Performance data processing method and device, computer equipment and storage medium

Abstract

The invention relates to a performance data processing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a performance log generated by a static module packaging tool in a construction process; extracting the performance log to obtain at least one piece of performance data; analyzing the performance data to obtain a performance analysis report, the performance analysis report comprising abnormal performance data and optimization suggestions; obtaining a new configuration file reconstructed by the static module packaging tool; and when identifying that the new configuration file comprises the abnormal performance data, feeding back an optimization suggestion to the equipment where the developer is located. By adopting the method, the efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a performance data processing method, apparatus, computer equipment, and storage medium. Background Art

[0002] As modern mobility becomes increasingly complex, building web applications for the industry is becoming increasingly complex. Front-end build tools play a crucial role in development. The performance of mobility web applications directly impacts the driver and passenger experience. Optimizing code splitting, resource loading, and caching strategies during the build process has become a key step in improving mobility app performance.

[0003] However, current front-end build tools can only determine that there is a problem in the build process when the build package is successfully built and cannot be run or a reminder appears. In this way, it is necessary to return to the front-end build tool for reconstruction, which leads to low development efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a performance data processing method, device, computer equipment and storage medium that can improve development efficiency in response to the above technical problems.

[0005] A performance data processing method, the method comprising:

[0006] Get the performance logs generated by the static module bundling tool during the build process;

[0007] Extracting the performance log to obtain at least one performance data;

[0008] Analyze each performance data to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions;

[0009] Get the new configuration file built by the static module packaging tool;

[0010] When a new configuration file is identified to contain abnormal performance data, optimization suggestions are fed back to the developer's device.

[0011] In one embodiment, obtaining the performance log generated by the static module packaging tool during the construction process includes:

[0012] Get the code files needed to build the application through plugins in static modules;

[0013] The modules in the code file are processed by the plug-in to obtain multiple modules with associated dependencies;

[0014] Package each processed module and generate a performance log.

[0015] In one embodiment, the performance log is extracted to obtain at least one performance data, including:

[0016] Parse the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time;

[0017] Identify module dependencies, at least one module loading time, resource size, code splitting, and build time as performance data.

[0018] In one embodiment, each performance data is analyzed to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions, including:

[0019] Anomaly detection is performed on module dependencies, at least one module load time, resource size, code segmentation, and build time to identify abnormal performance data;

[0020] Generate optimization suggestions corresponding to abnormal performance data, and bind the abnormal performance data with the optimization suggestions to obtain a performance analysis report.

[0021] In one embodiment, each performance data is analyzed to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions, including:

[0022] Obtain the trained performance optimization prediction model;

[0023] The module dependencies, at least one module loading time, resource size, code segmentation, and build time are input into the performance optimization prediction model. The performance optimization prediction model analyzes the input data and outputs abnormal performance data and corresponding optimization suggestions.

[0024] In one embodiment, after obtaining a new configuration file constructed again by the static module packaging tool, the following steps are included:

[0025] Get the new configuration files needed to build new applications through the plug-in in the static module packaging tool;

[0026] Check new configuration files for abnormal performance data;

[0027] If so, the step of feeding back the optimization suggestions to the developer's device is executed;

[0028] If not, the new configuration file is packaged through the plug-in.

[0029] In one embodiment, the optimization suggestions are fed back to the developer's device, including:

[0030] Generate visual reports based on optimization suggestions and abnormal performance data;

[0031] The visualization report is sent to the developer's device, and the visualization report is displayed to the developer through a static module packaging tool installed on the device.

[0032] A performance data processing device, comprising:

[0033] The first acquisition module is used to obtain the performance logs generated by the static module packaging tool during the construction process;

[0034] An extraction module, configured to extract the performance log to obtain at least one performance data;

[0035] The analysis module is used to analyze various performance data and obtain a performance analysis report, which includes abnormal performance data and optimization suggestions;

[0036] The second acquisition module is used to obtain a new configuration file built again by the static module packaging tool;

[0037] The identification module is used to provide optimization suggestions to the developer's device when it is identified that a new configuration file contains abnormal performance data.

