Data display method, device, electronic device and storage medium

Abstract

The present disclosure relates to a data display method, device, electronic device and storage medium, including: obtaining first data, wherein the first data includes monitoring data corresponding to at least one mini-program; detecting the first data, and in the case that the monitoring data of any mini-program is abnormal, determining any mini-program as a candidate program, and obtaining the log data of the candidate program; analyzing the monitoring data and the log data, obtaining an abnormal analysis result of the candidate program, and displaying the abnormal analysis result. In this way, a one-stop architecture is adopted to realize the operation data generated when the mini-program is running, and the entire processing flow from collection to feedback is gradually streamlined. The automated process drives the abnormal analysis of the mini-program when it is running, greatly reducing the cost from data collection to feedback display.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a data display method, device, electronic device, and storage medium. Background Art

[0002] As the mini program ecosystem continues to improve, there are more and more mini programs online, and a large amount of operating data is reported every day. These operating data are very important for maintaining the healthy operation of mini programs.

[0003] In the existing technology, operation data can only be collected by the host application of the mini program and is not reported to the developer in real time. When an exception occurs in the mini program, the developer can only actively go to the server of the host application to obtain the operation data, which makes it difficult to resolve the exception of the mini program in a timely manner. Therefore, a data collection and feedback method for automatically obtaining mini program data is needed. Summary of the Invention

[0004] The present disclosure provides a data display method, device, electronic device, and storage medium to at least solve the problem of the inability to automatically collect and feedback data from mini-programs in related technologies. The technical solutions of the present disclosure are as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a data presentation method is provided, including:

[0006] Acquire first data, where the first data includes monitoring data corresponding to at least one mini-program;

[0007] detecting the first data, and if the monitoring data of any of the mini-programs is abnormal, determining the mini-program as a candidate program, and obtaining log data of the candidate program;

[0008] The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

[0009] Optionally, the detecting the first data, and if the monitoring data of any of the mini-programs is abnormal, determining the mini-program as a candidate program, and obtaining log data of the candidate program, includes:

[0010] Filtering the first data and retaining abnormal data in the first data;

[0011] Perform aggregate analysis on the abnormal data, determine candidate programs based on the aggregate analysis results, and obtain log data of the candidate programs.

[0012] Optionally, performing aggregate analysis on the abnormal data, determining a candidate program based on the aggregate analysis result, and obtaining log data of the candidate program includes:

[0013] Performing aggregate analysis on the abnormal data, and determining at least one abnormal problem based on the aggregate analysis result;

[0014] According to a preset first priority, the mini-program corresponding to the at least one abnormal problem is selected as a candidate program, wherein the preset first priority is used to indicate the priority of the abnormal problem;

[0015] Obtain log data of the candidate program.

[0016] Optionally, performing aggregate analysis on the abnormal data, determining a candidate program based on the aggregate analysis result, and obtaining log data of the candidate program includes:

[0017] Performing aggregate analysis on the abnormal data, and determining candidate programs based on the aggregate analysis results;

[0018] According to a preset second priority, the type of target log data is determined, and the target log data of the candidate program is obtained. The preset second priority is used to indicate the priority of different types of log data.

[0019] Optionally, the monitoring data includes buried data and / or crash data, and filtering the first data to retain abnormal data in the first data includes:

[0020] Check the crash data according to a preset period, and retain the newly acquired crash data as abnormal data; and / or,

[0021] The buried data are monitored, and when any buried data exceeds a preset threshold, the any buried data is regarded as abnormal data and retained.

[0022] Optionally, analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes:

[0023] Downsampling the monitoring data and the log data to obtain sampled data;

[0024] The sampled data is analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

[0025] Optionally, analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes:

[0026] Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program;

[0027] The abnormality analysis result, the monitoring data and the log data are respectively stored in corresponding storage spaces, and the abnormality analysis result, the monitoring data and the log data are respectively displayed.

[0028] Optionally, analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes:

[0029] The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, the abnormality analysis result is filtered, and the filtered abnormality analysis result is displayed.

[0030] Optionally, presenting the abnormality analysis result includes:

[0031] Using a web page to display the abnormality analysis results; or,

[0032] According to the pre-acquired push request information, the abnormality analysis result corresponding to the push request information is pushed.

[0033] According to a second aspect of an embodiment of the present disclosure, there is provided a data display device, comprising:

[0034] A data collection platform is configured to obtain first data, wherein the first data includes monitoring data corresponding to at least one mini-program;

[0035] a data analysis platform configured to perform detection on the first data, and if the monitoring data of any of the mini-programs is abnormal, determine the mini-program as a candidate program, and obtain log data of the candidate program;

[0036] The problem display platform is configured to analyze the monitoring data and the log data, obtain an abnormality analysis result of the candidate program, and display the abnormality analysis result.

