Methods, devices, media, electronic equipment and software products for testing operating environments

Abstract

This disclosure relates to a method, apparatus, medium, electronic device, and program product for detecting the operating environment. The method includes: acquiring the operating environment information and operating requirements information of a target application on a server; determining the target operation and maintenance information corresponding to the target application from the server's corresponding operation and maintenance knowledge base; the operation and maintenance information includes the application's environment configuration information, historical operation anomaly information, and corresponding first solutions; and determining the first environment detection result of the target application's operating environment based on the operating environment information, operating requirements information, and target operation and maintenance information through a pre-trained detection model. This transforms the detection of the operating environment in the server from a single-point rule verification relying on static scripts to a comprehensive analysis process combining the model's semantic understanding capabilities, improving the automation and accuracy of operating environment detection, and helping to reduce operation and maintenance costs and improve the overall operational reliability of the server.

Description

Technical Field

[0001] This disclosure relates to the field of server operation and maintenance technology, and more specifically, to a method for detecting the operating environment, a device for detecting the operating environment, a non-transitory computer-readable storage medium, an electronic device, and a computer program product. Background Technology

[0002] In the deployment and maintenance of enterprise-level business systems, the configuration of server operating environments is complex, and different software or middleware have significantly different requirements for the operating environment. In actual projects, when problems such as failure to start, frequent abnormal exits, and significant performance degradation occur after installing or upgrading software on a server, maintenance personnel usually need to troubleshoot item by item in the command line according to the installation manual, configuration instructions, and error logs. The detection process is highly dependent on personal experience and is inefficient. Summary of the Invention

[0003] The purpose of this disclosure is to provide a method, apparatus, medium, electronic device, and program product for detecting the operating environment, which can transform the detection of the operating environment in the server from a single-point rule verification that relies on static scripts to a comprehensive analysis process that combines the semantic understanding capabilities of the model. This improves the automation and accuracy of the operating environment detection, helps to reduce operation and maintenance costs, and enhances the overall reliability of the server.

[0004] To achieve the above objectives, in one aspect, this disclosure provides a method for detecting an operating environment, comprising: Obtain the runtime environment and runtime requirements information of the target application on the server; The target operation and maintenance information corresponding to the target application is determined from the operation and maintenance knowledge base corresponding to the server; the operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server, and the operation and maintenance information includes the environment configuration information of the application, historical operation anomaly information, and the first solution corresponding to the historical operation anomaly information. Based on the runtime environment information, the runtime requirement information, and the target operation and maintenance information, a first environment detection result is determined by a pre-trained detection model to indicate whether the runtime environment is abnormal.

[0005] Optionally, the operation and maintenance knowledge base is generated in the following ways: Obtain historical runtime anomaly information and solutions for each application in the server; Establish the correspondence between the historical operational anomaly information and the solutions; The operation and maintenance knowledge base is generated based on the environment configuration information of each application and the corresponding relationship.

[0006] Optionally, the first environment detection result, which determines the operating environment of the target application based on the operating environment information, the operating requirement information, and the target operation and maintenance information using a pre-trained detection model, includes: The operating environment information, the operating requirement information, and the target operation and maintenance information are input into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and the operating requirement information to determine abnormal information, and to determine the target historical operation and maintenance information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation and maintenance information is determined, and the first environment detection result is determined based on the target historical operation and maintenance information and the target solution.

[0007] Optionally, the method further includes: The environmental configuration of the operating environment is adjusted based on the first environmental detection results; In the event of adjustment failure, the detection model is used to infer a second environmental detection result based on the abnormal information.

[0008] Optionally, the method further includes: Obtain the abnormal information and corresponding second solution from the second environmental detection result; The operation and maintenance knowledge base is updated based on the anomaly information and the second solution.

[0009] Optionally, the method further includes: The processing priority corresponding to the first environmental detection result is determined according to the degree of abnormality of the abnormal information. Based on the processing priority corresponding to the first environment detection result, a detection report is generated, which includes abnormal information of the operating environment and corresponding solutions.

[0010] Optionally, the first environmental detection result includes multiple sub-detection results, and the method further includes: The multiple sub-detection results are aggregated according to a preset aggregation method to obtain an aggregated detection result. The preset aggregation method includes hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. A test report is generated based on the aggregated test results.

[0011] In another aspect, this disclosure provides a device for detecting an operating environment, comprising: The environment modeling module is used to obtain the runtime environment information of the target application on the server; The requirements parsing module is used to obtain the runtime requirements information of the target application on the server; The operation and maintenance knowledge base management module is used to determine the target operation and maintenance information corresponding to the target application from the operation and maintenance knowledge base corresponding to the server; the operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server, and the operation and maintenance information includes the environment configuration information of the application, historical operation anomaly information, and the first solution corresponding to the historical operation anomaly information. The model detection module is used to determine the first environment detection result of the target application's operating environment based on the operating environment information, the operating requirement information, and the target operation and maintenance information, using a pre-trained detection model. The first environment detection result characterizes whether the operating environment is abnormal.

