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

Methods, devices and equipment for full life cycle quality monitoring of middle platform capabilities

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] 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

[0003] The present application relates to the field of smart middle-end technology, and in particular to a method, device and equipment for full life cycle quality monitoring of middle-end capabilities. Background Art

[0004] With the development of digital technology and industrial transformation, the smart middle platform has become a key platform for building a new type of information infrastructure. Currently, the operations domain (O domain) middle platform is a key development direction for the smart middle platform, 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 the middle platform's capabilities is necessary to ensure its normal operation.

[0005] In the related art, manual monitoring is generally used when performing quality monitoring of the O-domain middleware, mainly including manual data processing and manual configuration of monitoring dimensions. However, the quality monitoring methods in the above-mentioned related arts are labor-intensive and inefficient in actual applications. Moreover, the monitoring results may deviate significantly from the actual situation, and the monitoring accuracy cannot meet the requirements. Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a full life cycle quality monitoring method for the middle platform capabilities. This method performs statistical analysis on multiple dimensional data in the full life cycle stages of the middle platform capabilities, improves the efficiency and accuracy of the middle platform quality monitoring, and solves the problems of large manual participation in the middle platform capability monitoring dimensions, inability to clearly grasp the capability quality, and not adapting to the continuous iterative development of the middle platform.

[0008] The second purpose of this application is to propose a full life cycle quality monitoring device for mid-stage capabilities;

[0009] The third object of this application is to provide an electronic device;

[0010] The fourth object of this application is to provide a computer-readable storage medium.

[0011] To achieve the above objectives, the first aspect of this application is to propose a full life cycle quality monitoring method for middleware capabilities, which includes the following steps:

[0012] Obtain multiple types of historical detailed 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;

[0013] Aggregate the multi-type historical detailed data according to the preset granularity to generate monthly granularity dimension aggregated data for each dimension;

[0014] Based on the aggregated data at the monthly granularity, calculate the quality benchmark of the middle platform capability;

[0015] According to the monthly granularity dimension aggregation data and the scoring rules, the quality monitoring score of each dimension is calculated, and the middle platform capability quality scoring report is generated by combining the middle platform capability quality benchmark and the quality monitoring score of each dimension.

[0016] Optionally, according to an 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.

[0017] Optionally, according to one embodiment of the present application, the full life cycle of the middle platform capability includes: a capability release stage, a capability subscription stage, and a capability call stage. The dimensions of quality monitoring based on the full life cycle of the middle platform capability are divided, 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 average monthly capability call success rate, the average capability sharing time, and the capability online connection test status are divided.

[0018] Optionally, according to one embodiment of the present application, the multi-type historical detailed data are aggregated and processed according to a preset granularity to generate monthly granularity dimension aggregate data for each dimension, including: counting the operation monitoring results of each type of historical detailed data within a day to generate daily granularity capability aggregate data; counting the daily granularity capability aggregate data for each day in the target month to generate monthly granularity capability aggregate data; calculating the corresponding aggregate value of each dimension in the target month based on the monthly granularity capability aggregate data, and summarizing the corresponding aggregate value of each dimension to generate the monthly granularity dimension aggregate data.

[0019] Optionally, according to one embodiment of the present application, the data aggregated based on the monthly granularity dimensions is used to calculate the quality benchmark of the middle-station capability, including: calculating the 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 aggregated data benchmark for the quality monitoring of the middle-station capability; calculating the 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 score benchmark for the quality monitoring of the middle-station capability.

[0020] 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; and determining the abnormal conditions of the middle platform capability within the target month based on the target dimensions.

[0021] 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 according to the abnormal situation, and continuously monitoring the aggregated value of the target dimension.

[0022] To achieve the above objectives, the second aspect of this application also proposes a full life cycle quality monitoring device for middle platform capabilities, including the following modules:

[0023] An acquisition module is used to obtain 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;

[0024] An aggregation module is used to aggregate the multiple types of historical detailed data according to preset granularities to generate monthly granularity dimension aggregated data for each dimension;

[0025] A calculation module, configured to aggregate data based on the monthly granularity dimension and calculate the quality benchmark of the middle platform capability;

[0026] 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.

