Software system self-checking method, device and equipment and storage medium

Abstract

The invention discloses a software system self-checking method and device, equipment and a storage medium, and the method comprises the steps: carrying out the data detection of data in a data set corresponding to each data type under the condition that the data type contained in the collected to-be-detected data of a software system meets a preset condition, obtaining a first analysis result corresponding to the data set of each data type; processing the data in the data set of the data type with the corresponding association relationship by using an association data detection rule to obtain a second analysis result of the data set of the data type in each association relationship; whether each analysis result meets a corresponding preset data qualification condition or not is judged, and a detection report is generated. According to the method, the relevance between the data is considered, and the comprehensiveness of software system detection is improved. And the preset data qualification condition is obtained by analyzing historical data of the software system, so that the objectivity and accuracy of the detection result are improved.

Description

Technical Field

[0001] The present application relates to the field of data detection technology, and more specifically, to a software system self-checking method, device, equipment and storage medium. Background Art

[0002] With the rapid development of the Internet and information technology, the scale of software systems is getting larger and larger, and software updates and iterations are becoming more and more frequent. Therefore, the reliability and stability of core software systems are becoming more and more important. At present, through automated detection of software systems, hidden system problems can be discovered in advance, providing guarantee for the stable operation of software systems.

[0003] Usually, the automated detection method of the software system often relies on the experience of the operation and maintenance personnel to determine whether the data of each detection point meets the requirements. It requires a high level of experience from the operation and maintenance personnel, and the accuracy and objectivity of the detection results are low. Summary of the invention

[0004] In view of this, the present application provides a software system self-check method, apparatus, device and storage medium for solving the problem of low accuracy and objectivity of software system detection results.

[0005] In order to solve the above problems, the proposed solutions are as follows:

[0006] A software system self-checking method, comprising:

[0007] Collecting the data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range;

[0008] Determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is the first analysis result corresponding to the data set of the data type;

[0009] Acquire at least one pre-established association relationship between the data types, and an association data detection rule corresponding to each association relationship, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types;

[0010] Using the associated data detection rules respectively, the data in the data set of the data type having the corresponding associated relationship are processed to obtain a second analysis result of the data set of the data type in each associated relationship;

[0011] Determine respectively the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively judge whether each analysis result satisfies the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected;

[0012] Generate a test report containing the judgment results of each data set of the data type in the data to be tested.

[0013] A software system self-checking device, comprising:

[0014] A data collection unit, used for collecting data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range;

[0015] A first data detection unit is used to determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is a first analysis result corresponding to the data set of the data type;

[0016] An association relationship acquisition unit, used to acquire at least one association relationship established in advance between the data types, and an associated data detection rule corresponding to each of the association relationships, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types;

[0017] A second data detection unit, configured to process data in a data set of the data type having the corresponding association relationship by using the association data detection rules respectively, and obtain a second analysis result of the data set of the data type in each association relationship;

[0018] A result detection unit, used to respectively determine the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively determine whether each analysis result meets the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected;

[0019] The report generating unit is used to generate a detection report including the judgment result of the data set of each data type in the data to be detected.

[0020] A software system self-checking device, comprising a memory and a processor;

[0021] The memory is used to store programs;

[0022] The processor is used to execute the program to implement each step of any one of the software system self-checking methods.

[0023] A storage medium stores a computer program, which, when executed by a processor, implements each step of any one of the software system self-checking methods.

[0024] In the process of testing the data to be tested in the software system, in addition to independently testing the data of each data type, the present application also performs correlation analysis on the data of the data types with correlation relationships. Compared with the prior art, the correlation between the software system data is taken into account, and the comprehensiveness of the detection is improved. Furthermore, each first analysis result and second result obtained by independent detection and correlation analysis are compared with the corresponding preset data qualification conditions, respectively, to determine whether there is an unqualified situation, and generate a test report. Among them, the preset data qualification conditions are obtained by analyzing the historical data of the server to be tested, which improves the objectivity and accuracy of the test results compared with the experience judgment of the operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0026] Figure 1 A flowchart of a method for implementing a software system self-check provided in an embodiment of the present application;

[0027] Figure 2 A structural example diagram of an application system provided in an embodiment of the present application;

[0028] Figure 3 An example flow chart of a method for implementing a software system self-checking method for an application system provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of the structure of a software system self-checking device provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of the structure of a software system self-checking device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0032] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] The detection of software systems is complex and specific. It is necessary to detect the status of the software itself, the connectivity status of the upstream and downstream links, and the hardware equipment where the software is located. The main method of current software system detection is to collect and detect data for each link of the software itself and the relevant indicators of the hardware infrastructure environment on which the software depends. The applicant found that there are several problems in the detection process. First, only the various detection points or detection indicators of the software system are checked, and there is a lack of correlation analysis of the system data. The test results obtained are too one-sided and not accurate enough. Secondly, the qualified indicators or qualified data based on the software system detection need to be set by the operation and maintenance personnel based on experience, or manually judged. If the accuracy of the test results is to be improved, the experience requirements of the operation and maintenance personnel are relatively high, and the subjectivity is relatively strong.