[0012] Optionally, the model detection module is used for: The operating environment information, the operating requirement information, and the target operation and maintenance information are input into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and the operating requirement information to determine abnormal information, and to determine the target historical operation and maintenance information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation and maintenance information is determined, and the first environment detection result is determined based on the target historical operation and maintenance information and the target solution.

[0013] Optionally, the model detection module is further configured to: The environmental configuration of the operating environment is adjusted based on the first environmental detection results; In the event of adjustment failure, the detection model is used to infer a second environmental detection result based on the abnormal information.

[0014] Optionally, the device further includes an arrangement and presentation module, the arrangement and presentation module being used for: Obtain the abnormal information and corresponding second solution from the second environmental detection result; The operation and maintenance knowledge base is updated based on the anomaly information and the second solution.

[0015] Optionally, the device further includes an arrangement and presentation module, the arrangement and presentation module being used for: The processing priority corresponding to the first environmental detection result is determined according to the degree of abnormality of the abnormal information. Based on the processing priority corresponding to the first environment detection result, a detection report is generated, which includes abnormal information of the operating environment and corresponding solutions.

[0016] Optionally, the first environmental detection result includes multiple sub-detection results, and the device further includes an arrangement and presentation module, which is used for: The multiple sub-detection results are aggregated according to a preset aggregation method to obtain an aggregated detection result. The preset aggregation method includes hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. Based on the aggregated detection results, a detection report is generated, which includes anomaly information of the operating environment and corresponding solutions.

[0017] On the other hand, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for detecting the operating environment.

[0018] In another aspect, this disclosure provides an electronic device, comprising: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the steps of the above-described method for detecting the operating environment.

[0019] In another aspect, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for detecting the operating environment.

[0020] Through the above technical solution, the disclosed solution obtains the runtime environment information and runtime requirements information of the target application in the server; it determines the target operation and maintenance information corresponding to the target application from the server's corresponding operation and maintenance knowledge base. This operation and maintenance information includes the application's environment configuration information, historical runtime anomaly information, and the first solution corresponding to the historical runtime anomaly information; based on the runtime environment information, runtime requirements information, and target operation and maintenance information, it determines the first environment detection result of the target application's runtime environment through a pre-trained detection model. In this way, the runtime environment detection in the server is transformed from a single-point rule verification relying on static scripts to a comprehensive analysis process combining the model's semantic understanding capabilities. This improves the automation and accuracy of runtime environment detection, helps reduce operation and maintenance costs, and enhances the overall operational reliability of the server.

[0021] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1This is a flowchart illustrating a method for detecting an operating environment according to an exemplary embodiment; Figure 2 This is a schematic diagram illustrating the detection process of an application runtime environment detection method according to an exemplary embodiment; Figure 3 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment; Figure 4 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment; Figure 5 It is based on Figure 4 The illustrated embodiment presents a flowchart of a method for detecting an operating environment; Figure 6 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment; Figure 7 This is a schematic diagram of a detection device for an operating environment according to an exemplary embodiment; Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment; Figure 9 This is a block diagram illustrating an electronic device according to another exemplary embodiment. Detailed Implementation

[0023] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0024] In some related technologies in this field, the deployment and operation of enterprise-level business systems involve complex configurations of server operating environment components such as operating system version, kernel parameters, system services, language runtime, dependent libraries, and third-party tools. Different applications or middleware have significantly different requirements for the operating environment. In actual projects, when problems such as inability to start, frequent abnormal exits, and significant performance degradation occur after installing or upgrading an application on a server, operations and maintenance personnel typically need to rely on installation manuals, configuration instructions, and error logs to check the operating system version, patch status, whether necessary application packages are installed, whether critical services are enabled, and whether configuration files meet requirements item by item in the command line. This diagnostic process is highly dependent on personal experience and is inefficient.

[0025] Current environment diagnostic methods primarily rely on fixed rule scripts or dedicated testing tools for single products. These methods can only check a predefined set of items and have limited adaptability to different application versions, complex dependency chains, and dynamic runtime behavior. When a single server hosts multiple middleware and business modules, and the underlying runtime environment changes due to patch updates, security hardening, or configuration adjustments, existing rule scripts often fail to promptly cover new dependencies and conflicts. While the numerous logs, error messages, and natural language instructions in installation wizards generated during operation contain rich diagnostic clues, they are difficult to utilize effectively with traditional scripts. This often results in environments that do not meet requirements requiring multiple rounds of log collection and manual comparison with documentation to pinpoint the problem, leading to slow diagnosis, problem recurrence, and undetected potential risks.

