Fault processing method and device oriented to credential environment, storage medium and computer equipment

Abstract

The invention discloses a fault processing method and device oriented to a credential environment, a storage medium and computer equipment. Comprising the following steps: constructing a hardware adaptation layer based on a processor architecture of a credential environment in which a power grid terminal is located; calling an operation system interface supported by a hardware adaptation layer to acquire equipment operation data through a data acquisition agent deployed at a power grid terminal; inputting the equipment operation data into a machine learning model, analyzing the equipment operation data through the machine learning model, and determining a fault object in the credential environment; carrying out association relationship analysis on the fault object based on the knowledge graph, and determining an affected association object set; and on the basis of the fault object and the associated object set, a corresponding repair strategy is matched from a repair strategy library, and targeted repair operation is executed. According to the method provided by the invention, the accuracy and efficiency of fault positioning are improved, the time consumption and errors of manual troubleshooting are reduced, the linkage problem caused by fault diffusion can be accurately determined, and the fault conduction path identification capability is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a fault handling method, apparatus, storage medium, and computer equipment for the domestic IT innovation environment. Background Technology

[0002] Currently, the types of domestically developed (IT) terminal equipment used in the power grid industry are diverse, with frequent system version updates and significant differences in hardware terminal equipment configuration and system architecture. With the rapid development of IT platform services, the complexity of these services is increasing, which not only increases the workload of operation and maintenance but also makes the systems more complex. An effective system and application monitoring system is crucial for understanding the usage of business resources and promptly detecting potential system failures, thus ensuring system operation and security. Summary of the Invention

[0003] In view of this, this application provides a fault handling method, apparatus, storage medium and computer equipment for the information technology innovation environment, which can quickly locate the root cause of the problem by utilizing equipment operation data and improve the efficiency of fault diagnosis and handling under multiple hardware architectures and operating systems.

[0004] According to one aspect of this application, a fault handling method for a domestically developed information technology application environment is provided, comprising: A hardware adaptation layer is constructed based on the processor architecture of the domestic IT innovation environment in which the power grid terminal is located, so as to obtain the underlying hardware performance indicators of the hardware device. By deploying a data acquisition agent on the power grid terminal, the operating system interface supported by the hardware adaptation layer is invoked to collect device operation data, wherein the device operation data includes at least hardware performance data and upper-layer application operation data; The device operation data is input into a machine learning model, and the machine learning model is used to analyze the device operation data to identify faulty objects in the information technology innovation environment. The faulty objects include faulty hardware devices or faulty applications. The association relationship analysis of the faulty objects is performed based on the knowledge graph to determine the set of affected associated objects. The knowledge graph includes the association relationship between hardware devices, drivers and applications in the information technology innovation environment. Based on the faulty object and the set of associated objects, a corresponding repair strategy is matched from the repair strategy library, and a targeted repair operation is performed.

[0005] Optionally, the fault handling method for the information technology innovation environment further includes: The system collects attribute information of applications and hardware devices in the power grid terminal from the automated deployment platform, application performance monitoring system, log system, configuration management database and driver repository. The attribute information includes at least: name, version number, deployment path, dependent driver version, list of associated hardware devices, start / stop logs and resource utilization. Extract the entities and relationships between entities from the attribute information, wherein the entities include at least: application nodes, driver nodes, and hardware device nodes, and the relationships include at least: the dependency relationship between the application node and the driver node, and the driver relationship between the driver node and the hardware device node; The knowledge graph is constructed based on the entities and relationships, and the knowledge graph is stored in the database.

[0006] Optionally, the step of performing association analysis on the faulty object based on the knowledge graph to determine the set of affected associated objects includes: If the faulty object is a faulty application, then based on the knowledge graph, reverse tracing is performed along the dependency relationship and the driver relationship to determine the target driver node that drives the faulty application and the target hardware device node that depends on the target driver node, and the target driver node and the target hardware device node are determined as the upstream fault root cause. If the faulty object is a faulty hardware device, then based on the knowledge graph, reverse tracing is performed along the driving relationship to determine the target driver node that drives the faulty hardware device, and the target application node that depends on the target driver node, and the target driver node and the target application node are determined as the upstream fault root cause. Based on the upstream root cause of the failure, a forward traversal is performed along the dependency and driving relationships to identify other hardware devices and / or other applications involved in the upstream root cause of the failure as the set of associated objects.

[0007] Optionally, the remediation strategy includes remote patch pushing, configuration restoration, or service restart; the execution of targeted remediation operations includes: Download, verify, and install system patches or driver patches related to the faulty object and the set of associated objects from the patch management center; and / or, Remotely restore the critical configurations of the faulty object and the set of associated objects to a pre-stored healthy backup version; and / or, Restart the faulty object and the set of associated objects.

