System kernel reliability evaluation method and system based on fault sequence generation

Abstract

The invention provides a system kernel reliability evaluation method and system based on fault sequence generation, and relates to the technical field of linux system kernel reliability evaluation, and the method comprises the steps: collecting performance indexes and log information of a target system through a data collector, various fault types and attribute definitions possibly encountered in the system are put into a fault model library, and a fault sequence is provided for a fault injection manager according to a specific fault injection scene; the fault injection manager sends instructions to a target system through the system adapter, and the fault injection executor executes the instructions to simulate a fault scene; and the result analyzer receives data generated after the fault injection actuator executes the fault, carries out deep analysis and records an error log. Wherein in the fault model library, an LSTM algorithm is adopted to extract the rule of fault signals in fault information to establish a fault sequence generation model, and the fault sequence is predicted.

Description

Technical Field

[0001] The present disclosure relates to the technical field of Linux system kernel reliability evaluation, and in particular to a system kernel reliability evaluation method and system based on fault sequence generation. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] The stability of the operating system is the basis for ensuring technological progress, data security, and improving user experience. The stability of the operating system depends largely on the reliability of the kernel system. Evaluating the reliability of the kernel system is of great significance for improving system reliability, ensuring data security, optimizing resource utilization, and enhancing user experience.

[0004] System kernel reliability evaluation tools play a vital role in ensuring the stability and reliability of computer systems, especially operating system kernels. Operating system kernel reliability evaluation requires obtaining a sufficient amount of valid fault data in a short period of time, and artificial fault injection into the target system can greatly reduce the time required to obtain fault data, accelerate system failure, and promote the system to foresee and respond to potential faults and anomalies during the design and development stage.

[0005] Fault injection technology is an important technology for evaluating system reliability. It simulates the fault response of the system in a real environment, collects the system's response information, and provides data support for evaluating the reliability of the system.

[0006] Currently commonly used fault injection methods still have some limitations and challenges, including that current fault injection methods may require specific environments or configurations, limiting their applicability in different environments; fault injection cannot fully simulate the dependencies between all components in the system, and thus cannot accurately control the type, location, and time of the fault, affecting the accuracy of the test. Summary of the invention

[0007] In order to solve the above problems, the present disclosure proposes a system kernel reliability evaluation method and system based on fault sequence generation, which generates a fault sequence based on historical fault data and characteristics related to fault occurrence and propagation, overcomes the influence of dependencies between components and time on fault sequence generation, and obtains a fault sequence with more accurate fault type, location and time. Through the fault injection process, the reliability of the operating system kernel is evaluated, and the performance of the system under various fault conditions is effectively evaluated, making the evaluation process more systematic and standardized.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: The system kernel reliability evaluation method based on fault sequence generation includes: Obtain performance data and log information of the target system; Generate injected fault sequences based on possible fault types, attribute definitions, and specific fault injection scenarios; Inject the generated fault sequence into the target system, obtain the operating status and response status of the target system in real time during the fault injection process, identify the behavior pattern of the target system under fault conditions, evaluate the performance indicators of the target system after fault injection, and its stability and recovery ability in the face of faults; Identify and analyze abnormal conditions after fault injection in the target system, generate detailed evaluation reports based on the identification results, and present the analysis results in the form of graphs or charts; Among them, generating the injected fault sequence includes: extracting the fault signal rules in the historical fault information, establishing a fault sequence generation model, and predicting the possible propagation path and conditions of the fault, the probability of fault propagation, and the possibility of propagation under different conditions according to the possible fault types, attribute definitions and specific fault injection scenarios.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: The system kernel reliability evaluation system based on fault sequence generation includes: It includes a master end and a target end, wherein the master end includes a fault model library, a fault injection manager, a system adapter, a fault injection executor, a data collector and a result analyzer; the fault model library is configured to: store predefined fault models for simulating various system faults; The fault injection manager is configured to: generate fault injection instructions, including fault type, parameters and target system information and distribute the fault injection instructions to the fault injection executor at the target end; monitor the system status, during the fault injection process, monitor the operating status and response of the target system, and collect data of the target system during the fault injection; The system adapter is configured to: adapt and send the instructions of the fault injection manager to the fault injection executor at the target end; The fault injection executor is configured to: receive the fault injection instruction issued by the fault injection manager, parse the parameters in the fault instruction, and execute the fault injection on the target system according to the parsed parameters and the generated fault sequence to simulate the predetermined fault scenario; The data collector is configured to: collect performance data and log information of the target system during the fault injection execution; The result analyzer is configured to collect and analyze the data after the fault injection execution to evaluate the performance and stability of the system.