[0027] To achieve the above-mentioned purpose, the third aspect of the present application further proposes an electronic device, comprising:

[0028] processor;

[0029] a memory for storing instructions executable by the processor;

[0030] In which, the processor is configured to execute the instructions to implement the full life cycle quality monitoring method of the middleware capabilities as described in any one of the first aspects above.

[0031] To achieve the above-mentioned purpose, the fourth aspect of this application also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor, it implements the full life cycle quality monitoring method of the middle platform capabilities described in any one of the above-mentioned first aspects.

[0032] The technical solution provided by the embodiment of the present application brings at least the following beneficial effects: the present application first analyzes and integrates various historical information data of the middle platform capabilities, divides the data quality dimensions of the middle platform capabilities life cycle, aggregates and counts the data according to the divided dimensions, and obtains periodic historical statistical data; then generates the benchmark value of each capability indicator according to the benchmark rule, and outputs a scoring report on the capability quality in combination with the scoring rules of each dimension indicator, which distinguishes the monitoring rules of the 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 capabilities is analyzed, and the quality monitoring method of the middle platform capabilities life cycle is changed from manual analysis and calculation to the system automatically calculating and outputting the scoring report according to 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 the continuous optimization and upgrading of the middle platform capabilities for both subscribers through statistical analysis of the data of the life cycle stage dimensions of the capabilities. And through multi-layer aggregation, from a single capability to a single system, the overall multi-faceted nature of quality monitoring is achieved, which helps to tap the development advantages of the middle platform. In addition, by setting monitoring items of different dimensions according to the different stages of the capability life cycle, the quality of each stage of the capability is monitored, thereby improving the accuracy and comprehensiveness of the middle-end quality monitoring.

[0033] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above 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, in which:

[0035] FIG1 is a flow chart of a middle platform capability quality monitoring method in a related embodiment;

[0036] FIG2 is a flow chart of a method for monitoring the quality of a mid-stage capability throughout its life cycle, as proposed in an embodiment of the present application;

[0037] FIG3 is a flowchart of a method for generating monthly granularity dimension aggregated data proposed in an embodiment of the present application;

[0038] FIG4 is a flowchart of a specific method for monitoring the quality of the entire life cycle of the middle platform capabilities proposed in an embodiment of the present application;

[0039] FIG5 is a schematic diagram of the structure of a full life cycle quality monitoring device for mid-stage capabilities proposed in an embodiment of the present application;

[0040] FIG6 is a schematic structural diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following describes in detail embodiments of the present application, examples of which 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.

[0042] It should be noted that the smart middle platform (Middle Firmware Platform, MFP) is an operating mechanism that integrates multiple elements such as application software, hardware equipment and related specifications. In some related embodiments, the implementation process of the manual middle platform capability quality monitoring solution is shown in Figure 1. As can be seen from Figure 1, the middle platform capability quality monitoring solution in the related technology requires manual participation of operators to configure the global monitoring dimension baseline during the data quality evaluation stage, which will lead to problems such as large workload, operational errors, unfamiliarity with data, different capability calls between systems, and insensitivity to the values ​​of each monitoring dimension, which will lead to a large gap between the baseline configuration and the actual situation. In addition, the solution cannot form a reliable monitoring dimension system, cannot respond to changes in the platform capability monitoring dimension in a timely manner, and cannot automatically perform periodic baseline adjustments for individual monitoring dimensions, which will affect the decision-making direction of the responsible personnel of each capability provider to discover anomalies and upgrade or repair capabilities.

