Full-life-cycle quality monitoring method and apparatus for middle platform capability, and device

Abstract

The present application provides a full-life-cycle quality monitoring method and apparatus for a middle platform capability, and a device. The method comprises: acquiring a plurality of types of historical detailed data related to a middle platform capability, on the basis of a full life cycle of the middle platform capability, performing division on dimensions of quality monitoring, and determining a scoring rule corresponding to each dimension; according to a preset granularity, separately performing aggregation processing on the plurality of types of historical detailed data, to generate monthly-granularity dimension convergence data for each dimension; on the basis of the monthly-granularity dimension convergence data, computing a middle platform capability quality reference; and, on the basis of the monthly-granularity dimension convergence data and the scoring rule, computing a quality monitoring score of each dimension, and, taking into account the middle platform capability quality reference and the quality monitoring score of each dimension, generating a middle platform capability quality score report.

Description

[0001] Cross-reference to Related Applications This application claims priority to Chinese Patent Application No. 202311867568.9 filed in China on December 29, 2023, the entire contents of which are incorporated herein by reference. Technical Field This application relates to the field of smart middle platforms, and more particularly to a method, device, and apparatus for monitoring the quality of middle platform capabilities throughout their lifecycle. Background: With the development of digital technology and industrial transformation, smart middle platforms have become an important platform for building new information infrastructure. Currently, the operation domain (O domain) middle platform is a key development direction for smart middle platforms, enabling the aggregation, access, and sharing of basic network management capabilities. However, with the increasing complexity of systems and the diversification of application scenarios, quality monitoring of middle platform capabilities is necessary to ensure the normal operation of the middle platform. In related technologies, manual monitoring is generally used when performing quality monitoring of the O domain middle platform, primarily involving manual data processing and manual configuration of monitoring dimensions. However, the quality monitoring methods in the above-mentioned related technologies have a large workload in actual applications, low monitoring efficiency, and there may be a large deviation between the monitoring results and the actual situation, and the monitoring accuracy cannot meet the requirements. SUMMARY OF THE INVENTION The present application aims to solve one of the technical problems in the related technologies, at least to a certain extent. To this end, the first purpose of the present application is to propose a full-life cycle quality monitoring method for the capabilities of the middle platform. The method performs statistical analysis on multiple dimensional data in the full life cycle stage of the capabilities of the middle platform, thereby improving the efficiency and accuracy of the quality monitoring of the middle platform, and solving the problems of large manual participation in the monitoring dimensions of the capabilities of the middle platform, inability to clearly grasp the quality of the capabilities, and unsuitability for the continuous iterative development of the middle platform. The second purpose of the present application is to propose a full-life cycle quality monitoring device for the capabilities of the middle platform; the third purpose of the present application is to propose an electronic device; the fourth purpose of the present application is to propose a computer-readable storage medium. To achieve the above-mentioned purpose, the first aspect of the present application is to propose a full life cycle quality monitoring method for the middle platform capabilities, which includes the following steps: obtaining multi-type historical detailed data related to the middle platform capabilities, and dividing the quality monitoring dimensions based on the full life cycle of the middle platform capabilities, and determining the scoring rules corresponding to each dimension; aggregating the multi-type historical detailed data according to the preset granularity to generate monthly granularity dimension aggregated data for each dimension; calculating the middle platform capability quality benchmark based on the monthly granularity dimension aggregated data; calculating the quality monitoring score of each dimension according to the monthly granularity dimension aggregated data and the scoring rules, and generating a middle platform capability quality score report in combination with the middle platform capability quality benchmark and the quality monitoring score of each dimension.Optionally, according to one embodiment of the present application, the multi-type historical detailed data includes: capability information registered by capability providers, capability information subscribed by capability subscribers, capability online connection test data, and capability call request logs of capability subscribers. Optionally, according to one embodiment of the present application, the full lifecycle of the middle platform capability includes: a capability release phase, a capability subscription phase, and a capability call phase. The dimension of quality monitoring based on the full lifecycle of the middle platform capability includes: for the capability release phase, the capability release scale and capability incubation period; for the capability subscription phase, the capability sharing type and capability activity; and for the capability call phase, the average monthly capability call success rate, average capability sharing time, and capability online connection test status. Optionally, according to one embodiment of the present application, aggregating the multiple types of historical detailed data at preset granularities to generate monthly granularity dimension aggregate data for each dimension includes: calculating the operational monitoring results of each type of historical detailed data within a day to generate daily granularity capability aggregate data; calculating the daily granularity capability aggregate data for each day within a target month to generate monthly granularity capability aggregate data; calculating the aggregated value corresponding to each dimension within the target month based on the monthly granularity capability aggregate data, and summing the aggregated values ​​corresponding to each dimension to generate the monthly granularity dimension aggregate data. Optionally, according to one embodiment of the present application, calculating the middle platform capability quality benchmark based on the monthly granularity dimension aggregate data includes: calculating a first average value of the aggregated value corresponding to each dimension within a preset number of months, and using the first average value as the middle platform capability quality monitoring aggregate data benchmark; and calculating a second average value of the quality monitoring score of each dimension within the preset number of months, and using the second average value as the middle platform capability quality monitoring score benchmark. Optionally, according to one embodiment of the present application, the middle platform capability quality benchmark and the quality monitoring score of each dimension are combined to generate a middle platform capability quality score report, including: determining the deduction index of the middle platform capability based on the quality monitoring scores of all dimensions; comparing the quality monitoring score of each dimension with the corresponding middle platform capability quality monitoring score benchmark, and screening out the target dimensions whose quality monitoring scores are lower than the corresponding middle platform capability quality monitoring score benchmark; determining the abnormal conditions of the middle platform capability within the target month based on the target dimensions. Optionally, according to one embodiment of the present application, after generating the middle platform capability quality score report, it also includes: adjusting the service strategy of the middle platform based on the abnormal conditions, and continuously monitoring the aggregated values ​​of the target dimensions.To achieve the above objectives, the second aspect of the present application further proposes a full lifecycle quality monitoring device for middle platform capabilities, comprising the following modules: an acquisition module for acquiring multiple types of historical detailed data related to middle platform capabilities, and dividing the quality monitoring dimensions based on the full lifecycle of the middle platform capabilities, and determining the scoring rules corresponding to each dimension; an aggregation module for aggregating the multiple types of historical detailed data according to a preset granularity to generate monthly granularity dimension aggregated data for each dimension; a calculation module for calculating the middle platform capability quality benchmark based on the monthly granularity dimension aggregated data; a generation module for calculating the quality monitoring score for each dimension based on the monthly granularity dimension aggregated data and the scoring rules, and generating a middle platform capability quality score report based on the middle platform capability quality benchmark and the quality monitoring score of each dimension. To achieve the above objectives, the third aspect of the present application further proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the full lifecycle quality monitoring method for middle platform capabilities as described in any one of the first aspects above. To achieve the above objectives, the fourth aspect of the present application further proposes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the full lifecycle quality monitoring method for the middle platform capability described in any one of the first aspects above. The technical solutions provided by the embodiments of the present application bring at least the following beneficial effects: the present application first analyzes and integrates various historical information data of the middle platform capability, divides the data quality dimensions of the middle platform capability lifecycle, aggregates and counts the data according to the divided dimensions, and