[0037] Optionally, the data analysis platform is configured to perform:

[0038] Filtering the first data and retaining abnormal data in the first data;

[0039] Perform aggregate analysis on the abnormal data, determine candidate programs based on the aggregate analysis results, and obtain log data of the candidate programs.

[0040] Optionally, the data analysis platform is configured to perform:

[0041] Performing aggregate analysis on the abnormal data, and determining at least one abnormal problem based on the aggregate analysis result;

[0042] According to a preset first priority, the mini-program corresponding to the at least one abnormal problem is selected as a candidate program, wherein the preset first priority is used to indicate the priority of the abnormal problem;

[0043] Obtain log data of the candidate program.

[0044] Optionally, the data analysis platform is configured to perform:

[0045] Performing aggregate analysis on the abnormal data, and determining candidate programs based on the aggregate analysis results;

[0046] According to a preset second priority, the type of target log data is determined, and the target log data of the candidate program is obtained. The preset second priority is used to indicate the priority of different types of log data.

[0047] Optionally, the data analysis platform is configured to perform:

[0048] Check the crash data according to a preset period, and retain the newly acquired crash data as abnormal data; and / or,

[0049] The buried data are monitored, and when any buried data exceeds a preset threshold, the any buried data is regarded as abnormal data and retained.

[0050] Optionally, the problem display platform is configured to execute:

[0051] Downsampling the monitoring data and the log data to obtain sampled data;

[0052] The sampled data is analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

[0053] The problem display platform is configured to execute:

[0054] Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program;

[0055] The abnormality analysis result, the monitoring data and the log data are respectively stored in corresponding storage spaces, and the abnormality analysis result, the monitoring data and the log data are respectively displayed.

[0056] Optionally, the problem display platform is configured to execute:

[0057] The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, the abnormality analysis result is filtered, and the filtered abnormality analysis result is displayed.

[0058] Optionally, the problem display platform is configured to execute:

[0059] Using a web page to display the abnormality analysis results; or,

[0060] According to the pre-acquired push request information, the abnormality analysis result corresponding to the push request information is pushed.

[0061] According to a third aspect of an embodiment of the present disclosure, there is provided a data display electronic device, comprising:

[0062] processor;

[0063] a memory for storing instructions executable by the processor;

[0064] The processor is configured to execute the instructions to implement the data display method described in the first item above.

[0065] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of a data display electronic device, the data display electronic device is enabled to execute any of the data display methods described above.

[0066] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program / instruction, which implements the data display method described in the first item above when executed by a processor.

[0067] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0068] Obtain first data, the first data including monitoring data corresponding to at least one mini program; detect the first data, and if the monitoring data of any mini program is abnormal, determine any mini program as a candidate program, and obtain log data of the candidate program; analyze the monitoring data and log data to obtain abnormal analysis results of the candidate program, and display the abnormal analysis results.

[0069] A one-stop architecture is adopted to automatically detect the monitoring data of the mini program and automatically obtain the log data of the mini program under abnormal circumstances. The cause of the mini program abnormality is analyzed based on the monitoring data and log data, so as to realize the operation data generated when the mini program is running. The entire processing flow from collection to feedback is gradually simplified. The automated process drives the abnormal analysis of the mini program during operation, greatly reducing the cost from data collection to feedback display.

[0070] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0072] Figure 1 The figure is an architecture diagram of a data display system according to an exemplary embodiment.

[0073] Figure 2 The figure is a logic diagram showing a data display method according to an exemplary embodiment.

[0074] Figure 3 The figure is a system diagram showing a data display method according to an exemplary embodiment.

[0075] Figure 4 It is a functional display diagram showing a data display method according to an exemplary embodiment.

[0076] Figure 5 The present invention is an automated flowchart of a data display method according to an exemplary embodiment.

[0077] Figure 6 The figure is a block diagram of a data display device according to an exemplary embodiment.

[0078] Figure 7 The figure is a block diagram of an electronic device for data presentation according to an exemplary embodiment.

[0079] Figure 8 The figure is a block diagram showing a device for data presentation according to an exemplary embodiment. DETAILED DESCRIPTION

[0080] In order to enable ordinary people in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0081] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0082] Figure 1 is a flow chart showing a data display method according to an exemplary embodiment. Figure 1 As shown, the data display method includes the following steps.