[0010] Furthermore, the target end is a target system kernel and an application program.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the system kernel reliability evaluation method based on fault sequence generation is implemented.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the system kernel reliability evaluation method based on fault sequence generation.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a system kernel reliability evaluation system based on fault sequence generation, which divides each functional component into a test management module, a fault simulation and injection module, a system monitoring and logging module, and a performance and stability evaluation module. These modules constitute the main flow of interaction between the master end and the target end, ensuring that the kernel reliability evaluation tool can effectively evaluate the performance of the system under various fault conditions, making the evaluation process more systematic and standardized.

[0014] The present invention discloses a system kernel reliability evaluation system based on fault sequence generation. In the system kernel reliability evaluation tool, fault mode selection and fault sequence generation based on the fault model library and fault history data are key steps in fault injection and system reliability testing. The fault sequence is generated based on historical fault data and features related to fault occurrence and propagation, which overcomes the influence of dependencies between components and time on fault sequence generation, and obtains a fault sequence with more accurate fault type, location and time. The fault sequence generation process disclosed in the present invention adopts the LSTM algorithm, trains the LSTM model to extract the rules of fault signals in fault information, and obtains the optimal parameters to establish a fault sequence generation model. Compared with traditional methods, this method does not need to make any simplifying assumptions, and does not consider the dependencies between system groups. It uses its superior performance in feature extraction and time series prediction to make a more accurate prediction of the fault sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0016] Figure 1 A schematic diagram of a system kernel reliability evaluation system based on fault sequence generation according to an embodiment of the present disclosure; Figure 2 This is a functional architecture diagram of a system kernel reliability evaluation system based on fault sequence generation according to an embodiment of the present disclosure; Figure 3 A flowchart of the LSTM-based fault sequence generation of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0019] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0020] Example 1 In one embodiment of the present disclosure, a system kernel reliability evaluation method based on fault sequence generation is provided, comprising: Obtain performance data and log information of the target system; Generate injected fault sequences based on possible fault types, attribute definitions, and specific fault injection scenarios; Inject the generated fault sequence into the target system, obtain the operating status and response status of the target system in real time during the fault injection process, identify the behavior pattern of the target system under fault conditions, evaluate the performance indicators of the target system after fault injection, and its stability and recovery ability in the face of faults; Identify and analyze abnormal conditions after fault injection in the target system, generate detailed evaluation reports based on the identification results, and present the analysis results in the form of graphs or charts; Among them, generating the injected fault sequence includes: extracting the fault signal rules in the historical fault information, establishing a fault sequence generation model, and predicting the possible propagation path and conditions of the fault, the probability of fault propagation, and the possibility of propagation under different conditions according to the possible fault types, attribute definitions and specific fault injection scenarios.

[0021] As an embodiment, a system kernel reliability evaluation method based on fault sequence generation disclosed in the present invention is implemented based on a master end and a target end, wherein the master end includes a fault model library, a fault injection manager, a system adapter, a fault injection executor, a data collector, and a result analyzer; Fault model library, which stores predefined fault models used to simulate various system faults; The fault injection manager generates fault injection instructions, including fault type, parameters and target system information, and distributes the fault injection instructions to the fault injection executor at the target end; monitors the system status, during the fault injection process, monitors the operating status and response of the target system, and collects data of the target system during the fault injection; The system adapter adapts the instructions of the fault injection manager and sends them to the fault injection executor at the target end; The fault injection executor receives the fault injection instruction issued by the fault injection manager, parses the parameters in the fault instruction, and executes the fault injection on the target system according to the parsed parameters and the generated fault sequence to simulate the predetermined fault scenario; Specifically, the failure scenarios include hardware failure, software failure, network failure, power failure, human error and environmental factors, among which hardware failure includes memory corruption, hard disk failure and network interface failure. Software failure includes program crash, resource overload and software deadlock. Network failure includes network delay, network packet loss and network partition (simulating network failure causing the system to be divided into multiple parts). Power failure includes power outage and voltage fluctuation. Human error includes configuration error and security vulnerability. Environmental factors include abnormal temperature and abnormal humidity.