[0043] To this end, this application proposes a full life cycle quality monitoring method for the capabilities of the middle platform. This method conducts quality monitoring of the capability life cycle for the O-domain middle platform, evolving from the stage of manually formulating monitoring dimensions to the stage of automatically refining monitoring dimensions by the system, reducing the process of manually processing system data, saving labor costs, and improving system efficiency. Monitoring refinement is achieved for each monitoring dimension data, and the values ​​of each monitoring dimension are adjusted more accurately. The entire process is monitored and traced from the data source and underlying data to the problem closed loop, making it convenient for each system to understand the development of the O-domain middle platform and grasp the use of capabilities in real time. In addition, this method conducts quality monitoring from the perspective of the full life cycle of quality indicator definition, all-round testing, problem tracking and operation monitoring, effectively promoting the quality improvement and optimization of the release capabilities of each system and the consumption efficiency of consumer users, improving the output efficiency of capabilities, and quickly supporting the company's business development.

[0044] The following describes, with reference to the accompanying drawings, a method, device, and equipment for full life cycle quality monitoring of middleware capabilities proposed in an embodiment of the present application.

[0045] It should be noted that the full life cycle quality monitoring method of the middle-office capability implemented in this application is configured in a full life cycle quality monitoring method device of the middle-office capability for illustration. The full life cycle quality monitoring method of the middle-office capability can be applied to any electronic device so that the electronic device can perform the function of full life cycle quality monitoring of the middle-office capability.

[0046] Among them, 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, and other hardware devices with various operating systems, touch screens and / or display screens.

[0047] FIG2 is a flow chart of a method for monitoring the quality of the entire life cycle of a middleware capability proposed in an embodiment of the present application. As shown in FIG1 , the method includes the following steps:

[0048] Step S101: Obtain multiple types of historical detailed data related to the middle platform capabilities, divide the dimensions of quality monitoring based on the entire life cycle of the middle platform capabilities, and determine the scoring rules corresponding to each dimension.

[0049] The capability of the middle platform (MFP) in this application refers to the basic application cell (BAC) running on the middle platform. This application performs quality monitoring (QM) on the middle platform capability from the perspective of the complete life cycle of a certain capability.

[0050] Specifically, when this 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 capabilities. The multiple types of historical detailed data may include relevant information about the middle platform capabilities, as well as consumption data subscribed to the target capability, etc.

[0051] In one embodiment of the present application, the acquired multiple types of historical detailed data include: 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. Several specific examples are provided below to illustrate this.

[0052] 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, can be obtained in the form shown in the following Table 1:

[0053] Table 1 Capability information table registered by capability provider

[0054] As a second example, the capability information subscribed by the capability subscriber, that is, the capability information data subscribed by the capability subscriber system on the middle platform application, can be obtained in the form shown in the following Table 2:

[0055] Table 2 Capability subscription information table

[0056] As a third example, the capability online connection test data may be obtained in the form shown in the following Table 3:

[0057] Table 3 Capability online connection test data table

[0058] As a fourth example, the capability subscription request log is the request log data of the capability subscription system calling the target capability in the middle platform gateway. This information can be obtained in the form shown in the following Table 4:

[0059] Table 4 Subscriber call request log table

[0060] Therefore, various types of data can be continuously collected and recorded in the form of the above examples to obtain multiple types of historical detailed data.

[0061] Furthermore, the quality monitoring dimensions of the middle-office capability lifecycle data are divided, that is, the quality monitoring dimensions are divided according to the lifecycle of the middle-office capability. Specifically, the quality monitoring dimensions are classified according to the various indicators involved in each stage of the complete lifecycle of the middle-office capability from the beginning to the end.

[0062] In one embodiment of the present application, the entire life cycle of the middle platform capability includes: capability release stage, capability subscription stage and capability call stage. The dimensions of quality monitoring are divided based on the entire 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 average monthly capability call success rate, the average capability sharing time and the capability online connection test status are divided.

[0063] For example, in this embodiment, the categories of capability publishing, capability subscription, and capability invocation are divided as shown in Table 5 below:

[0064] Table 5 Data quality dimension table of middle platform capability life cycle

[0065] Therefore, this embodiment develops a unified quality monitoring calculation model for the lifecycle quality monitoring of mid-tier capabilities, treating each indicator to be monitored and evaluated as a monitoring dimension to facilitate subsequent statistical analysis of each dimension. This includes, but is not limited to, statistics on the release scale of capabilities, capability reuse, capability activity during the non-incubation period, average monthly capability response time, average monthly capability call success rate, and capability online connection and testing statistics.

