Computer fault diagnosis and repair system and method based on artificial intelligence

Abstract

The invention provides a computer fault diagnosis and repair system and method based on artificial intelligence, and belongs to the technical field of computer fault diagnosis and repair, the computer fault diagnosis and repair system based on artificial intelligence comprises a data acquisition module, a fault diagnosis module, a repair scheme generation module and a repair execution module; an artificial intelligence technology is utilized, computer faults can be quickly and accurately diagnosed, a corresponding repair scheme is generated, the efficiency and accuracy of fault diagnosis and repair are improved, computer operation data are collected in real time through a data collection module, a fault diagnosis module analyzes the data through a machine learning algorithm, a fault diagnosis model is established, and the fault diagnosis efficiency is improved. Whether the computer has a fault or not and the type of the fault can be rapidly and accurately judged, the repair scheme generation module generates a corresponding repair scheme according to the fault diagnosis result, the repair execution module can rapidly execute the repair scheme, and the fault diagnosis and repair efficiency is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer fault diagnosis and repair, and specifically relates to a computer fault diagnosis and repair system and method based on artificial intelligence. Background Art

[0002] With the rapid development of computer technology, computers have been widely used in various fields. However, various faults will inevitably occur in the operation of computers, such as software faults, hardware faults, etc. At present, computer fault diagnosis and repair mainly rely on manual experience, which is inefficient and difficult to guarantee accuracy. In addition, for some complex faults, professional technicians are often required to handle them, which not only increases the maintenance cost, but also may cause the fault repair time to be too long, affecting the normal use of users. Therefore, there is an urgent need for a more efficient and accurate computer fault diagnosis and repair method.

[0003] This solution aims to provide a computer fault diagnosis and repair system and method based on artificial intelligence to solve the problems existing in the prior art. By utilizing artificial intelligence technology to collect and analyze the computer's operating data in real time, the fault type and cause can be diagnosed quickly and accurately, and a corresponding repair plan can be generated. At the same time, the system can automatically perform some simple repair operations, and for complex repair operations, detailed operation guides can also be provided to facilitate users to perform manual repairs. This can greatly improve the efficiency and accuracy of computer fault diagnosis and repair, reduce maintenance costs, and improve the reliability and stability of computers. Summary of the invention

[0004] The purpose of the present invention is to provide a computer fault diagnosis and repair system and method based on artificial intelligence, aiming to solve the problem that traditional computer fault diagnosis and repair in the prior art rely on manual experience, are inefficient and difficult to ensure accuracy, and complex faults require professional technicians to handle, resulting in increased maintenance costs and long fault repair time, affecting normal user use.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A computer fault diagnosis and repair system based on artificial intelligence, comprising a data acquisition module, a fault diagnosis module, a repair solution generation module and a repair execution module;

[0007] The data acquisition module is used to collect computer operation data, including system logs, hardware status information, software operation status, etc. The collected data will be transmitted to the fault diagnosis module for analysis. The data acquisition module obtains data by reading system files, calling system APIs, monitoring network traffic, etc. The data collection frequency is once an hour;

[0008] The fault diagnosis module uses artificial intelligence technology to analyze the collected data to determine whether the computer has a fault and the type of fault. It uses machine learning algorithms, such as support vector machines, decision trees, neural networks, etc., to learn a large amount of historical fault data and establish a fault diagnosis model. When new data is received, the model is used to perform fault diagnosis and output the fault type and possible cause. The fault diagnosis formula is: F=f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%;

[0009] The repair solution generation module generates a corresponding repair solution according to the fault type and cause output by the fault diagnosis module. It also uses artificial intelligence technology to learn from a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the repair solution is generated using the model and output to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes;

[0010] The repair execution module is responsible for executing the repair plan output by the repair plan generation module. It can automatically execute some simple repair operations, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.

