Fault safety self-checking system based on computer hardware

Abstract

The invention discloses a fault safety self-checking system based on computer hardware, and relates to the technical field of hardware fault detection, and the system comprises a hardware monitoring module which collects the working state data of target computer hardware in real time; the fault diagnosis module is used for performing real-time analysis according to the data acquired by the hardware monitoring module and detecting potential faults of the hardware component; the fault prediction module is used for predicting potential faults of the target computer hardware based on the historical fault data, the working state data and a machine learning algorithm and carrying out early warning in advance; the automatic repairing module is used for automatically executing preset repairing operation when hardware faults and potential faults are detected; and the user notification module is used for sending fault alarm information, repair progress and result to a user. According to the method, highly-integrated hardware monitoring, fault diagnosis, prediction, automatic repair and notification functions are provided, the reliability, stability and safety of computer hardware are remarkably improved, and loss and repair time when a system fault occurs are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hardware fault detection, and more specifically, to a fail-safe self-checking system for computer hardware. Background Art

[0002] With the wide application of computer hardware, especially in the fields of data centers, cloud computing, and high-performance computing, the impact of hardware failures on the system has become increasingly serious. Hardware failures can not only lead to a decline in system performance but also may cause data loss and system downtime. Therefore, it is particularly important to monitor the status of computer hardware in real time and perform early diagnosis and prediction of faults.

[0003] Traditional hardware fault detection methods often rely on regular inspections and manual maintenance, and there are many limitations. Fault diagnosis can only be carried out after a fault occurs, and the repair process usually requires manual intervention, which greatly reduces the reliability and efficiency of the system. To improve the stability and availability of computer systems, modern computer hardware fault detection systems increasingly tend to integrate functions such as real-time monitoring, fault diagnosis, fault prediction, automatic repair, and user notification, minimizing the impact of hardware failures through intelligent means. Summary of the Invention

[0004] To solve the above technical problems, a fail-safe self-checking system for computer hardware is provided, and this technical solution solves the above problems.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A fail-safe self-checking system for computer hardware, comprising:

[0007] A hardware monitoring module for real-time collection of the working state data of the target computer hardware;

[0008] A fault diagnosis module electrically connected to the hardware monitoring module, which is used for real-time analysis of the data collected by the hardware monitoring module, detecting potential faults of hardware components, and generating a fault report;

[0009] A fault prediction module electrically connected to the fault diagnosis module, which predicts potential faults of the target computer hardware based on historical fault data, working state data, and machine learning algorithms, and issues an early warning;

[0010] An automatic repair module electrically connected to the fault diagnosis module and the fault prediction module, which is used for automatically executing preset repair operations when detecting hardware faults and potential faults;

[0011] The user notification module is electrically connected to the automatic repair module. The user notification module is used to send fault alarm information, repair progress and results to the user, ensuring that the user can timely understand the status of hardware fault handling.

[0012] Preferably, the hardware monitoring module includes:

[0013] The hardware data acquisition unit, which acquires the temperature, usage rate, voltage and current operation parameters of the hardware components in real time;

[0014] The sensor unit, which is connected to the hardware data acquisition unit, is used to collect the hardware physical environment data and perform correlation analysis with the hardware data.

[0015] Preferably, the internal of the fault diagnosis module includes:

[0016] The data preprocessing unit, which is used to clean and standardize the real-time data collected by the hardware monitoring module to eliminate noise;

[0017] The fault analysis unit, based on statistical analysis and rule engine, evaluates the hardware operation status, identifies potential faults and marks the health risk indicators of the hardware.

[0018] Preferably, the internal of the fault analysis unit includes:

[0019] Statistically analyze the monitoring data of the hardware to obtain key indicators, including mean, standard deviation, maximum value, minimum value, skewness and kurtosis;

[0020] Perform time series analysis on the hardware operation data to identify the change trend of the device status and find the signs of device performance decline and sudden faults;

[0021] Set the threshold of the hardware operation, and perform real-time inspection on the collected data according to the predefined rule engine.

[0022] Preferably, the fault prediction module includes:

[0023] The historical data collection unit, which collects the historical operation data and fault records of the target computer hardware;

[0024] The machine learning prediction unit, based on the historical data and machine learning algorithms, models the operation status of the hardware, predicts the possible future hardware faults, and generates a prediction report. Among them, the risk prediction formula is:

[0025]

[0026] In the formula, x i is the i-th feature variable, w iw is the weight of the corresponding feature, b is the bias term, P(t) represents the failure probability at time t, and σ is the activation function.