[0038] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0039] Get the performance logs generated by the static module bundling tool during the build process;

[0040] Extracting the performance log to obtain at least one performance data;

[0041] Analyze each performance data to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions;

[0042] Get the new configuration file built by the static module packaging tool;

[0043] When a new configuration file is identified to contain abnormal performance data, optimization suggestions are fed back to the developer's device.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0045] Get the performance logs generated by the static module bundling tool during the build process;

[0046] Extracting the performance log to obtain at least one performance data;

[0047] Analyze each performance data to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions;

[0048] Get the new configuration file built by the static module packaging tool;

[0049] When a new configuration file is identified to contain abnormal performance data, optimization suggestions are fed back to the developer's device.

[0050] The above-mentioned performance data processing method, device, computer equipment and storage medium obtain the performance log generated by the static module packaging tool during the construction process, extract the performance log to obtain at least one performance data, analyze each performance data to obtain a performance analysis report, the performance analysis report includes abnormal performance data and optimization suggestions, obtain a new configuration file rebuilt by the static module packaging tool, and when it is identified that the new configuration file includes abnormal performance data, the optimization suggestions are fed back to the device where the developer is located.

[0051] Therefore, through the performance logs of the build process, it is possible to intelligently identify abnormal performance data of the static module packaging tool during the build process and provide optimization suggestions. There is no need to wait until the build and packaging are successful to determine whether an abnormality occurred during the build process. Abnormal performance data can be identified and optimization suggestions can be provided before packaging, greatly improving development efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A diagram of an application environment of a performance data processing method in one embodiment;

[0053] Figure 2 1 is a flow chart of a performance data processing method according to an embodiment;

[0054] Figure 3 A flowchart of the steps for obtaining a performance log in one embodiment;

[0055] Figure 4 A schematic diagram of a process for extracting performance logs in one embodiment;

[0056] Figure 5 1 is a flow chart of a performance data analysis step in one embodiment;

[0057] Figure 6 1 is a flow chart of a performance data analysis step in one embodiment;

[0058] Figure 7 A schematic diagram of a flow chart of a configuration file detection step in one embodiment;

[0059] Figure 8A flowchart of an optimization suggestion feedback step in one embodiment;

[0060] Figure 9 is a structural block diagram of a performance data processing device in one embodiment;

[0061] Figure 10 is a diagram of the internal structure of a computer device in one embodiment;

[0062] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0064] The performance data processing method provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0065] Specifically, the server 104 obtains the performance log generated by the static module packaging tool during the construction process, extracts the performance log, obtains at least one performance data, analyzes each performance data, obtains a performance analysis report, the performance analysis report includes abnormal performance data and optimization suggestions, obtains a new configuration file rebuilt by the static module packaging tool, and when it is identified that the new configuration file includes abnormal performance data, the optimization suggestions are fed back to the terminal 102 where the developer is located.

[0066] In another embodiment, the terminal 102 obtains the performance log generated by the static module packaging tool during the construction process, extracts the performance log to obtain at least one performance data, analyzes each performance data, obtains a performance analysis report, the performance analysis report includes abnormal performance data and optimization suggestions, obtains a new configuration file constructed again by the static module packaging tool, and when it is identified that the new configuration file includes abnormal performance data, the optimization suggestions are fed back to the device where the developer is located.

[0067] In one embodiment, Figure 2 As shown, a performance data processing method is provided, which is applied to Figure 1 The following steps are used as an example to illustrate the terminal or server in the example:

[0068] Step 202: Obtain the performance log generated by the static module packaging tool during the construction process.

[0069] Among them, a static module bundling tool is a tool for packaging all dependencies in a project (such as JavaScript modules, images, CSS, etc.) into one or more bundles for use in browsers. Among them, Webpack is one of the most popular static module bundling tools.

[0070] Specifically, the performance log generated by the static module packaging tool when building an application or project is obtained. The performance log records the running status and performance indicators of the project or application, and is mainly used for fault diagnosis, performance optimization and resource monitoring.