[0036] In addition, for software systems deployed on multiple servers with large amounts of data, the self-check process often needs to span multiple platforms. For example, some functions of software A are deployed on Windows servers, some are deployed on Linux servers, and some require databases and redis middleware. To detect the health of software A, it is often necessary to detect each link, so you need to log in to a specific platform (such as logging in to the database management platform to view the database, logging in to the redis manager to view the service status, logging in to the Linux server to view the server and service status, etc.) to obtain the test results. This is time-consuming, the test process is complicated, the test results are not easy to collect, and the test efficiency is low.

[0037] To solve the above problems, the present application provides a software system self-checking method, which can be applied to any server deployed by the software system, or to a server independent of the software system, to achieve Figure 1 The steps shown here complete the automated testing of the software system. Figure 1 , the process of implementing the software system automated detection method may include the following steps:

[0038] Step S110, collecting the data to be tested of the software system.

[0039] The data to be tested is the data of at least one server to be tested within a preset time range. The server to be tested is a server where the software system to be tested is deployed. The data of multiple servers to be tested are collected to a unified testing address for testing, which is convenient for generating a unified test report, improving the testing efficiency and the integrity of the test results.

[0040] The data in the data to be tested may include: processes, CPU, IO, memory, Socket connection, performance log, application log of each server to be tested, and the response results of the server to the simulation request. The data in the data to be tested can be divided into units of servers to be tested, and the data corresponding to each server to be tested includes multiple data types and data sets of each data type.

[0041] Before collecting data, the operation and maintenance personnel can manually set a preset time range. For example, the operation and maintenance personnel noticed that an anomaly occurred at time A through the front-end response of the software system. In order to locate the specific servers where the problem of the software system occurred, the operation and maintenance personnel can use the neighborhood of time A as the preset time range, so as to detect the data to be detected within the preset time range and locate the cause of the anomaly.

[0042] Optionally, to ensure the stability of the software system during operation and to detect system problems in a timely manner, a timing module may be configured on the service end or server running the software system self-check method to periodically obtain the data to be tested within a preset time range.

[0043] In the scenario of scheduled detection, the process of collecting the data to be detected of the software system may include: obtaining the time of the last collection of the data to be detected of the server to be detected corresponding to the software system as the first time; based on the first time and the preset scheduled detection interval, determining the time for collecting the data to be detected of the server to be detected this time as the second time; collecting the data of the server to be detected within a preset time range as the data to be detected, the preset time range being the time range limited by the first time and the second time.

[0044] In the embodiment of the present application, although it is a timed detection mode, the data to be detected each time is the data from the last timing to the current timing, but the data at every moment in the operation of the software system is detected, thereby improving the comprehensiveness of the software system detection.

[0045] It is understandable that each data type in the server at each moment corresponds to at least one data. Since the data obtained is within a preset time range, the data types in the data to be detected are complex and the data volume is huge. Therefore, in addition to being divided into units based on the server to be detected, the data to be detected obtained by the embodiment of the present application can also be divided into units based on data types. Each data type corresponds to a data set, and the data set includes all data belonging to the same data type. You can refer to Table 1 to understand the data structure of the data to be detected. By standardizing the data format of the data to be detected, the data comparison confusion that may occur during the detection process can be avoided.

[0046] Table 1 Data structure of the data to be detected

[0047] Step S120, determining whether the data type included in the data to be detected meets the preset condition. If it is determined that the data type included in the data to be detected meets the preset condition, step S130 is executed.

[0048] In an optional situation, the operation and maintenance personnel can set preset conditions for the data to be tested, such as whether the data to be tested is complete and has no redundant data, etc., or they can set the preset conditions to be empty and continue to execute subsequent steps.

[0049] Optionally, when the preset condition is that the data to be tested is complete, the process of judging whether the data to be tested meets the preset condition based on the type of data contained in the data to be tested may include the following steps:

[0050] Obtain a pre-stored reference data list, which stores the data types that need to be detected by the server to be detected; query whether the data to be detected contains each data type in the reference data list; if the data to be detected contains each data type in the reference data list, determine that the data types contained in the data to be detected meet preset conditions; if the data to be detected does not completely contain each data type in the reference data list, execute the steps of collecting the data to be detected by the software system until the data to be detected contains each data type in the reference data list.

[0051] The reference data list can be pre-stored on the server or service end that runs the software system self-check method, wherein the reference data list may include: server cluster information, server information, service information and other static data of the software system that need to be detected, and may also include real-time collected indicators, logs, links, system performance and other dynamic data that need to be detected.

[0052] The reference data list stores the data types of static data and dynamic data. For example, the log data in the collected dynamic data includes: timestamp-9:00, user or system identifier-user name, operation type-storage operation, operation parameter-storage data size and system status-on, etc. Taking "timestamp-9:00" as an example, the timestamp belongs to the data type, and 9:00 belongs to the time data corresponding to the timestamp. In the process of pre-storing the reference data list, the data type included in the log data is extracted and stored in the reference data list as the data type as the basis for judging whether the data to be detected is complete.

[0053] The reference data list can refer to Table 2. The data types to be detected by each server to be detected may be the same or completely different. The specific data type to be detected by each server to be detected depends on the data type corresponding to the data collected in advance from each server to be detected. All data types of the server are included as much as possible, so as to conduct a comprehensive detection of the server running the software system in the future.