[0027] Preferably, the failure prediction module further includes:

[0028] A model training unit that uses historical data to train the prediction model to improve the accuracy of model prediction;

[0029] A failure risk assessment unit that scores the failure risk of the hardware component based on the prediction result and provides repair suggestions to the user. Among them, the risk assessment formula is:

[0030]

[0031] In the formula, V c is the current voltage of the hardware component, V n is the rated voltage of the hardware component, T c is the current temperature of the hardware component, T max is the maximum operating temperature of the hardware component, P f is the historical failure rate, P r is the reliability index of the component, and σ, β, γ are weighting coefficients.

[0032] Preferably, the automatic repair module includes:

[0033] A hardware restart unit that automatically restarts the target hardware component and the computer system when a temporary failure is detected;

[0034] A spare hardware switching unit that automatically switches to a preset spare hardware component when a serious failure occurs in the hardware component to ensure the continuous operation of the system;

[0035] A repair result verification unit that, after repair, re-verifies the system status to ensure that the repair operation has taken effect and solved the original problem. If the repair fails, it triggers a secondary repair process and notifies the user for manual intervention.

[0036] Preferably, the automatic repair module further includes:

[0037] A software update unit that automatically downloads and installs software drivers compatible with the hardware during the hardware failure repair process to ensure good cooperation between the hardware and software;

[0038] A log recording unit that records the logs of all repair operations, including the failure type, repair measures, and execution time information.

[0039] Preferably, the user notification module includes:

[0040] A fault information receiving unit that receives fault information from an automatic repair module, a fault diagnosis module, and a fault prediction module, including the type of fault, the time of occurrence, and the severity of the fault, and transmits the received fault information to the subsequent notification processing unit;

[0041] A notification content generation unit that generates user notification content based on the received fault information, including fault alarm information, repair progress, and repair results;

[0042] A multi-channel notification unit that notifies users of the fault information, repair progress, and results through email, SMS, and mobile application push;

[0043] A user interface module that provides a friendly graphical interface to display fault reports, repair operations, and historical records for users to view and manage.

[0044] Preferably, the user notification module further includes:

[0045] A user feedback unit that receives user feedback on the fault handling process and adjusts the fault handling strategy according to the feedback;

[0046] A data backup module that automatically performs data backup and recovery operations during the repair process of hardware faults;

[0047] A hardware health report generation unit that regularly generates hardware health reports for users to view the long-term trends of hardware operation status and potential fault warnings.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] The present invention proposes highly integrated functions of hardware monitoring, fault diagnosis, prediction, automatic repair, and notification, significantly improving the reliability, stability, and security of computer hardware, and effectively reducing the losses and repair time when system faults occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a system framework diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0052] Referring to Figure 1 shown, a fault-safe self-checking system based on computer hardware includes: a hardware monitoring module for real-time collecting working state data of a target computer hardware;

[0053] A fault diagnosis module, electrically connected to the hardware monitoring module, is used to perform real-time analysis on the data collected by the hardware monitoring module, detect potential faults of hardware components, and generate a fault report;

[0054] A fault prediction module, electrically connected to the fault diagnosis module, predicts potential faults of the target computer hardware based on historical fault data, working status data, and machine learning algorithms, and issues an early warning;

[0055] An automatic repair module, electrically connected to the fault diagnosis module and the fault prediction module, is used to automatically execute preset repair operations when detecting hardware faults and potential faults;

[0056] A user notification module, electrically connected to the automatic repair module, is used to send fault alarm information, repair progress, and results to the user to ensure that the user can timely grasp the status of hardware fault handling.

[0057] The hardware monitoring module includes:

[0058] A hardware data acquisition unit that obtains the temperature, usage rate, voltage, and current operation parameters of hardware components in real time;

[0059] A sensor unit, connected to the hardware data acquisition unit, is used to collect hardware physical environment data and perform correlation analysis with the hardware data;

[0060] By real-time monitoring the working status of hardware components and combining the environmental data collected by the sensor unit, a comprehensive analysis of the hardware status is achieved, potential problems can be discovered in real time, and the occurrence of hardware faults is reduced.

[0061] Inside the fault diagnosis module includes:

[0062] A data preprocessing unit, used to clean and standardize the real-time data collected by the hardware monitoring module to eliminate noise;

[0063] A fault analysis unit, based on statistical analysis and a rule engine, evaluates the hardware operation status, identifies potential faults, and marks the health risk indicators of the hardware;

[0064] By combining data preprocessing and a rule engine, the status of hardware components is analyzed in real time, potential hardware faults can be identified, and a fault report can be generated in time for early warning, avoiding the delayed response in the traditional method.

