A method, system, device and medium for self-detecting abnormality of a component

Abstract

The present invention relates to the field of performance detection. The present invention proposes a method, system, device and medium for component self-detection of abnormalities. The method includes: receiving a trigger request, a preset detection template parsing the trigger request to generate a detection task; passing the detection task and the corresponding authorization information to the probe of the component to be detected; the probe collects the performance data of the component; all performance data and the preset abnormality library are double-judged to obtain the corresponding abnormal cause and solution. The present invention defines a template and a related common component abnormality library, uses interfaces and scripts to self-detect the status of components, quickly locates problems of various components, and infers the possible causes of corresponding problems based on the manifestation of the problems, defines relevant solutions, and can automatically detect, analyze and locate various configuration differences and potential problems in the system. The efficiency and accuracy of abnormality troubleshooting are greatly improved, and the service interruption time caused by system abnormalities is reduced.

Description

Technical Field

[0001] The present invention relates to the field of performance detection, and in particular to a method, system, device and medium for self-detecting abnormality of a component. Background Art

[0002] The distributed environment complexity faced by some customers is increasing day by day. Due to the differences and diversity in the configuration of the Central Control System and various types of probes (such as performance monitoring probes, security audit probes, business logic probes, etc.), the coordination between various components in the entire system has become particularly complex and prone to various abnormal problems. These configuration differences may be caused by different hardware platforms, operating system versions, middleware types, and the characteristics of the application itself.

[0003] In actual operation, these differences often lead to unforeseen compatibility issues or performance bottlenecks, and the specific causes of these problems are often difficult to directly display on the user interface. Users can usually only see abnormal system performance, such as response delays, service interruptions, or data errors, but cannot directly obtain information such as specific configuration errors, resource conflicts, or code defects that caused these component abnormalities.

[0004] In order to locate and solve these problems, technicians have to rely on cumbersome and time-consuming command line tools to search and troubleshoot each component one by one. This process is not only inefficient, but also prone to missing key information, which further prolongs the time to solve the problem. In addition, frequent exceptions and troubleshooting also seriously affect the normal operation of the system and the daily business operations of users, reduce the availability and stability of the system, and thus have a great negative impact on the user experience. Summary of the invention

[0005] Based on the above purpose, the present invention proposes a method for component self-detection abnormality, comprising:

[0006] Upon receiving the trigger request, the preset detection template parses the trigger request to generate a detection task;

[0007] Pass the detection task and the corresponding authorization information to the probe of the component to be detected;

[0008] The probes collect performance data of the components;

[0009] All performance data and the preset exception library are double-judged to obtain the corresponding exception causes and solutions.

[0010] In some embodiments, the step of generating a detection task by parsing a preset detection template trigger request includes:

[0011] The server reads the file configuration of the preset detection template, and configures the access resources, triggers the request and selects the self-detection components according to the template file;

[0012] Prioritize the detection of the basic configuration of the entire system according to the configuration in the template file, and when there is no abnormality in the server environment, generate a detection task for the selected self-detection component;

[0013] Dividing the detection task into status detection, configuration detection and performance detection;

[0014] Each component generates a return code corresponding to the detection result.

[0015] In some embodiments, the step of transmitting the detection task and the corresponding authorization information to the probe of the component to be detected is:

[0016] Set the authorization information to the permission type and permission level;

[0017] Traverse all target components, execute permission detection rules for each component, and obtain authorized permission items and unauthorized permission items;

[0018] The preset detection template automatically transfers the corresponding authorization information to the unauthorized permission item for authorization.

[0019] In some embodiments, all performance data and a preset abnormality library are double judged to obtain the corresponding abnormality cause:

[0020] Compare the collected performance data with the exception library, where the performance data includes the running status, occupied resources and related connection status;

[0021] Match the return codes corresponding to all performance data with the preset configuration exception library, locate the abnormal components, and obtain the abnormal reasons for the running status of all components;

[0022] The return code of the abnormal component is matched with the preset performance abnormality library to obtain the abnormal reason of the performance of the abnormal component.

[0023] In some embodiments, the step of performing a double judgment on all performance data and a preset abnormality library to obtain the corresponding abnormality cause further includes:

[0024] If the abnormal reasons for the running status of all components and the abnormal reasons for the performance of the abnormal components are not found, the general reason is selected;

[0025] The detection is annotated and collected into the anomaly library for later maintenance.