obtains periodic historical statistical data; then, based on the benchmark rules, generates a baseline value for each capability indicator, and outputs a scoring report for the capability quality in combination with the scoring rules for each dimension indicator. The report distinguishes the monitoring rules for the capability at different stages and can dynamically adjust the rules; finally, by comparing the capability score and the benchmark value, the quality of the middle platform capability is analyzed, achieving a transformation of the quality monitoring method of the middle platform capability lifecycle from manual analysis and calculation to a system that automatically calculates and outputs a scoring report based on the rules, thereby improving the efficiency of the middle platform quality monitoring. Therefore, based on long-term end-to-end monitoring, the present application can provide objective reference data for both subscribers to continuously optimize and upgrade the middle platform capability through statistical analysis of the capability lifecycle stage dimension data. Furthermore, through a multi-layered aggregation approach, individual capabilities are aggregated into a single system, achieving comprehensive and multifaceted quality monitoring, helping to leverage the development advantages of the middle platform. Furthermore, by setting monitoring items across different dimensions based on the different stages of the capability lifecycle, the quality of each capability is monitored, improving the accuracy and comprehensiveness of the middle platform's quality monitoring.Additional aspects and advantages of the present application will be described in part in the following description, and in part will become apparent from the following description or be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS The above-mentioned and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, wherein: FIG1 is a flowchart of a middleware capability quality monitoring flow chart in a related embodiment; FIG2 is a flowchart of a method for monitoring the quality of a middleware capability throughout its lifecycle proposed in an embodiment of the present application; FIG3 is a flowchart of a method for generating monthly granularity dimension aggregated data proposed in an embodiment of the present application; FIG4 is a flowchart of a specific method for monitoring the quality of a middleware capability throughout its lifecycle proposed in an embodiment of the present application; FIG5 is a schematic structural diagram of a device for monitoring the quality of a middleware capability throughout its lifecycle proposed in an embodiment of the present application; and FIG6 is a schematic structural diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application and should not be construed as limiting the present application. It should be noted that the smart middle platform (MFP) is an operating mechanism that integrates multiple elements, including application software, hardware devices, and related specifications. In some relevant embodiments, the implementation process of a manual middle platform capability quality monitoring solution is shown in Figure 1. As shown in Figure 1, the middle platform capability quality monitoring solution in related technologies requires manual participation by operators to configure global monitoring dimension baselines during the data quality assessment phase. This can lead to problems such as heavy workload, operational errors, unfamiliarity with data, inconsistent capability calls between systems, and insensitivity to the values ​​of various monitoring dimensions, resulting in a significant gap between the baseline configuration and actual performance. Furthermore, this solution fails to form a reliable monitoring dimension system, cannot promptly respond to changes in platform capability monitoring dimensions, and cannot automatically perform periodic baseline adjustments for individual monitoring dimensions. This in turn affects the decision-making direction of personnel responsible for each capability provider to detect anomalies and upgrade or repair capabilities. To this end, this application proposes a full lifecycle quality monitoring method for middle platform capabilities. This method monitors the quality of the capability lifecycle for the 0-domain middle platform, evolving from the manual formulation of monitoring dimensions to the system's automatic refinement of monitoring dimensions. This reduces the manual processing of system data, saves labor costs, and improves system efficiency.This method refines monitoring of each monitoring dimension data, more accurately adjusting the values ​​of each monitoring dimension. This allows for full-process monitoring and tracing, from the source and underlying data to the closed-loop problem loop. This facilitates real-time understanding of the development of the 0-domain middleware and its utilization by each system. Furthermore, this method performs quality monitoring from a full lifecycle perspective, encompassing quality indicator definition, comprehensive testing, problem tracking, and operational monitoring. This effectively drives quality improvement and optimization of each system's release capabilities, improves consumer efficiency, enhances capability output efficiency, and rapidly supports the company's business development. The following describes a method, apparatus, and device for full-lifecycle quality monitoring of middleware capabilities, as proposed in an embodiment of the present application, with reference to the accompanying drawings. It should be noted that the full-lifecycle quality monitoring method for middleware capabilities implemented in this application is configured within a device for full-lifecycle quality monitoring of middleware capabilities for illustration purposes only. This method can be applied to any electronic device, enabling the electronic device to perform full-lifecycle quality monitoring of middleware capabilities. The electronic device can be any device with computing capabilities, such as a personal computer (PC), a mobile terminal, etc. The mobile terminal can be, for example, a tablet computer, a personal digital assistant, a wearable device, or other hardware device with various operating systems, touch screens, and / or display screens. FIG2 is a flowchart of a method for full-lifecycle quality monitoring of middleware capabilities proposed in an embodiment of the present application. As shown in FIG1 , the method includes the following steps: Step S101: Obtaining multiple types of historical detailed data related to the middleware capabilities, dividing the quality monitoring dimensions based on the full lifecycle of the middleware capabilities, and determining the scoring rules corresponding to each dimension. The capabilities of the middleware (MFP) in this application refer to the basic application cells (BACs) running on the middleware. This application performs quality monitoring (QM) on the middleware capabilities from the perspective of the complete lifecycle of a capability. oSpecifically, when the present application performs quality monitoring on the target capability to be monitored, it first uses the existing functions of the middle platform to conduct long-term tracking and monitoring of the relevant data of the target capability, and obtains multiple types of historical detailed data related to the middle platform capability. The multiple types of historical detailed data may include relevant information about the middle platform capability, as well as consumption data of the target capability being subscribed, etc. In one embodiment of the present application, the multiple types of historical detailed data obtained include: capability information registered by the capability provider, capability information subscribed by the capability subscriber, capability online connection test data, and capability call request log of the capability subscriber. Several specific examples are given below to illustrate. As a first example, the capability information registered by the capability provider, that is, the capability information data registered by the capability provider's system on the middle platform application. The information can be obtained in the form shown in the following Table 1: Table 1 Capability information table registered by capability provider As a second example, the capability information subscribed by the capability subscriber is the capability information data subscribed by the capability subscriber system on the middle platform application. This information can be obtained in the form shown in the following Table 2: Table 2 Capability Subscription Information Table As a third example, the capability online connection test data can be obtained in the form shown in the following Table 3: Table 3 Capability Online Connection Test Data Table As a fourth example, the capability call request log of the capability subscriber is the request log data of the capability subscriber system calling the target capability in the middle platform gateway. This information can be obtained in the form shown in the following Table 4: Table 4 Subscriber call request log table Thus, various types of data can be collected and recorded continuously in the form of the above example to obtain multi-type historical detailed data. Further, the quality monitoring dimension of the middle platform capability life cycle data is divided, that is, the quality monitoring dimension is divided according to the life cycle of the middle platform capability, specifically, the dimension classification targeted by the quality monitoring is divided according to the various indicators involved in each stage of the complete life cycle of the middle platform capability from the beginning to the end. In one embodiment of the present application, the full life cycle of the middle platform capability includes: capability release stage, capability subscription stage and capability call stage. The quality monitoring dimension is divided based on the full life cycle of the middle platform capability, including: for the capability release stage, the capability release scale and capability incubation period are divided; for the capability subscription stage, the capability sharing type and capability activity are divided; for the capability call stage, the capability average monthly call success rate, the average time consumed by capability sharing and the capability online connection test status are divided. For example, in this embodiment, the dimensional classifications of capability release, capability subscription and capability call are divided as shown in the following Table 5: Table 5 Middle platform capability life cycle data quality dimension table