[0008] Optionally, after performing the targeted repair operation, the fault handling method for the information technology innovation environment further includes: Monitor the restart process of the faulty object and the set of associated objects; Based on the running data collected during the restart process and the running status after the restart, the repair effect of the repair operation is verified; If the repair effect reaches the preset standard, the information of the faulty object or the set of associated objects whose repair effect reaches the preset standard, along with the repair strategy corresponding to the repair operation, is fed back to the repair strategy library to optimize the repair strategy library.

[0009] Optionally, the fault handling method for the information technology innovation environment further includes: Extract key features related to various faults from historical equipment operation data to construct a fault feature sample library; Based on the fault feature sample library and the corresponding, manually verified historical diagnostic results, the machine learning model is trained under supervision to obtain the machine learning diagnostic model capable of fault classification and root cause analysis.

[0010] Optionally, the fault handling method for the information technology innovation environment further includes: The historical fault frequency and repair records of each entity in the knowledge graph are periodically detected, and fault propagation weights are configured for the relationships between entities. Based on the equipment operation data corresponding to the faulty object, early abnormal signs of the corresponding entity in the knowledge graph are determined; Starting from the node where the early abnormal signs appear, risk propagation simulation is performed on the associations in the knowledge graph where the fault propagation weight is greater than a preset threshold to predict potential fault propagation paths and a set of high-risk associated objects. Based on the prediction results, preventive maintenance operations are performed on the set of high-risk associated objects, wherein the preventive maintenance operations include at least one of the following: resource reservation, performance baseline adjustment, or preloading of repair patches.

[0011] According to another aspect of this application, a fault handling device for the information technology innovation environment is provided, comprising: The acquisition module is used to build a hardware adaptation layer based on the processor architecture of the information technology innovation environment in which the power grid terminal is located, so as to obtain the underlying hardware performance indicators of the hardware device. The data acquisition module is used to collect device operation data by calling the operating system interface supported by the hardware adaptation layer through the data acquisition agent deployed on the power grid terminal. The device operation data includes at least hardware performance data and upper-layer application operation data. A fault detection module is used to input the device operating data into a machine learning model, analyze the device operating data through the machine learning model, and identify faulty objects in the domestic IT innovation environment. The faulty objects include faulty hardware devices or faulty applications. The association relationship analysis of the faulty objects is performed based on the knowledge graph to determine the set of affected associated objects. The knowledge graph includes the association relationship between hardware devices, drivers and applications in the information technology innovation environment. The fault handling module is used to match the corresponding repair strategy from the repair strategy library based on the fault object and the set of associated objects, and to perform targeted repair operations.

[0012] Optionally, the acquisition module is further configured to collect attribute information of the application and hardware devices in the power grid terminal from the automated deployment platform, application performance monitoring system, log system, configuration management database and driver repository, wherein the attribute information includes at least: name, version number, deployment path, dependent driver version, list of associated hardware devices, start / stop logs and resource utilization rate; The fault handling device for the information technology innovation environment also includes: A relationship identification module is used to extract entities and relationships between entities from the attribute information. The entities include at least: application nodes, driver nodes, and hardware device nodes. The relationships include at least: the dependency relationship between the application node and the driver node, and the driver relationship between the driver node and the hardware device node. The knowledge graph construction module is used to construct the knowledge graph based on the entities and the relationships, and to store the knowledge graph in a database.

[0013] Optionally, the fault detection module is specifically configured to: if the faulty object is a faulty application, then, based on the knowledge graph, perform reverse tracing along the dependency and driver relationships to determine the target driver node driving the faulty application, and the target hardware device node dependent on the target driver node, and identify the target driver node and the target hardware device node as upstream fault roots; if the faulty object is a faulty hardware device, then, based on the knowledge graph, perform reverse tracing along the driver relationships to determine the target driver node driving the faulty hardware device, and the target application node dependent on the target driver node, and identify the target driver node and the target application node as upstream fault roots; based on the upstream fault roots, perform forward traversal along the dependency and driver relationships to determine other hardware devices and / or other applications involved in the upstream fault roots as the set of associated objects.

[0014] Optionally, the fault handling module is specifically used to download, verify, and install system patches or driver patches related to the faulty object and the set of associated objects from the patch management center.

[0015] Optionally, the fault handling module is specifically used to remotely restore the critical configurations involved in the faulty object and the set of associated objects to a pre-stored healthy backup version.

[0016] Optionally, the fault handling module is specifically used to restart the faulty object and the set of associated objects.

[0017] Optionally, the fault handling device for the information technology innovation environment further includes: The repair verification module is used to monitor the restart process of the faulty object and the set of associated objects; and to verify the repair effect of the repair operation based on the running data collected during the restart process and the running status after the restart. An optimization module is used to feed back the information of the faulty object or the set of associated objects whose repair effect reaches the preset standard, along with the repair strategy corresponding to the repair operation, to the repair strategy library if the repair effect reaches the preset standard, so as to optimize the repair strategy library.