[0026] In some embodiments, the step of performing a double judgment on all performance data and a preset abnormal library to obtain a solution includes:

[0027] Experts evaluate the abnormal reasons of the operating status of all components, the abnormal reasons of the performance of the abnormal components, or the common reasons, and combine them to obtain a system abnormal status code;

[0028] Get the solution from the preset system exception library according to the system exception status code.

[0029] In some embodiments, the design process of the preset exception library is as follows:

[0030] For abnormal library priority setting, set gradient thresholds for abnormal libraries with different priorities;

[0031] Statistics on the calculation accuracy and processing time of each abnormal library within a historical period;

[0032] The calculation accuracy and processing time are weighted and combined to obtain an evaluation value;

[0033] When the evaluation value exceeds the corresponding gradient threshold, the abnormal library is evaluated as being ready for use.

[0034] The present invention proposes a system for self-detecting abnormalities of components, comprising:

[0035] A parsing unit, configured to receive a trigger request, parse the trigger request using a preset detection template to generate a detection task;

[0036] An authorization unit configured to transmit a detection task and corresponding authorization information to a probe of a component to be detected;

[0037] A collection unit configured to collect performance data of the component for the probe;

[0038] The exception unit is configured to perform double judgment on all performance data and a preset exception library to obtain corresponding exception causes and solutions.

[0039] The present invention provides a computer device, comprising:

[0040] At least one processor; and a memory storing a computer program executable on the processor, wherein the processor executes the steps of the method for self-detecting abnormality of a component when executing the program.

[0041] The present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for self-detecting abnormalities of a component are executed.

[0042] The present invention has at least the following beneficial technical effects:

[0043] The present invention proposes a method, system, device and medium for component self-detection of abnormalities. The method includes: receiving a trigger request, a preset detection template parsing the trigger request to generate a detection task; passing the detection task and corresponding authorization information to a probe of the component to be detected; the probe collects performance data of the component; and performing double judgment on all performance data and a preset abnormality library to obtain corresponding abnormal causes and solutions.

[0044] The present invention defines a template and a related common component exception library, uses interfaces and scripts to self-detect component status, quickly locates various component problems, and infers the causes of the corresponding problems based on the manifestation of the problems, defines related solutions, and can automatically detect, analyze and locate various configuration differences and potential problems in the system. This greatly improves the efficiency and accuracy of exception troubleshooting, reduces service interruption time caused by system exceptions, and thus improves user experience and overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flow chart of a method for self-detecting abnormality of a component provided by the present invention;

[0047] Figure 2 A system module diagram of a component self-detection abnormality provided by the present invention;

[0048] Figure 3 A flowchart of an embodiment of a method for self-detecting abnormality of a component provided by the present invention;

[0049] Figure 4 A schematic diagram of the structure of an embodiment of a computer device provided by the present invention;

[0050] Figure 5 A schematic diagram of the structure of an embodiment of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0052] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0053] The present invention proposes a method for component self-detection of abnormalities, please refer to Figure 1 and Figure 3, including:

[0054] Upon receiving the trigger request, the preset detection template parses the trigger request to generate a detection task;

[0055] Pass the detection task and the corresponding authorization information to the probe of the component to be detected;

[0056] The probes collect performance data of the components;

[0057] All performance data and the preset exception library are double-judged to obtain the corresponding exception causes and solutions.

[0058] The detection template can define the component modules that need to be detected, and determine which corresponding components need to be detected based on the enabled attribute in the configuration.

[0059] The specific implementation process of the present invention is as follows:

[0060] 1. The client initiates an interface request;

[0061] 2. The corresponding program on the server reads the template file configuration;

[0062] 3. Determine which components to self-test based on the configuration in the template file;

[0063] 4. The detection resource ratio of the corresponding component can be specified in the template file, and the content with a high ratio will be detected first;

[0064] 5. The basic system configuration will be tested first according to the configuration in the template file. After confirming that the entire server environment is OK, the components will be tested (to ensure that the memory usage, CPU usage, etc. are within a normal range);

[0065] 6. Test the corresponding components according to the allocation of test resources. The test contents in the components are divided into three types:

[0066] 6.1. Status detection;

[0067] 6.1.1. Check the running status of components;

[0068] 6.1.2. Check the resources occupied by components;

[0069] 6.1.3. Detect the relevant connection status of the component;

[0070] 6.2.Configuration detection;

[0071] The basic configuration of the detection component operation is generally used for further troubleshooting if there is an abnormality in 6.1.1;

[0072] Check the connection configuration of the components. If there are any abnormalities in 6.1.2, further troubleshooting will be carried out;

[0073] 6.3. Performance testing;

[0074] Detect the response time of the component and compare it with the configured threshold time in the template file to obtain a return code; compare the peak response time recorded by the component with the configuration of the template file to obtain a return code; make a combined comparison based on the results returned by the component-related configuration and the operating status resource occupancy to obtain a return code.