[0002] Therefore, this embodiment has formulated a unified quality monitoring calculation model for the life cycle quality monitoring of the middle platform capabilities, and regards each indicator to be monitored and evaluated as a monitoring dimension, so as to facilitate subsequent statistical analysis of each dimension. In this embodiment, it mainly includes but is not limited to: statistics on the release scale of capabilities, statistics on the reuse of capabilities, statistics on the activity of capabilities in the non-incubation period, statistics on the average monthly capability response time, statistics on the average monthly capability call success rate, and statistics on the online connection and online test of capabilities. Furthermore, for each of the divided dimensions, the scoring rules corresponding to each dimension are determined. Continuing to refer to the example shown in Table 5, this application sets the corresponding scoring rules for each dimension in Table 5 as shown in the following Table 6: Table 6 Middle platform capability data quality scoring setting table Therefore, the embodiment of the present application sets corresponding scoring rules for each dimensional classification, facilitating the subsequent generation of scores for each dimension. Step S102: Aggregate and process multiple types of historical detailed data at preset granularity to generate monthly granularity aggregated data for each dimension. Specifically, aggregate and statistically analyze the collected middle-station capability data. Based on the dimensional classification for the middle-station capability lifecycle quality monitoring obtained in the previous step, the acquired multiple types of historical detailed data are aggregated and processed into periodic statistical data for each dimension. As a possible implementation method, the multiple types of historical detailed data are first aggregated into daily granularity statistical data, that is, the daily operational quality of the target middle-station capability is statistically analyzed. The data recorded for each type of historical detailed data within a day is summarized and clustered to obtain daily granularity statistical data corresponding to each type of historical detailed data. Then, all daily granularity statistical data for each type of historical detailed data within a month are aggregated and further statistically analyzed to obtain monthly granularity statistical data corresponding to each type of historical detailed data. Then, based on the above-mentioned dimensional divisions, all monthly granular statistical data are statistically calculated. That is, for each dimension, relevant data is extracted from the monthly granular statistical data corresponding to all types of historical detailed data and calculated to obtain the aggregated value corresponding to each dimension. For example, for the dimension of capability activity shown in Table 5 in the above example, the corresponding aggregated value calculated is the activity percentage, that is, the ratio of the number of called capabilities to the total number. For another example, for the dimension of reuse capability in the above example, the corresponding aggregated value calculated includes the number of reuse capabilities and the reuse percentage. Finally, the aggregated values ​​corresponding to each dimension are summarized to obtain the monthly granular dimension aggregated data. Step S103: Based on the monthly granular dimension aggregated data, the middle platform capability quality benchmark is calculated. Specifically, the middle platform capability data quality benchmark is generated based on the monthly granular dimension aggregated data. The middle platform capability quality benchmark may include a benchmark value for the aggregated value corresponding to each dimension, a benchmark value for the quality monitoring score of each dimension, and other benchmark values ​​that reflect the quality of the middle platform capability. In one embodiment of the present application, the middle platform capability quality benchmark is calculated based on monthly granularity dimension aggregated data, including: calculating a first average value of the aggregated values ​​corresponding to each dimension within a preset number of months, and using the first average value as the middle platform capability quality monitoring aggregated data benchmark; calculating a second average value of the quality monitoring score of each dimension within a preset number of months, and using the second average value as the middle platform capability quality monitoring score benchmark. Specifically, in this embodiment, for the dimension aggregated data benchmark of the middle platform capability quality monitoring, the monthly granularity dimension aggregated data obtained in the previous step is used as the data source, and the benchmark is obtained by calculating the average value of the historical aggregated data of each indicator.The preset number of months can be the previous months adjacent to the current month, and the specific number can be adjusted based on factors such as calculation accuracy requirements. As an example, the definition and calculation method of the mid-stage capability quality monitoring aggregated data benchmark are shown in Table 7 below: Table 7 Mid-stage capability data quality monitoring aggregated data benchmark table Among them, for each dimension, the monthly aggregated values ​​within a preset number of months can be obtained, and then calculated according to the relevant formulas in the above table. Furthermore, in this embodiment, for the middle platform capability quality monitoring scoring benchmark, the quality monitoring score of each dimension is calculated in subsequent steps and then calculated. When calculating the benchmark value, the final middle platform capability data quality scoring report information is used as the data source, and the scoring benchmark is obtained by calculating the average value of the monthly historical scoring statistics within a preset number of months. As an example, continue to refer to the scoring rules shown in Table 6 above. For each scoring item, the definition and calculation method of the middle platform capability quality monitoring scoring benchmark obtained are shown in Table 8 below: Table 8 Middle platform capability data quality monitoring scoring benchmark table Among them, for each scoring item, the monthly scoring values ​​within a preset number of months can be obtained, and then calculated according to the relevant formulas in the above table. Step S104: According to the monthly granularity dimension aggregation data and scoring rules, calculate the quality monitoring score of each dimension, and combine the middle platform capability quality benchmark and the quality monitoring score of each dimension to generate a middle platform capability quality scoring report. Specifically, the monthly granularity dimension aggregation data obtained in the above embodiment is substituted into the corresponding scoring rules for calculation to obtain the quality monitoring score of each dimension. The corresponding aggregated value and score of each dimension in the current month can be obtained, and then the specific data and score of each dimension are added to the scoring report. As an example, the scoring report finally output by quality monitoring of the middle