[0066] Furthermore, for each of the divided dimensions, the scoring rules corresponding to each dimension are determined.

[0067] Continuing with the example shown in Table 5, this application sets corresponding scoring rules for each dimension in Table 5 as shown in Table 6 below:

[0068] Table 6 Middle platform capability data quality score setting table

[0069] Therefore, the embodiment of the present application sets corresponding scoring rules for each dimension classification to facilitate the subsequent generation of scores for each dimension.

[0070] Step S102: Aggregate multiple types of historical detailed data according to preset granularities to generate monthly granularity dimension aggregated data for each dimension.

[0071] Specifically, the collected middle-office capability data are aggregated and statistically analyzed, and according to the dimension classification of the middle-office capability life cycle quality monitoring obtained in the previous step, the obtained multi-type historical detailed data are aggregated and processed into periodic statistical data of each dimension.

[0072] As a possible implementation approach, multiple types of historical detailed data are first aggregated into daily granularity statistics. This means that the daily operational service quality of the target middleware capabilities is statistically analyzed. The data recorded for each type of historical detailed data within a single day is aggregated and clustered to obtain daily granularity statistics for each type of historical detailed data. Then, all daily granularity statistics for each type of historical detailed data within a month are aggregated and further statistically analyzed to obtain monthly granularity statistics for each type of historical detailed data.

[0073] Then, based on the above-mentioned dimensional divisions, all monthly granular statistical data are statistically calculated, that is, for each dimension in turn, relevant data is extracted from the monthly granular statistical data corresponding to all types of historical detailed data for calculation, and the corresponding aggregate value of each dimension is obtained. For example, for the dimension of capability activity shown in Table 5 in the above example, the calculated corresponding aggregate value is the activity ratio, 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 calculated corresponding aggregate value includes the number of reuse capabilities and the reuse ratio.

[0074] Finally, the aggregated values ​​corresponding to each dimension are summarized to obtain the aggregated data of the monthly granularity dimension.

[0075] Step S103: Aggregate data based on the monthly granularity dimension and calculate the quality benchmark of the middle platform capability.

[0076] Specifically, the data quality benchmark for the middle platform capability is generated based on the aggregated data at the monthly granularity dimension. The middle platform capability quality benchmark may include the benchmark value of the aggregated numerical value corresponding to each dimension, and may also include the benchmark value of the quality monitoring score of each dimension, and other benchmark values ​​that reflect the quality of the middle platform capability.

[0077] In one embodiment of the present application, based on the monthly granularity dimension aggregated data, the middle-station capability quality benchmark is calculated, including: calculating the 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-station capability quality monitoring aggregated data benchmark; calculating the 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-station capability quality monitoring score benchmark.

[0078] Specifically, in this embodiment, the dimensional aggregated data benchmark for mid-stage capability quality monitoring uses the monthly granularity dimensional aggregated data obtained in the previous step as the data source, and is obtained by calculating the average 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.

[0079] As an example, the definition and calculation method of the aggregated data benchmark for the middle platform capability quality monitoring are shown in Table 7 below:

[0080] Table 7 Middle platform capability data quality monitoring aggregated data benchmark table

[0081] For each dimension, you can obtain the aggregated values ​​for each month within a preset number of months, and then calculate according to the relevant formula in the table above.

[0082] Furthermore, in this embodiment, the middle-station capability quality monitoring score benchmark is calculated after the quality monitoring score of each dimension is calculated in subsequent steps. When calculating the benchmark value, the final middle-station capability data quality score report information is used as the data source, and the score benchmark is obtained by calculating the average of the monthly historical score statistics within a preset number of months.

[0083] As an example, continuing to refer to the scoring rules shown in Table 6 above, for each scoring item, the definition and calculation method of the mid-stage capability quality monitoring scoring benchmark are shown in Table 8 below:

[0084] Table 8 Middle platform capability data quality monitoring score benchmark

[0085] For each scoring item, the monthly scoring values ​​within a preset number of months can be obtained, and then calculated according to the relevant formula in the above table.