[0026] To address the aforementioned issues, the solution disclosed herein acquires the runtime environment information and runtime requirements information of the target application on the server; it then determines the target operation and maintenance information corresponding to the target application from the server's corresponding operation and maintenance knowledge base. This operation and maintenance information includes the application's environment configuration information, historical runtime anomaly information, and corresponding first solutions for those anomalies. Based on the runtime environment information, runtime requirements information, and target operation and maintenance information, a pre-trained detection model determines the first environment detection result for the target application's runtime environment. This transforms the runtime environment detection on the server from a single-point rule verification relying on static scripts into a comprehensive analysis process combining the model's semantic understanding capabilities. This improves the automation and accuracy of runtime environment detection, helps reduce operation and maintenance costs, and enhances the overall reliability of the server.

[0027] Figure 1 This is a flowchart illustrating a method for detecting an operating environment according to an exemplary embodiment, such as... Figure 1 As shown, the methods for detecting the operating environment include: S101: Obtain the runtime environment and runtime requirements information of the target application in the server; S102: Determine the target operation and maintenance information corresponding to the target application from the operation and maintenance knowledge base corresponding to the server; the operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server, and the operation and maintenance information includes the application's environment configuration information, historical operation anomaly information, and the first solution corresponding to the historical operation anomaly information. S103: Based on runtime environment information, runtime requirements information, and target operation and maintenance information, the first environment detection result of the target application's runtime environment is determined through a pre-trained detection model. The first environment detection result indicates whether the runtime environment is abnormal.

[0028] For example, when problems occur during the installation, upgrade, or operation of an application on a server, the application's runtime environment can be checked to determine if the runtime environment is abnormal. The target application refers to the application being installed, upgraded, or run.

[0029] like Figure 2 As shown, an environment detection module and an information collection module can be deployed on the server to be monitored. These modules can automatically collect runtime environment data of the target application on the server without affecting its business operations. Data collection can be performed through system commands, configuration file parsing, and API calls.

[0030] The runtime environment data may include relevant data about the runtime environment of the target application and relevant data generated by the actual operation of the target application. For example, the environment detection module may collect relevant data about the runtime environment of the target application, and the information collection module may collect relevant data generated by the actual operation of the target application.

[0031] In one example, runtime environment data includes the operating system and kernel version on the server, installed application packages and runtime versions, start / stop status of critical system services, port usage, resource limit configuration, and log fragments and error messages generated by the target application during actual operation.

[0032] It is understandable that runtime environment data consists of fragmented state data. This data can be organized to obtain runtime environment information. Organization involves transforming the fragmented runtime environment data into a structured, ordered, and standardized description that facilitates subsequent testing. The specific organization method can be determined based on actual application needs and is not limited here.

[0033] The runtime requirements information is obtained by parsing runtime requirements data. Runtime requirements data may include the target application's environment requirements, installation wizard prompts, official compatibility lists, and standard environment baselines. Runtime requirements data may be included with the target application, for example, within its installation package. Runtime requirements data is presented in natural language and contains a large amount of information. Parsing this data to extract key information yields the runtime requirements information. The specific parsing method can be determined based on actual application needs and is not limited here. The runtime requirements information includes the target application's version minimums, required components, configuration constraints, and recommended parameter ranges.

[0034] In some embodiments, an operation and maintenance knowledge base corresponding to the server can be pre-generated, and the target operation and maintenance information can be obtained through this knowledge base. For example, a vectorized retrieval based on the target application can be performed in the operation and maintenance knowledge base, and the target operation and maintenance information corresponding to the target application can be determined from the knowledge base. The target operation and maintenance information may include the environment configuration information, historical operation anomaly information, and corresponding first solution of the target application.

[0035] Subsequently, based on the runtime environment information, runtime requirements information, and target maintenance information, a pre-trained detection model can comprehensively understand and reason to determine whether the target application's runtime environment meets the runtime requirements. If the runtime environment meets the runtime requirements, it is considered normal; if it does not meet the runtime requirements, it is considered abnormal. In the case of an abnormal runtime environment, the detection model can determine a solution to resolve the abnormal runtime environment based on the target maintenance information. The first environment detection result output by the detection model can include the runtime anomaly information of the target application and the corresponding solution. The first environment detection result can be provided to maintenance personnel for server maintenance.

[0036] In one example, the pre-trained detection model can take the form of a pre-trained large language model.