platform capability is specifically shown in the following Table 9: Table 9 Middle platform capability data quality scoring report information table Furthermore, the quality monitoring score of each dimension can be compared with the corresponding middle platform capability quality benchmark to determine the dimension indicators with abnormalities, and generate the dimension indicators with abnormalities in the middle platform capability quality score report, so as to adjust the operation strategy of the middle platform capability to eliminate the abnormalities. In summary, the full life cycle quality monitoring method of the middle platform capability of the embodiment of the present application first analyzes and integrates various historical information data of the middle platform capability, divides the middle platform capability life cycle data quality dimensions, aggregates and counts the data according to the divided dimensions, and obtains periodic historical statistical data; then, based on the benchmark rules, a benchmark value for each capability indicator is generated, and a scoring report is output for the capability quality in combination with the scoring rules for each dimension indicator. The report distinguishes the monitoring rules for capabilities at different stages and can dynamically adjust the rules; finally, by comparing the capability score and the benchmark value, the quality of the middle platform capability is analyzed, realizing the transformation of the quality monitoring method in the middle platform capability life cycle from manual analysis and calculation to the system automatically calculating and outputting the scoring report based on the rules, thereby improving the efficiency of the middle platform quality monitoring. Therefore, this method, based on long-term end-to-end monitoring and through statistical analysis of capability lifecycle stage-dimensional data, can provide objective reference data for both subscribers to continuously optimize and upgrade the capabilities of the middle platform. Furthermore, through a multi-layered aggregation approach, individual capabilities are aggregated into a single system, achieving comprehensive and multifaceted quality monitoring, helping to leverage the development advantages of the middle platform. Furthermore, by setting monitoring items in different dimensions based on the different lifecycle stages of the capability, the quality of each capability stage is monitored, improving the accuracy and comprehensiveness of the middle platform's quality monitoring. Based on the above embodiments, to more clearly illustrate the specific implementation process of this application's generation of monthly granularity aggregated data for each dimension by aggregating historical data, a specific data processing method embodiment is described in detail below. Figure 3 is a flowchart of a method for generating monthly granularity aggregated data proposed in this embodiment. As shown in Figure 3, the method includes the following steps: Step S301: Counting the operational monitoring results of each type of historical detailed data within a day to generate daily granularity capability aggregated data. For example, the detailed historical data of the capabilities called as shown in Table 4 in the above example, ie, the call request log of the subscriber, is aggregated into the data shown in Table 10 below: Table 10 Day-granularity capability aggregated data table Among them, the number of times the subscriber calls the middle platform application in one day is counted to obtain the total number of requests in the above Table 10, and the request time of each call request is accumulated to obtain the total request time data in the above Table 10. All the data in the table can be obtained by statistics and calculations of historical data. Step S302, count the daily granularity capability aggregation data for each day in the target month, and generate monthly granularity capability aggregation data. Among them, the target month is the month for the current quality monitoring, such as the current month. Continuing to refer to the above example, the data shown in Table 10 can be counted every day in the target month, and then all the daily granularity capability aggregation data in the target month are further aggregated to generate the monthly granularity data shown in the following Table 11: Table 11 Monthly granularity capability aggregation data table Step S303: Based on the monthly granularity capability aggregate data, the corresponding aggregated values ​​for each dimension in the target month are calculated, and the aggregated values ​​corresponding to each dimension are summarized to generate monthly granularity dimension aggregated data. Specifically, the monthly granularity capability aggregated data table corresponding to all types of historical detail data is used as the data source. For each dimension in Table 5 of the above embodiment, relevant data is extracted from all the monthly granularity capability aggregated data and calculated to obtain the aggregated values ​​corresponding to each dimension. As an example, for each dimension in Table 5, the aggregated values ​​to be calculated and the final generated monthly granularity dimension aggregated data are shown in Table 12 below: Table 12 Monthly Granularity Dimension Aggregated Data Table For example, for the aggregated value of the reuse rate (MultiUseRate) in Table 12, the total number of reuse capabilities (MultiUseCount) can be calculated from the relevant monthly granularity capability aggregation data, and then the total number of application release capabilities (ApplicationCount) can be extracted. The reuse rate is then obtained by dividing the number of reuse capabilities by the total number of application release capabilities. Thus, the method of the embodiment of the present application can obtain the aggregated value corresponding to each dimension through multi-level data aggregation statistics and calculations, facilitating the subsequent calculation of the quality monitoring score for each dimension. Based on the above embodiment, in order to more clearly and in detail illustrate the specific implementation process of the full lifecycle quality monitoring method of the middle platform capabilities of the present application, a specific application example in actual application is described in detail below. Figure 4 is a flowchart of a specific full lifecycle quality monitoring method for middle platform capabilities proposed in the embodiment of the present application. As shown in Figure 4, the method includes the following steps: Step S401: Utilize the middle platform gateway to collect request and response information of the subscriber system calling capabilities. Specifically, the system registered in the middle platform allows the subscriber system to call the published capability, and then produces the actual call data of the capability. Therefore, this embodiment uses the middle platform gateway to collect historical data. Taking a capability provider system and the corresponding capability subscriber system as an example, after the capability subscriber system calls the capability through the middle platform gateway, it obtains the capability request and response data, and performs subsequent processes. Step S402, at the first time point of each day, the daily capability monitoring information is statistically