[0065] Inside the fault analysis unit includes:

[0066] Perform statistical analysis on the monitoring data of the hardware to obtain key indicators, including mean, standard deviation, maximum value, minimum value, skewness, and kurtosis;

[0067] Perform time - series analysis on the hardware operation data, identify the change trend of the device status, and detect signs of device performance degradation and sudden failures;

[0068] Set the threshold for hardware operation and perform real - time inspection on the collected data according to a predefined rule engine.

[0069] The fault prediction module includes:

[0070] A historical data collection unit that collects the historical operation data and fault records of the target computer hardware;

[0071] A machine learning prediction unit that models the operation status of the hardware based on historical data and machine learning algorithms, predicts possible future hardware failures, and generates a prediction report. Among them, the risk prediction formula is:

[0072]

[0073] In the formula, x i is the i - th feature variable, w i is the weight corresponding to the feature, b is the bias term, P(t) represents the fault probability at time t, and σ is the activation function;

[0074] Using machine learning algorithms to model historical data, combining with the actual working status of the hardware for fault prediction, and leveraging the combination of deep learning and historical data improves the accuracy and foresight of the prediction, significantly enhancing the system's proactive fault prevention ability.

[0075] The fault prediction module further includes:

[0076] A model training unit that trains the prediction model using historical data to improve the accuracy of model prediction;

[0077] A fault risk assessment unit that scores the fault risk of hardware components based on the prediction results and provides repair suggestions to the user. Among them, the risk assessment formula is:

[0078]

[0079] In the formula, V c is the current voltage of the hardware component, V n is the rated voltage of the hardware component, T c is the current temperature of the hardware component, T max is the maximum operating temperature of the hardware component, P f is the historical failure rate, P r is the reliability index of the component, and σ, β, γ are weighting coefficients.

[0080] The automatic repair module includes:

[0081] A hardware restart unit, which is used to automatically restart the target hardware component and the computer system when a temporary fault is detected;

[0082] A spare hardware switching unit, which automatically switches to a preset spare hardware component when a serious fault occurs in the hardware component to ensure the continuous operation of the system;

[0083] A repair result verification unit, which, after the repair, re-verifies the system status to ensure that the repair operation has taken effect and solved the original problem. If the repair fails, it triggers a secondary repair process and notifies the user for manual intervention;

[0084] The automatic repair module can automatically perform repairs when a temporary fault occurs in the hardware. By restarting the hardware or automatically switching to the spare hardware, it greatly reduces manual intervention and improves the self-healing ability of the system. Through the repair result verification unit, it ensures that each repair operation can be effectively verified and the problem can be solved.

[0085] The automatic repair module further includes:

[0086] A software update unit, which is used to automatically download and install software drivers compatible with the hardware during the hardware fault repair process to ensure the good cooperation between the hardware and the software;

[0087] A log recording unit, which records the logs of all repair operations, including the fault type, repair measures, and execution time information;

[0088] Adopting a spare hardware switching mechanism, when a key hardware fault occurs, the system automatically switches to the spare hardware to ensure the continuous operation of the system and avoid system interruption caused by hardware faults.

[0089] The user notification module includes:

[0090] A fault information receiving unit, which receives fault information from the automatic repair module, the fault diagnosis module, and the fault prediction module, including the fault type, occurrence time, and severity of the fault, and transmits the received fault information to the subsequent notification processing unit;

[0091] A notification content generation unit, which generates user notification content based on the received fault information, including fault alarm information, repair progress, and repair results;

[0092] A multi-channel notification unit, which is used to notify the user of the fault information, repair progress, and results by means of email, SMS, and mobile application push;

[0093] A user interface module, which provides a friendly graphical interface to display fault reports, repair operations, and historical records for the user to view and manage;

[0094] The notification method of the channel ensures that users can timely understand hardware failures and repair progress through different channels. Different notification means meet the needs of different users, improving the efficiency and response speed of failure notifications.

[0095] The user notification module also includes:

[0096] A user feedback unit, which is used to receive user feedback on the fault handling process and adjust the fault handling strategy according to the feedback;

[0097] A data backup module, which automatically performs data backup and recovery operations during the hardware failure repair process;

[0098] A hardware health report generation unit, which regularly generates hardware health reports for users to view the long-term trends of hardware operation status and potential fault warnings.