[0086] Step S104: Aggregate data and scoring rules based on the monthly granularity dimension, calculate the quality monitoring score for 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 score report.

[0087] Specifically, the monthly granularity dimension aggregated 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.

[0088] As an example, the final scoring report output by quality monitoring of the middle platform capabilities is shown in Table 9 below:

[0089] Table 9 Middle platform capability data quality score report information table

[0090] 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 operating strategy of the middle-platform capability to eliminate abnormalities.

[0091] 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 data quality dimension of the middle platform capability life cycle, aggregates and counts the data according to the divided dimensions, and obtains periodic historical statistical data; then generates a benchmark value for each capability indicator based on the benchmark rule, and outputs a scoring report on the capability quality in combination with the scoring rules of each dimension indicator, which distinguishes the monitoring rules of 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, and the quality monitoring method of the middle platform capability life cycle is changed from manual analysis and calculation to the system automatically calculating and outputting the scoring report according to the rules, thereby improving the efficiency of the middle platform quality monitoring. Therefore, the method is based on long-term end-to-end monitoring, and through statistical analysis of the life cycle stage dimension data of the capability, it can provide objective reference data for the continuous optimization and upgrading of the middle platform capability for both subscribers. And through multi-layer aggregation, from a single capability to a single system, the overall multi-faceted nature of quality monitoring is achieved, which helps to tap the development advantages of the middle platform. In addition, by setting monitoring items of different dimensions according to the different stages of the capability life cycle, the quality of each stage of the capability is monitored, thereby improving the accuracy and comprehensiveness of the middle platform quality monitoring.

[0092] Based on the above embodiment, in order to more clearly illustrate the specific implementation process of this application by aggregating historical data to generate monthly granularity dimension aggregate data for each dimension, the following is a detailed description of a specific data processing method embodiment. Figure 3 is a flowchart of a method for generating monthly granularity dimension aggregate data proposed in an embodiment of this application.

[0093] As shown in FIG3 , the method includes the following steps:

[0094] Step S301 : Count the operation monitoring results of each type of historical detailed data within one day to generate daily granularity capability aggregation data.

[0095] For example, the detailed historical data of the capability calls shown in Table 4 of the above example, that is, the call request log of the subscriber, is aggregated into the data shown in the following Table 10:

[0096] Table 10-day granularity capability aggregation data table

[0097] 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 this table can be obtained by statistics and calculations on historical data.

[0098] Step S302 , collecting statistics on daily granularity capability aggregation data for each day in the target month to generate monthly granularity capability aggregation data.

[0099] The target month is the month for which quality monitoring is currently to be performed, such as the current month.

[0100] Continuing with the above example, the data shown in Table 10 can be obtained daily within the target month. All daily granularity capability aggregation data within the target month are further aggregated to generate the monthly granularity data shown in Table 11 below:

[0101] Table 11 Granularity Capability Aggregate Data

[0102] Step S303: Aggregate data at the monthly granularity, calculate the aggregate value corresponding to each dimension in the target month, and summarize the aggregate value corresponding to each dimension to generate monthly granularity dimension aggregate data.

[0103] Specifically, the monthly granularity capability aggregation data table corresponding to all types of historical detailed data is used as the data source. For each dimension in Table 5 in the above embodiment, relevant data is extracted from all the monthly granularity capability aggregation data for calculation to obtain the aggregation value corresponding to each dimension.

[0104] 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 the following Table 12:

[0105] Table 12 Granularity dimension aggregated data table

[0106] For example, for the aggregated value of the reuse ratio (MultiUseRate) in Table 12, the total number of reuse capabilities (MultiUseCount) can be counted from the relevant monthly granularity capability aggregation data, and then the total number of application release capabilities (AbilityCount) can be extracted. Then, the number of reuse capabilities can be divided by the total number of application release capabilities to obtain the reuse ratio.

[0107] Therefore, 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, which facilitates the subsequent calculation of the quality monitoring score of each dimension.

