase coefficient is matched, thereby increasing the check code length transmitted across nodes. Reacquire the disorder index of the performance data form, mark it as the final value of the disorder in the performance data form, and determine whether to issue an early warning for the generation process of the performance data form.

7. The performance data management method based on a mini-program according to claim 6, characterized in that, The specific process for determining whether to issue an alert during the generation of the performance data form is as follows: Compare the disordered final value of the performance data form with the disordered limit value of the performance data form; When the final value of the performance data form disorder is lower than the performance data form disorder threshold, it is determined that no warning will be issued during the generation process of the performance data form. When the final value of the disorder in the performance data form is not lower than the disorder threshold, an early warning is issued for the performance data form generation process. At the same time, the parallel processing and asynchronous task queue of the performance data form generation process are optimized based on the disorder final value.

8. A performance data management system based on mini-programs, using the performance data management method based on mini-programs as described in any one of claims 1 to 7, characterized in that: include: The interaction monitoring module is used to monitor and analyze the data interaction process parameters between the mini-program and the server when high-concurrency scenarios are detected, thereby determining whether the high-frequency data interaction process between the mini-program and the server is in a stable state. The data interaction process parameters reflect the running status of the mini-program in a high-concurrency environment. The interaction adjustment module is used for the mini program to receive raw performance data returned by the server. If the high-frequency data interaction process is in an unstable state, the received raw performance data is preprocessed to obtain the final performance data. At the same time, the data interaction process between the mini program and the server is adjusted and optimized. If the high-frequency data interaction process is in a stable state, the received raw performance data is initially processed to obtain the final performance data. The disorder monitoring module is used to generate performance data forms based on the final performance data, parse and obtain the parameters of the performance data form generation process, and thus determine whether data disorder has occurred in the performance data forms, thereby realizing the performance data management of the mini program.

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