[0083] In step S11, first data is obtained, where the first data includes monitoring data corresponding to at least one mini-program.

[0084] In this step, the monitoring data of the applet running process can be collected. Among them, the monitoring data includes buried data and crash data. The buried data is the information collected during the operation of the applet according to the data buried data set in advance by the developer. After the buried data is reported, it can be displayed through various forms of dashboards. For example, the time taken for a network request from initiation to completion can be collected through data buried data. After the collected request time data is reported, it can be displayed through the network request dashboard. A large amount of such buried data can form a time interval distribution in the dashboard. Developers can discover potential problems by monitoring the network request time. Crash data is the on-site information after the applet crashes during operation. By reporting the crash data, the crash problem of the applet can be monitored, which is conducive to solving the crash problem.

[0085] The tracking data and crash data of the mini program can be stored on the aggregation platform, which is used to display and store various operating data reported by the client. Among them, the tracking data and crash data of the mini program will have their own corresponding aggregation platforms. For example, the tracking data can correspond to the recovery platform. Through the recovery platform, the tracking data of a certain user or device can be recovered, and various recovery tasks can also be managed. Correspondingly, the crash platform is the place where crash data is displayed. In addition, the aggregation platform can also include a reporting platform, which is mainly responsible for generating reports and displaying monitoring data. Through these aggregation platforms, various types of data of the mini program are managed and displayed, which facilitates the analysis and processing of specific problems arising during the operation of the mini program.

[0086] In step S12, the first data is detected. If the monitoring data of any mini-program is abnormal, the mini-program is determined as a candidate program, and the log data of the candidate program is obtained.

[0087] Log data, like mini-program monitoring data, is collected in real time during mini-program operation and is a crucial basis for resolving online mini-program issues. Log data records various types of information during mini-program operation. The more detailed the log data, the more accurately it can restore the mini-program's operational scene, facilitating analysis and resolution of mini-program anomalies. After a mini-program is identified as a candidate, its log data can be retrieved from the log data aggregation platform.

[0088] In this step, by processing and classifying the first data of the mini-program, we can preliminarily analyze which mini-programs have abnormalities, and then obtain the log data of these mini-programs to facilitate further analysis of the causes of the abnormalities of the mini-programs.

[0089] In one implementation, the first data is detected, and when any mini-program has monitoring anomalies, the mini-program is determined as a candidate program. The step of obtaining log data of the candidate program may include:

[0090] The first data is filtered to retain abnormal data in the first data; an aggregate analysis is performed on the abnormal data, a candidate program is determined based on the aggregate analysis result, and log data of the candidate program is obtained.

[0091] Among them, the priorities of different aggregated analysis results or log data can be set in advance, and based on the preset priorities, the abnormal problems of the mini program can be solved more reasonably and efficiently.

[0092] For example, in one case, aggregate analysis is performed on abnormal data, candidate programs are determined based on the aggregate analysis results, and log data of the candidate programs is obtained, including:

[0093] Perform aggregate analysis on the abnormal data and determine at least one abnormal problem based on the aggregate analysis results; based on a preset first priority, select the mini-program corresponding to at least one abnormal problem as a candidate program, and the preset first priority is used to indicate the priority of the abnormal problem; obtain log data of the candidate program.

[0094] In other words, different abnormal problems of the mini program can be divided into multiple priorities according to their severity or the degree of impact on the overall business process, that is, the first priority is preset. In this way, when multiple abnormal problems occur in the mini program within a period of time, the abnormal problems with high priority will be solved first.

[0095] Alternatively, in another case, aggregate analysis is performed on the abnormal data, candidate programs are determined based on the aggregate analysis results, and log data of the candidate programs is obtained, including:

[0096] Perform aggregate analysis on the abnormal data and determine the candidate program based on the aggregate analysis results; determine the type of target log data based on the preset second priority, and obtain the target log data of the candidate program. The preset second priority is used to indicate the priority of different types of log data.

[0097] That is to say, log data usually includes multiple different types of data and the data volume is large. Different types of log data can be divided into multiple priorities according to the criticality of each log data or the degree of impact on the overall business process, that is, the second priority is preset. In this way, when an abnormal problem occurs in the mini program, high-priority log data is obtained first to resolve the mini program's abnormality as soon as possible.

[0098] In one implementation, when the monitoring data includes buried data and / or crash data, filtering the first data and retaining abnormal data in the first data includes:

[0099] According to the preset period, the crash data is checked and the newly acquired crash data is retained as abnormal data; and / or, the buried point data is monitored. If any buried point data exceeds the preset threshold, any buried point data is treated as abnormal data and the abnormal data is retained.