[0075] 6.4. According to the return code result of 6.1, configure the exception library for matching, obtain the recommended return content according to the return result, and after some scenarios are met, execute the command of 6.2, and configure the exception library according to the return code of 6.2 to match the content to obtain the running status conclusion and related solution ideas.

[0076] 6.5. Under the premise of meeting some of the requirements in 6.1, perform the test in 6.3 and match the content in the performance anomaly database according to the return code in 6.3 to obtain the solution ideas for the relevant performance conclusions.

[0077] 6.6.According to the returned content of 6.4 and 6.5, a component troubleshooting conclusion and whether the running status is normal are obtained. If no relevant status conclusion is obtained in the exception library of 6.4 and 6.5, a general exception return will be used, and the conclusion content will be marked as only a possibility, and the relevant content will be recorded in the built-in Log file to facilitate subsequent manual troubleshooting and update the component library definition content. After authorization, it can be sent to the official email address of the corresponding maintenance personnel for problem collection.

[0078] 6.7. Sort out the return contents of multiple components, get the status code of the entire system, then match it with the system exception library, and get a summary as the final output.

[0079] In some embodiments, see Figure 1 3, the steps of generating a detection task by parsing the preset detection template trigger request include:

[0080] The server reads the file configuration of the preset detection template, and configures the access resources, triggers the request and selects the self-detection components according to the template file;

[0081] Prioritize the detection of the basic configuration of the entire system according to the configuration in the template file, and when there is no abnormality in the server environment, generate a detection task for the selected self-detection component;

[0082] Dividing the detection task into status detection, configuration detection and performance detection;

[0083] Each component generates a return code corresponding to the detection result.

[0084] Return codes provide a standardized way to communicate detection results. Different components or system modules can use the same return code system, making it easier to exchange and understand information across systems. Consistency ensures that different developers, testers, or operators have a unified understanding and response when faced with the same detection results. Return codes are usually associated with specific errors or statuses, which allows technicians to quickly identify the problem without having to read lengthy error descriptions or logs. Specific return codes can directly point to the root cause of the problem, such as configuration errors, insufficient resources, permission issues, etc., thereby speeding up the problem-solving process.

[0085] For end users, although they may not see the return code directly, the existence of the return code enables the system to report the problem more accurately and provide users with more specific solutions or suggestions. When the system can automatically fix certain problems based on the return code, the user experience will be improved because they do not have to wait for manual intervention to solve the problem.

[0086] In some embodiments, see Figure 1 3, the steps of passing the detection task and the corresponding authorization information to the probe of the component to be detected are:

[0087] Set the authorization information to the permission type and permission level;

[0088] Traverse all target components, execute permission detection rules for each component, and obtain authorized permission items and unauthorized permission items;

[0089] The preset detection template automatically transfers the corresponding authorization information to the unauthorized permission item for authorization.

[0090] Automatically passing authorization information through preset templates avoids the tedious process of manually configuring permissions and significantly improves work efficiency. The automated process reduces human intervention, thereby reducing the risk of configuration errors and ensuring the accuracy of permission configuration.

[0091] It can ensure that only authorized components or services can access specific resources or perform specific operations, thereby strengthening the security of the system. Automated permission management helps organizations comply with relevant laws, regulations and industry standards to ensure system compliance.

[0092] It can achieve unified management of permissions for different components or services, simplifying the management process. When the permission configuration needs to be updated or modified, you only need to adjust the preset template, without having to modify the configuration of each component one by one, reducing maintenance costs. Automated permission management reduces system interruptions or failures caused by permission issues, thereby improving user experience. When a new component or service needs to access specific resources, it can be quickly authorized through the preset template, improving response speed.