analyzed by hierarchical aggregation to obtain monitoring statistical information. For example, at 1 a.m. every day, the Extract Transform Load (ETL) tool Kettle is used to analyze the daily capability monitoring information by hierarchical aggregation to obtain the monitoring detailed data of the capability being called in the target month as shown in the following Table 13: Table 13 Monitoring data details table Furthermore, the statistical results obtained through hierarchical aggregation are shown in Table 14 below: Table 14 Monitoring Statistics Information Table In step S403, at the second time point each day, the monitoring statistical information is classified according to preset monitoring rules to generate a customized report. For example, at 3:00 a.m. each day, the ETL tool Kettle is used to classify the monitoring statistical information according to preset monitoring rules to generate a customized report. Specifically, the statistical results obtained in the previous step (Table 14) are statistically analyzed and calculated according to the capability lifecycle data quality dimensions described in Table 5 of the above embodiment to generate the following multi-dimensional monitoring statistical customized report: Table 15 Multi-dimensional Monitoring Statistics Customized Report Step S404: Obtain basic statistical data for capability dimensional scoring based on the statistical data in the customized report. Specifically, the aggregated values ​​for each dimension in the multi-dimensional monitoring statistics customized report shown in Table 15 are calculated accordingly to obtain the multi-dimensional scoring statistical basic data table shown in Table 16 below: Table 16 Basic Data Table for Multi-Dimensional Scoring Statistics Step S405: Associate the capability dimension scoring statistical base data with the scoring rules to generate a capability quality score report. Specifically, the scoring statistical base data for each dimension in Table 16 above is substituted into the scoring rules set for each dimension in Table 6 of the above embodiment, and relevant calculations are performed to obtain the scores for each dimension as shown in Table 17 below: Table 17: Quality Monitoring Scoring Table for Each Dimension Furthermore, based on the quality monitoring score table for each dimension and in combination with the determined middle platform capability quality benchmark, a quality score report for the middle platform capability can be generated. In one embodiment of the present application, generating a middle platform capability quality score report based on the middle platform capability quality benchmark and the quality monitoring score for each dimension includes the following steps: First, determining a deduction indicator for the middle platform capability based on the quality monitoring scores of all dimensions. Specifically, the numerical values ​​of the quality monitoring scores for all dimensions are determined, and the dimensions with lower scores are used as deduction indicators. Referring to the example shown in Table 17, the data in the table shows that the scores for the release scale, incubation scale, and reuse score are lower than the scores of the other dimension indicators. Therefore, the deduction items for the capability are determined to be mainly concentrated on the release scale, incubation scale, and reuse score dimensions. Furthermore, based on the deduction indicators, it can be determined that AAAA applications have problems such as a small number of released capabilities and low capability reuse. Then, the quality monitoring score of each dimension is compared with the corresponding middle platform capability quality monitoring score benchmark, and the target dimensions whose quality monitoring scores are lower than the corresponding middle platform capability quality monitoring score benchmark are screened out. Continuing with the example shown in Table 17, Table 17 also lists the pre-calculated baseline for the quality monitoring score of the middle platform capabilities, including the base values ​​corresponding to each scoring item. The quality monitoring score of each dimension is compared with the corresponding scoring baseline, and the dimension indicator whose quality monitoring score is lower than the corresponding scoring baseline is used as the target dimension. In this example, by comparing the baseline data, it can be determined that the target dimension is the incubation scale. Finally, the abnormality of the middle platform capabilities in the target month is determined based on the target dimension. Specifically, when the incubation scale score is lower than the baseline value, it indicates that the abnormality of the middle platform capabilities in the target month is that the application has not recently released new capabilities. Furthermore, by comparison, it is determined that the application's total score is lower than the baseline total score, and the quality level of the application in the target month is determined to be unqualified. Therefore, the middle platform capability quality score report output by this method embodiment can reflect the quality status of the middle platform capabilities in the target month from multiple perspectives. Furthermore, in one embodiment of the present application, after generating the middle platform capability quality score report, the method further includes: adjusting the middle platform service policy based on the abnormality and continuously monitoring the aggregated value of the target dimension. For example, if the application in the example above has not recently released a new capability defect, the middleware capability operation strategy can be adjusted to favor the development of this capability and increase sharing. Furthermore, over a period of time, the relevant data from the capability incubation period can be continuously monitored to determine whether the adjustment strategy improves the anomaly in the target dimension, until the anomaly is eliminated.In summary, the full-lifecycle quality monitoring method for middle-end capabilities in the embodiments of this application has strong practicality and standardization. By establishing diverse monitoring items tailored to actual business operation scenarios, it can effectively enhance the ability to detect problems within the middle-end. Furthermore, through precise end-to-end monitoring, it can promote joint focus on issues between subscribers, timely problem location, and improve the efficiency of closed-loop problem resolution. This method can be applied to multiple scenarios, including home service provisioning, 5G vertical industries, and network management and operation support. It is beneficial for improving resource accuracy, enabling the perception of capability changes, enhancing the quality of middle-end capabilities, and improving the accuracy of problem location. To implement the above embodiments, this application also proposes a full-lifecycle quality monitoring device for middle-end capabilities. Figure 5 is a schematic structural diagram of a full-lifecycle quality monitoring device for middle-end capabilities proposed in an embodiment of this application. As shown in Figure 5, the device includes an acquisition module 100, an aggregation module 200, a calculation module 300, and a generation module 400. The acquisition module 100 is used to acquire multiple types of