[0037] The above technical solution utilizes the comprehensive understanding and reasoning capabilities of a pre-trained detection model. Based on runtime environment information, runtime requirements information, and target maintenance information, it determines whether the runtime environment of the target application meets the runtime requirements and outputs the first environment detection result, forming an intelligent detection result for maintenance personnel. This transforms runtime environment detection in servers from a single-point rule verification relying on static scripts into a comprehensive analysis process combining the model's semantic understanding capabilities. Without altering the existing application implementation, it improves the automation and accuracy of runtime environment detection, which helps reduce maintenance costs and enhance the overall reliability of server operation.

[0038] The process of generating the operations and maintenance knowledge base is described in detail below.

[0039] In some embodiments, the operations and maintenance knowledge base can be generated in the following ways: Obtain historical runtime exception information and solutions for each application on the server; Establish a correspondence between historical operational anomaly information and solutions; An operations and maintenance knowledge base is generated based on the environment configuration information and corresponding relationships of each application.

[0040] For example, the content sources of the operations and maintenance knowledge base include official installation and configuration documents for various applications, internal enterprise operations and maintenance specifications, historical fault tickets and handling records, standard environment baseline configurations, common problems and corresponding solutions, etc. In some examples, the content sources of the operations and maintenance knowledge base also include operational requirement information. Based on the above content sources, multi-source knowledge data can be obtained, which can include structured configuration items, dependencies, version constraints, resource requirements, etc., as well as installation instructions, error descriptions, and troubleshooting experience expressed in natural language.

[0041] The acquired multi-source knowledge data undergoes data cleaning, classification, and indexing. This multi-source knowledge data can be categorized into environmental configuration information, historical operational anomaly information, and solutions. Structured modeling is then performed on the historical operational anomaly information and solutions to establish a correspondence between them. Structured modeling refers to re-encoding unstructured natural language to obtain vectorized data, facilitating subsequent retrieval.

[0042] Then, the environment configuration information and corresponding relationships of each application are stored according to the preset storage method to obtain the operation and maintenance knowledge base. For example, the environment configuration information and corresponding relationships can be stored in a relational database to obtain the operation and maintenance knowledge base.

[0043] It should be noted that the operation and maintenance knowledge base can provide a unified knowledge query interface for the detection model, enabling the detection model to perform reasoning and comparison by combining the operation and maintenance information in the operation and maintenance knowledge base during the detection process.

[0044] The above technical solutions introduce an operations and maintenance knowledge base, which unifies official documents, internal specifications, and historical fault experience into a searchable knowledge system. The detection model determines the first environment detection result with the support of the operations and maintenance knowledge base, which significantly reduces the reliance on manual comparison of documents and manual troubleshooting experience, and improves the automation and accuracy of operating environment detection.

[0045] The detection process of the detection model is described in detail below.

[0046] In some embodiments, the first environment detection result (i.e., S130) for determining the runtime environment of the target application based on runtime environment information, runtime requirement information, and target operation and maintenance information using a pre-trained detection model includes: The operating environment information, operating requirements information, and target operation and maintenance information are input into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and operating requirements information to identify abnormal information, and to identify the target historical operation abnormal information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation abnormal information is determined, and the first environment detection result is determined based on the target historical operation abnormal information and the target solution.

[0047] For example, the detection model can retrieve the target operation and maintenance information corresponding to the target application from the operation and maintenance knowledge base, and then read the target operation and maintenance information. After receiving the runtime environment information and runtime requirement information, the detection model uses the runtime environment information and runtime requirement information together with the target operation and maintenance information as input data for the detection model.

[0048] The detection model can compare runtime environment information and runtime requirements information. This comparison can be performed at the field level, which involves identifying differences between two data structures field by field. By comparing the runtime environment information and runtime requirements information, the corresponding exception information for the target application can be obtained. This exception information can include differences between the runtime environment information and runtime requirements information, as well as corresponding error codes and exception logs. The differences represent items in the runtime environment that do not meet the runtime requirements.

[0049] By searching the target operation and maintenance information based on the anomaly information, the target historical operation anomaly information that matches the anomaly information can be identified. The search can be based on similarity or other arbitrary search methods. Since a correspondence between historical operation anomaly information and the first solution has been established, the target solution corresponding to the target historical operation anomaly information can be determined based on the target operation and maintenance information.

[0050] It should be noted that searching the target operation and maintenance information can identify multiple historical operational anomalies that match the anomaly information, and correspondingly, multiple target solutions can be identified. The detection model can comprehensively understand the input operating environment information, operating requirement information, and target operation and maintenance information to determine a single historical operational anomaly and its corresponding target solution from multiple historical operational anomalies and their corresponding target solutions.