[0071] Step 204: extract the performance log to obtain at least one performance data.

[0072] Among them, performance data is the data in the performance log that reflects the performance of the project or application status. After obtaining the performance log, the performance log is parsed to extract various performance data in the build process, including but not limited to module loading time, resource size, code segmentation, cache hit rate, module dependency and other information.

[0073] Among them, module loading time is the length of time required for a module in an application to be successfully loaded, resource size is the byte size of the resource in the application, code splitting is the situation in which the resource is split into resource bundles, cache hit rate is the ratio of the number of requests that successfully obtain data from the cache to the total number of requests, and module dependency is the mutual dependence or dependency relationship between modules in the application.

[0074] Step 206: Analyze each performance data to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions.

[0075] The performance analysis report identifies possible performance bottlenecks and optimization suggestions. Possible performance bottlenecks are abnormal performance data. That is, after obtaining the performance data in the performance log, a detailed performance analysis report is automatically generated based on this performance data. The performance analysis report identifies abnormal performance data and optimization suggestions. Abnormal performance data is performance data that has problems or performance bottlenecks, and optimization suggestions are optimization operations or measures given for this abnormal performance data.

[0076] In some embodiments, a large model may be used to perform predictive analysis on various performance data, and output a performance analysis report including abnormal performance data and optimization suggestions.

[0077] Step 208: Obtain a new configuration file constructed again by the static module packaging tool.

[0078] Step 210 : When it is identified that the new configuration file includes abnormal performance data, optimization suggestions are fed back to the device where the developer is located.

[0079] Among them, after obtaining the performance analysis report, it is stored in the static module packaging tool, or installed in the static module packaging tool as a detection plug-in. Specifically, the static module packaging tool re-builds the configuration file of the new application or project. At this time, the configuration file has not been packaged by the static module packaging tool. Before packaging, the configuration file is tested. When the configuration file includes abnormal performance data, optimization suggestions are obtained and fed back to the device where the developer is located. The static module packaging tool is installed on the device where the developer is located. Displaying optimization suggestions and abnormal performance data in the new configuration file through the static module packaging tool can help developers to perform effective optimization and package the optimized configuration file through the static module packaging tool.

[0080] In the above-mentioned performance data processing method, the performance log generated by the static module packaging tool during the construction process is obtained, the performance log is extracted to obtain at least one performance data, each performance data is analyzed to obtain a performance analysis report, the performance analysis report includes abnormal performance data and optimization suggestions, and a new configuration file constructed again by the static module packaging tool is obtained. When it is identified that the new configuration file includes abnormal performance data, the optimization suggestions are fed back to the device where the developer is located.

[0081] Therefore, through the performance logs of the build process, it is possible to intelligently identify abnormal performance data of the static module packaging tool during the build process and provide optimization suggestions. There is no need to wait until the build and packaging are successful to determine whether an abnormality occurred during the build process. Abnormal performance data can be identified and optimization suggestions can be provided before packaging, greatly improving development efficiency.

[0082] In one embodiment, Figure 3 As shown, obtain the performance logs generated by the static module packaging tool during the build process, including:

[0083] Step 302: Obtain the code files required to build the application through the plug-in in the static module.

[0084] Step 304: Process the modules in the code file through the plug-in to obtain multiple modules with associated dependencies.

[0085] Step 306: Package the processed modules and generate a performance log.

[0086] Among them, a static module bundling tool is a tool for packaging all dependencies in a project (such as JavaScript modules, images, CSS, etc.) into one or more bundles for use in browsers. Among them, Webpack is one of the most popular static module bundling tools.

[0087] Among them, a plug-in is installed in the static module packaging tool to generate performance logs, and the plug-in is also used to process and package the code files.

[0088] Specifically, when building an application or project, a plug-in of a static module packaging tool obtains the code files required to build the application. The code files record the code required to build the application. Furthermore, the plug-in in the static module packaging tool processes the dependencies between the modules in the code files, determines the interdependence, mutual dependence, and collaborative working relationships between each module, and obtains multiple modules with associated dependencies.