[0022] The data collector is responsible for collecting performance data and log information of the target system during the fault injection execution; Result Analyzer, responsible for collecting and analyzing data after fault injection execution to evaluate the performance and stability of the system.

[0023] The specific implementation process of the system kernel reliability evaluation method based on fault sequence generation disclosed in the present invention is as follows: 1) Collect the performance indicators and log information of the target system through the data collector, put the various fault types and attribute definitions that may be encountered in the system into the fault model library, and provide fault sequences for the fault injection manager according to the specific fault injection scenario.

[0024] Specifically, the data collector is responsible for collecting the performance data and log information of the target system during the execution of fault injection. The data collector monitors the target system in real time, including the performance indicators of the target system, such as CPU usage, memory usage, disk I / O, etc.; performs logging, recording all key logs and events of the target system during fault injection; performs data collection, collecting various data generated by the target system during fault injection, including system status, error information, performance counters, etc.; performs data storage, storing the collected data in appropriate storage media to ensure data integrity and accessibility; ensures data security, and ensures compliance with relevant data security and privacy protection specifications during data collection.

[0025] The fault model library contains predefined fault models for simulating various system faults. The main responsibilities include: storing fault models, maintaining a library containing various fault types, each with detailed attributes and triggering conditions; providing fault definitions, providing accurate fault definitions for the fault injection manager, including fault type, impact range, duration, etc.; maintaining fault attributes, maintaining detailed attributes of each fault, such as fault severity, recoverability, etc.; supporting custom faults, providing a LSTM-based fault sequence generation method and adding custom fault models according to user-specific evaluation requirements; recording fault history, recording used fault models and related evaluation results, and providing support for historical data analysis.

[0026] 2) The fault injection manager sends instructions to the target system through the system adapter, and the fault injection executor executes these instructions to simulate the fault scenario.

[0027] The main functions of the fault injection manager include fault simulation and verification, fault tolerance testing, performance bottleneck analysis, fault recovery and restart testing, security vulnerability mining, and monitoring and alarm system verification. The main responsibilities include: monitoring system status, monitoring the operating status and response of the target system during the fault injection process; data collection coordination, coordinating the data collector to collect data from the target system during the fault injection period; user interaction, providing a user interface to allow users to configure evaluation parameters, start evaluation tasks, and view evaluation results; task scheduling, scheduling fault injection tasks according to the evaluation strategy, and determining the execution order and time of tasks; instruction generation and distribution, generating fault injection instructions, including fault type, parameters, and target system information, and distributing the fault injection instructions to the fault injection executor on the target side; triggering system response, observing and recording the system's response to faults, including error handling, exception capture, etc.

[0028] Implementation process: Design a user interface in which users can choose to configure evaluation parameters, start evaluation tasks, and view evaluation results. Implement a task queue to store and manage tasks to be executed, develop a scheduler to select and schedule tasks from the task queue based on the evaluation strategy and resource status, and determine the execution order and time of tasks based on the dependencies between tasks. There are three steps to generate instructions: first, select a template, create a template that contains all necessary fields, such as fault type, injection point, duration, recovery conditions, etc., and collect test parameters such as the time, location, and conditions of fault injection through the user interface; second, fill in parameters, fill the verified parameters into the template, and generate specific fault injection instructions; third, format the instructions to ensure that the generated instructions are in the correct format and can be understood and executed by the execution system.

[0029] The system adapter is responsible for adapting the fault injection manager's instructions and sending them to the target fault injection executor. The main responsibilities include: instruction adaptation, adapting the fault injection instructions generated by the fault injection manager to a format recognizable by the target system; ensuring platform compatibility, ensuring that the fault injection instructions are compatible with the hardware and software platforms of the target system; communication protocol conversion, converting the communication protocol to ensure smooth communication between the master and the target; error handling, handling errors that may occur during instruction adaptation or sending, and providing corresponding feedback; security assurance: ensuring compliance with relevant data security and privacy protection specifications during instruction adaptation and sending; performance optimization: optimizing the instruction sending process to reduce the impact on the target system performance.