[0051] The initial environment detection results may include anomaly information and a target solution. Anomaly information can pinpoint specific components, configurations, or version conditions; the target solution may include an analysis of the cause of the anomaly and adjustment suggestions for the current operating environment. These suggestions may include installing missing application packages (providing the application package name and installation method), adjusting configuration items and values, changing application versions (providing version ranges), and enabling system services.

[0052] In the above technical solutions, the detection model, supported by the operation and maintenance knowledge base, completes intelligent matching and difference analysis between the current status of the operating environment and the operating requirements, determines the first environment detection result, and enables anomalies in the operating environment to be automatically identified and located to specific components, configurations or version conditions. This significantly reduces the reliance on manual comparison of documents and manual troubleshooting experience, helps to shorten the problem location time, and improves the automation and accuracy of operating environment detection.

[0053] Figure 3 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment, as shown below. Figure 3 As shown, the methods for detecting the operating environment also include: S140: Adjust the environmental configuration of the operating environment based on the first environmental detection results; S150: In the event of adjustment failure, the second environment detection result is obtained by reasoning through the detection model based on the abnormal information.

[0054] For example, after determining the first environment detection result based on the detection model, the environment configuration of the operating environment can be adjusted based on the target solution in the first environment detection result. If the target application can be installed, upgraded, or run normally after the environment configuration is adjusted, the adjustment is considered successful; at this time, the detection of the operating environment is completed, and a detection report can be generated and provided to the operation and maintenance personnel. The generation of the detection report will be described in detail later.

[0055] If problems persist in installing, upgrading, or running the target application after the environment configuration is adjusted, the adjustment is deemed a failure. This indicates that the anomaly information identified in the aforementioned embodiments is not historical operational anomaly information but a new type of anomaly information. In this case, the detection model can comprehensively understand the input operational environment information, operational requirement information, and target operation and maintenance information, and infer based on the anomaly information to obtain a second environment detection result. The second environment detection result includes the anomaly information and the corresponding second solution.

[0056] In the above technical solutions, for anomalies that have not appeared before, if the target solution in the first environment detection result cannot solve the problem, a new solution is determined by the detection model. This overcomes the limitations of historical experience, providing new solutions for newly emerging problems, further improving the automation level of runtime environment detection, and enhancing the deployment success rate and operational stability of applications in complex server environments.

[0057] Figure 4 It is based on Figure 3 The illustrated embodiment presents a flowchart of a method for detecting an operating environment, as shown below. Figure 4 As shown, the methods for detecting the operating environment also include: S160: Obtain the abnormal information and corresponding second solution from the second environmental detection results; S170: Update the operations and maintenance knowledge base based on anomaly information and second solutions.

[0058] For example, after obtaining the second environmental detection result, the abnormal information and the corresponding second solution can be obtained, the abnormal information and the second solution can be written into the operation and maintenance knowledge base, and the operation and maintenance knowledge base can be updated so that the abnormal information and the second solution can be used as operation and maintenance knowledge for subsequent operation and maintenance.

[0059] In this way, the operation and maintenance knowledge base can continuously evolve with actual operation and maintenance practices, and the detection capabilities of the detection model can also continuously evolve with actual operation and maintenance practices, expanding the coverage of automated operation and maintenance. Under the premise of minimal intrusion into the current system and low transformation cost, a unified intelligent detection capability that can be reused in multi-server and multi-application version scenarios can be formed, which has good engineering application value and promotion prospects.

[0060] In some cases, the environment configuration of the operating environment can be adjusted first based on the second solution. If the adjustment is successful, that is, if it is determined that the second solution can effectively solve the problem, the operation and maintenance knowledge base can be updated based on the anomaly information and the second solution.

[0061] The process of generating a search report is described in detail below.

[0062] Figure 5 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment, as shown below. Figure 5 As shown, the methods for detecting the operating environment also include: S180: Determine the processing priority corresponding to the first environmental detection result according to the degree of abnormality of the abnormal information; S190: Based on the processing priority corresponding to the first environmental detection result, generate a detection report. The detection report includes abnormal information of the operating environment and corresponding solutions.

[0063] For example, the abnormal information has different degrees of abnormality. For instance, a performance parameter below a preset threshold is a type of abnormal information. If the performance parameter is within a first preset range and will not cause problems with the installation, upgrade, or operation of the target application, then the degree of abnormality is low. If the performance parameter is within a second preset range and will cause problems with the installation, upgrade, or operation of the target application, then the degree of abnormality is high.

[0064] It is understandable that abnormal information with a high degree of abnormality directly affects the installation, upgrade or operation of the target application and needs to be resolved first. On the other hand, abnormal information with a low degree of abnormality does not currently affect the installation, upgrade or operation of the target application and can be temporarily postponed or not resolved.