historical detailed data related to the middle platform capabilities, divide the quality monitoring dimensions based on the full lifecycle of the middle platform capabilities, and determine the scoring rules corresponding to each dimension. The aggregation module 200 is used to aggregate the multiple types of historical detailed data at a preset granularity to generate monthly granularity aggregated data for each dimension. The calculation module 300 is used to calculate the middle platform capability quality benchmark based on the monthly granularity aggregated data. The generation module 400 is used to calculate the quality monitoring score for each dimension based on the monthly granularity aggregated data and scoring rules, and generate a middle platform capability quality score report by combining the middle platform capability quality benchmark and the quality monitoring score for each dimension. In one embodiment of the present application, the acquisition module 100 is specifically used to: divide the capability release phase into capability release scale and capability incubation period; divide the capability subscription phase into capability sharing type and capability activity; and divide the capability call phase into the monthly average capability call success rate, average capability sharing time, and capability online connection test status. In one embodiment of the present application, the aggregation module 200 is specifically configured to: compile statistics on the operational monitoring results of each type of historical detailed data within a day to generate daily granularity capability aggregation data; compile statistics on the daily granularity capability aggregation data for each day within a target month to generate monthly granularity capability aggregation data; and calculate the corresponding aggregation value of each dimension within the target month based on the monthly granularity capability aggregation data, and summarize the corresponding aggregation values ​​of each dimension to generate monthly granularity dimension aggregation data.In one embodiment of the present application, the calculation module 300 is specifically used to: calculate the first average value of the aggregated value corresponding to each dimension within a preset number of months, and use the first average value as the aggregated data benchmark for the quality monitoring of the middle platform capability; calculate the second average value of the quality monitoring score of each dimension within a preset number of months, and use the second average value as the quality monitoring score benchmark for the middle platform capability. In one embodiment of the present application, the generation module 400 is specifically used to: determine the deduction index of the middle platform capability based on the quality monitoring scores of all dimensions; compare the quality monitoring score of each dimension with the corresponding middle platform capability quality monitoring score benchmark, and screen out the target dimension whose quality monitoring score is lower than the corresponding middle platform capability quality monitoring score benchmark; determine the abnormal conditions of the middle platform capability within the target month based on the target dimension. In one embodiment of the present application, the system also includes an adjustment module, which is specifically used to: adjust the service strategy of the middle platform according to the abnormal conditions, and continuously monitor the aggregated values ​​of the target dimensions. It should be noted that the aforementioned explanations of the embodiment of the method for monitoring the quality of the middle platform's capabilities throughout their lifecycle also apply to the apparatus of this embodiment and will not be elaborated upon here. In summary, the full lifecycle quality monitoring of the middle platform's capabilities in the embodiments of this application, based on long-term end-to-end monitoring and statistical analysis of data across the capability's lifecycle stages, can provide objective reference data for both subscribers to continuously optimize and upgrade the middle platform's capabilities. Furthermore, the apparatus achieves comprehensive and multifaceted quality monitoring through a multi-layered aggregation approach, from individual capabilities to a single system, helping to leverage the development advantages of the middle platform. Furthermore, by setting monitoring items of different dimensions based on the different lifecycle stages of the capability, the quality of each capability is monitored, thereby improving the accuracy and comprehensiveness of the middle platform's quality monitoring. To implement the above embodiments, this application also proposes an electronic device. As shown in FIG6 , the electronic device 600 includes: a processor 610; and a memory 620 for storing instructions executable by the processor 610. The processor 610 is configured to execute the instructions to implement the method for monitoring the quality of the middle platform's capabilities throughout their lifecycle as described in any of the embodiments of the first aspect above. To implement the above embodiments, the present application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the full lifecycle quality monitoring method for middleware capabilities as described in any of the embodiments of the first aspect above. Throughout this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present application.In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples, unless mutually inconsistent. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined. Any process or method description depicted in a flowchart or otherwise herein can be understood to represent a module, segment, or portion of code comprising one or more executable instructions for implementing a custom logic function or process step. The scope of the preferred embodiments of this application includes alternative implementations in which functions may be performed out of the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of this application pertain. The logic and / or steps depicted in a flowchart or otherwise described herein can, for example, be considered a sequenced list of executable instructions for implementing the logic function and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system capable of fetching and executing instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (not an exhaustive list) of computer-readable media include: an electrical connector with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). oFurthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in other suitable ways as necessary, and then stored in a computer memory. It should be understood that various portions of this application may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. Those skilled in the art will appreciate that all or part of the steps of the above-described method embodiments can be performed by instructing the relevant hardware through a program. The program may be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules. If the integrated modules are implemented as software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are illustrative and are not to be construed as limiting the present application. Persons of ordinary skill in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