[0030] The fault injection executor is a key component of the target end, which is responsible for actually executing the fault injection instructions sent by the master end on the target system. Specifically, it includes: receiving fault instructions, receiving fault injection instructions issued by the fault injection manager of the master end; parsing fault parameters, parsing the parameters in the fault instructions, including fault type, impact range, duration, etc.; executing fault injection: according to the parsed parameters and the generated fault sequence, execute fault injection on the target system to simulate the predetermined fault scenario.

[0031] Specifically, parsing fault parameters, parsing parameters in fault instructions, including: Parsing fault parameters is a key step in fault injection testing, which involves extracting and processing key information from fault injection instructions in order to correctly perform fault injection. The fault injection instructions are in JSON format, and the parsing steps are as follows: 1. Read JSON string: Read the string in JSON format in the request.

[0032] 2. Parse JSON: Use the JSON library provided by the programming language to parse the JSON string into the corresponding data structure.

[0033] 3. Extract parameters: Extract the required parameters from the parsed data structure.

[0034] Furthermore, according to the parsed parameters and the generated fault sequence, fault injection is performed on the target system to simulate the predetermined fault scenario. The injection and simulation process is as follows: Use the configured fault injection tool to perform fault injection on the target system, monitor the system response, record key data and system behavior during fault injection, monitor and evaluate the system for anomaly detection, and ensure timely response when the system exhibits abnormalities.

[0035] The result analyzer receives the data generated by the fault injection executor after the fault is executed, performs in-depth analysis, and records the error log. The result analyzer is responsible for collecting and analyzing the data after the fault injection is executed to evaluate the performance and stability of the system. The result analyzer receives the logs and performance data collected by the fault injection executor during the fault injection process, and performs in-depth analysis on the collected data to identify the behavior patterns of the system under fault conditions, evaluate the performance indicators of the system after fault injection, such as response time, throughput, etc., and evaluate the stability and recovery capabilities of the system in the face of faults; perform anomaly detection on the system to identify anomalies in the data, such as performance bottlenecks or potential system problems; based on the analysis results, generate a detailed evaluation report, present the analysis results in the form of graphics or charts, improve the readability and intuitiveness of the information, and provide suggestions for system optimization and improvement to help improve the reliability and performance of the system.

[0036] According to the above components, the fault injection system can be divided into five functional modules: test management module, fault simulation and injection module, system monitoring and logging module, performance and stability evaluation module, result analysis and reporting module, such as Figure 2 shown.

[0037] Furthermore, the LSTM-based fault sequence generation method generates an injected fault sequence, including: Extract fault signal rules from historical fault information, establish a fault sequence generation model, and predict the possible fault propagation path and conditions, the probability of fault propagation, and the possibility of propagation under different conditions based on possible fault types, attribute definitions, and specific fault injection scenarios.

[0038] Specifically, 1) Obtain historical fault data of component status, including fault type, occurrence time, duration, impact range, etc. Select features related to fault occurrence and propagation, including system load, environmental conditions, component status, etc. Preprocess historical data, remove abnormal data, complete data, and convert non-numeric data into numerical data.

[0039] The LSTM algorithm is used to extract the rules of fault signals in fault information to establish a fault sequence generation model that takes the current system state and environmental conditions as input and outputs a predicted fault sequence, including: 1. Obtaining historical fault data on fault type, occurrence time, duration, impact range, system load, environmental conditions, and component status; 2. Use the LSTM algorithm to extract the rules of fault signals in fault information, use historical fault data to train the model, and learn fault modes and sequences; 3. Use accuracy metrics to evaluate model performance and perform cross-validation to ensure the generalization ability of the model; 4. Provide the model with input data of the current system status and environmental conditions, and predict future fault sequences based on the input data.