[0054] Table 2 Reference data list

[0055] For the data type of the data contained in each server to be tested in the data to be tested, check whether it contains each data type in the reference data list. If the data to be tested lacks any data type, it proves that the currently collected data to be tested is incomplete, and it is necessary to re-collect the data to be tested of the software system until the collected data to be tested is complete, and further perform data testing on the complete data to be tested.

[0056] Optionally, if the data to be detected does not completely contain every data type in the reference data list, the step of collecting the data to be detected by the software system is executed, and the number of times the data to be detected by the software system is collected is recorded; when the number of times the data to be detected by the software system reaches a preset number, and the data to be detected does not completely contain every data type in the reference data list, the step of performing data detection on the data sets corresponding to each data type in the data to be detected is executed on the data to be detected, and an alarm message indicating incomplete data collection is sent to the front end.

[0057] In the case that the complete data to be tested cannot be collected for many times, in order to avoid the self-test program entering an endless loop, a target number of times can be set. If the number of collections reaches the target number of times and the complete data to be tested is still not collected, the collection of the data to be tested can be terminated and an alarm message of data collection abnormality can be sent to the front end. In addition, data detection can be performed on the incomplete data to be tested that was collected last time to detect whether there is an abnormality in the existing data.

[0058] Optionally, when the preset condition is that there is no redundant data in the data to be detected, or the data corresponding to a specific data type is in a specific data format, the data to be detected can be checked, cleaned, parsed or format converted in combination with the above-mentioned reference data list, so that the data to be detected finally collected meets the preset conditions.

[0059] Step S130, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within a preset time range, where the change information is the first analysis result corresponding to the data set of the data type.

[0060] In the embodiment of the present application, a data set corresponding to each data type in the data to be detected is used as a detection unit for detection, and the change of data of each data type in the data to be detected within a preset time range is determined. For example, in the data to be detected, the data in the data set corresponding to the message response rate is detected, and the message response rate corresponding to each moment is counted according to the time sequence to obtain a line graph representing the change of the message response rate within the preset time range, and the line graph is used as the first analysis result corresponding to the data set of the message response rate.

[0061] It can be understood that for message response rate, error rate, remaining memory, etc., the data contained in the corresponding data sets are numerical data, and the line graph can be used to subsequently determine whether the data type has been detected to be qualified. The above-mentioned detection method can be used, but for the judgment of abnormal situations such as message character abnormalities and connection status abnormalities, it is impossible to obtain the line graph corresponding to the message characters or connection status based on the data to be detected. Therefore, different data types correspond to different detection methods, but the common point is to detect the changes or change information of the data in the data set of the data type.

[0062] Therefore, when detecting the data set corresponding to each of the data types, the following steps can be executed: determining the preset detection rules corresponding to the data type; based on the preset detection rules, performing data detection on the data in the data set corresponding to the data type to obtain change information of the data set of the data type within a preset time range.

[0063] Optionally, the operation and maintenance personnel may pre-set detection rules corresponding to different data types according to whether the data corresponding to different data types are character data, numerical data, etc. Alternatively, the server or service end running the software system self-checking method may determine the detection rules corresponding to the data types according to the data qualification conditions corresponding to the data types, so that the change information or the first analysis result obtained by detecting the data contained in the data set of the data type based on the detection rules can be easily compared with the data qualification conditions to improve the data detection efficiency.

[0064] Step S140, obtaining at least one pre-established association relationship between data types, and an association data detection rule corresponding to each association relationship.

[0065] Step S150 , using the associated data detection rules respectively, the data in the data set of the data type having the corresponding associated relationship are processed to obtain a second analysis result of the data set of the data type in each associated relationship.

[0066] The association relationship indicates that there is a preset association between the data in the data set between at least two data types. In the embodiment of the present application, the association relationship between the data types can be preset by the operation and maintenance personnel, such as detecting whether the server responds correctly to the simulation request within a preset time range, which involves at least two data types: simulation request data and response data, and the response content changes with the change of the simulation request, and the qualified conditions for comparison are also different, so there is an association relationship between the simulation request data and the response data.

[0067] For data types with obvious correlations like the above, the operation and maintenance personnel can pre-set them. For data types that are not easy for the operation and maintenance personnel to detect but have correlations, the server can analyze and mine the specific correlations between the data of each data type, and based on at least two data types with clear correlations, gradually improve the correlations between the set data types during the detection process.

[0068] It can be understood that each association relationship corresponds to an association data detection rule, and the association data detection rule is an analysis rule or analysis method for performing association analysis on data in a data set of a data type having the association relationship.

[0069] For example, the rules for performing association analysis on the data contained in the data sets corresponding to the simulated request data and the response data may include: first, according to the request type, classify the various simulated request data contained in the data sets corresponding to the simulated request data type, and obtain the collections of similar request data corresponding to request 1, request 2, ... request n. Then, match the response data in the data set corresponding to the response data type with the simulated request data in each collection of similar request data, so that each simulated request data in the collection of similar request data corresponds to the matching response data. Further, using the collection of similar request data as the detection unit, analyze the similarities and differences between the response data corresponding to the same type of simulated request data, analyze the reasons for the differences, and analyze whether the differences in the format, character content, substantive request content, etc. between the simulated request data have an impact on the response data of the server. Alternatively, compare the differences between different types of simulated request data, analyze the impact of the differences on the response data, and other methods may be used for association analysis.