[0011] As a preferred solution of the present invention, the following steps are included:

[0012] Step 1: Data Collection

[0013] The data collection module collects computer operation data, including system logs, hardware status information, software operation status, etc. The data collection frequency is once an hour;

[0014] Step 2: Troubleshooting

[0015] The fault diagnosis module is used to analyze the collected data to determine whether the computer has a fault and the type of fault. First, the data is preprocessed, including data cleaning and feature extraction. Then, the preprocessed data is analyzed using a machine learning algorithm to establish a fault diagnosis model. When new data is received, the model is used to diagnose the fault and output the fault type and possible cause. The fault diagnosis formula is: F = f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%;

[0016] Step 3: Generate a repair plan

[0017] According to the fault type and cause output by the fault diagnosis module, the repair solution generation module generates a corresponding repair solution. First, the fault type and cause are analyzed to determine the possible repair method. Then, artificial intelligence technology is used to learn a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the model is used to generate a repair solution and output it to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes.

[0018] Step 4: Repair execution

[0019] The repair execution module executes the repair plan output by the repair plan generation module. Some simple repair operations can be automatically executed, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. In this solution, artificial intelligence technology is used to quickly and accurately diagnose computer faults and generate corresponding repair plans, which improves the efficiency and accuracy of fault diagnosis and repair. The data acquisition module collects the computer's operating data in real time. The fault diagnosis module uses machine learning algorithms to analyze the data and establish a fault diagnosis model. It can quickly and accurately determine whether the computer has a fault and the type of fault. The repair plan generation module generates a corresponding repair plan based on the fault diagnosis results. The repair execution module can quickly execute the repair plan, which greatly improves the efficiency of fault diagnosis and repair.

[0022] 2. In this solution, some simple repair operations are automatically performed, which reduces the need for human intervention and reduces maintenance costs. The system can automatically diagnose and repair some common faults without the need for professional technicians to handle them, reducing labor costs. At the same time, fast and accurate fault diagnosis and repair can reduce losses caused by faults, improve computer availability, and thus reduce overall operating costs. Timely diagnosis and repair of computer faults improves computer reliability and stability and extends the service life of computers. By real-time monitoring of the computer's operating status, potential fault hazards can be discovered and resolved in a timely manner, which can effectively avoid the occurrence or expansion of faults and improve the reliability and stability of the computer. At the same time, fast and accurate fault repair can reduce damage to computer hardware and software caused by faults and extend the service life of the computer. DETAILED DESCRIPTION

[0023] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Example 1

[0025] The present invention provides the following technical solutions:

[0026] A computer fault diagnosis and repair system based on artificial intelligence, comprising a data acquisition module, a fault diagnosis module, a repair solution generation module and a repair execution module;

[0027] The data acquisition module is used to collect computer operation data, including system logs, hardware status information, software operation status, etc. The collected data will be transmitted to the fault diagnosis module for analysis. The data acquisition module obtains data by reading system files, calling system APIs, monitoring network traffic, etc. The data collection frequency is once an hour;

[0028] The fault diagnosis module uses artificial intelligence technology to analyze the collected data to determine whether the computer has a fault and the type of fault. It uses machine learning algorithms, such as support vector machines, decision trees, neural networks, etc., to learn a large amount of historical fault data and establish a fault diagnosis model. When new data is received, the model is used to perform fault diagnosis and output the fault type and possible cause. The fault diagnosis formula is: F=f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%;

[0029] The repair solution generation module generates a corresponding repair solution according to the fault type and cause output by the fault diagnosis module. It also uses artificial intelligence technology to learn from a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the repair solution is generated using the model and output to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes;

[0030] The repair execution module is responsible for executing the repair plan output by the repair plan generation module. It can automatically execute some simple repair operations, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.

[0031] In a specific embodiment of the present invention, first, the data acquisition module collects computer operation data once an hour through various methods (reading system files, calling system APIs, monitoring network traffic, etc.), including system logs, hardware status information, software operation status, etc., to provide a comprehensive and timely information basis for subsequent fault diagnosis. This high-frequency data acquisition can ensure that the system can quickly capture changes in the computer's operating status and promptly discover potential fault hazards. At the same time, the combination of multiple acquisition methods can obtain data from different angles, improving the accuracy and comprehensiveness of fault diagnosis.