[0089] Finally, the modules that have dependencies after processing are packaged and a performance log is generated. The performance log records the running status and performance indicators of the application during the construction and packaging process.

[0090] In one embodiment, Figure 4 As shown, the performance log is extracted to obtain at least one performance data, including:

[0091] Step 402: parse the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time.

[0092] Step 404 : Determine module dependencies, at least one module loading time, resource size, code segmentation, and build time as performance data.

[0093] Among them, after obtaining the performance log, parsing is performed based on the performance log to parse the various performance data in the performance log. Specifically, the performance log can be parsed to extract various performance data recorded in the performance log during the construction process, including at least one module loading time, resource size, code segmentation, cache hit rate, module dependencies, total construction time and other information.

[0094] Among them, module load time is the length of time required to successfully load a module in an application, resource size is the byte size of the resources in the application, code splitting is the situation of splitting resources into resource bundles, cache hit rate is the ratio of the number of requests that successfully retrieve data from the cache to the total number of requests, module dependency is the mutual dependence or dependency relationship between modules in the application, and build time is the total length of time it takes for the static module packaging tool to successfully build the application.

[0095] Finally, the above module dependencies, at least one module loading time, resource size, code segmentation and build time are determined as performance data.

[0096] In one embodiment, Figure 5 As shown, analyze each performance data to obtain a performance analysis report. The performance analysis report includes abnormal performance data and optimization suggestions, including:

[0097] Step 502 : Perform anomaly detection on module dependencies, at least one module loading time, resource size, code segmentation, and build time to determine abnormal performance data.

[0098] Step 504 : Generate optimization suggestions corresponding to the abnormal performance data, and bind the abnormal performance data with the optimization suggestions to obtain a performance analysis report.

[0099] Specifically, after obtaining module dependencies, at least one module loading time, resource size, code segmentation, and build time, anomaly detection is performed on the aforementioned performance data to determine abnormal performance data. Anomaly detection can include obtaining a standard threshold or standard size for the performance data, comparing the performance data with the standard threshold or standard size, and determining performance data that exceeds the standard threshold or standard size as abnormal performance data. The standard threshold or standard size can be determined in advance based on actual business needs, actual product needs, or actual application scenarios.

[0100] For example, the module load time anomaly detection process may include obtaining a preset module load time threshold and determining whether the module load time exceeds the preset module load threshold. If so, the module load time is determined to be too long, which constitutes abnormal performance data. Another example is resource size anomaly detection, which includes obtaining a preset resource size threshold and determining whether the resource size exceeds the preset resource size threshold. If so, the resource size is determined to be too large, which constitutes abnormal performance data.

[0101] Finally, after identifying abnormal performance data, optimization recommendations corresponding to the abnormal performance data are generated and associated with the optimization recommendations to produce a performance analysis report. The performance analysis report thus identifies possible bottlenecks or issues associated with the abnormal performance data, along with recommendations for improving or resolving the issues or bottlenecks.

[0102] In one embodiment, Figure 6 As shown, analyze each performance data to obtain a performance analysis report. The performance analysis report includes abnormal performance data and optimization suggestions, including:

[0103] Step 602: Obtain a trained performance optimization prediction model.

[0104] In step 604, the module dependencies, at least one module loading time, resource size, code segmentation, and build time are input into a performance optimization prediction model. The input data is analyzed by the performance optimization prediction model to output various abnormal performance data and corresponding optimization suggestions.

[0105] In some embodiments, a large model may be used to perform predictive analysis on various performance data, and output a performance analysis report including abnormal performance data and optimization suggestions.

[0106] Therefore, a trained performance optimization prediction model is obtained. The performance optimization prediction model is used to predict and generate a performance analysis report. The performance optimization prediction model can be trained in advance using a large amount of performance data carrying performance analysis report labels to obtain a trained performance optimization prediction model.