[0100] In other words, the acquisition of log data can be automatically triggered by a scheduled task, such as regularly checking online crash data, and starting the acquisition process when a new crash is found. Or, it can also be triggered by alarm monitoring. For example, when the startup time of a small program exceeds the preset threshold, it will automatically trigger the acquisition of the log data of the small program.

[0101] For example, if the embedded data is the startup time data of the mini program, then if it is detected that the startup time of a mini program is very long, the startup time data of each stage of the startup process can be automatically extracted from the embedded data, and the relevant log data can be retrieved at the same time. Then, anomaly analysis can be performed through the log data and embedded data.

[0102] In step S13, the monitoring data and log data are analyzed to obtain abnormality analysis results of the candidate program, and the abnormality analysis results are displayed.

[0103] In one implementation, monitoring data and log data are analyzed to obtain abnormality analysis results of candidate programs, and the abnormality analysis results are displayed, including: downsampling the monitoring data and log data to obtain sampled data; analyzing the sampled data to obtain abnormality analysis results of candidate programs, and displaying the abnormality analysis results.

[0104] For example, instead of analyzing all of the outliers, we can randomly select a few cases for analysis based on the percentage of mini-programs launched, referring to the ratio of the number of mini-program launches to the total number of mini-program launches. Mini-program online operations are counted daily. If there are 100,000 mini-program launches on a given day, and mini-program A accounts for 40%, it can be considered a top mini-program. Therefore, if sampling is performed based on the percentage, the data for mini-program A should account for approximately 40% of the total sampled data.

[0105] In the present disclosure, unprocessed exception cases can be processed through a manually triggered fallback method, which can reduce the large daily startup volume and the occupation of physical resources by log data acquisition tasks.

[0106] In one implementation, monitoring data and log data are analyzed to obtain abnormality analysis results of candidate programs, and the abnormality analysis results are displayed, including: analyzing monitoring data and log data to obtain abnormality analysis results of candidate programs; storing the abnormality analysis results, monitoring data and log data in corresponding storage spaces respectively, and displaying the abnormality analysis results, monitoring data and log data respectively.

[0107] In other words, the processing results are stored in different persistent containers according to their nature. Monitoring data is stored in the problem storage pool, which records the phenomena observed after analyzing abnormal data, that is, the abnormal problems that occurred. Retrieved logs are stored in the log storage pool, and analysis results are stored in the analysis result storage pool, which records the results of analyzing abnormal data, that is, the causes of the abnormal problems. Each mini-program exception analysis task has a unique task scheduling identifier, which associates different data stored in different storage spaces.

[0108] In one implementation, monitoring data and log data are analyzed to obtain abnormality analysis results of candidate programs, and the abnormality analysis results are displayed, including: analyzing monitoring data and log data to obtain abnormality analysis results of candidate programs, filtering the abnormality analysis results, and displaying the filtered abnormality analysis results.

[0109] Specifically, when addressing a specific issue, faced with massive amounts of monitoring data and retrieved log data, it's necessary to filter out irrelevant information. For example, for video-related issues, only the relevant monitoring data and log data should be considered. To achieve this, a set of customized log generation and analysis rules is required, such as using specific prefixes when recording logs. Furthermore, additional filtering capabilities are required for the log analysis results to filter out sensitive information.

[0110] In one implementation, displaying the abnormality analysis result includes: displaying the abnormality analysis result using a web page; or, based on pre-acquired push request information, pushing the abnormality analysis result corresponding to the push request information.

[0111] In other words, the feedback result display is divided into two parts: static display and dynamic push. Static display can display the exception analysis results in the form of a web page. Developers can choose to check specific exception analysis results on the web page according to their needs. Dynamic push pushes the exception analysis results in the form of subscription information. For developers who have subscribed to a certain indicator, whenever a new exception analysis result of the indicator is generated, they will be notified through message push, email, etc.

[0112] In the present disclosure, each of the above steps can be visually displayed on the client through the front-end display module of the aggregation platform, such as the first data of each mini program currently obtained, the mini program that is currently undergoing data acquisition or exception analysis, the mini program waiting for data acquisition or exception analysis, etc., and an operation entry for manual intervention priority can also be provided, etc.

[0113] like Figure 2 As shown, it is a logical diagram of a data display method in the present disclosure. For the embedded data, crash data and log data generated when the mini program is running, the entire processing flow from collection to feedback will be gradually simplified and finally generate feedback indicators.