[0032] Second, the fault diagnosis module uses artificial intelligence technology, especially machine learning algorithms (support vector machines, decision trees, neural networks, etc.) to learn a large amount of historical fault data and establish a fault diagnosis model. When receiving new data, the module can accurately determine whether the computer has a fault and the type of fault, and output the type of fault and possible causes. Its fault diagnosis accuracy is not less than 90%, ensuring that the system can quickly and accurately identify computer faults in most cases, providing a reliable basis for subsequent repair work. By learning from historical data, the module can continuously optimize the diagnosis model and improve the accuracy and efficiency of diagnosis.

[0033] 3. The repair solution generation module uses artificial intelligence technology to learn a large number of historical repair cases based on the fault type and cause output by the fault diagnosis module to establish a repair solution generation model. When receiving the fault type and cause, the module can generate a corresponding repair solution in no more than 5 minutes and output it to the repair execution module. This ability to quickly generate repair solutions can greatly shorten the repair time of computer failures and reduce the losses caused by failures. At the same time, by learning from historical repair cases, the module can generate more scientific and reasonable repair solutions and improve the success rate of repairs.

[0034] 4. Repair execution module is responsible for executing the repair plan output by the repair plan generation module. For some simple repair operations, such as restarting the computer, repairing software vulnerabilities, etc., they can be executed automatically to improve the repair efficiency; for some complex repair operations, detailed operation guides are provided for users to perform manually to ensure the accuracy and safety of the repair work. The requirement that the success rate of automatic repair operations is not less than 80% ensures that the system can automatically and effectively repair computer failures in most cases, reducing user intervention and operation difficulty. At the same time, the manual operation guide for complex failures can help users better understand and solve problems, improving user satisfaction.

[0035] The specific scheme includes the following steps:

[0036] Step 1: Data Collection

[0037] The data collection module collects computer operation data, including system logs, hardware status information, software operation status, etc. The data collection frequency is once an hour;

[0038] Step 2: Troubleshooting

[0039] The fault diagnosis module is used to analyze the collected data to determine whether the computer has a fault and the type of fault. First, the data is preprocessed, including data cleaning and feature extraction. Then, the preprocessed data is analyzed using a machine learning algorithm to establish a fault diagnosis model. When new data is received, the model is used to diagnose the fault and output the fault type and possible cause. The fault diagnosis formula is: F = f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%;

[0040] Step 3: Generate a repair plan

[0041] According to the fault type and cause output by the fault diagnosis module, the repair solution generation module generates a corresponding repair solution. First, the fault type and cause are analyzed to determine the possible repair method. Then, artificial intelligence technology is used to learn a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the model is used to generate a repair solution and output it to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes.

[0042] Step 4: Repair execution

[0043] The repair execution module executes the repair plan output by the repair plan generation module. Some simple repair operations can be automatically executed, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.

[0044] In this embodiment: 1. Fault diagnosis

[0045] The fault diagnosis module plays a key role in the entire system. First, the collected data is preprocessed, including data cleaning and feature extraction. Data cleaning can remove noise, outliers and incomplete data to ensure the quality of data input into the model. Feature extraction is to extract features that can effectively characterize computer faults from the original data, providing a basis for subsequent fault diagnosis.

[0046] Next, the preprocessed data is analyzed using machine learning algorithms to establish a fault diagnosis model. You can choose a variety of machine learning algorithms such as support vector machines, decision trees, neural networks, etc., and select and optimize them according to the actual situation. By learning a large amount of historical fault data, the model can gradually grasp the characteristics and patterns of different fault types, so that when new data is received, it can accurately diagnose faults and output the fault type and possible causes.