[0040] Specifically, LSTM is a special type of recurrent neural network (RNN) that is designed to solve the gradient vanishing or gradient exploding problems that occur in traditional RNNs when processing long sequence data. The core idea of ​​LSTM is to introduce three gating mechanisms: Forget Gate, Input Gate, and Output Gate, which allow the network to learn long-term dependencies in the data.

[0041] The forget gate determines what information the network should discard at the current time step. The calculation formula is: (1) in, is the sigmoid activation function, and are weights and biases, is the hidden state at the previous time step, is the input for the current time step.

[0042] The input gate consists of two parts: a sigmoid layer that decides which values ​​to update, and a tanh layer that creates a new vector of candidate values ​​that will be added to the state.

[0043] Calculation of sigmoid layer: (2) Calculation of tanh layer: (3) The candidate memory state is updated by the following formula: (4) The output gate determines the next hidden state The value of . It is calculated by the following formula: (5) Final hidden state Computed from the output gate and the scaled memory state: (6) Through these gating mechanisms, LSTM can learn to ignore or update its internal state, thereby capturing the long-term dependencies of the fault sequence.

[0044] 3) Use accuracy metrics to evaluate model performance and perform cross-validation to ensure the generalization ability of the model; Specifically, the F1 score indicator is used to evaluate the performance of the model, and cross-validation is performed to ensure the generalization ability of the model; the F1 score is an indicator for evaluating the performance of a binary classification model, which is the harmonic mean of precision and recall. Cross-validation is a technique for evaluating the generalization ability of a model. It divides the data set into multiple subsets, uses one subset as the test set each time, and uses the rest as the training set. This process is repeated multiple times to calculate the performance indicators of the model on different data sets.

[0045] 4) Cross-validation process: For K subsets, perform the following steps K times: Combine the data of K-1 subsets as the training set; The remaining subset is used as the test set; Train the model using the training set; Evaluate the model using the test set and calculate the F1 score; For each cross validation, the F1 score of the model is calculated. The calculation formula of the F1 score is:

[0046] Among them, the precision and recall can be calculated by the following formula:

[0047] Calculate the average F1 score of all K cross-validations. Evaluate the generalization ability of the model based on the average F1 score, and select the model with a higher average F1 score.

[0048] Provide the model with input data of the current system state and environmental conditions, and predict future fault sequences based on the input data. Use the model to predict how faults propagate in the system, consider possible propagation paths and conditions, evaluate the probability of fault propagation, and consider the possibility of propagation under different conditions.

[0049] Example 2 In one embodiment of the present disclosure, a system kernel reliability evaluation system based on fault sequence generation is provided, including a master end and a target end, wherein the master end includes a fault model library, a fault injection manager, a system adapter, a fault injection executor, a data collector, and a result analyzer; The fault model library is configured to: store predefined fault models for simulating various system faults; The fault injection manager is configured to: generate fault injection instructions, including fault type, parameters and target system information and distribute the fault injection instructions to the fault injection executor at the target end; monitor the system status, during the fault injection process, monitor the operating status and response of the target system, and collect data of the target system during the fault injection; The system adapter is configured to: adapt and send the instructions of the fault injection manager to the fault injection executor at the target end; The fault injection executor is configured to: receive the fault injection instruction issued by the fault injection manager, parse the parameters in the fault instruction, and execute the fault injection on the target system according to the parsed parameters and the generated fault sequence to simulate the predetermined fault scenario; The data collector is configured to: collect performance data and log information of the target system during the fault injection execution; The result analyzer is configured to collect and analyze the data after the fault injection execution to evaluate the performance and stability of the system.

[0050] The target end is the target system kernel and application program.

[0051] Among them, the fault model library belongs to the test management module, the fault injection manager, system adapter and fault injection executor belong to the fault simulation and injection module, the data collector belongs to the system monitoring and logging module, and the result analyzer belongs to the result analysis and reporting module and the performance and stability evaluation module.

[0052] Example 3 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the system kernel reliability evaluation method based on fault sequence generation is implemented.

[0053] Example 4 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the system kernel reliability evaluation method based on fault sequence generation.