[0114] Specifically, the data display method can be divided into three parts: data collection, data analysis, and indicator feedback. Among them, data collection can be divided into two parts: data source and data aggregation. The data source refers to the source of embedded data, crash data, and log data. After the embedded data is reported, it is displayed through various forms of dashboards, such as network request dashboards, video conversion rate dashboards, audio abnormal event dashboards, component insert failure rate dashboards, package download failure rate dashboards, and page opening failure rate dashboards, etc.

[0115] The endpoints of data collection are various aggregation platforms developed by developers. For example, the reporting platform generates reports and displays tracking data, the crash platform displays crash data, and the log platform displays log data. Additionally, there are retrieval platforms, which can be used to retrieve tracking data for a specific user or device and manage retrieval tasks. These aggregation platforms manage and display tracking data, crash data, and log data, enabling developers to analyze and address specific issues.

[0116] The data analysis part is responsible for processing and classifying the embedded data, crash data, and log data obtained by the data collection part, and preliminarily analyzing the causes to determine in which aspects the mini program anomalies occur. For example, the mini program anomalies may be crash problems, video playback problems, page rendering problems, audio playback problems, JS errors (javascript code errors) or network errors, etc.

[0117] Finally, based on the data analysis results, readable result data can be generated and finally presented through the indicator feedback part.

[0118] like Figure 3 As shown, it is a system diagram of a data display method in the present disclosure, wherein the data entry represents the source of the buried data, crash data and log data, such as Figure 2 The data aggregation platform represented by the data collection part also includes the collaboration platform, feedback platform, or alarm platform used by developers in their daily work. The embedded data, crash data, and log data can come from multiple data sources, such as data entry 1, data entry 2, data entry 3, and data entry 4, etc.

[0119] The scheduling module accesses the data passed in by each data entry through the external docking interface. According to different scheduling strategies, the log data is obtained through the external docking interface. At the same time, a data retrieval task can be generated, and the retrieval task module can retrieve the buried data through the external docking interface. For example, according to the priority strategy, you can first obtain the data corresponding to the abnormal problems with higher priority, or process the data with higher priority first, and so on. For example, a surge in crashes in a short period of time will have a higher priority than a small number of crashes in a long period of time, and the monitoring data that takes 200% of the alarm threshold to start the mini program will have a higher priority than the monitoring data that takes 100% of the alarm threshold. In this way, when there is a lot of data that needs to be processed at the same time, it can adapt to the processing capabilities of the tool and give priority to the data with higher priority.

[0120] The analysis module analyzes the acquired tracking data, crash data, and log data to obtain anomaly analysis results for candidate programs. The front-end information display module then displays these anomaly analysis results. Furthermore, the data adaptation module stores the candidate program's anomaly issues, log data, and anomaly analysis results for subsequent query and processing.

[0121] like Figure 4 As shown, this is a functional display diagram of a data display method in the present disclosure. Specifically, from the overall function, the architecture of the data display method can be divided into a display layer, a routing layer, a logic layer, a database adaptation layer and a data layer. To the outside world, the visibility gradually decreases from top to bottom.

[0122] Among them, the display layer includes multiple parts such as feedback result display, triggering retrieval entrance, retrieval information display and other information display, providing visualization and operational capabilities. In the feedback result display part, the problem type and log details of the mini program can be displayed. The problem type refers to the specific type of problem, such as crash, startup timeout, rendering failure, etc. The log details may include log summary, detailed log download entrance, retrieval task information and operation details. In the triggering retrieval entrance part, the triggering method and triggering conditions of the triggering retrieval entrance can be displayed. The triggering method may include manual triggering, timed triggering and other triggering methods, etc. The triggering conditions may include restrictions on user ID, device ID and operating platform, etc. In the retrieval information display part, information such as retrieval task ID, triggering event, retrieval status, creator, creation time, creation method, retrieval error type, user ID, operating platform, time consumption and whether the log has been processed can be displayed. In the other information display part, other current information when the mini program is running can be displayed, such as the current mini program version number, etc., which is not specifically limited.

[0123] The routing layer can be considered as a connecting component. For example, the presentation layer and the logic layer are two independent components that may have very different functions and implementations. Both of them can work independently, but the routing layer can combine the two.