[0018] Optionally, the fault handling device for the information technology innovation environment further includes: The model training module is used to extract key features related to various faults from historical equipment operation data and construct a fault feature sample library. Based on the fault feature sample library and the corresponding manually verified historical diagnostic results, the machine learning model is trained under supervision to obtain the machine learning diagnostic model that can perform fault classification and root cause analysis.

[0019] Optionally, the fault handling device for the information technology innovation environment further includes: The early warning module is used to periodically detect the historical fault frequency and repair records of each entity in the knowledge graph, and configure fault propagation weights for the relationships between entities; based on the equipment operation data corresponding to the faulty object, it determines the early abnormal symptoms of the corresponding entity in the knowledge graph; starting from the node where the early abnormal symptoms appear, it performs risk propagation simulation on the relationships in the knowledge graph where the fault propagation weight is greater than a preset threshold, predicts potential fault propagation paths and a set of high-risk associated objects; based on the prediction results, it performs preventive maintenance operations on the set of high-risk associated objects, wherein the preventive maintenance operations include at least one of the following: resource reservation, performance baseline adjustment, or preloading of repair patches.

[0020] According to another aspect of this application, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the fault handling method for the information technology innovation environment described above.

[0021] According to another aspect of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the fault handling method for the information technology innovation environment described above.

[0022] By employing the aforementioned technical solutions and utilizing machine learning models to automatically analyze hardware performance and application operation data, faulty objects can be identified efficiently and accurately. Based on knowledge graph analysis encompassing hardware-driver-application relationships, the associated impact of faulty objects can be rapidly pinpointed, allowing for the rapid identification of the set of related objects affected by the fault. Finally, targeted repair strategies are matched from a repair strategy library and automatically executed, shortening fault recovery time. On one hand, through the tight integration of the hardware adaptation layer and the operating system interface, intelligent diagnosis and analysis using machine learning and knowledge graph technologies improve the accuracy and efficiency of fault location, reducing the time and error associated with manual troubleshooting. On the other hand, by leveraging the complex relationships between hardware, drivers, and applications, the system systematically analyzes the relevant components that a fault may affect, accurately determining the cascading problems caused by the fault's propagation, and enhancing the ability to identify fault propagation paths. This effectively constructs a full-process, intelligent, and automated fault handling system for the domestic IT innovation environment, improving the stability and operational efficiency of power grid hardware and applications within that environment.

[0023] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a fault handling method for an information technology innovation environment provided in an embodiment of this application is shown. Figure 2 This paper shows a structural block diagram of a fault handling device for the information technology innovation environment provided in an embodiment of this application; Figure 3 A schematic diagram of the electronic structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0025] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0027] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “attached” to another element, it can be directly connected or attached to the other element, or there may be intermediate elements. Furthermore, “connected” or “attached” as used herein can include wireless connections or wireless interconnections. The term “and / or” as used herein includes all or any unit and all combinations of one or more associated listed items.

[0028] Exemplary embodiments according to this application will now be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in many different forms and should not be construed as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of this application is thorough and complete, and that the concept of these exemplary embodiments is fully conveyed to those skilled in the art.

[0029] This embodiment provides a fault handling method for the information technology innovation environment, such as... Figure 1 As shown, the method includes: Step 101: Construct a hardware adaptation layer based on the processor architecture of the information technology innovation environment in which the power grid terminal is located.

[0030] In this embodiment, a hardware adaptation layer for a specific processor architecture is constructed to obtain underlying hardware performance metrics. This ensures that drivers and applications can access hardware resources correctly and efficiently, avoiding compatibility issues caused by architectural differences. Furthermore, the acquired underlying hardware performance metrics provide data on hardware operating status, helping to promptly identify performance bottlenecks and anomalies, and ensuring stable operation of devices and systems.

[0031] Among them, the underlying hardware performance metrics are used to ensure that the data acquisition agent can correctly understand and access the hardware of the architecture.

[0032] For example, low-level hardware access interfaces are designed for different hardware architectures (such as x86, ARM, etc.) and terminal device types. For CPU utilization monitoring, the performance monitoring unit (PMU) interface provided by different processor architectures is used to obtain data such as core frequency and instruction execution cycle. Taking the ARM architecture as an example, CPU-related information is obtained by reading the ARM core's performance monitoring registers. For memory usage, the memory management interface provided by the operating system kernel, combined with relevant registers of the hardware memory controller, is used to obtain real-time memory usage, memory bandwidth, and other data. For storage devices, the storage device driver interface and hardware controller registers are used to obtain metrics such as storage read / write speed and remaining capacity. For network devices, the network interface card (NIC) driver is used to collect data such as network traffic, bandwidth utilization, and packet loss rate.

[0033] Step 102: Collect device operation data by calling the operating system interface supported by the hardware adaptation layer through the data acquisition agent deployed on the power grid terminal.

[0034] The device operation data includes at least hardware performance data and upper-layer application operation data.