Claims 1. A full life cycle quality monitoring method for middle platform capabilities, including the following steps: Obtain multi-type historical detail data related to the middle platform capabilities, divide the dimensions of quality monitoring based on the full life cycle of the middle platform capabilities, and determine the scoring rules corresponding to each dimension; perform aggregation processing on the multi-type historical detail data respectively according to a preset granularity to generate monthly-granularity dimension aggregation data for each dimension; calculate the quality benchmark of the middle platform capabilities based on the monthly-granularity dimension aggregation data; calculate the quality monitoring score of each dimension according to the monthly-granularity dimension aggregation data and the scoring rules, and generate a quality score report of the middle platform capabilities by combining the quality benchmark of the middle platform capabilities and the quality monitoring score of each dimension.

2. The method according to claim 1, wherein The multi-type historical detail data includes: the capability information registered by the capability provider, the capability information subscribed by the capability subscriber, the capability online connection test data, and the capability call request logs of the capability subscriber.

3. The method according to claim 1, wherein The full life cycle of the middle platform capabilities includes: the capability release stage, the capability subscription stage, and the capability call stage. The division of the dimensions of quality monitoring based on the full life cycle of the middle platform capabilities includes: for the capability release stage, dividing the capability release scale and the capability incubation period; for the capability subscription stage, dividing the capability sharing type and the capability activity; for the capability call stage, dividing the monthly average call success rate of the capability, the average sharing time of the capability, and the capability online connection test status.