[0108] Based on the above embodiments, in order to more clearly and in detail illustrate the specific implementation process of the full life cycle quality monitoring method of the middle platform capability of this application, a specific application embodiment in actual application is described in detail below. Figure 4 is a flowchart of a specific full life cycle quality monitoring method of the middle platform capability proposed in the embodiment of this application.

[0109] As shown in FIG4 , the method includes the following steps:

[0110] Step S401: Use the gateway of the middle platform to collect the request and response information of the subscriber system call capability.

[0111] Specifically, systems registered with the middle platform allow subscriber systems to call published capabilities, thereby generating actual call data for the capabilities. Therefore, this embodiment uses the middle platform gateway to collect historical data. Taking a capability provider system and its 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 proceeds with the subsequent process.

[0112] Step S402 : At the first time point of each day, daily capacity monitoring information is aggregated in a hierarchical manner to obtain monitoring statistical information.

[0113] For example, at 1:00 AM every day, the Extract Transform Load (ETL) tool Kettle is used to aggregate daily capability monitoring information using hierarchical aggregation. The detailed monitoring data of the capability being called in the target month is shown in Table 13 below:

[0114] Table 13 Monitoring data details

[0115] Furthermore, the statistical results obtained through hierarchical aggregation are shown in Table 14 below:

[0116] Table 14 Monitoring statistics table

[0117] Step S403: At the second time point every day, the monitoring statistical information is classified according to the preset monitoring rules to generate a customized report.

[0118] For example, at 3 a.m. every day, the ETL tool Kettle is used to classify the monitoring statistical information according to the preset monitoring rules to generate a customized report. That is, 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:

[0119] Table 15 Multi-dimensional monitoring statistics customized report

[0120] Step S404: Obtain basic statistical data of capability dimension scores based on the statistical data of the customized report.

[0121] Specifically, the aggregated values ​​of each dimension in the multi-dimensional monitoring statistics customized report shown in Table 15 above are calculated accordingly to obtain the multi-dimensional scoring statistics basic data table shown in Table 16 below:

[0122] Table 16 Basic data table of multi-dimensional scoring statistics

[0123] Step S405: Associating the capability dimension scoring statistical basic data with the scoring rules to generate a capability quality scoring report.

[0124] Specifically, the scoring statistical basic data of each dimension in Table 16 above are substituted into the scoring rules set for each dimension shown in Table 6 of the above embodiment, and relevant calculations are performed to obtain the scores of each dimension as shown in Table 17 below:

[0125] Table 17 Quality monitoring score table for each dimension

[0126] Furthermore, based on the quality monitoring score sheet of each dimension and combined with the determined middle platform capability quality benchmark, a quality score report of the middle platform capability can be generated.

[0127] In one embodiment of the present application, a middle platform capability quality score report is generated by combining the middle platform capability quality benchmark and the quality monitoring score of each dimension, including the following steps: First, based on the quality monitoring scores of all dimensions, the deduction indicators of the middle platform capability are determined.

[0128] Specifically, the quality monitoring scores for all dimensions are determined, with dimensions with lower scores used as penalty indicators. Referring to the example shown in Table 17, the data in this table shows that the scores for the release scale, incubation scale, and reuse are lower than those for the other dimensional indicators. Therefore, the penalty for this capability is primarily focused on the release scale, incubation scale, and reuse dimensions. Furthermore, based on these penalty indicators, it can be determined that AAAA applications have problems such as a small number of released capabilities and low reuse of capabilities.

[0129] Then, the quality monitoring score of each dimension is compared with the corresponding middle-office capability quality monitoring score benchmark, and the target dimensions whose quality monitoring scores are lower than the corresponding middle-office capability quality monitoring score benchmark are screened out.

[0130] Continuing with the example shown in Table 17, the table also lists the pre-calculated baseline quality monitoring scores for the middle platform capabilities, including the base values ​​for each scoring item. The quality monitoring score for each dimension is compared with the corresponding scoring benchmark, and the dimension indicator with a quality monitoring score lower than the corresponding scoring benchmark is used as the target dimension. In this example, by comparing the baseline data, it can be seen that the target dimension is the incubation scale.