[0093] Record detailed information of each permission change, including the time and content of the change, to facilitate subsequent auditing and monitoring. By monitoring permission changes, potential security risks or violations can be discovered in a timely manner, and appropriate measures can be taken to prevent them.

[0094] In some embodiments, see Figure 1 As shown in Figure 3, all performance data and the preset abnormality library are double judged to obtain the corresponding abnormality causes:

[0095] Compare the collected performance data with the exception library, where the performance data includes the running status, occupied resources and related connection status;

[0096] Match the return codes corresponding to all performance data with the preset configuration exception library, locate the abnormal components, and obtain the abnormal reasons for the running status of all components;

[0097] The return code of the abnormal component is matched with the preset performance abnormality library to obtain the abnormal reason of the performance of the abnormal component.

[0098] By comparing performance data with preset anomalies in the configuration anomaly library and performance anomaly library, the anomaly type can be accurately identified to avoid false positives or false negatives. The dual judgment mechanism can reduce misjudgments caused by a single data source or judgment criteria and improve the reliability of anomaly detection. Once the performance data successfully matches an anomaly in the anomaly library, the cause and location of the anomaly can be quickly located. Based on the located anomaly cause, solutions can be developed more quickly, reducing the time for troubleshooting and repair.

[0099] By continuously monitoring performance data and comparing it with the configuration anomaly library and performance anomaly library, potential system problems can be discovered and prevented in a timely manner. The dual judgment mechanism helps improve the system's ability to respond to abnormal situations and enhance the system's stability and reliability.

[0100] In some embodiments, see Figure 1 As shown in FIG3 , all performance data and the preset abnormality library are double judged to obtain the corresponding abnormality cause, and the steps also include:

[0101] If the abnormal reasons for the running status of all components and the abnormal reasons for the performance of the abnormal components are not found, the general reason is selected;

[0102] The detection is annotated and collected into the anomaly library for later maintenance.

[0103] In cases where the specific reason cannot be determined, providing a general explanation can maintain user information continuity and avoid users being confused or dissatisfied due to the lack of a clear answer. Even if the precise reason cannot be provided, by giving a reasonable general explanation, users can maintain their trust in the system or service.

[0104] The common cause can serve as a starting point for subsequent detailed investigations, providing a general direction for technicians to further troubleshoot and solve the problem. After the common cause is initially determined, resources can be allocated more specifically.

[0105] In some embodiments, see Figure 1 As shown in FIG. 3 , the steps of performing double judgment on all performance data and the preset abnormal library to obtain a solution include:

[0106] Experts evaluate the abnormal reasons of the operating status of all components, the abnormal reasons of the performance of the abnormal components, or the common reasons, and combine them to obtain a system abnormal status code;

[0107] Get the solution from the preset system exception library according to the system exception status code.

[0108] It is necessary to comprehensively collect and record the reasons for abnormal operation status of all components and abnormal performance of abnormal components. This information may come from multiple channels such as system monitoring logs, user feedback, and technician diagnosis. When the specific cause cannot be determined, a reasonable general cause can be provided as an alternative based on the existing information and professional knowledge. This is the source of information for the configuration exception library and the performance exception library.

[0109] The collected abnormal causes are reviewed and evaluated one by one. During the expert evaluation process, the accuracy, relevance and importance of each abnormal cause will be evaluated based on their own professional knowledge and experience.

[0110] Based on the results of expert evaluation, a system of system abnormality status codes is designed. The system abnormality status codes should be able to accurately reflect the nature, location and severity of the abnormality, so that technical personnel can quickly locate and solve the problem. Each abnormal cause is mapped and combined with the corresponding system abnormality status code. For complex abnormal situations, multiple status codes are combined to describe their complete characteristics. During the mapping and combination process, the accuracy and uniqueness of the status code must be ensured to avoid confusion and misjudgment.

[0111] Through the system abnormal status code, technicians can quickly identify the abnormal type and location, thus shortening the time to locate and solve the problem. The system abnormal status code system provides strong support for system maintenance and optimization. By analyzing the frequency and trend of the status code, potential problems and weaknesses of the system can be identified, and improvements and optimizations can be made accordingly.