[0070] It can be seen from the above content that there are various ways to perform association analysis on data in a data set of data types with associated relationships. In order to improve the accuracy and uniformity of the detection results, the embodiment of the present application can pre-set associated data detection rules corresponding to each associated relationship.

[0071] Among them, the process of pre-setting the associated data detection rule can be explained by taking the associated data detection rule corresponding to the association relationship between the simulated request data and the response data as an example. Assuming that the method of performing association analysis or detection on the simulated request data and the response data can include: Rule 1, Rule 2, Rule 3, based on the simulated request data and response data contained in the historical data of the server to be detected as sample data, the sample data is respectively subjected to association analysis by Rule 1, Rule 2, and Rule 3 to obtain the corresponding association analysis results 1, association analysis results 2, and association analysis results 3. According to the accuracy, complexity and other standards of each association analysis result, an association analysis result with high accuracy and easy to understand for the operation and maintenance personnel is determined, and the rule corresponding to the association analysis result is determined as the associated data detection rule for the association relationship between the simulated request data and the response data.

[0072] Further, for each data type in the data to be detected, it is identified whether there is an association relationship matching the data type in the pre-established association relationship, and if there is a matching association relationship, another data type with which there is an association relationship is determined, and data sets corresponding to at least two data types with which there is an association relationship are extracted from the data to be detected as an associated data combination. According to the associated data detection rule corresponding to the association relationship, the associated data combination is processed to obtain a second analysis result corresponding to the data sets of at least two data types with which there is an association relationship.

[0073] Step S160, respectively determining the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively judging whether each analysis result satisfies the corresponding preset data qualification condition.

[0074] It is understandable that not every data type has a data type with an associated relationship. Therefore, the analysis result for a data type without a corresponding associated relationship may include: a first analysis result, and the analysis result for a data type with a corresponding associated relationship may include: a first analysis result and a second analysis result.

[0075] In the embodiment of the present application, the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected.

[0076] Optionally, a method for determining data eligibility conditions based on historical data may include: collecting historical data sets of the data type that has detection requirements in each server to be detected, the historical data sets including historical data belonging to the data type, and the historical data of the server to be detected also records abnormal data and abnormal events corresponding to the abnormal data.

[0077] Take the analysis process of the historical data set corresponding to any data type as an example. If the historical data corresponding to data type A is character data, the normal historical data is "string m", "string n", etc., and the abnormal data that causes abnormal events are abnormal key characters such as "string a", "string b", etc. The data qualification condition corresponding to the data type to be detected is "whether the first analysis result contains abnormal key characters such as 'string a', 'string b', etc.". If the historical data corresponding to data type B is numerical data, and the abnormal data reflected by the historical data is historical data in which all values ​​exceed "value a", the data qualification condition corresponding to the data type is "whether the broken line of the first analysis result exceeds the baseline of 'value a'", or it can also be judged based on the numerical change trend of the first analysis result whether the data has a trend exceeding "value a", and use this as the data qualification condition to achieve the effect of predicting abnormalities in advance.

[0078] If data type A is associated with data type B, the historical data in the historical data sets corresponding to the two data types are analyzed. Assume that when analyzing and detecting the data of data type A alone, "character string m" is a normal character, but "character string m" will affect the numerical change trend of the historical data of data type B, causing the numerical value of the historical data of data type B to tend to the "abnormal critical value b". If the character information of data type A is not modified for a long time, the numerical value corresponding to the data of data type B will exceed the critical value, causing the server to become abnormal. Therefore, the data qualification condition for the second analysis result of data types A and B that are associated can be set as "whether there is a 'character string m' that affects data type B in the data corresponding to data type A in the second analysis result".

[0079] The above content is only used as an example to illustrate how to determine data eligibility conditions based on historical data. The specific method of setting the data eligibility conditions corresponding to each analysis result is based on the role of the data type in the server and is not limited to a single method.

[0080] Based on the preset data qualification conditions corresponding to each data type determined in advance, the first analysis result and the second analysis result corresponding to the data set of each data type are judged to determine whether they meet the preset data qualification conditions.

[0081] Step S170, generating a test report including the determination result of the data set of each data type in the data to be tested.

[0082] For analysis results that meet the preset data qualification conditions, it is determined that there is no abnormal data in the data set corresponding to the data type, and the test report can be marked with a corresponding label information indicating that there is no abnormality; for analysis results that do not meet the preset data qualification conditions, it can be further determined which specific data in the data set does not meet the preset data qualification conditions, or the reasons why the data set does not meet the preset data qualification conditions, etc.

[0083] Based on this, a test report is generated for the judgment results corresponding to the data set of each data type, so that the test report can be directly displayed on the front end of the server, so that the operation and maintenance personnel can understand the status of each server in the software system and maintain the abnormal data that appears on the server.

[0084] Optionally, when any of the analysis results does not meet the corresponding preset data qualification conditions, an alarm message is sent to the front end, and the alarm message includes at least an abnormal data type, and the abnormal data type is a data type corresponding to the analysis result that does not meet the preset data qualification conditions.

[0085] Based on the alarm settings of the operation and maintenance personnel for abnormal situations of the software system, when abnormal data is detected in the data to be tested, the corresponding alarm information will be issued to enable the operation and maintenance personnel to notice and perform timely maintenance on the software system. In order to improve the maintenance efficiency of the operation and maintenance personnel, detailed information such as the data causing the abnormality and the corresponding data type will be marked in the test report and alarm information.