[0124] The logic layer encompasses various data processing functions and is primarily divided into a data preprocessing layer and an aggregation analysis layer. The data preprocessing layer filters embedded data, crash data, and log data, processing only abnormal data for further analysis. Preprocessed data is then transmitted to the aggregation analysis layer, where data preprocessing can include user ID extraction, version number parsing, and server configuration information analysis. The aggregation analysis layer is divided into several parts according to its functions, including log retrieval, log retrieval, log analysis and log analysis rule pool. Among them, log retrieval includes several functions such as active retrieval through interactive portals, timed retrieval or alarm retrieval. Log retrieval includes retrieval status display and retrieval result notification. Log analysis includes obtaining analysis rules, obtaining analysis status and processing analysis results. The log analysis rule pool includes various log analysis rules such as startup problems, rendering problems, network problems, video problems and audio problems, which respectively specify the correspondence between log information and problems. For example, video-related log data contains a unified prefix "video", and audio-related log data contains a unified prefix "audio". Then, when formulating analysis rules, you can analyze video-related abnormal problems by extracting logs containing "video", and analyze audio-related problems by extracting logs containing "audio".

[0125] The logic layer analyzes and processes tracking data, crash data, and log data to generate anomaly analysis results. The database adaptation layer stores the analysis results and corresponding data in corresponding storage spaces within the data layer, facilitating subsequent queries and further analysis. The data layer can be divided into multiple storage spaces, each storing different types of data, including anomaly issues, log data, anomaly analysis results, and summary information.

[0126] like Figure 5 As shown, it is an automated flow chart of a data display method in the present invention, which includes four parts: timed triggering script, monitoring alarm, manual triggering and data aggregation analysis. That is to say, through the three methods of timed triggering script, monitoring alarm and manual triggering, the buried data, crash data and log data can be automatically obtained, and then, the buried data, crash data and log data can be automatically aggregated and analyzed to obtain abnormal analysis results.

[0127] As can be seen from the above, the technical solution provided by the embodiments of the present disclosure adopts a one-stop architecture to automatically detect the monitoring data of the mini program, and automatically obtain the log data of the mini program under abnormal circumstances, and analyze the causes of the mini program's abnormalities based on the monitoring data and log data, thereby realizing the operation data generated when the mini program is running. The entire processing flow from collection to feedback is gradually streamlined, and the automated process drives the abnormal analysis of the mini program during runtime, greatly reducing the cost from data collection to feedback display.

[0128] Figure 6 1 is a block diagram of a data display device according to an exemplary embodiment, the device comprising:

[0129] The data collection platform 201 is configured to obtain first data, where the first data includes monitoring data corresponding to at least one mini-program;

[0130] The data analysis platform 202 is configured to perform a test on the first data, and if the monitoring data of any of the mini-programs is abnormal, determine the mini-program as a candidate program, and obtain log data of the candidate program;

[0131] The problem display platform 203 is configured to analyze the monitoring data and the log data, obtain an abnormality analysis result of the candidate program, and display the abnormality analysis result.

[0132] In one implementation, the data analysis platform 202 is configured to perform:

[0133] Filtering the first data and retaining abnormal data in the first data;

[0134] Perform aggregate analysis on the abnormal data, determine candidate programs based on the aggregate analysis results, and obtain log data of the candidate programs.

[0135] In one implementation, the data analysis platform 202 is configured to perform:

[0136] Performing aggregate analysis on the abnormal data, and determining at least one abnormal problem based on the aggregate analysis result;

[0137] According to a preset first priority, the mini-program corresponding to the at least one abnormal problem is selected as a candidate program, wherein the preset first priority is used to indicate the priority of the abnormal problem;

[0138] Obtain log data of the candidate program.

[0139] In one implementation, the data analysis platform 202 is configured to perform:

[0140] Performing aggregate analysis on the abnormal data, and determining candidate programs based on the aggregate analysis results;

[0141] According to a preset second priority, the type of target log data is determined, and the target log data of the candidate program is obtained. The preset second priority is used to indicate the priority of different types of log data.

[0142] In one implementation, the data analysis platform 202 is configured to perform:

[0143] Check the crash data according to a preset period, and retain the newly acquired crash data as abnormal data; and / or,

[0144] The buried data are monitored, and when any buried data exceeds a preset threshold, the any buried data is regarded as abnormal data and retained.

[0145] In one implementation, the question display platform 203 is configured to execute:

[0146] Downsampling the monitoring data and the log data to obtain sampled data;

[0147] The sampled data is analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

[0148] In one implementation, the question display platform 203 is configured to execute:

[0149] Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program;

[0150] The abnormality analysis result, the monitoring data and the log data are respectively stored in corresponding storage spaces, and the abnormality analysis result, the monitoring data and the log data are respectively displayed.