[0131] Finally, based on the target dimension, determine the abnormality of the middle platform capabilities in the target month. Specifically, if 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, if the application's total score is lower than the baseline total score, it can be determined that the application's quality level in the target month is unqualified.

[0132] Therefore, the middle platform capability quality score report output by the embodiment of this method can reflect the quality status of the middle platform capability in the target month from multiple angles.

[0133] Furthermore, in 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 according to abnormal conditions, and continuously monitoring the aggregated values ​​of the target dimensions.

[0134] For example, if the application in the example above has not recently released a new capability defect, the middle platform capability operation strategy can be adjusted to cultivate this capability for greater sharing. Furthermore, the relevant data from the capability incubation period can be continuously monitored over a period of time to determine whether the adjustment strategy improves the target dimension anomaly until the anomaly is eliminated.

[0135] In summary, the full life cycle quality monitoring method of the middle platform capabilities of the embodiment of the present application has strong feasibility and standardization. Through the establishment of various monitoring items that are in line with the actual business operation scenarios, it can effectively improve the problem discovery ability of the middle platform, and through fine end-to-end monitoring, it can promote both subscribers to focus on the problem together, locate the problem in a timely manner, and improve the efficiency of closed-loop problem solving. This method can be applied to multiple scenarios such as the launch of home services, 5G vertical industries, and network management and operation support. It is conducive to improving resource accuracy, realizing changes in perception capabilities, improving the quality of middle platform capabilities, and improving the accuracy of problem location.

[0136] In order to implement the above-mentioned embodiments, the present application also proposes a full life cycle quality monitoring device for the middle platform capabilities. Figure 5 is a structural schematic diagram of a full life cycle quality monitoring device for the middle platform capabilities proposed in an embodiment of the present application. As shown in Figure 5, the device includes an acquisition module 100, a convergence module 200, a calculation module 300 and a generation module 400.

[0137] Among them, the acquisition module 100 is used to obtain 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.

[0138] The aggregation module 200 is used to aggregate multiple types of historical detailed data according to preset granularities to generate monthly granularity dimension aggregated data for each dimension.

[0139] The calculation module 300 is used to aggregate data based on the monthly granularity dimension and calculate the quality benchmark of the middle platform capabilities.

[0140] Generation module 400 is used to aggregate data and scoring rules based on the monthly granularity dimension, calculate the quality monitoring score of each dimension, and generate a middle platform capability quality score report by combining the middle platform capability quality benchmark and the quality monitoring score of each dimension.

[0141] In one embodiment of the present application, the acquisition module 100 is specifically used to: divide the capability release scale and capability incubation period for the capability release stage; divide the capability sharing type and capability activity for the capability subscription stage; and divide the capability monthly average call success rate, average capability sharing time and capability online connection test status for the capability call stage.

[0142] In one embodiment of the present application, the aggregation module 200 is specifically used to: count the operation monitoring results of each type of historical detailed data within one day to generate daily granularity capability aggregation data; count the daily granularity capability aggregation data for each day in the target month to generate monthly granularity capability aggregation data; calculate the corresponding aggregation value of each dimension in the target month based on the monthly granularity capability aggregation data, and summarize the corresponding aggregation value of each dimension to generate monthly granularity dimension aggregation data.

[0143] 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 capabilities; 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 benchmark for the quality monitoring score of the middle platform capabilities.

[0144] 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 dimensions whose quality monitoring scores are 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 dimensions.

[0145] 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 abnormal conditions, and continuously monitor the aggregated values ​​of the target dimension.

[0146] It should be noted that the above explanation of the embodiment of the method for monitoring the quality of the entire life cycle of the middle platform capability is also applicable to the device of this embodiment and will not be repeated here.