[0047] The fault diagnosis formula is F = f(D), where f represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. In order to ensure the accuracy of fault diagnosis, the fault diagnosis accuracy is required to be no less than 90%. This means that in practical applications, the fault diagnosis module can accurately determine whether the computer has a fault and the type of fault in most cases, providing a reliable basis for subsequent repair work.

[0048] 2. Repair plan generation

[0049] The repair solution generation module generates the corresponding repair solution based on the fault type and cause output by the fault diagnosis module. First, the fault type and cause are analyzed to determine the possible repair methods. This requires an in-depth understanding of different fault types, and combining historical repair cases and professional knowledge to propose feasible repair solutions.

[0050] Then, artificial intelligence technology is used to study a large number of historical repair cases and establish a repair solution generation model. This model can automatically generate a corresponding repair solution based on the input fault type and cause. When the fault type and cause are received, the repair solution generation module will use the model to generate a repair solution and output it to the repair execution module.

[0051] The formula for generating a repair solution is R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. In order to improve the repair efficiency, the repair solution generation time is required to be no more than 5 minutes. In this way, when a computer fails, an effective repair solution can be provided to the user as soon as possible, reducing the impact of the failure on the user's work and life.

[0052] 3. Repair Execution

[0053] The repair execution module is responsible for executing the repair solution output by the repair solution generation module. Some simple repair operations, such as restarting the computer and repairing software vulnerabilities, can be automatically executed. These operations are usually common and can be completed automatically by the program without manual intervention by the user.

[0054] For some complex repair operations, the repair execution module will provide detailed operation guides for users to perform manually. These operations may require users to have certain computer knowledge and skills, or require users to perform some specific operation steps. By providing detailed operation guides, users can follow the guides to complete the repair of the fault.

[0055] The automatic repair operation success rate is required to be no less than 80%, which means that in most cases, the automatic repair operation can successfully solve the computer failure. For some failures that cannot be automatically repaired, by providing detailed operation guides, users can also solve the problems themselves to a certain extent, thus improving the success rate and efficiency of failure repair.

[0056] IV. Advantages and application scenarios of this embodiment

[0057] The computer fault diagnosis and repair system and method based on artificial intelligence in this embodiment have the following advantages:

[0058] Efficient and accurate: Using artificial intelligence technology, computer faults can be diagnosed quickly and accurately, and corresponding repair plans can be generated, improving the efficiency and accuracy of fault diagnosis and repair.

[0059] Reduce costs: Automatically perform some simple repair operations, reducing the need for human intervention and lowering maintenance costs.

[0060] Improve reliability: Timely diagnosis and repair of computer failures improves computer reliability and stability and extends the service life of the computer.

[0061] The system and method are applicable to various computer usage scenarios, such as corporate office, home use, school education, etc. In corporate office, it can improve the availability of computers and reduce work interruptions and losses caused by failures. In home use, it can facilitate users to solve computer failures by themselves and improve the user experience. In school education, it can help students and teachers better maintain computer equipment and improve teaching efficiency.

[0062] The working principle and use process of the present invention: Data collection:

[0063] Through the data collection module, the computer's operating data is collected once an hour by reading system files, calling system APIs, monitoring network traffic, and other methods, including system logs, hardware status information, software operating status, etc.

[0064] The collected data will be transmitted to the fault diagnosis module for analysis.

[0065] Troubleshooting:

[0066] The fault diagnosis module preprocesses the collected data, including data cleaning and feature extraction, to remove noise, outliers and incomplete data, and extract features that can effectively characterize computer faults from the original data.

[0067] The preprocessed data is analyzed using machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.) to establish a fault diagnosis model. The model gradually grasps the characteristics and patterns of different fault types by learning from a large amount of historical fault data.

[0068] When new data is received, the fault diagnosis model is used to perform fault diagnosis and output the fault type and possible cause. The fault diagnosis accuracy is not less than 90%.

[0069] Repair plan generation:

[0070] The repair solution generation module first analyzes the fault type and cause according to the fault type and cause output by the fault diagnosis module and determines the possible repair method.