[0151] In one implementation, the question display platform 203 is configured to execute:

[0152] The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, the abnormality analysis result is filtered, and the filtered abnormality analysis result is displayed.

[0153] In one implementation, the question display platform 203 is configured to execute:

[0154] Using a web page to display the abnormality analysis results; or,

[0155] According to the pre-acquired push request information, the abnormality analysis result corresponding to the push request information is pushed.

[0156] As can be seen from the above, the technical solution provided by the embodiments of the present disclosure adopts a one-stop architecture to automatically detect the monitoring data of the mini program, and automatically obtain the log data of the mini program under abnormal circumstances, and analyze the causes of the mini program's abnormalities based on the monitoring data and log data, thereby realizing the operation data generated when the mini program is running. The entire processing flow from collection to feedback is gradually streamlined, and the automated process drives the abnormal analysis of the mini program during runtime, greatly reducing the cost from data collection to feedback display.

[0157] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0158] Figure 7 The figure is a block diagram of an electronic device for data presentation according to an exemplary embodiment.

[0159] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory including instructions, wherein the instructions can be executed by a processor of an electronic device to perform the above method. Alternatively, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0160] In an exemplary embodiment, a computer program product is also provided. When the computer program product is executed on a computer, the computer is enabled to implement the above-mentioned data presentation method.

[0161] As can be seen from the above, the technical solution provided by the embodiments of the present disclosure adopts a one-stop architecture to automatically detect the monitoring data of the mini program, and automatically obtain the log data of the mini program under abnormal circumstances, and analyze the causes of the mini program's abnormalities based on the monitoring data and log data, thereby realizing the operation data generated when the mini program is running. The entire processing flow from collection to feedback is gradually streamlined, and the automated process drives the abnormal analysis of the mini program during runtime, greatly reducing the cost from data collection to feedback display.

[0162] Figure 8 It is a block diagram of a device 800 for data presentation according to an exemplary embodiment.

[0163] For example, apparatus 800 may be a mobile phone, a computer, a digital broadcast electronic device, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

[0164] Reference Figure 8 , the apparatus 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0165] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0166] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0167] The power supply component 807 provides power to the various components of the device 800. The power supply component 807 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 800.

[0168] The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0169] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0170] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0171] Sensor assembly 814 includes one or more sensors for providing various aspects of the status assessment of device 800. For example, sensor assembly 814 can detect the open / closed state of device 800, the relative positioning of components, such as the display and keypad of device 800. Sensor assembly 814 can also detect changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and changes in the temperature of device 800. Sensor assembly 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 can also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0172] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0173] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described in the first and second aspects.

[0174] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the device 800 to perform the above method. Alternatively, for example, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0175] In an exemplary embodiment, a computer program product including instructions is further provided. When the computer program product is run on a computer, the computer is caused to execute the data presentation method described in the first embodiment above.

[0176] As can be seen from the above, the technical solution provided by the embodiments of the present disclosure adopts a one-stop architecture to automatically detect the monitoring data of the mini program, and automatically obtain the log data of the mini program under abnormal circumstances, and analyze the causes of the mini program's abnormalities based on the monitoring data and log data, thereby realizing the operation data generated when the mini program is running. The entire processing flow from collection to feedback is gradually streamlined, and the automated process drives the abnormal analysis of the mini program during runtime, greatly reducing the cost from data collection to feedback display.

[0177] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0178] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data display method, characterized in that: include: Acquire first data, where the first data includes monitoring data corresponding to at least one mini-program; detecting the first data, and if the monitoring data of any of the mini-programs is abnormal, determining the mini-program as a candidate program, and obtaining log data of the candidate program; Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program, and displaying the abnormality analysis result; The detecting of the first data, determining any mini-program as a candidate program when the monitoring data of any mini-program is abnormal, and obtaining log data of the candidate program includes: Filtering the first data and retaining abnormal data in the first data; Performing aggregate analysis on the abnormal data, determining a candidate program based on the aggregate analysis result, and obtaining log data of the candidate program; The monitoring data includes buried data and / or crash data, and filtering the first data to retain abnormal data in the first data includes: Check the crash data according to a preset period, and retain the newly acquired crash data as abnormal data; and / or, The buried data are monitored, and when any buried data exceeds a preset threshold, the any buried data is regarded as abnormal data and retained.