[0065] Therefore, the processing priority of the first environmental detection result can be determined according to the degree of abnormality of the abnormal information. The specific determination method can be set and adjusted according to the actual application. For example, the degree of abnormality of the abnormal information is divided into severe abnormality and general abnormality, and the processing priority can be divided into two levels accordingly. The first level corresponds to severe abnormal information, which means immediate processing, and the second level corresponds to general abnormal information, which means that attention is recommended and processing should be postponed.

[0066] Next, the results of the first environment detection can be structured and organized, and a detection report corresponding to the first environment detection results can be generated based on the determined processing priorities. The first environment detection results output by the detection model are JavaScript Object Notation (JSON) strings. Structured organization refers to converting the JSON string into a structured report. The detection report includes abnormal information of the runtime environment and corresponding solutions, and indicates the processing priority of the abnormal information.

[0067] In some embodiments, the detection report includes command-line suggestions, configuration snippet examples, and change impact warnings. Command-line suggestions refer to commands that can be executed directly on the server. Configuration snippet examples refer to specific parameters or code segments in the configuration file that need to be modified. Change impact warnings explain what effects will occur after performing a certain operation.

[0068] The detection report is provided to operations and maintenance personnel, who can then adjust the operating environment based on the solutions and corresponding processing priorities outlined in the report. It's important to note that the solution can be executed by operations and maintenance personnel or directly by the detection model. When the detection model executes the solution, the output detection report can include a "Resolved" label for any anomalies.

[0069] In some cases, the first environmental detection result includes multiple sub-detection results, each with a corresponding processing priority determined according to the severity of the anomaly. The first environmental detection result also includes multiple passed detection items without any anomalies. In this case, the processing priority can be divided into three levels: Level 1 corresponds to severe anomalies, indicating immediate processing; Level 2 corresponds to general anomalies, indicating a suggestion to pay attention and postpone processing; and Level 3 corresponds to passed detection items, indicating no processing is required. Multiple sub-detection results and passed detection items are displayed in the detection report according to their processing priority.

[0070] In the above technical solutions, based on the detection results of the detection model, a detection report is generated for operation and maintenance personnel according to the processing priority of abnormal information. This makes it easy to directly incorporate into the enterprise's existing operation and maintenance processes and shorten the problem location and handling cycle.

[0071] Figure 6 It is based on Figure 1 The illustrated embodiment presents a flowchart of a method for detecting an operating environment, as shown below. Figure 6 As shown, the first environment detection result includes multiple sub-detection results, and the detection method for the operating environment also includes: S1100: Aggregate multiple sub-detection results according to a preset aggregation method to obtain aggregated detection results. The preset aggregation methods include hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. S1110: Generate a test report based on the aggregated test results.

[0072] For example, multiple sub-detection results can be aggregated according to the hardware dimension, or according to the software dimension, or according to the anomaly type dimension.

[0073] Aggregation refers to classifying and summarizing multiple scattered sub-detection results in a specific way. Hardware-based aggregation means summarizing multiple sub-detection results according to their corresponding hardware categories. For example, servers include master servers and slave servers. Multiple sub-detection results related to master servers are summarized together, and multiple sub-detection results related to slave servers are summarized together.

[0074] The software dimension aggregation method involves summarizing multiple sub-detection results according to their corresponding software (i.e., applications). For example, multiple sub-detection results related to database software are summarized together, and multiple sub-detection results related to system software are summarized together.

[0075] The anomaly type-based aggregation method involves summarizing multiple sub-detection results according to their corresponding anomaly types. For example, multiple sub-detection results related to configuration anomalies are summarized together, and multiple sub-detection results related to performance anomalies are summarized together.

[0076] After obtaining the aggregated detection results, a detection report can be generated based on them. The detection report includes the aggregated sub-detection results and their corresponding solutions.

[0077] In the above technical solution, the detection results of the detection model are aggregated according to a preset aggregation method to generate a detection report. In this way, the scattered multiple sub-detection results are organized into a structured view that conforms to human thinking patterns, which can be adapted to the internal operation and maintenance process specifications of enterprises.

[0078] Figure 7 This is a schematic diagram of a detection device 200 for an operating environment according to an exemplary embodiment, as shown below. Figure 7 As shown, the operating environment detection device 200 may include an environment modeling module 210, a requirements analysis module 220, an operation and maintenance knowledge base management module 230, and a model detection module 240.

[0079] Environment modeling module 210 is used to obtain the runtime environment information of the target application in the server.

[0080] The requirement parsing module 220 is used to obtain the runtime requirement information of the target application in the server.