[0035] Understandably, to address compatibility issues in monitoring diverse devices, the collected data undergoes standardization. Specifically, a unified data format and data dictionary are defined, converting data into a standard format regardless of the device's hardware architecture and operating system. For example, CPU utilization is uniformly represented as a value from 0-100%, and storage capacity is standardized as bytes.

[0036] Step 103: Input the equipment operation data into the machine learning model, analyze the equipment operation data through the machine learning model, and identify the faulty objects in the information technology innovation environment.

[0037] The faulty objects include faulty hardware devices or faulty applications.

[0038] Understandably, the data acquisition agent sends standardized data to the machine learning models of the centralized data analysis and management platform via secure communication protocols (such as SSL / TLS encrypted HTTP or MQTT) to enhance data security. In cases of unstable network environments, data caching and breakpoint resumption mechanisms can be employed to ensure no data loss.

[0039] In practical applications, before step 103, the fault handling method for the information technology innovation environment also includes: extracting key features related to various faults from historical equipment operation data and constructing a fault feature sample library; based on the fault feature sample library and the corresponding, manually verified historical diagnostic results, supervising the training of the machine learning model to obtain a machine learning diagnostic model that can perform fault classification and root cause analysis.

[0040] In this embodiment, a comprehensive sample library covering the entirety of historical faults is constructed, providing a complete learning foundation for the model. Based on this, supervised training is performed using manually verified diagnostic results, ensuring the model can deeply understand the complex causal relationships between different fault modes and their representations. This enables the final diagnostic model to not only quickly and accurately classify and locate the root causes of real-time faults, but also to effectively mine potential hazards from massive amounts of data, significantly improving fault identification efficiency.

[0041] Step 104: Analyze the relationships between the faulty objects based on the knowledge graph to determine the set of affected related objects.

[0042] The knowledge graph includes the relationships between hardware devices, drivers, and applications in the domestic IT innovation environment.

[0043] In practical applications, before step 104, the fault handling method for the information technology innovation environment includes: collecting attribute information of applications and hardware devices in the power grid terminal from the automated deployment platform, application performance monitoring system, log system, configuration management database and driver repository; extracting entities and relationships between entities from the attribute information; constructing a knowledge graph based on entities and relationships, and storing the knowledge graph in the database.

[0044] The attribute information must include at least: name, version number, deployment path, dependent driver versions, list of associated hardware devices, start / stop logs, and resource utilization. Entities must include at least: application nodes, driver nodes, and hardware device nodes, and relationships must include at least: the dependency relationship between application nodes and driver nodes, and the driver relationship between driver nodes and hardware device nodes.

[0045] In this embodiment, cross-system data collection connects different systems, aggregating and integrating multi-dimensional application, driver, and hardware-related data to improve the comprehensiveness of data acquisition. Entities and relationships are extracted from the collected data, transforming abstract adaptation logic and operational associations into a structured knowledge graph. When hardware failures or application performance anomalies occur, the system can immediately locate which applications are driven by the affected hardware, or which hardware the abnormal applications depend on, based on the knowledge graph. This allows for rapid identification of the fault propagation chain and the root cause of the problem, significantly improving operational efficiency.

[0046] In one embodiment, step 104, namely, performing association analysis on the faulty objects based on the knowledge graph to determine the set of affected associated objects, specifically includes the following steps: Step 104-1: If the faulty object is a faulty application, then based on the knowledge graph, reverse tracing is performed along the dependency and driver relationships to determine the target driver node that drives the faulty application, as well as the target hardware device node that depends on the target driver node, and the target driver node and target hardware device node are identified as the upstream fault root cause.

[0047] Step 104-2: If the faulty object is a faulty hardware device, then based on the knowledge graph, reverse tracing is performed along the driving relationship to determine the target driver node that drives the faulty hardware device, as well as the target application node that depends on the target driver node, and the target driver node and the target application node are identified as the upstream fault root cause.

[0048] Step 104-3: Based on the upstream fault root cause, perform a forward traversal along the dependency and driving relationships to identify other hardware devices and / or other applications involved in the upstream fault root cause as a set of associated objects.

[0049] In this embodiment, for faulty applications or hardware, the upstream nodes are traced back based on the application-driver-hardware relationship in the knowledge graph. This allows for rapid location of the corresponding driver and hardware device; faulty hardware can directly pinpoint the upstream root cause of the fault, such as the driver that adapts to it and the applications that depend on it. After locating the root cause, the solution then traverses the relationship chain forward to fully identify other affected hardware or applications. This avoids overlooking potentially affected objects and allows for early prediction of potential cascading problems caused by the fault, providing accurate data support for subsequent fault isolation, emergency plan activation, and impact analysis, thereby reducing the scope and severity of the fault's impact on the overall operation of the power grid terminals.

[0050] Step 105: Based on the faulty object and its associated object set, match the corresponding repair strategy from the repair strategy library and perform targeted repair operations.