[0071] Artificial intelligence technology is used to study a large number of historical restoration cases and establish a restoration plan generation model.

[0072] When the fault type and cause are received, the model is used to generate a repair plan and output it to the repair execution module. The repair plan generation time does not exceed 5 minutes.

[0073] Repair execution:

[0074] The repair execution module is responsible for executing the repair plan output by the repair plan generation module.

[0075] Some simple repair operations, such as restarting the computer and repairing software vulnerabilities, can be performed automatically to improve repair efficiency.

[0076] For some complex repair operations, detailed operation guides will be provided for users to perform manually.

[0077] The success rate of automatic repair operations is no less than 80%. For some faults that cannot be repaired automatically, users can solve the problems themselves according to the operation guide to improve the success rate and efficiency of fault repair.

[0078] Through the above operation process, the system can use artificial intelligence technology to quickly and accurately diagnose computer faults and generate corresponding repair plans. At the same time, it automatically performs simple repair operations or provides detailed operation guides, thereby greatly improving the efficiency and accuracy of computer fault diagnosis and repair, reducing maintenance costs, and improving computer reliability and stability.

[0079] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A computer fault diagnosis and repair system based on artificial intelligence, characterized in that: It includes a data acquisition module, a fault diagnosis module, a repair plan generation module and a repair execution module; The data acquisition module is used to collect computer operation data, including system logs, hardware status information, software operation status, etc. The collected data will be transmitted to the fault diagnosis module for analysis. The data acquisition module obtains data by reading system files, calling system APIs, monitoring network traffic, etc. The data collection frequency is once an hour; The fault diagnosis module uses artificial intelligence technology to analyze the collected data to determine whether the computer has a fault and the type of fault. It uses machine learning algorithms, such as support vector machines, decision trees, neural networks, etc., to learn a large amount of historical fault data and establish a fault diagnosis model. When new data is received, the model is used to perform fault diagnosis and output the fault type and possible cause. The fault diagnosis formula is: F=f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%; The repair solution generation module generates a corresponding repair solution according to the fault type and cause output by the fault diagnosis module. It also uses artificial intelligence technology to learn from a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the repair solution is generated using the model and output to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes; The repair execution module is responsible for executing the repair plan output by the repair plan generation module. It can automatically execute some simple repair operations, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.

2. The computer fault diagnosis and repair system and method based on artificial intelligence according to claim 1, characterized in that: The following steps are involved: Step 1: Data Collection The data collection module collects computer operation data, including system logs, hardware status information, software operation status, etc. The data collection frequency is once an hour; Step 2: Troubleshooting The fault diagnosis module is used to analyze the collected data to determine whether the computer has a fault and the type of fault. First, the data is preprocessed, including data cleaning and feature extraction. Then, the preprocessed data is analyzed using a machine learning algorithm to establish a fault diagnosis model. When new data is received, the model is used to diagnose the fault and output the fault type and possible cause. The fault diagnosis formula is: F = f(D), where F represents the fault type and cause, D represents the collected data, and f represents the fault diagnosis model function. The fault diagnosis accuracy is not less than 90%; Step 3: Generate a repair plan According to the fault type and cause output by the fault diagnosis module, the repair solution generation module generates a corresponding repair solution. First, the fault type and cause are analyzed to determine the possible repair method. Then, artificial intelligence technology is used to learn a large number of historical repair cases to establish a repair solution generation model. When the fault type and cause are received, the model is used to generate a repair solution and output it to the repair execution module. The repair solution generation formula is: R = g(F), where R represents the repair solution, F represents the fault type and cause, and g represents the repair solution generation model function. The repair solution generation time does not exceed 5 minutes. Step 4: Repair execution The repair execution module executes the repair plan output by the repair plan generation module. Some simple repair operations can be automatically executed, such as restarting the computer, repairing software vulnerabilities, etc. For some complex repair operations, detailed operation guides will be provided for users to execute manually. The success rate of automatic repair operations is not less than 80%.