[0107] Specifically, the module dependencies, at least one module loading time, resource size, the code segmentation situation and construction time are jointly input into the performance optimization prediction model. The input data is analyzed and predicted in the performance optimization prediction model to obtain abnormal performance data in the input data, and optimization suggestions corresponding to the abnormal performance data are obtained. Finally, the performance optimization prediction model jointly outputs each abnormal performance data and the corresponding optimization suggestions.

[0108] In one embodiment, Figure 7 As shown, after obtaining the new configuration file built by the static module packaging tool, it includes:

[0109] Step 702: Obtain a new configuration file required for building a new application through the plug-in in the static module.

[0110] Step 704: Check whether the new configuration file contains abnormal performance data.

[0111] Step 706: If yes, then the step of feeding back the optimization suggestion to the device where the developer is located is executed.

[0112] Step 708: If not, the new configuration file is packaged through the plug-in.

[0113] Among them, after obtaining the performance analysis report, it is stored in the static module packaging tool, or installed in the static module packaging tool as a detection plug-in. Specifically, the static module packaging tool re-builds the configuration file of the new application or project. At this time, the configuration file has not been packaged by the static module packaging tool. Before packaging, the configuration file is tested. When the configuration file includes abnormal performance data, optimization suggestions are obtained and fed back to the device where the developer is located. The static module packaging tool is installed on the device where the developer is located. Displaying optimization suggestions and abnormal performance data in the new configuration file through the static module packaging tool can help developers to perform effective optimization and package the optimized configuration file through the static module packaging tool.

[0114] On the contrary, when the configuration file does not include abnormal performance data, the new configuration file is packaged and processed through the plug-in, which ensures that the new application can be successfully built and run.

[0115] In one embodiment, Figure 8 As shown, optimization suggestions are fed back to the developer's device, including:

[0116] Step 802: Generate a visualization report based on the optimization suggestions and abnormal performance data.

[0117] Step 804 : Send the visualization report to the device where the developer is located, and display the visualization report to the developer through the static module packaging tool installed on the device.

[0118] Among them, the visual report can intuitively display abnormal performance data and optimization suggestions to developers. Developers can intuitively identify abnormal performance data through the visual report and refer to the optimization suggestions for optimization processing.

[0119] Specifically, a visualization report is generated based on optimization recommendations and abnormal performance data. The format of the visualization report can be tables, graphs, or other forms, depending on actual business needs, product requirements, or application scenarios. Furthermore, the visualization report is sent to the developer's device and displayed to the developer via a static module packaging tool installed on the device.

[0120] It should be understood that, although the various steps in the above flow chart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0121] In one embodiment, Figure 9 As shown, a performance data processing device 900 is provided, comprising: a first acquisition module 902, an extraction module 904, an analysis module 906, a second acquisition module 908 and an identification module 910, wherein:

[0122] The first acquisition module 902 is used to obtain the performance log generated by the static module packaging tool during the construction process.

[0123] The extraction module 904 is configured to extract the performance log to obtain at least one performance data.

[0124] The analysis module 906 is used to analyze various performance data to obtain a performance analysis report, which includes abnormal performance data and optimization suggestions.

[0125] The second acquisition module 908 is used to obtain a new configuration file constructed again by the static module packaging tool.

[0126] The identification module 910 is configured to feed back optimization suggestions to the developer's device when it is identified that a new configuration file includes abnormal performance data.

[0127] In one embodiment, the first acquisition module 902 obtains the code file required to build the application through the plug-in in the static module, processes the module in the code file through the plug-in to obtain multiple modules with associated dependencies, packages each processed module, and generates a performance log.

[0128] In one embodiment, the extraction module 904 parses the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time, and determines the module dependencies, at least one module loading time, resource size, code segmentation, and build time as performance data.

[0129] In one embodiment, the analysis module 906 performs anomaly detection on module dependencies, at least one module loading time, resource size, code segmentation, and build time, determines abnormal performance data, generates optimization suggestions corresponding to the abnormal performance data, and binds the abnormal performance data with the optimization suggestions to obtain a performance analysis report.