[0086] Optionally, in the embodiment of the present application, the operation and maintenance personnel can also pre-set the corresponding alarm level when different types of abnormal data appear according to the impact of the abnormal situation of the software system on the software system. Before issuing the corresponding alarm information, the alarm level corresponding to the abnormal data type and the alarm method corresponding to the alarm level can be determined based on the preset alarm level corresponding to each data type; according to the alarm method, the alarm information is sent to the front end.

[0087] For example, a software system has the function of guarding service processes. If the service process is lost abnormally, the software system will automatically restart. For this feature setting, if the number of processes is abnormal for several consecutive minutes, and there is no process automatic restart keyword in the log, the operation and maintenance personnel need to intervene to restart the process, otherwise more service process data will be lost. The software system identifies this abnormal situation during the automatic detection process, and the alarm level corresponding to this abnormal situation is high. The operation and maintenance personnel need to be reminded as soon as possible by telephone notification. Based on this, an alarm message is sent to the operation and maintenance personnel by telephone notification. If the number of processes is abnormal for several consecutive minutes, but there is a process automatic restart keyword in the log, and the software system automatically restarts, you only need to notify the operation and maintenance personnel that the process is lost but has been successfully restarted. You can choose to send information to the front end by reminder method such as SMS or email.

[0088] In summary, in the process of detecting the data to be detected in the software system, the embodiment of the present application not only performs independent detection on the data of each data type, but also performs correlation analysis on the data of the data type with a correlation relationship. Compared with the prior art, the correlation between the software system data is taken into account, and the comprehensiveness of the detection is improved. Furthermore, each first analysis result and second result obtained by independent detection and correlation analysis are compared with the corresponding preset data qualification conditions, respectively, to determine whether there is an unqualified situation, and generate a detection report. Among them, the preset data qualification conditions are obtained by analyzing the historical data of the server to be detected, which improves the objectivity and accuracy of the detection results compared with the experience judgment of the operation and maintenance personnel.

[0089] Next, refer to Figure 2 The structure example diagram of an application system provided by the embodiment of the present application is shown to illustrate the practical application of the above-mentioned software system self-checking method. Figure 3 The example flow chart of the application system provided in the embodiment of the present application to implement the software system self-check method further details the various steps of the software system self-check method.

[0090] Figure 2 The application system shown may include: a data acquisition module, a parsing and storage module, an analysis and judgment module, a CMDB, a monitoring and alarm module, and a display module.

[0091] Among them, the data collection module includes collection probes installed on each server end of the software system, which collect data on each server in real time. Collection probe 1 corresponds to collecting data from server 1 to be tested, and collection probe 2 corresponds to collecting data from server 2 to be tested... and sends these data to the analysis and storage module for subsequent data consumption and data analysis.

[0092] The parsing and storage module includes a data parsing sub-module and a database. The data parsing sub-module is first used to parse the collected data, such as clarifying and formatting the data according to the agreed format, and storing the processed data in the database as a backup, and as an input to the analysis and judgment module, to avoid the original collected data being of various types and possibly not conforming to the input format of the analysis and judgment module, so as to facilitate analysis and judgment.

[0093] The analysis and judgment module first obtains the reference data list stored in the CMDB, checks the integrity of the data input into the analysis and judgment module, and if the data is complete, analyzes the data sets of data types such as the operating system, server status, process status, simulation request, Socket connection status, message volume, error keywords, message response rate, and error rate range of the server data to obtain the analysis results corresponding to the data sets of each data type, which may include the first analysis results and / or the second analysis results.

[0094] Furthermore, the analysis and judgment module stores preset data qualification conditions to judge each analysis result. For example:

[0095] For data sets of operating system and server status, query whether the CPU, IO, memory, and other server status of all clusters and services from the last timing to the current timing are within the baseline range;

[0096] For data sets of process status type, query whether the process status and number of processes of all clusters and services from the last timing to the current timing are normal;

[0097] For the data in the simulated request type data set, query all clusters and services for simulated requests from the last timing to the current timing to determine whether there is a problem with the entire link and whether the request is sent successfully and receives a correct response;

[0098] For the data in the Socket connection status data set, query whether there is a continuous accumulation of Q in the sockets of all clusters and services from the last timing to the current timing, and whether the socket connection status is normal;

[0099] For the data in the packet volume type data set, query whether the packet volume of all clusters and services from the last timing to the current timing is within the baseline range;

[0100] For data in the dataset with the wrong keyword type, query all clusters and services to see whether fatal keywords or unknown keywords appear during the time period from the last timing to the current timing.

[0101] For the data in the data set of the message response rate type, query whether the message response rate of all clusters and services from the last timing to the current timing meets expectations and is in a relatively stable state;

[0102] For data in the error rate range type data set, query whether the error rate of all clusters and services from the last timing to the current timing exceeds the baseline range.

[0103] Based on the above data qualification conditions, the judgment results of each analysis result are generated, and the test report is displayed on the front-end page through the display module. The contents of several types of test reports are listed as follows:

[0104] If there are no abnormalities in Server 1 and Server 4 in Cluster J, the self-check results feedback that the data in the data set of each analysis result or data type is normal;

[0105] If the operating system status of server 2 is abnormal, the detection report displayed on the front end will show that the operating system of server 2 in cluster 1 is abnormal, and display detailed information;

[0106] If the socket connection status of server 5 is abnormal, the detection report displayed on the front end will show the socket product Q or connection Xichang of server 5 in cluster J, and display detailed information;

[0107] If a process of IPa or IPb of server 2 appears to be dead, the front end will show that the status of IPa or IPb process of cluster 1 is abnormal and display detailed information.