4. The method according to claim 1, wherein The performing aggregation processing on the multi-type historical detail data respectively according to a preset granularity to generate monthly-granularity dimension aggregation data for each dimension includes: counting the operation monitoring results of each type of the historical detail data within one day to generate daily-granularity capability aggregation data; counting the daily-granularity capability aggregation data within the target month to generate monthly-granularity capability aggregation data; calculating the aggregation value corresponding to each dimension within the target month according to the monthly-granularity capability aggregation data, and summarizing the aggregation values corresponding to each dimension to generate the monthly-granularity dimension aggregation data.

5. The method according to claim 1, wherein The calculating the quality benchmark of the middle platform capabilities based on the monthly-granularity dimension aggregation data includes: calculating the first average value of the aggregation values corresponding to each dimension within a preset number of months, and taking the first average value as the benchmark of the quality monitoring aggregation data of the middle platform capabilities; calculating the second average value of the quality monitoring scores of each dimension within the preset number of months, and taking the second average value as the benchmark of the quality monitoring score of the middle platform capabilities.

6. The method according to claim 5, wherein The combining the quality benchmark of the middle platform capabilities and each dimension The quality monitoring score is used to generate a middle platform capability quality score report, including: determining the deduction index of the middle platform capability according to the quality monitoring scores of all dimensions; comparing the quality monitoring score of each dimension with the corresponding middle platform capability quality monitoring score benchmark, and screening out the target dimensions whose quality monitoring scores are lower than the corresponding middle platform capability quality monitoring score benchmark; determining the abnormal conditions of the middle platform capability within the target month according to the target dimensions.

7. The method according to claim 6, wherein After generating the middle platform capability quality score report, it also includes: adjusting the service strategy of the middle platform according to the abnormal situation, and continuously monitoring the aggregated value of the target dimension.

8. A full life cycle quality monitoring device for middle platform capabilities, comprising: An acquisition module is used to acquire multiple types of historical detailed data related to the middle platform capabilities, and divide the dimensions of quality monitoring based on the full life cycle of the middle platform capabilities, and determine the scoring rules corresponding to each dimension; an aggregation module is used to aggregate the multiple types of historical detailed data according to preset granularity, and generate monthly granularity dimension aggregation data for each dimension; a calculation module is used to calculate the quality benchmark of the middle platform capabilities based on the monthly granularity dimension aggregation data; A generation module is used to aggregate data and the scoring rules according to the monthly granularity dimension, calculate the quality monitoring score of each dimension, and generate a middle platform capability quality scoring report in combination with the middle platform capability quality benchmark and the quality monitoring score of each dimension.

9. An electronic device, comprising: processor; A memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement a full life cycle quality monitoring method for middleware capabilities as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for monitoring the quality of the middleware capability throughout the life cycle as described in any one of claims 1 to 7 is implemented.

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