[0130] In one embodiment, the analysis module 906 obtains a trained performance optimization prediction model, inputs module dependencies, at least one module loading time, resource size, code segmentation and build time into the performance optimization prediction model, analyzes the input data through the performance optimization prediction model, and outputs various abnormal performance data and corresponding optimization suggestions.

[0131] In one embodiment, the performance data processing device 900 obtains a new configuration file required to build a new application through a plug-in in a static module, and detects whether the new configuration file contains abnormal performance data. If so, it executes the step of feeding back optimization suggestions to the device where the developer is located. If not, it packages the new configuration file through the plug-in.

[0132] In one embodiment, the identification module 910 generates a visualization report based on the optimization suggestions and abnormal performance data, sends the visualization report to the developer's device, and displays the visualization report to the developer through a static module packaging tool installed on the device.

[0133] The specific definition of the performance data processing device can be found in the definition of the performance data processing method above and will not be repeated here. Each module in the above-mentioned performance data processing device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0134] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store performance analysis reports. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a performance data processing method is implemented.

[0135] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a performance data processing method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0136] Those skilled in the art will understand that Figure 10 or Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0137] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: obtaining a performance log generated by a static module packaging tool during a construction process, extracting the performance log to obtain at least one performance data, analyzing each performance data to obtain a performance analysis report, the performance analysis report including abnormal performance data and optimization suggestions, obtaining a new configuration file reconstructed by the static module packaging tool, and when it is identified that the new configuration file includes abnormal performance data, feeding back the optimization suggestions to the developer's device.

[0138] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining the code file required to build the application through the plug-in in the static module, processing the module in the code file through the plug-in to obtain multiple modules with associated dependencies, packaging each processed module, and generating a performance log.

[0139] In one embodiment, when the processor executes the computer program, the processor also implements the following steps: parsing the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time, and determining the module dependencies, at least one module loading time, resource size, code segmentation, and build time as performance data.

[0140] In one embodiment, when the processor executes the computer program, it also implements the following steps: performing anomaly detection on module dependencies, at least one module loading time, resource size, code segmentation, and build time, determining abnormal performance data, generating optimization suggestions corresponding to the abnormal performance data, and binding the abnormal performance data with the optimization suggestions to obtain a performance analysis report.

[0141] In one embodiment, when the processor executes the computer program, it also implements the following steps: obtaining a trained performance optimization prediction model, inputting module dependencies, at least one module loading time, resource size, code segmentation and build time into the performance optimization prediction model, analyzing the input data through the performance optimization prediction model, and outputting various abnormal performance data and corresponding optimization suggestions.

[0142] In one embodiment, when the processor executes the computer program, it also implements the following steps: obtaining a new configuration file required to build a new application through a plug-in in a static module packaging tool, detecting whether the new configuration file contains abnormal performance data, and if so, executing the step of feeding back optimization suggestions to the device where the developer is located; if not, packaging the new configuration file through the plug-in.

[0143] In one embodiment, when the processor executes the computer program, it also implements the following steps: generating a visualization report based on the optimization suggestions and abnormal performance data, sending the visualization report to the developer's device, and displaying the visualization report to the developer through a static module packaging tool installed on the device.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtaining a performance log generated by a static module packaging tool during a construction process, extracting the performance log to obtain at least one performance data, analyzing each performance data to obtain a performance analysis report, the performance analysis report including abnormal performance data and optimization suggestions, obtaining a new configuration file rebuilt by the static module packaging tool, and when it is recognized that the new configuration file includes abnormal performance data, feeding back the optimization suggestions to the device where the developer is located.

[0145] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the code file required to build the application is obtained through the plug-in in the static module, the module in the code file is processed by the plug-in to obtain multiple modules with associated dependencies, each processed module is packaged and processed, and a performance log is generated.

[0146] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: parsing the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time, and determining the module dependencies, at least one module loading time, resource size, code segmentation, and build time as performance data.

[0147] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: performing anomaly detection on module dependencies, at least one module loading time, resource size, code segmentation, and build time, determining abnormal performance data, generating optimization suggestions corresponding to the abnormal performance data, and binding the abnormal performance data with the optimization suggestions to obtain a performance analysis report.