[0112] In some embodiments, see Figure 1 3, the design process of the preset exception library is as follows:

[0113] For abnormal library priority setting, set gradient thresholds for abnormal libraries with different priorities;

[0114] Statistics on the calculation accuracy and processing time of each abnormal library within a historical period;

[0115] The calculation accuracy and processing time are weighted and combined to obtain an evaluation value;

[0116] When the evaluation value exceeds the corresponding gradient threshold, the abnormal library is evaluated as being ready for use.

[0117] After the anomaly library has been rigorously evaluated and reaches the gradient threshold, the anomaly patterns and rules it contains are more precise and comprehensive, allowing the system to more accurately identify potential abnormal behaviors and reduce false positives and negatives. Once the anomaly library is confirmed to be ready for use, the system can immediately use the rules in it for anomaly detection. This greatly shortens the time from the occurrence of an anomaly to its identification, allowing the system to respond more quickly.

[0118] Through the rules in the exception library, the system can detect potential security risks and vulnerabilities in advance. This helps organizations take timely measures to prevent and repair, and avoid potential security threats. The use of the exception library can reduce the probability of system failures. By real-time monitoring and detecting abnormal behaviors, the system can warn and intervene before failures occur, thereby avoiding failures and helping organizations allocate resources more reasonably. By accurately identifying abnormal behaviors, organizations can focus limited resources on key businesses and improve resource utilization efficiency.

[0119] The present invention proposes a system for self-detecting abnormalities of components, see FIG2, comprising:

[0120] The parsing unit 100 is configured to receive a trigger request, and parse the trigger request using a preset detection template to generate a detection task;

[0121] An authorization unit 200 is configured to transmit a detection task and corresponding authorization information to a probe of a component to be detected;

[0122] A collection unit 300 is configured to collect performance data of the probe assembly;

[0123] The abnormality unit 400 is configured to perform a double judgment on all performance data and a preset abnormality library to obtain corresponding abnormality causes and solutions.

[0124] The present invention defines a template and a related common component exception library, uses interfaces and scripts to self-detect component status, quickly locates various component problems, and infers the causes of the corresponding problems based on the manifestation of the problems, defines related solutions, and can automatically detect, analyze and locate various configuration differences and potential problems in the system. This greatly improves the efficiency and accuracy of exception troubleshooting, reduces service interruption time caused by system exceptions, and thus improves user experience and overall system performance.

[0125] In some embodiments, please refer to FIG. 3 , the template file and component exception library defined in the present invention compare the contents defined in the template file with the solutions in the component exception library file to define the error causes and solutions for the corresponding component exceptions.

[0126] Define the configuration conditions of the template file: the file is in JSON format; the file needs to define the name of the component; the file needs to define the installation path of the component; the file needs to define the command for starting the component; the error correction commands are organized in the file, and a command list is provided, which is mapped to the corresponding return code according to the return value of different commands; there will be some common commands in the file for detecting the current machine's environment variables and other configurations; detecting the status of the disk; detecting the memory status of the defined directory; detecting the earliest file storage time under the relevant directory.

[0127] Define the configuration conditions of the component exception library file: the component exception library file is in JSON format; define error prompts related to code mapping; define compound error prompts that meet multiple codes; define solutions for related component exception codes.

[0128] The process of executing corresponding commands in multiple threads by reading template files and component exception libraries is as follows: execute corresponding commands according to priority; system status related commands, process status commands and process configuration verification commands; extract the return content of the command based on the return; define the return code based on the extracted content keywords; collect the code contents of multiple component processes and compare them with the component exception library; determine the returned data format and solution.

[0129] Based on the same inventive concept, according to another aspect of the present invention, Figure 4As shown, an embodiment of the present invention further provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, the steps of the above method are performed.

[0130] Based on the same inventive concept, according to another aspect of the present invention, Figure 5 As shown, an embodiment of the present invention further provides a computer-readable storage medium 40, which stores a computer program 410 for executing the above method when executed by a processor.

[0131] The embodiment of the present invention may also include a corresponding computer device. The computer device includes a memory, at least one processor, and a computer program stored in the memory and executable on the processor, and the processor executes any one of the above methods when executing the program.

[0132] The memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules in the embodiments of the present application. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions and modules stored in the memory, that is, implementing the above method.

[0133] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In an embodiment, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] Finally, it should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program can be a disk, an optical disk, a read-only storage memory (ROM) or a random access memory (RAM), etc. The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding above-mentioned arbitrary method embodiments.