[0054] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A system kernel reliability evaluation method based on fault sequence generation, characterized in that: include: Obtain performance data and log information of the target system; Generate injected fault sequences based on possible fault types, attribute definitions, and specific fault injection scenarios; Inject the generated fault sequence into the target system, obtain the operating status and response status of the target system in real time during the fault injection process, identify the behavior pattern of the target system under fault conditions, evaluate the performance indicators of the target system after fault injection, and its stability and recovery ability in the face of faults; Identify and analyze abnormal conditions after fault injection in the target system, generate detailed evaluation reports based on the identification results, and present the analysis results in the form of graphs or charts; Among them, generating the injected fault sequence includes: extracting the fault signal rules in the historical fault information, establishing a fault sequence generation model, and predicting the possible propagation path and conditions of the fault, the probability of fault propagation, and the possibility of propagation under different conditions according to the possible fault types, attribute definitions and specific fault injection scenarios.

2. The system kernel reliability evaluation method based on fault sequence generation according to claim 1, characterized in that: Obtain the performance data and log information of the target system, including CPU usage, memory usage, and disk I / O, and record key logs and events of the target system.

3. The system kernel reliability evaluation method based on fault sequence generation according to claim 1, characterized in that: Generate injected fault sequences based on possible fault types, attribute definitions, and specific fault injection scenarios, including: extracting predefined fault models in the fault model library for simulating various system faults, the attributes and triggering conditions of each fault, extracting precise fault definitions for fault injection, including fault type, impact range, and duration, and maintaining detailed attributes of each fault.

4. The system kernel reliability evaluation method based on fault sequence generation according to claim 3, characterized in that: Generating the injected fault sequence includes: obtaining historical fault data of component status, including fault type, occurrence time, duration and impact range, selecting features related to fault occurrence and propagation, and using LSTM algorithm to extract the rules of fault signals in fault information to establish a fault sequence generation model. The fault sequence generation model generates possible fault propagation paths and conditions, the probability of fault propagation and the possibility of propagation under different conditions.

5. The system kernel reliability evaluation method based on fault sequence generation according to claim 1, characterized in that: During the fault injection process, monitor the operating status and response of the target system and collect data of the target system during the fault injection period; provide a user interface to allow users to configure evaluation parameters, start evaluation tasks and view evaluation results, schedule fault injection tasks according to evaluation tasks, and determine the execution order and time of tasks; Generate fault injection instructions, including fault type, parameters and target system information, and distribute the fault injection instructions to the fault injection executor, trigger the target system response, observe and record the target system's response to the fault, including error handling and exception capture.

6. The system kernel reliability evaluation method based on fault sequence generation according to claim 1, characterized in that: The fault injection instruction is issued by the fault injection manager, and the fault instruction is issued to the system adapter. The system adapter adapts the fault injection instruction and sends it to the fault injection executor. The system adapter adapts the fault injection instruction generated by the fault injection manager into a format recognizable by the target system to ensure that the fault injection instruction is compatible with the hardware and software platform of the target system.

7. A system kernel reliability evaluation system based on fault sequence generation, characterized in that: It includes a master end and a target end, wherein the master end includes a fault model library, a fault injection manager, a system adapter, a fault injection executor, a data collector and a result analyzer; The fault model library is configured to: store predefined fault models for simulating various system faults; The fault injection manager is configured to: generate fault injection instructions, including fault type, parameters and target system information and distribute the fault injection instructions to the fault injection executor at the target end; Monitor system status. During the fault injection process, monitor the operating status and response of the target system and collect data of the target system during the fault injection process. The system adapter is configured to: adapt and send the instructions of the fault injection manager to the fault injection executor at the target end; The fault injection executor is configured to: receive the fault injection instruction issued by the fault injection manager, parse the parameters in the fault instruction, and execute the fault injection on the target system according to the parsed parameters and the generated fault sequence to simulate the predetermined fault scenario; The data collector is configured to: collect performance data and log information of the target system during the fault injection execution; The result analyzer is configured to collect and analyze the data after the fault injection execution to evaluate the performance and stability of the system.

8. The system kernel reliability evaluation system based on fault sequence generation according to claim 7, characterized in that: The target end is the target system kernel and application program.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the system kernel reliability evaluation method based on fault sequence generation according to any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the system kernel reliability evaluation method based on fault sequence generation as described in any one of claims 1-6.

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