[0051] The fault handling method for the domestic IT innovation environment provided in this application embodiment utilizes machine learning models to automatically analyze hardware performance and application operation data, enabling efficient and accurate identification of faulty objects. Based on a knowledge graph analysis containing hardware-driver-application relationships, the method analyzes the associated impact of faulty objects, quickly identifying the set of related objects affected by the fault. Finally, it matches targeted repair strategies from a repair strategy library and executes them automatically, shortening fault recovery time. On one hand, through the tight integration of the hardware adaptation layer and the operating system interface, intelligent diagnosis and analysis using machine learning and knowledge graph technologies improve the accuracy and efficiency of fault location, reducing the time and error of manual troubleshooting. On the other hand, by utilizing the complex relationships between hardware, drivers, and applications, the method systematically analyzes the relevant components that may be affected by the fault, accurately determining the chain reactions caused by the fault propagation, and improving the ability to identify fault propagation paths. This effectively constructs a full-process, intelligent, and automated fault handling system for the domestic IT innovation environment, improving the stability and operational efficiency of power grid hardware equipment and applications within that environment.

[0052] It is worth mentioning that the fault handling method for the information technology innovation environment provided in this application embodiment can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the fault handling method for the information technology innovation environment, etc., but is not limited to the above forms.

[0053] In practical applications, remediation strategies include remote patch pushing, configuration restoration, or service restart.

[0054] Furthermore, performing targeted repair operations in step 105 may include the following methods: Method 1: Download, verify, and install system patches or driver patches related to the faulty object and its associated object set from the patch management center.

[0055] For example, a patch management center is established to collect, store, and manage patches for operating systems and applications. The patch management center maintains close contact with software developers and operating system vendors to obtain the latest patch information in a timely manner. For each patch, detailed information such as the applicable operating system version, software version, and the issues it fixes is recorded. Before pushing patches, compatibility testing is performed. Based on the hardware architecture, operating system version, and software configuration of the terminal device, it is ensured that the patch can be correctly installed and run on the target device. Different types of terminal devices can be simulated in a test environment to conduct comprehensive testing of the patch, including functional testing, compatibility testing, and performance testing. Remote push and installation: Patches are pushed to terminal devices through a secure remote connection channel (such as SSH, Windows remote management, etc.). During the push process, interrupted download resume and encrypted transmission technologies are used to ensure the integrity and security of the patch files. On the terminal device, the patch installer runs automatically, and related services or applications are automatically restarted after installation. During the installation process, logging and progress feedback mechanisms allow administrators to understand the patch installation status in real time.

[0056] Method 2: Remotely restore the critical configurations of the faulty object and its associated object set to a pre-stored healthy backup version.

[0057] For example, critical system configuration files (such as network configuration and system service configuration) are backed up regularly while the device is running normally. When a configuration error is diagnosed as causing a device malfunction, the backed-up configuration files are restored to the device via remote control or an automated script. During the restoration process, the configuration files are checked for validity and compatibility to ensure that the restored configuration enables the device to function normally.

[0058] Method 3: Restart the faulty object and its associated object set.

[0059] For example, for repair measures that require a restart to take effect, an optimized restart strategy can be developed. For instance, a suitable time can be chosen to restart without affecting the user's ongoing critical tasks. This can be achieved by interacting with the user or application to remind them to save their work, and then automatically performing the restart operation. During the restart process, the system startup process should be monitored to ensure that the system can start normally, and after startup, the device's operating status should be rechecked to verify the repair effect.

[0060] In one embodiment, after step 105, the fault handling method for the information technology innovation environment further includes: monitoring the restart process of the fault object and the set of associated objects; verifying the repair effect of the repair operation based on the running data collected during the restart process and the running status after the restart; if the repair effect reaches the preset standard, feeding back the information of the fault object or the set of associated objects whose repair effect reaches the preset standard and the repair strategy corresponding to the repair operation to the repair strategy library.

[0061] In this embodiment, by monitoring the restart process and subsequent status, successful "fault-repair" cases, namely fault object information, associated objects, and effective repair strategies, are fed back to the strategy library. This achieves closed-loop optimization of the repair strategy library, enabling the efficiency and intelligence level of the entire power grid operation and maintenance system to continuously improve with the increase of operating time.

[0062] In one embodiment, the fault handling method for the domestic IT innovation environment further includes: periodically detecting the historical fault frequency and repair records of each entity in the knowledge graph, and configuring fault propagation weights for the relationships between entities; determining early abnormal signs of the corresponding entity in the knowledge graph based on the equipment operation data corresponding to the fault object; starting from the node where early abnormal signs appear, performing risk propagation simulation on the relationships in the knowledge graph where the fault propagation weight is greater than a preset threshold, and predicting potential fault propagation paths and a set of high-risk associated objects; and performing preventive maintenance operations on the set of high-risk associated objects based on the prediction results.

[0063] Among them, preventative maintenance operations include at least one of the following: resource reservation, performance baseline adjustment, or preloading of repair patches.