[0108] At the same time, the monitoring alarm module identifies abnormal situations in the detection report and the corresponding alarm levels. In this example, the alarm classification is based on whether the abnormal situation requires operation and maintenance personnel to intervene in maintenance. If operation and maintenance personnel are required to intervene in maintenance, the alarm information is sent to the operation and maintenance personnel by telephone alarm. If operation and maintenance personnel do not need to intervene, the alarm information is sent to the operation and maintenance personnel by email or SMS alarm.

[0109] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0111] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0112] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0113] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] The following is a description of a software system self-checking device provided in an embodiment of the present application. The software system self-checking device described below and the software system self-checking method described above can be referenced to each other.

[0115] First, combine Figure 4 , introduce the software system self-check device, such as Figure 4 As shown, the software system self-checking device may include:

[0116] The data collection unit 100 is used to collect the data to be detected of the software system, wherein the data to be detected is the data of at least one server to be detected within a preset time range;

[0117] The first data detection unit 200 is used to determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain the change information of the data set of each data type within the preset time range, and the change information is the first analysis result corresponding to the data set of the data type;

[0118] The association relationship acquisition unit 300 is used to acquire at least one association relationship established in advance between the data types, and an associated data detection rule corresponding to each of the association relationships, wherein the association relationship indicates that there is a preset association between the data in the data sets between at least two of the data types;

[0119] The second data detection unit 400 is used to process the data in the data set of the data type having the corresponding association relationship by using the association data detection rule respectively, and obtain the second analysis result of the data set of the data type in each association relationship;

[0120] The result detection unit 500 is used to respectively determine the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively determine whether each analysis result meets the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected;

[0121] The report generating unit 600 is used to generate a detection report including the determination result of each data set of the data type in the data to be detected.

[0122] Optionally, the unit implementing the process of determining whether the data type contained in the data to be detected meets a preset condition in the first data detection unit includes:

[0123] A list acquisition subunit is used to acquire a pre-stored reference data list, wherein the reference data list stores the type of data that needs to be detected by the server to be detected;

[0124] A data query subunit, used for querying whether the data to be detected contains each of the data types in the reference data list;

[0125] A result determination subunit, used for determining whether the data type contained in the to-be-detected data meets a preset condition when the query result of the data query subunit is yes;

[0126] The re-collection subunit is used to execute the steps of collecting the data to be detected of the software system if the data to be detected does not completely contain each of the data types in the reference data list, until the data to be detected contains each of the data types in the reference data list.

[0127] Optionally, the data detection first unit also includes:

[0128] A times recording subunit, used for executing the step of collecting the data to be detected by the software system and recording the times of collecting the data to be detected by the software system if the data to be detected does not completely contain each of the data types in the reference data list;

[0129] The acquisition abnormality alarm sub-unit is used to execute the step of respectively detecting the data in the data sets corresponding to each data type in the data to be detected, and send an alarm message indicating incomplete data acquisition to the front end when the number of data to be detected in the acquisition software system reaches a preset number and the data to be detected does not completely contain each data type in the reference data list.

[0130] Optionally, the data acquisition unit 100 includes:

[0131] A first time acquisition subunit is used to acquire the time when the to-be-detected data of the to-be-detected server corresponding to the software system was last collected as the first time;

[0132] A second time determination subunit is used to determine the time for collecting the data to be detected of the server to be detected as the second time based on the first time and a preset timed detection interval;

[0133] The data collection subunit is used to collect data of the server to be detected within a preset time range as the data to be detected, and the preset time range is a time range limited by the first time and the second time.

[0134] Optionally, the data detection first unit includes

[0135] For each data set corresponding to the data type, perform the following steps:

[0136] A rule determination subunit, used to determine a preset detection rule corresponding to the data type;

[0137] The detection subunit is used to perform data detection on the data in the data set corresponding to the data type based on the preset detection rule, and obtain change information of the data set of the data type within the preset time range.

[0138] Optionally, the software system self-checking device may further include:

[0139] An alarm unit is used to send an alarm message to the front end when any of the analysis results does not meet the corresponding preset data qualification conditions, and the alarm message at least includes an abnormal data type, and the abnormal data type is a data type corresponding to the analysis result that does not meet the preset data qualification conditions.

[0140] Optionally, the alarm unit may include:

[0141] A level matching subunit, used to determine the alarm level corresponding to the abnormal data type and the alarm mode corresponding to the alarm level based on the preset alarm level corresponding to each of the data types;

[0142] The alarm subunit is used to send alarm information to the front end according to the alarm mode.

[0143] In summary, in the process of detecting the data to be detected in the software system, the embodiment of the present application not only performs independent detection on the data of each data type, but also performs correlation analysis on the data of the data type with a correlation relationship. Compared with the prior art, the correlation between the software system data is taken into account, and the comprehensiveness of the detection is improved. Furthermore, each first analysis result and second result obtained by independent detection and correlation analysis are compared with the corresponding preset data qualification conditions, respectively, to determine whether there is an unqualified situation, and generate a detection report. Among them, the preset data qualification conditions are obtained by analyzing the historical data of the server to be detected, which improves the objectivity and accuracy of the detection results compared with the experience judgment of the operation and maintenance personnel.