[0148] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining a trained performance optimization prediction model, inputting module dependencies, at least one module loading time, resource size, code segmentation and build time into the performance optimization prediction model, analyzing the input data through the performance optimization prediction model, and outputting various abnormal performance data and corresponding optimization suggestions.

[0149] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining a new configuration file required to build a new application through a plug-in in a static module packaging tool, detecting whether the new configuration file contains abnormal performance data, and if so, executing the step of feeding back optimization suggestions to the device where the developer is located; if not, packaging the new configuration file through the plug-in.

[0150] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: generating a visualization report based on the optimization suggestions and abnormal performance data, sending the visualization report to the device where the developer is located, and displaying the visualization report to the developer through a static module packaging tool installed on the device.

[0151] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0152] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A performance data processing method, the method comprising: Get the performance logs generated by the static module bundling tool during the build process; Extracting the performance log to obtain at least one performance data; Analyze each of the performance data to obtain a performance analysis report, wherein the performance analysis report includes abnormal performance data and optimization suggestions; Obtain a new configuration file constructed again by the static module packaging tool; When it is identified that the new configuration file includes the abnormal performance data, the optimization suggestion is fed back to the device where the developer is located.

2. The method according to claim 1, characterized in that The performance logs generated by the static module packaging tool during the construction process are obtained, including: Obtain the code files required to build the application through the plug-in in the static module; Processing the modules in the code file by the plug-in to obtain a plurality of modules associated with dependencies; The modules after each processing are packaged to generate the performance log.

3. The method according to claim 1, characterized in that The extracting of the performance log to obtain at least one performance data includes: Parse the performance log to obtain module dependencies, at least one module loading time, resource size, code segmentation, and total build time; The module dependency, at least one module loading time, the resource size, the code segmentation status, and the build duration are determined as the performance data.

4. The method according to claim 3, characterized in that The performance data is analyzed to obtain a performance analysis report, wherein the performance analysis report includes abnormal performance data and optimization suggestions, including: Performing anomaly detection on the module dependencies, at least one module loading time, the resource size, the code segmentation status, and the build time to determine abnormal performance data; An optimization suggestion corresponding to the abnormal performance data is generated, and the abnormal performance data is bound to the optimization suggestion to obtain the performance analysis report.

5. The method according to claim 3, characterized in that The performance data is analyzed to obtain a performance analysis report, wherein the performance analysis report includes abnormal performance data and optimization suggestions, including: Obtain the trained performance optimization prediction model; The module dependencies, at least one module loading time, the resource size, the code segmentation situation and the build time are input into the performance optimization prediction model, the input data is analyzed by the performance optimization prediction model, and the abnormal performance data and corresponding optimization suggestions are output.

6. The method according to claim 1, characterized in that After obtaining the new configuration file constructed by the static module packaging tool, the following steps are included: Obtain a new configuration file required to build a new application through the plug-in in the static module; detecting whether the new configuration file contains the abnormal performance data; If yes, then executing the step of feeding back the optimization suggestion to the device where the developer is located; If not, the new configuration file is packaged by the plug-in.

7. The method according to claim 1, characterized in that Feedback of the optimization suggestion to the developer's device includes: generating a visual report based on the optimization suggestions and the abnormal performance data; The visualization report is sent to the device where the developer is located, and the visualization report is displayed to the developer through the static module packaging tool installed on the device.

8. A performance data processing device, characterized in that: The device comprises: The first acquisition module is used to obtain the performance logs generated by the static module packaging tool during the construction process; An extraction module, configured to extract the performance log to obtain at least one performance data; An analysis module, configured to analyze each of the performance data to obtain a performance analysis report, wherein the performance analysis report includes abnormal performance data and optimization suggestions; A second acquisition module is used to obtain a new configuration file constructed again by the static module packaging tool; The identification module is configured to feed back the optimization suggestion to the device where the developer is located when it is identified that the new configuration file includes the abnormal performance data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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