[0064] In this embodiment, by periodically analyzing the historical failure frequency and repair records of entities, fault propagation weights are configured for related relationships to quantify the probability of fault propagation between applications, drivers, and hardware. By monitoring real-time operational data and matching it with an early anomaly symptom database, the risk propagation of high-weighted associations is simulated starting from potentially abnormal nodes with early anomaly symptoms. This accurately predicts the possible path of fault spread and high-risk associated objects, and proactively addresses high-risk hazards through preventative maintenance. Thus, potential risk sources can be identified before a fault actually occurs, in the early stages of performance degradation, enabling proactive prevention of power grid terminal faults in the context of information technology innovation. By preventing faults in advance and blocking risk propagation, the probability of fault occurrence and impact losses are significantly reduced, minimizing the impact of faults on power grid hardware and applications.

[0065] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0066] Furthermore, such as Figure 2 As shown, as a specific implementation of the above-mentioned fault handling method for the information technology innovation environment, this application embodiment provides a fault handling device 200 for the information technology innovation environment. The fault handling device 200 for the information technology innovation environment includes: an acquisition module 201, a data acquisition module 202, a fault detection module 203, and a fault handling module 204.

[0067] Among them, the acquisition module 201 is used to build a hardware adaptation layer based on the processor architecture of the information technology innovation environment in which the power grid terminal is located, so as to obtain the underlying hardware performance indicators of the hardware device. The data acquisition module 202 is used to collect device operation data by calling the operating system interface supported by the hardware adaptation layer through the data acquisition agent deployed on the power grid terminal. The device operation data includes at least hardware performance data and upper-layer application operation data. The fault detection module 203 is used to input equipment operation data into a machine learning model, analyze the equipment operation data through the machine learning model, identify faulty objects in the information technology innovation environment, wherein the faulty objects include faulty hardware devices or faulty applications; and perform correlation analysis on the faulty objects based on a knowledge graph to determine the set of affected related objects, wherein the knowledge graph includes the correlation between hardware devices, drivers and applications in the information technology innovation environment. The fault handling module 204 is used to match the corresponding repair strategy from the repair strategy library based on the fault object and the set of associated objects, and to perform targeted repair operations.

[0068] Furthermore, the acquisition module 201 is also used to collect attribute information of applications and hardware devices in the power grid terminal from the automated deployment platform, application performance monitoring system, log system, configuration management database and driver repository. The attribute information includes at least: name, version number, deployment path, dependent driver version, list of associated hardware devices, start / stop logs and resource utilization. The fault handling device 200 for the information technology innovation environment also includes: The relationship identification module (not shown in the figure) is used to extract entities and relationships between entities from attribute information. The entities include at least: application nodes, driver nodes and hardware device nodes. The relationships include at least: the dependency relationship between application nodes and driver nodes, and the driver relationship between driver nodes and hardware device nodes. The knowledge graph construction module (not shown in the figure) is used to build knowledge graphs based on entities and relationships and store the knowledge graphs in the database.

[0069] Furthermore, the fault detection module 203 is specifically used to: if the faulty object is a faulty application, then based on the knowledge graph, perform reverse tracing along the dependency and driver relationships to determine the target driver node driving the faulty application, and the target hardware device node that depends on the target driver node, and identify the target driver node and the target hardware device node as the upstream fault root cause; if the faulty object is a faulty hardware device, then based on the knowledge graph, perform reverse tracing along the driver relationships to determine the target driver node driving the faulty hardware device, and the target application node that depends on the target driver node, and identify the target driver node and the target application node as the upstream fault root cause; based on the upstream fault root cause, perform forward traversal along the dependency and driver relationships to determine other hardware devices and / or other applications involved in the upstream fault root cause as a set of associated objects.

[0070] Furthermore, the fault handling module 204 is specifically used to download, verify, and install system patches or driver patches related to the faulty object and its associated object set from the patch management center.

[0071] Furthermore, the fault handling module 204 is specifically used to remotely restore the key configurations involved in the faulty object and the set of associated objects to a pre-stored healthy backup version.

[0072] Furthermore, the fault handling module 204 is specifically used to restart the faulty object and the set of associated objects.

[0073] Furthermore, the fault handling device 200 for the information technology innovation environment also includes: The repair verification module (not shown in the figure) is used to monitor the restart process of the faulty object and its associated object set; based on the running data collected during the restart process and the running status after the restart, it verifies the repair effect of the repair operation; The optimization module (not shown in the figure) is used to feed back the information of the faulty objects or related object sets whose repair effect meets the preset standard and the repair strategy corresponding to the repair operation to the repair strategy library if the repair effect meets the preset standard, so as to optimize the repair strategy library.

[0074] Furthermore, the fault handling device 200 for the information technology innovation environment also includes: The model training module (not shown in the figure) is used to extract key features related to various faults from historical equipment operation data and build a fault feature sample library. Based on the fault feature sample library and the corresponding, manually verified historical diagnostic results, the machine learning model is trained under supervision to obtain a machine learning diagnostic model that can perform fault classification and root cause analysis.