[0147] In summary, the full life cycle quality monitoring of the middle platform capabilities in the embodiment of the present application is based on long-term end-to-end monitoring. Through statistical analysis of the life cycle stage dimension data of the capabilities, it can provide objective reference data for the continuous optimization and upgrading of the middle platform capabilities by both subscribers. In addition, the device achieves the overall multi-faceted nature of quality monitoring by converging a single capability to a single system through multiple layers, which helps to tap the development advantages of the middle platform. In addition, according to the different stages of the life cycle of the capability, by setting monitoring items of different dimensions, the quality of each stage of the capability is monitored, thereby improving the accuracy and comprehensiveness of the quality monitoring of the middle platform.

[0148] In order to implement the above embodiments, the present application also proposes an electronic device, as shown in Figure 6, the electronic device 600 includes: a processor 610; a memory 620 for storing executable instructions of the processor 610; wherein the processor 610 is configured to execute instructions to implement the full life cycle quality monitoring method of the middle platform capabilities as described in any of the above-mentioned first aspect embodiments.

[0149] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the full life cycle quality monitoring method of the middle platform capabilities as described in any of the above first aspect embodiments.

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the 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. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0151] 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 being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0152] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0153] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, 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 that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0154] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0156] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A full life cycle quality monitoring method for middle platform capabilities, including the following steps: Obtain multiple types of historical detailed 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; Aggregate the multi-type historical detailed data according to the preset granularity to generate monthly granularity dimension aggregated data for each dimension; Based on the aggregated data at the monthly granularity, calculate the quality benchmark of the middle platform capability; According to the monthly granularity dimension aggregation data and the scoring rules, the quality monitoring score of each dimension is calculated, and the middle platform capability quality scoring report is generated by combining the middle platform capability quality benchmark and the quality monitoring score of each dimension.

2. The method according to claim 1, wherein, 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.

3. The method according to claim 1, wherein, The full life cycle of the middle platform capability includes: capability release phase, capability subscription phase and capability call phase. The dimension of quality monitoring based on the full life cycle of the middle platform capability is divided into: Based on the capability release stage, the capability release scale and capability incubation period are divided; According to the capability subscription stage, capability sharing types and capability activity levels are divided; For the capability calling stage, the monthly average capability calling success rate, the average capability sharing time and the capability online connection test status are divided.

4. The method according to claim 1, wherein, The aggregating the multi-type historical detailed data according to the preset granularity to generate monthly granularity dimension aggregated data for each dimension includes: Counting the operation monitoring results of each type of historical detailed data within one day to generate daily granularity capability aggregation data; Collect the daily granularity capability aggregation data for each day in the target month to generate monthly granularity capability aggregation data; According to the monthly granularity capability aggregation data, the aggregation value corresponding to each dimension in the target month is calculated, and the aggregation value corresponding to each dimension is summarized to generate the monthly granularity dimension aggregation data.

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

6. The method according to claim 5, wherein, The combination of the middle platform capability quality benchmark and the quality monitoring score of each dimension generates a middle platform capability quality score report, including: Determine the deduction indicators for the middle platform capabilities 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 filter out the target dimensions whose quality monitoring score is lower than the corresponding middle platform capability quality monitoring score benchmark; Determine the abnormal conditions of the middle platform capabilities within the target month based on the target dimensions.

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

8. A full-life cycle quality monitoring device for middleware capabilities, comprising: An acquisition module, configured to acquire multi-type historical detail data related to middleware capabilities, divide the dimensions of quality monitoring based on the full life cycle of the middleware capabilities, and determine the scoring rules corresponding to each dimension; An aggregation module, configured to perform aggregation processing on the multi-type historical detail data respectively according to a preset granularity, and generate monthly granularity dimension aggregation data for each dimension; A calculation module, configured to calculate the quality benchmark of the middleware capabilities based on the monthly granularity dimension aggregation data; A generation module, configured to calculate the quality monitoring score of each dimension according to the monthly granularity dimension aggregation data and the scoring rules, and generate a middleware capabilities quality score report in combination with the middleware capabilities quality benchmark and the quality monitoring score of each dimension.

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

10. A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the full-life cycle quality monitoring method for middleware capabilities as described in any one of claims 1-7.

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