[0135] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0136] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention. The functions, steps and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0137] It should be understood that, as used herein, the singular forms "a", "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations including one or more of the associated listed items.

[0138] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention is limited to these examples; under the idea of ​​the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for self-detecting abnormality of a component, characterized in that: include: Upon receiving a trigger request, a preset detection template parses the trigger request to generate a detection task, wherein the server reads the file configuration of the preset detection template, obtains the selected self-detection component according to the configuration of the access resources in the template file and the trigger request; the basic configuration of the entire system is preferentially detected according to the configuration in the template file, and when there is no abnormality in the server environment, a detection task is generated for the selected self-detection component, the detection resource ratio of the corresponding component is specified from the template file, and the content with a high ratio is detected preferentially; the detection task is divided into status detection, configuration detection and performance detection; each component generates a return code corresponding to the detection result; Pass the detection task and the corresponding authorization information to the probe of the component to be detected; The probes collect performance data of the components; All performance data and the preset exception library are double-judged to obtain the corresponding exception causes and solutions.

2. A method for self-detecting abnormality of a component according to claim 1, characterized in that: The step of passing the detection task and the corresponding authorization information to the probe of the component to be detected: Set the authorization information to the permission type and permission level; Traverse all target components, execute permission detection rules for each component, and obtain authorized permission items and unauthorized permission items; The preset detection template automatically transfers the corresponding authorization information to the unauthorized permission item for authorization.

3. A method for self-detecting abnormality of a component according to claim 1, characterized in that: Double-judge all performance data and the preset exception library to obtain the corresponding abnormal cause steps: Compare the collected performance data with the exception library, where the performance data includes the running status, occupied resources and related connection status; Match the return codes corresponding to all performance data with the preset configuration exception library, locate the abnormal components, and obtain the abnormal reasons for the running status of all components; The return code of the abnormal component is matched with the preset performance abnormality library to obtain the abnormal reason of the performance of the abnormal component.

4. A method for self-detecting abnormality of a component according to claim 3, characterized in that: The steps of performing double judgment on all performance data and the preset abnormality library to obtain the corresponding abnormality cause also include: If the abnormal reasons for the running status of all components and the abnormal reasons for the performance of the abnormal components are not found, the general reason is selected; The detection is annotated and collected into the anomaly library for later maintenance.

5. A method for self-detecting abnormality of a component according to claim 4, characterized in that: The steps of performing double judgment on all performance data and the preset abnormal library to obtain a solution include: Experts evaluate the abnormal reasons of the operating status of all components, the abnormal reasons of the performance of the abnormal components, or the common reasons, and combine them to obtain a system abnormal status code; Get the solution from the preset system exception library according to the system exception status code.

6. A method for self-detecting abnormality of a component according to claim 1, characterized in that: The design process of the preset exception library is as follows: For abnormal library priority setting, set gradient thresholds for abnormal libraries with different priorities; Statistics on the calculation accuracy and processing time of each abnormal library within a historical period; The calculation accuracy and processing time are weighted and combined to obtain an evaluation value; When the evaluation value exceeds the corresponding gradient threshold, the abnormal library is evaluated as being ready for use.

7. A system for self-detecting abnormality of components, characterized in that: include: A parsing unit is configured to receive a trigger request, and a preset detection template parses the trigger request to generate a detection task, wherein the server reads the file configuration of the preset detection template, and obtains the selected self-detection component according to the configuration of the access resources in the template file and the trigger request; the basic configuration of the entire system is preferentially detected according to the configuration in the template file, and when there is no abnormality in the server environment, a detection task is generated for the selected self-detection component, the detection resource ratio of the corresponding component is specified from the template file, and the content with a high ratio is detected preferentially; the detection task is divided into state detection, configuration detection and performance detection; each component generates a return code corresponding to the detection result; An authorization unit configured to transmit a detection task and corresponding authorization information to a probe of a component to be detected; A collection unit configured to collect performance data of the component for the probe; The exception unit is configured to perform double judgment on all performance data and a preset exception library to obtain corresponding exception causes and solutions.

8. A computer device comprising: at least one processor; and a memory storing a computer program executable on the processor, wherein the processor executes the steps of a method for self-detecting abnormality of a component as claimed in any one of claims 1 to 6 when executing the program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for self-detecting abnormality of a component as claimed in any one of claims 1 to 6 are performed.

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