[0075] Furthermore, the fault handling device 200 for the information technology innovation environment also includes: The early warning module (not shown in the figure) is used to periodically detect the historical fault frequency and repair records of each entity in the knowledge graph, and configure fault propagation weights for the relationships between entities; based on the equipment operation data corresponding to the faulty object, it determines the early abnormal signs of the corresponding entity in the knowledge graph; starting from the node where the early abnormal signs appear, it performs risk propagation simulation on the relationships in the knowledge graph where the fault propagation weight is greater than a preset threshold, predicts potential fault propagation paths and a set of high-risk associated objects; based on the prediction results, it performs preventive maintenance operations on the set of high-risk associated objects, wherein the preventive maintenance operations include at least one of the following: resource reservation, performance baseline adjustment, or preloading of repair patches.

[0076] Specific limitations regarding fault handling devices for the domestic IT innovation environment can be found in the limitations of fault handling methods for the domestic IT innovation environment described above, and will not be repeated here. Each module in the aforementioned fault handling device for the domestic IT innovation environment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0077] Based on the above, Figure 1 Accordingly, embodiments of this application also provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 The fault handling method shown is for the domestic IT innovation environment.

[0078] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive) and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0079] Based on the above, Figure 1 The method shown, and Figure 2 The virtual device embodiment shown is designed to achieve the above objectives, such as... Figure 3 As shown in the figure, this application embodiment also provides a computer device 300, which includes a processor 301 and a memory 302. The memory 302 stores a program or instructions that can run on the processor 301. When the program or instructions are executed by the processor 301, they implement the above-mentioned... Figure 1 The fault handling method shown is for the domestic IT innovation environment.

[0080] The memory 302 can be used to store software programs and various data. The memory 302 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 302 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 302 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0081] Processor 301 may include one or more processing units; optionally, processor 301 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 301.

[0082] Computer equipment can specifically include personal computers, servers, network devices, etc.

[0083] Optionally, the computer device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB ports, card reader ports, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Bluetooth interfaces, Wi-Fi interfaces), etc.

[0084] Those skilled in the art will understand that the computer device structure provided in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0085] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or by hardware implementation based on the processor architecture of the power grid terminal in the domestic IT innovation environment to build a hardware adaptation layer to obtain the underlying hardware performance indicators of the hardware device; through the data acquisition agent deployed on the power grid terminal, the operating system interface supported by the hardware adaptation layer is called to collect device operation data, wherein the device operation data includes at least hardware performance data and upper-layer application operation data; the device operation data is input into a machine learning model, and the machine learning model analyzes the device operation data to determine the faulty objects in the domestic IT innovation environment, wherein the faulty objects include faulty hardware devices or faulty applications; based on the knowledge graph, the association relationship analysis of the faulty objects is performed to determine the set of affected associated objects, wherein the knowledge graph includes the association relationship between hardware devices, drivers and applications in the domestic IT innovation environment; based on the faulty objects and the set of associated objects, the corresponding repair strategy is matched from the repair strategy library and targeted repair operations are executed. In the embodiments of this application, the machine learning model is used to automatically analyze hardware performance and application operation data, which can efficiently and accurately identify faulty objects. By analyzing the correlations between hardware, drivers, and applications using a knowledge graph, the system can quickly identify the set of related objects affected by the fault. Finally, it matches targeted repair strategies from a repair strategy library and executes them automatically, shortening fault recovery time. On one hand, through the tight integration of the hardware adaptation layer and the operating system interface, machine learning and knowledge graph technologies are used for intelligent diagnosis and analysis, improving the accuracy and efficiency of fault location and reducing the time and error of manual troubleshooting. On the other hand, by leveraging the complex relationships between hardware, drivers, and applications, the system systematically analyzes the relevant components that a fault may affect, accurately determining the cascading problems caused by the fault's spread and improving the ability to identify fault propagation paths. This effectively constructs a full-process, intelligent, and automated fault handling system for the domestic IT innovation environment, improving the stability and operational efficiency of power grid hardware and applications within that environment.

[0086] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0087] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A fault handling method for an information technology innovation environment, characterized in that, The method includes: A hardware adaptation layer is constructed based on the processor architecture of the domestic IT innovation environment in which the power grid terminal is located, so as to obtain the underlying hardware performance indicators of the hardware device. By deploying a data acquisition agent on the power grid terminal, the operating system interface supported by the hardware adaptation layer is invoked to collect device operation data, wherein the device operation data includes at least hardware performance data and upper-layer application operation data. The device operation data is input into a machine learning model, and the machine learning model is used to analyze the device operation data to identify faulty objects in the information technology innovation environment. The faulty objects include faulty hardware devices or faulty applications. The association relationship analysis of the faulty objects is performed based on the knowledge graph to determine the set of affected associated objects. The knowledge graph includes the association relationship between hardware devices, drivers and applications in the information technology innovation environment. Based on the faulty object and the set of associated objects, a corresponding repair strategy is matched from the repair strategy library, and a targeted repair operation is performed.