[0144] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0145] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0146] The software system self-checking apparatus provided in the embodiment of the present application can be applied to software system self-checking equipment.

[0147] Figure 5 The schematic diagram of the software system self-checking device is shown. Figure 5 , the structure of the software system self-checking device may include: at least one processor 10, at least one memory 20, at least one communication bus 30 and at least one communication interface 40;

[0148] In the embodiment of the present application, the number of the processor 10, the memory 20, the communication bus 30, and the communication interface 40 is at least one, and the processor 10, the memory 20, and the communication interface 40 communicate with each other through the communication bus 30;

[0149] The processor 10 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention;

[0150] The memory 20 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory;

[0151] The memory stores a program, and the processor can call the program stored in the memory, and the program is used to implement each processing flow in the aforementioned software system self-check solution.

[0152] An embodiment of the present application also provides a storage medium, which can store a program suitable for execution by a processor, and the program is used to implement each processing flow in the aforementioned ... solution.

[0153] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0154] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0155] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0156] According to one or more embodiments of the present disclosure, Example 1 provides a software system self-checking method, including:

[0157] Collecting the data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range;

[0158] Determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is the first analysis result corresponding to the data set of the data type;

[0159] Acquire at least one pre-established association relationship between the data types, and an association data detection rule corresponding to each association relationship, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types;

[0160] Using the associated data detection rules respectively, the data in the data set of the data type having the corresponding associated relationship are processed to obtain a second analysis result of the data set of the data type in each associated relationship;

[0161] Determine respectively the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively judge whether each analysis result satisfies the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected;

[0162] Generate a test report containing the judgment results of each data set of the data type in the data to be tested.

[0163] According to one or more embodiments disclosed in the present application, Example 2 provides a process of determining whether the data type contained in the to-be-detected data satisfies a preset condition in the method of Example 1, including:

[0164] Acquire a pre-stored reference data list, wherein the reference data list stores the type of data that the to-be-detected server needs to detect;

[0165] Querying whether the data to be detected contains each of the data types in the reference data list;

[0166] If the data to be detected includes each of the data types in the reference data list, determining that the data type included in the data to be detected meets a preset condition;

[0167] If the data to be detected does not completely contain every data type in the reference data list, the step of collecting the data to be detected by the software system is executed until the data to be detected contains every data type in the reference data list.

[0168] According to one or more embodiments disclosed in the present application, Example 3 provides the method of Example 2, further comprising:

[0169] If the data to be detected does not completely contain each of the data types in the reference data list, executing the step of collecting the data to be detected by the software system, and recording the number of times the data to be detected by the software system is collected;

[0170] When the number of data to be detected in the acquisition software system reaches a preset number, and the data to be detected does not completely contain each of the data types in the reference data list, the step of respectively detecting the data in the data sets corresponding to each of the data types in the data to be detected is performed on the data to be detected, and an alarm message indicating incomplete data collection is sent to the front end.

[0171] According to one or more embodiments disclosed in the present application, Example 4 provides the method of Example 1, wherein the collecting data to be detected by the software system includes:

[0172] Obtain the time when the to-be-detected data of the to-be-detected server corresponding to the software system was last collected as the first time;

[0173] Based on the first time and a preset timed detection interval, determine the time for collecting the data to be detected of the server to be detected as the second time;

[0174] The data of the server to be detected within a preset time range is collected as the data to be detected, and the preset time range is a time range limited by the first time and the second time.

[0175] According to one or more embodiments disclosed in the present application, Example 5 provides a method in Example 1, wherein data detection is performed on the data in the data set corresponding to each data type in the data to be detected, and change information of the data set of each data type within the preset time range is obtained, including

[0176] For each data set corresponding to the data type, perform the following steps:

[0177] Determining a preset detection rule corresponding to the data type;

[0178] Based on the preset detection rule, data detection is performed on the data in the data set corresponding to the data type to obtain change information of the data set of the data type within the preset time range.

[0179] According to one or more embodiments disclosed in the present application, Example 6 provides the method of Example 1, further comprising:

[0180] When any of the analysis results does not meet the corresponding preset data qualification conditions, an alarm message is sent to the front end, and the alarm message at least includes an abnormal data type, and the abnormal data type is a data type corresponding to the analysis result that does not meet the preset data qualification conditions.

[0181] According to one or more embodiments disclosed in the present application, Example 7 provides the method of Example 6, wherein sending the alarm information to the front end includes:

[0182] Based on the preset alarm level corresponding to each of the data types, determine the alarm level corresponding to the abnormal data type and the alarm mode corresponding to the alarm level;

[0183] According to the alarm method, an alarm message is sent to the front end.