[0081] The operation and maintenance knowledge base management module 230 is used to determine the target operation and maintenance information corresponding to the target application from the operation and maintenance knowledge base corresponding to the server. The operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server. The operation and maintenance information includes the application's environment configuration information, historical operation exception information, and the first solution corresponding to the historical operation exception information.

[0082] The model detection module 240 is used to determine the first environment detection result of the target application's operating environment based on the operating environment information, operating requirement information, and target operation and maintenance information, through a pre-trained detection model. The environment detection result characterizes whether the operating environment is abnormal.

[0083] In some embodiments, the model detection module 240 is used to input the operating environment information, operating requirement information and target operation and maintenance information into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and operating requirement information to determine abnormal information, and to determine the target historical operation abnormal information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation abnormal information is determined, and the first environment detection result is determined based on the target historical operation abnormal information and the target solution.

[0084] In some embodiments, the model detection module 240 is used to adjust the environment configuration of the operating environment based on the first environment detection result; if the adjustment fails, it uses the detection model to infer based on the abnormal information to obtain the second environment detection result.

[0085] In some embodiments, the operating environment detection device 200 further includes an orchestration and presentation module 250. The orchestration and presentation module 250 is used to acquire abnormal information and corresponding second solutions from the second environment detection results; and to update the operation and maintenance knowledge base based on the abnormal information and the second solutions.

[0086] In some embodiments, the orchestration and presentation module 250 is further configured to determine the processing priority corresponding to the first environment detection result according to the degree of abnormality of the abnormal information; and generate a detection report based on the processing priority corresponding to the first environment detection result, the detection report including the abnormal information of the operating environment and the corresponding solution.

[0087] In some embodiments, the environmental detection results include multiple sub-detection results. The orchestration and presentation module 250 is further used to aggregate the multiple sub-detection results according to a preset aggregation method to obtain an aggregated detection result. The preset aggregation method includes hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. Based on the aggregated detection result, a detection report is generated.

[0088] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0089] Figure 8 This is a block diagram illustrating an electronic device 300 according to an exemplary embodiment. Figure 8 As shown, the electronic device 300 may include a processor 301 and a memory 302. The electronic device 300 may also include one or more of a multimedia component 304, an input / output (I / O) interface 304, and a communication component 305.

[0090] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the aforementioned method for detecting the operating environment. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 303 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 302 or transmitted via communication component 305. The audio component also includes at least one speaker for outputting audio signals. I / O interface 304 provides an interface between processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 305 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0091] In an exemplary embodiment, the electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described detection method for the operating environment.

[0092] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for detecting the operating environment. For example, the computer-readable storage medium may be the memory 302 including the program instructions, which may be executed by the processor 301 of the electronic device 300 to complete the above-described method for detecting the operating environment.

[0093] Figure 9 This is a block diagram illustrating an electronic device 400 according to an exemplary embodiment. For example, the electronic device 400 may be provided as a server. (Refer to...) Figure 9 The electronic device 400 includes a processor 422, which may be one or more, and a memory 432 for storing computer programs executable by the processor 422. The computer programs stored in the memory 432 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 422 may be configured to execute the computer program to perform the aforementioned method for detecting the operating environment.

[0094] Additionally, the electronic device 400 may also include a power supply component 426 and a communication component 450. The power supply component 426 may be configured to perform power management of the electronic device 400, and the communication component 450 may be configured to enable communication of the electronic device 400, such as wired or wireless communication. Furthermore, the electronic device 400 may also include an input / output (I / O) interface 458. The electronic device 400 can operate on an operating system stored in the memory 432.

[0095] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for detecting the operating environment. For example, the non-transitory computer-readable storage medium may be the memory 432 including the program instructions, which may be executed by the processor 422 of the electronic device 400 to complete the above-described method for detecting the operating environment.

[0096] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the detection method of the above-described operating environment when executed by the programmable device.

[0097] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0098] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0099] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for detecting an operating environment, characterized in that, include: Obtain the runtime environment and runtime requirements information of the target application on the server; The target operation and maintenance information corresponding to the target application is determined from the operation and maintenance knowledge base corresponding to the server; the operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server, and the operation and maintenance information includes the environment configuration information of the application, historical operation anomaly information, and the first solution corresponding to the historical operation anomaly information. Based on the runtime environment information, the runtime requirement information, and the target operation and maintenance information, a first environment detection result is determined by a pre-trained detection model to indicate whether the runtime environment is abnormal.