[0099] The usage process of the present invention is as follows:

[0100] Step 1: The user turns on the computer and starts the hardware failure safety self-check system, and the system starts to initialize and prepare for hardware monitoring;

[0101] Step 2: The hardware monitoring module starts to collect real-time operation parameters of hardware components such as temperature, usage rate, voltage, and current;

[0102] Step 3: The sensor unit starts to collect physical environment data of the hardware and conduct correlation analysis with the hardware data;

[0103] Step 4: The collected real-time data is transmitted to the fault diagnosis module through the hardware monitoring module;

[0104] Step 5: The fault diagnosis module cleans and standardizes the data transmitted by the hardware monitoring module to eliminate noise interference;

[0105] Step 6: The fault diagnosis module uses statistical analysis and rule engines to evaluate the health status of the hardware, identify potential faults and generate fault reports;

[0106] Step 7: According to the analysis results, the fault diagnosis module real-time identifies potential faults and issues warnings to the system;

[0107] Step 8: The fault analysis unit conducts time series analysis, monitors the change trend of the device status, and identifies signs of device performance decline and sudden faults;

[0108] Step 9: The fault diagnosis module checks whether the data exceeds the hardware operation threshold according to the predefined rule engine. If it exceeds, an alarm is immediately issued;

[0109] Step Ten: The fault prediction module collects the operation data and fault records of the target computer hardware through the historical data collection unit;

[0110] Step Eleven: The machine learning prediction unit establishes a prediction model based on the collected historical data and algorithms to conduct potential fault prediction;

[0111] Step Twelve: According to the machine learning model, the fault prediction module generates a prediction report to evaluate the future fault risk of the hardware;

[0112] Step Thirteen: The fault prediction module uses the historical data to train the prediction model to continuously improve the prediction accuracy;

[0113] Step Fourteen: The fault prediction module scores the hardware fault risk according to the prediction results and generates repair suggestions for the user's reference;

[0114] Step Fifteen: When a hardware fault or potential fault is detected, the automatic repair module is activated to execute preset repair operations, such as automatically restarting the hardware or switching to a standby hardware;

[0115] Step Sixteen: After the repair operation is completed, the system automatically verifies whether the hardware has returned to normal. If the repair is not successful, a secondary repair process is started;

[0116] Step Seventeen: The user notification module pushes the fault information, repair progress, and results to the user through email, SMS, or a mobile application;

[0117] Step Eighteen: The user provides feedback through the interface module. The system optimizes the fault handling strategy according to the feedback and regularly generates a hardware health report to facilitate the user to view the long-term operation trend and potential fault warnings.

[0118] In summary, the advantages of the present invention are as follows:

[0119] The hardware monitoring module can obtain the operation status data of the computer hardware in real time, such as temperature, voltage, current, and usage rate, etc., to ensure the real-time grasp of the hardware status, which helps to timely discover potential problems and prevent faults from occurring;

[0120] The fault diagnosis module can conduct real-time evaluation of the hardware status by analyzing the data provided by the hardware monitoring module, identify potential fault risks, and generate a detailed fault report. This enables faults to be discovered and processed in a timely manner, reducing human operation errors;

[0121] Based on historical fault data and machine learning algorithms, the fault prediction module can predict the future state of the hardware, identify potential hardware fault risks in advance. This warning mechanism provides the system administrator with sufficient time to perform necessary preventive maintenance;

[0122] The automatic repair module can automatically perform repair operations, such as hardware restart, standby hardware switching, etc., when detecting hardware failures or potential failures, ensuring the continuous operation of the system and reducing manual intervention. This automated repair mechanism greatly improves the speed and efficiency of fault recovery;

[0123] The user notification module can timely notify users of fault information, repair progress, and repair results through methods such as emails, text messages, and mobile application push, ensuring that users can always keep track of the hardware fault handling status. In addition, the user feedback unit allows users to provide feedback on the fault handling process, helping the system optimize the fault handling strategy;

[0124] During the fault repair process, the system can automatically perform data backup and recovery operations, avoiding the risk of data loss and enhancing the reliability of the system;

[0125] The system regularly generates hardware health reports to help users understand the long-term operating status of the hardware and early warnings of potential failures, supporting decision-makers in long-term planning and maintenance.

[0126] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A fail-safe self-checking system based on computer hardware, characterized in that, including: a hardware monitoring module for real-time collecting the working status data of the target computer hardware; a fault diagnosis module electrically connected to the hardware monitoring module, which is used for real-time analyzing the data collected by the hardware monitoring module, detecting potential faults of hardware components, and generating a fault report; a fault prediction module electrically connected to the fault diagnosis module, which predicts potential faults of the target computer hardware based on historical fault data, working status data and machine learning algorithms, and gives an early warning; an automatic repair module electrically connected to the fault diagnosis module and the fault prediction module, which is used for automatically executing preset repair operations when detecting hardware faults and potential faults; a user notification module electrically connected to the automatic repair module, which is used for sending fault alarm information, repair progress and results to the user to ensure that the user can timely master the hardware fault handling situation.