[0184] According to one or more embodiments disclosed in the present application, Example 8 provides a software system self-checking device, including:

[0185] A data collection unit, used for collecting data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range;

[0186] A first data detection unit is used to determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is a first analysis result corresponding to the data set of the data type;

[0187] An association relationship acquisition unit, used to acquire at least one association relationship established in advance between the data types, and an associated data detection rule corresponding to each of the association relationships, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types;

[0188] A second data detection unit, configured to process data in a data set of the data type having the corresponding association relationship by using the association data detection rules respectively, and obtain a second analysis result of the data set of the data type in each association relationship;

[0189] A result detection unit, used to respectively determine the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively determine whether each analysis result meets the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected;

[0190] The report generating unit is used to generate a detection report including the judgment result of the data set of each data type in the data to be detected.

[0191] According to one or more embodiments disclosed in the present application, Example 9 provides a software system self-checking device, including a memory and a processor;

[0192] The memory is used to store programs;

[0193] The processor is used to execute the program to implement each step of any one of the software system self-checking methods.

[0194] According to one or more embodiments disclosed in the present application, Example 10 provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of any one of the software system self-check methods is implemented.

[0195] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0196] Although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0197] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A software system self-checking method, characterized in that: include: Collecting the data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range; Determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is the first analysis result corresponding to the data set of the data type; Acquire at least one pre-established association relationship between the data types, and an association data detection rule corresponding to each association relationship, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types; Using the associated data detection rules respectively, the data in the data set of the data type having the corresponding associated relationship are processed to obtain a second analysis result of the data set of the data type in each associated relationship; Determine respectively the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively judge whether each analysis result satisfies the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected; Generate a test report containing the judgment results of each data set of the data type in the data to be tested.

2. The software system self-checking method according to claim 1, characterized in that: The process of determining whether the data type contained in the data to be detected meets the preset conditions includes: Acquire a pre-stored reference data list, wherein the reference data list stores the type of data that the to-be-detected server needs to detect; Querying whether the data to be detected contains each of the data types in the reference data list; If the data to be detected includes each of the data types in the reference data list, determining that the data type included in the data to be detected meets a preset condition; If the data to be detected does not completely contain every data type in the reference data list, the step of collecting the data to be detected by the software system is executed until the data to be detected contains every data type in the reference data list.

3. The software system self-checking method according to claim 2, characterized in that: Also includes: If the data to be detected does not completely contain each of the data types in the reference data list, executing the step of collecting the data to be detected by the software system, and recording the number of times the data to be detected by the software system is collected; When the number of data to be detected in the acquisition software system reaches a preset number, and the data to be detected does not completely contain each of the data types in the reference data list, the step of respectively detecting the data in the data sets corresponding to each of the data types in the data to be detected is performed on the data to be detected, and an alarm message indicating incomplete data collection is sent to the front end.

4. The software system self-checking method according to claim 1, characterized in that: The data to be detected by the acquisition software system includes: Obtain the time when the to-be-detected data of the to-be-detected server corresponding to the software system was last collected as the first time; Based on the first time and a preset timed detection interval, determine the time for collecting the data to be detected of the server to be detected as the second time; The data of the server to be detected within a preset time range is collected as the data to be detected, and the preset time range is a time range limited by the first time and the second time.

5. The software system self-checking method according to claim 1, characterized in that: The data detection is performed on the data in the data set corresponding to each data type in the data to be detected, and the change information of the data set of each data type within the preset time range is obtained, including For each data set corresponding to the data type, perform the following steps: Determining a preset detection rule corresponding to the data type; Based on the preset detection rule, data detection is performed on the data in the data set corresponding to the data type to obtain change information of the data set of the data type within the preset time range.

6. The software system self-checking method according to claim 1, characterized in that: Also includes: When any of the analysis results does not meet the corresponding preset data qualification conditions, an alarm message is sent to the front end, and the alarm message at least includes an abnormal data type, and the abnormal data type is a data type corresponding to the analysis result that does not meet the preset data qualification conditions.

7. The software system self-checking method according to claim 6, characterized in that: The sending of alarm information to the front end includes: Based on the preset alarm level corresponding to each of the data types, determine the alarm level corresponding to the abnormal data type and the alarm mode corresponding to the alarm level; According to the alarm method, an alarm message is sent to the front end.

8. A software system self-checking device, characterized in that: include: A data collection unit, used for collecting data to be detected of the software system, wherein the data to be detected is data of at least one server to be detected within a preset time range; A first data detection unit is used to determine the data type contained in the data to be detected, and when the contained data type meets the preset conditions, respectively perform data detection on the data in the data set corresponding to each data type in the data to be detected, and obtain change information of the data set of each data type within the preset time range, wherein the change information is a first analysis result corresponding to the data set of the data type; An association relationship acquisition unit, used to acquire at least one association relationship established in advance between the data types, and an associated data detection rule corresponding to each of the association relationships, wherein the association relationship indicates that there is a preset association between data in data sets between at least two of the data types; A second data detection unit, configured to process data in a data set of the data type having the corresponding association relationship by using the association data detection rules respectively, and obtain a second analysis result of the data set of the data type in each association relationship; A result detection unit, used to respectively determine the analysis results corresponding to the data sets of each data type in the data to be detected, and respectively determine whether each analysis result meets the corresponding preset data qualification condition, wherein the preset data qualification condition is a data qualification condition obtained by analyzing the historical data of the server to be detected; The report generating unit is used to generate a detection report including the judgment result of the data set of each data type in the data to be detected.

9. A software system self-checking device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the software system self-check method as described in any one of claims 1-7.

10. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, each step of the software system self-checking method as described in any one of claims 1 to 7 is implemented.

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