2. The data display method according to claim 1, characterized in that: The performing aggregate analysis on the abnormal data, determining a candidate program based on the aggregate analysis result, and obtaining log data of the candidate program includes: Performing aggregate analysis on the abnormal data, and determining at least one abnormal problem based on the aggregate analysis result; According to a preset first priority, the mini-program corresponding to the at least one abnormal problem is selected as a candidate program, wherein the preset first priority is used to indicate the priority of the abnormal problem; Obtain log data of the candidate program.

3. The data display method according to claim 1, characterized in that: The performing aggregate analysis on the abnormal data, determining a candidate program according to the aggregate analysis result, and obtaining log data of the candidate program includes: Performing aggregate analysis on the abnormal data, and determining candidate programs based on the aggregate analysis results; According to a preset second priority, the type of target log data is determined, and the target log data of the candidate program is obtained. The preset second priority is used to indicate the priority of different types of log data.

4. The data display method according to claim 1, characterized in that: The analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes: Downsampling the monitoring data and the log data to obtain sampled data; The sampled data is analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

5. The data display method according to claim 1, characterized in that: The analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes: Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program; The abnormality analysis result, the monitoring data and the log data are respectively stored in corresponding storage spaces, and the abnormality analysis result, the monitoring data and the log data are respectively displayed.

6. The data display method according to claim 1, characterized in that: The analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program and displaying the abnormality analysis result includes: The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, the abnormality analysis result is filtered, and the filtered abnormality analysis result is displayed.

7. The data display method according to claim 1, characterized in that: The displaying of the abnormality analysis result includes: Using a web page to display the abnormality analysis results; or, According to the pre-acquired push request information, the abnormality analysis result corresponding to the push request information is pushed.

8. A data display device, characterized in that: include: A data collection platform is configured to obtain first data, wherein the first data includes monitoring data corresponding to at least one mini-program; a data analysis platform configured to perform detection on the first data, and if the monitoring data of any of the mini-programs is abnormal, determine the mini-program as a candidate program, and obtain log data of the candidate program; a problem display platform configured to analyze the monitoring data and the log data, obtain an abnormality analysis result of the candidate program, and display the abnormality analysis result; The data analysis platform is configured to perform: Filtering the first data and retaining abnormal data in the first data; Performing aggregate analysis on the abnormal data, determining a candidate program based on the aggregate analysis result, and obtaining log data of the candidate program; The monitoring data includes tracking data and / or crash data, and the data analysis platform is configured to perform: Check the crash data according to a preset period, and retain the newly acquired crash data as abnormal data; and / or, The buried data are monitored, and when any buried data exceeds a preset threshold, the any buried data is regarded as abnormal data and retained.

9. The data display device according to claim 8, characterized in that: The data analysis platform is configured to perform: Performing aggregate analysis on the abnormal data, and determining at least one abnormal problem based on the aggregate analysis result; According to a preset first priority, the mini-program corresponding to the at least one abnormal problem is selected as a candidate program, wherein the preset first priority is used to indicate the priority of the abnormal problem; Obtain log data of the candidate program.

10. The data display device according to claim 8, characterized in that: The data analysis platform is configured to perform: Performing aggregate analysis on the abnormal data, and determining candidate programs based on the aggregate analysis results; According to a preset second priority, the type of target log data is determined, and the target log data of the candidate program is obtained. The preset second priority is used to indicate the priority of different types of log data.

11. The data display device according to claim 8, characterized in that: The problem display platform is configured to execute: Downsampling the monitoring data and the log data to obtain sampled data; The sampled data is analyzed to obtain an abnormality analysis result of the candidate program, and the abnormality analysis result is displayed.

12. The data display device according to claim 8, characterized in that: The problem display platform is configured to execute: Analyzing the monitoring data and the log data to obtain an abnormality analysis result of the candidate program; The abnormality analysis result, the monitoring data and the log data are respectively stored in corresponding storage spaces, and the abnormality analysis result, the monitoring data and the log data are respectively displayed.

13. The data display device according to claim 8, characterized in that: The problem display platform is configured to execute: The monitoring data and the log data are analyzed to obtain an abnormality analysis result of the candidate program, the abnormality analysis result is filtered, and the filtered abnormality analysis result is displayed.

14. The data display device according to claim 8, characterized in that: The problem display platform is configured to execute: Using a web page to display the abnormality analysis results; or, According to the pre-acquired push request information, the abnormality analysis result corresponding to the push request information is pushed.

15. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the data presentation method according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that When the instructions in the computer-readable storage medium are executed by a processor of a data presentation electronic device, the data presentation electronic device is enabled to perform the data presentation method according to any one of claims 1 to 7.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the data display method according to any one of claims 1 to 7 is implemented.

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