2. The fault handling method for the information technology innovation environment according to claim 1, characterized in that, The method further includes: The system collects attribute information of applications and hardware devices in the power grid terminal from the automated deployment platform, application performance monitoring system, log system, configuration management database and driver repository. The attribute information includes at least: name, version number, deployment path, dependent driver version, list of associated hardware devices, start / stop logs and resource utilization. Extract the entities and relationships between entities from the attribute information, wherein the entities include at least: application nodes, driver nodes, and hardware device nodes, and the relationships include at least: the dependency relationship between the application node and the driver node, and the driver relationship between the driver node and the hardware device node; The knowledge graph is constructed based on the entities and relationships, and the knowledge graph is stored in the database.

3. The fault handling method for the information technology innovation environment according to claim 2, characterized in that, The step of performing association analysis on the faulty objects based on knowledge graphs to determine the set of affected associated objects includes: If the faulty object is a faulty application, then based on the knowledge graph, reverse tracing is performed along the dependency relationship and the driver relationship to determine the target driver node that drives the faulty application and the target hardware device node that depends on the target driver node, and the target driver node and the target hardware device node are determined as the upstream fault root cause. If the faulty object is a faulty hardware device, then based on the knowledge graph, reverse tracing is performed along the driving relationship to determine the target driver node that drives the faulty hardware device, and the target application node that depends on the target driver node, and the target driver node and the target application node are determined as the upstream fault root cause. Based on the upstream root cause of the failure, a forward traversal is performed along the dependency and driving relationships to identify other hardware devices and / or other applications involved in the upstream root cause of the failure as the set of associated objects.

4. The fault handling method for the information technology innovation environment according to claim 1, characterized in that, The remediation strategy includes remote patch pushing, configuration restoration, or service restart; the execution of targeted remediation operations includes: Download, verify, and install system patches or driver patches related to the faulty object and the set of associated objects from the patch management center; and / or, Remotely restore the critical configurations of the faulty object and the set of associated objects to a pre-stored healthy backup version; and / or, Restart the faulty object and the set of associated objects.

5. The fault handling method for the information technology innovation environment according to claim 1, characterized in that, After performing the targeted repair operation, the method further includes: Monitor the restart process of the faulty object and the set of associated objects; Based on the running data collected during the restart process and the running status after the restart, the repair effect of the repair operation is verified; If the repair effect reaches the preset standard, the information of the faulty object or the set of associated objects whose repair effect reaches the preset standard, along with the repair strategy corresponding to the repair operation, is fed back to the repair strategy library to optimize the repair strategy library.

6. The fault handling method for the information technology innovation environment according to any one of claims 1 to 5, characterized in that, The method further includes: Extract key features related to various faults from historical equipment operation data to construct a fault feature sample library; Based on the fault feature sample library and the corresponding, manually verified historical diagnostic results, the machine learning model is trained under supervision to obtain the machine learning diagnostic model capable of fault classification and root cause analysis.

7. The fault handling method for the information technology innovation environment according to claim 1, characterized in that, The method further includes: The historical fault frequency and repair records of each entity in the knowledge graph are periodically detected, and fault propagation weights are configured for the relationships between entities. Based on the equipment operation data corresponding to the faulty object, early abnormal signs of the corresponding entity in the knowledge graph are determined; Starting from the node where the early abnormal signs appear, risk propagation simulation is performed on the associations in the knowledge graph where the fault propagation weight is greater than a preset threshold to predict potential fault propagation paths and a set of high-risk associated objects. Based on the prediction results, preventive maintenance operations are performed on the set of high-risk associated objects, wherein the preventive maintenance operations include at least one of the following: resource reservation, performance baseline adjustment, or preloading of repair patches.

8. A fault handling device for an information technology innovation environment, characterized in that, The device includes: The acquisition module is used to build a hardware adaptation layer based on the processor architecture of the information technology innovation environment in which the power grid terminal is located, so as to obtain the underlying hardware performance indicators of the hardware device. The data acquisition module is used to collect device operation data by calling the operating system interface supported by the hardware adaptation layer through the data acquisition agent deployed on the power grid terminal. The device operation data includes at least hardware performance data and upper-layer application operation data. A fault detection module is used to input the device operating data into a machine learning model, analyze the device operating data through the machine learning model, and identify faulty objects in the domestic IT innovation environment. The faulty objects include faulty hardware devices or faulty applications. The association relationship analysis of the faulty objects is performed based on the knowledge graph to determine the set of affected associated objects. The knowledge graph includes the association relationship between hardware devices, drivers and applications in the information technology innovation environment. The fault handling module is used to match the corresponding repair strategy from the repair strategy library based on the fault object and the set of associated objects, and to perform targeted repair operations.

9. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the fault handling method for the information technology innovation environment as described in any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the fault handling method for the information technology innovation environment as described in any one of claims 1 to 7.

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