2. The fail-safe self-checking system based on computer hardware according to claim 1, characterized in that, The hardware monitoring module includes: a hardware data collection unit for real-time obtaining the temperature, usage rate, voltage and current operation parameters of hardware components; a sensor unit connected to the hardware data collection unit for collecting hardware physical environment data and performing correlation analysis with the hardware data.

3. A fail-safe self-checking system based on computer hardware according to claim 2, characterized in that, Inside the fault diagnosis module includes: a data preprocessing unit for cleaning and standardizing the real-time data collected by the hardware monitoring module to eliminate noise; a fault analysis unit for evaluating the hardware operation status based on statistical analysis and a rule engine, identifying potential faults and marking the health risk indicators of the hardware.

4. The fail-safe self-checking system based on computer hardware according to claim 3, characterized in that, Inside the fault analysis unit includes: performing statistical analysis on the monitoring data of the hardware to obtain key indicators, including mean, standard deviation, maximum value, minimum value, skewness and kurtosis; performing time series analysis on the hardware operation data to identify the change trend of the device status and discover signs of device performance degradation and sudden faults; setting the threshold of the hardware operation, and performing real-time inspection on the collected data according to a predefined rule engine.

5. A fail-safe self-checking system based on computer hardware according to claim 4, characterized in that, The fault prediction module includes: a historical data collection unit for collecting the historical operation data and fault records of the target computer hardware; a machine learning prediction unit for modeling the operation status of the hardware based on historical data and machine learning algorithms, predicting possible future hardware faults, and generating a prediction report. The risk prediction formula is: where x i is the i-th feature variable, w i is the weight corresponding to the feature, b is the bias term, P(t) represents the failure probability at time t, and σ is the activation function.

6. The fail-safe self-checking system based on computer hardware according to claim 5, wherein The fault prediction module further includes: a model training unit for training the prediction model with historical data to improve the accuracy of model prediction; a fault risk assessment unit for scoring the fault risk of hardware components based on the prediction results and providing repair suggestions to the user. The risk assessment formula is: Where, V c is the current voltage of the hardware component, V n is the rated voltage of the hardware component, T c is the current temperature of the hardware component, T max is the maximum operating temperature of the hardware component, P f is the historical failure rate, P r is the reliability index of the component, and σ, β, γ are weighting coefficients.

7. The fail-safe self-checking system based on computer hardware according to claim 6, wherein The automatic repair module includes: a hardware restart unit for automatically restarting the target hardware components and the computer system when detecting a temporary fault; a spare hardware switching unit for automatically switching to a preset spare hardware component when a hardware component has a serious fault to ensure the continuous operation of the system; The repair result verification unit, after the repair, re-verifies the system status to ensure that the repair operation has taken effect and solved the original problem. If the repair fails, it triggers a secondary repair process and notifies the user for manual intervention.

8. A fail-safe self-checking system based on computer hardware according to claim 7, characterized in that, The automatic repair module further includes: The software update unit is used to automatically download and install software drivers compatible with the hardware during the hardware fault repair process to ensure good cooperation between the hardware and the software; The log recording unit records the logs of all repair operations, including the fault type, repair measures, and execution time information.

9. A fail-safe self-checking system based on computer hardware according to claim 8, characterized in that, The user notification module includes: The fault information receiving unit receives fault information from the automatic repair module, the fault diagnosis module, and the fault prediction module, including the type of the fault, the time of occurrence, and the severity of the fault, and transmits the received fault information to the subsequent notification processing unit; The notification content generation unit generates user notification content based on the received fault information, including fault alarm information, repair progress, and repair results; The multi-channel notification unit is used to notify the user of the fault information, repair progress, and results by means of email, SMS, and mobile application push; The user interface module provides a friendly graphical interface to display the fault report, repair operations, and historical records for the user to view and manage.

10. A fail-safe self-checking system based on computer hardware according to claim 9, characterized in that, The user notification module also includes: The user feedback unit is used to receive the user's feedback on the fault handling process and adjust the fault handling strategy according to the feedback; The data backup module automatically performs data backup and recovery operations during the hardware fault repair process; The hardware health report generation unit regularly generates hardware health reports for the user to view the long-term trend of the hardware operation status and potential fault warnings.

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