2. The method for detecting the operating environment according to claim 1, characterized in that, The operations and maintenance knowledge base is generated in the following ways: Obtain historical runtime anomaly information and solutions for each application in the server; Establish the correspondence between the historical operational anomaly information and the solutions; The operation and maintenance knowledge base is generated based on the environment configuration information of each application and the corresponding relationship.

3. The method for detecting the operating environment according to claim 1, characterized in that, The first environment detection result, which determines the operating environment of the target application based on the operating environment information, the operating requirement information, and the target operation and maintenance information using a pre-trained detection model, includes: The operating environment information, the operating requirement information, and the target operation and maintenance information are input into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and the operating requirement information to determine abnormal information, and to determine the target historical operation and maintenance information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation and maintenance information is determined, and the first environment detection result is determined based on the target historical operation and maintenance information and the target solution.

4. The method for detecting the operating environment according to claim 3, characterized in that, The method further includes: The environmental configuration of the operating environment is adjusted based on the first environmental detection results; In the event of adjustment failure, the detection model is used to infer a second environmental detection result based on the abnormal information.

5. The method for detecting the operating environment according to claim 4, characterized in that, The method further includes: Obtain the abnormal information and corresponding second solution from the second environmental detection result; The operation and maintenance knowledge base is updated based on the anomaly information and the second solution.

6. The method for detecting the operating environment according to claim 3, characterized in that, The method further includes: The processing priority corresponding to the first environmental detection result is determined according to the degree of abnormality of the abnormal information. Based on the processing priority corresponding to the first environment detection result, a detection report is generated, which includes abnormal information of the operating environment and corresponding solutions.

7. The method for detecting the operating environment according to any one of claims 1 to 5, characterized in that, The first environmental detection result includes multiple sub-detection results, and the method further includes: The multiple sub-detection results are aggregated according to a preset aggregation method to obtain an aggregated detection result. The preset aggregation method includes hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. A test report is generated based on the aggregated test results.

8. A device for detecting the operating environment, characterized in that, include: The environment modeling module is used to obtain the runtime environment information of the target application on the server; The requirements parsing module is used to obtain the runtime requirements information of the target application on the server; The operation and maintenance knowledge base management module is used to determine the target operation and maintenance information corresponding to the target application from the operation and maintenance knowledge base corresponding to the server; the operation and maintenance knowledge base includes the operation and maintenance information corresponding to each application in the server, and the operation and maintenance information includes the environment configuration information of the application, historical operation anomaly information, and the first solution corresponding to the historical operation anomaly information. The model detection module is used to determine the first environment detection result of the target application's operating environment based on the operating environment information, the operating requirement information, and the target operation and maintenance information, using a pre-trained detection model. The first environment detection result characterizes whether the operating environment is abnormal.

9. The operating environment detection device according to claim 8, characterized in that, The model detection module is used for: The operating environment information, the operating requirement information, and the target operation and maintenance information are input into the detection model to obtain the first environment detection result output by the detection model. The detection model is used to compare the operating environment information and the operating requirement information to determine abnormal information, and to determine the target historical operation and maintenance information that matches the abnormal information from the target operation and maintenance information. Based on the target operation and maintenance information, the target solution corresponding to the target historical operation and maintenance information is determined, and the first environment detection result is determined based on the target historical operation and maintenance information and the target solution.

10. The operating environment detection device according to claim 9, characterized in that, The model detection module is also used for: The environmental configuration of the operating environment is adjusted based on the first environmental detection results; In the event of adjustment failure, the detection model is used to infer a second environmental detection result based on the abnormal information.

11. The method for detecting the operating environment according to claim 10, characterized in that, The device further includes an arrangement and presentation module, the arrangement and presentation module being used for: Obtain the abnormal information and corresponding second solution from the second environmental detection result; The operation and maintenance knowledge base is updated based on the anomaly information and the second solution.

12. The operating environment detection device according to claim 9, characterized in that, The device further includes an arrangement and presentation module, the arrangement and presentation module being used for: The processing priority corresponding to the first environmental detection result is determined according to the degree of abnormality of the abnormal information. Based on the processing priority corresponding to the first environment detection result, a detection report is generated, which includes abnormal information of the operating environment and corresponding solutions.

13. The operating environment detection device according to any one of claims 8 to 12, characterized in that, The first environmental detection result includes multiple sub-detection results, and the device further includes an arrangement and presentation module, which is used for: The multiple sub-detection results are aggregated according to a preset aggregation method to obtain an aggregated detection result. The preset aggregation method includes hardware dimension aggregation, software dimension aggregation, or anomaly type dimension aggregation. A test report is generated based on the aggregated test results.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting the operating environment according to any one of claims 1 to 7.

15. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method for detecting the operating environment according to any one of claims 1 to 7.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting the operating environment according to any one of claims 1 to 7.

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