Evaluation management method for computer use state detection

Abstract

The invention provides an evaluation management method for computer use state detection. According to the technical scheme, the evaluation management method comprises the steps that S1, a hardware feature library and a software feature library are established according to computer data of a target computer; s2, obtaining hardware operation data of computer hardware and software operation data of computer software in the target computer; s3, analyzing to obtain a hardware sound abnormal coefficient, a hardware vibration abnormal coefficient and a hardware detection abnormal coefficient of the computer hardware; s4, analyzing to obtain a software sound abnormal coefficient, a software vibration abnormal coefficient and a software detection abnormal coefficient of the computer software; s5, analyzing to obtain a computer hardware exception coefficient and a computer software exception coefficient of the target computer; and S6, analyzing to obtain a computer state abnormal coefficient of the target computer, and obtaining a use state abnormal degree of the target computer according to the computer state abnormal coefficient. According to the method, the comprehensiveness and the accuracy of the computer use state detection result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer detection, and in particular to an evaluation and management method for computer usage status detection. Background Art

[0002] With the rapid development of information technology, computers have become an indispensable tool in modern society and are widely used in many fields such as office, scientific research, finance, and medical care. As computer application scenarios become increasingly complex and diverse, higher requirements are placed on the accuracy and real-time performance of computer usage status detection and the comprehensiveness and effectiveness of evaluation and management. During computer use, its usage status directly affects work efficiency, data security, and system stability. Therefore, accurate detection and effective evaluation and management of computer usage status are of great practical significance.

[0003] The evaluation and management methods for computer usage status detection in related technologies are often unable to conduct comprehensive and accurate status detection of both the hardware and software during computer use based on the actual parameters of the computer, resulting in a lack of comprehensiveness and accuracy in the detection results of the computer usage status, and there is room for improvement. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide an evaluation and management method for detecting the usage status of a computer, so as to improve the problem that the evaluation and management method for detecting the usage status of a computer in the related art is often unable to perform comprehensive and accurate status detection of both the hardware and software during the use of the computer based on the actual parameters of the computer, resulting in a lack of comprehensiveness and accuracy in the detection results of the computer usage status.

[0005] This application provides a method for evaluating and managing computer usage status detection, comprising the following steps:

[0006] Step S1: Obtain computer data of a target computer, and establish a hardware feature library and a software feature library based on the computer data; the hardware feature library contains hardware standard operation data of the computer hardware, and the software feature library contains software standard operation data of the computer software;

[0007] Step S2: performing a computer operation test on the target computer to obtain hardware operation data of the computer hardware and software operation data of the computer software in the target computer;

[0008] Step S3: comparing and analyzing the hardware operation data of the computer hardware with the hardware standard operation data to obtain a hardware sound abnormality coefficient and a hardware vibration abnormality coefficient of the computer hardware, and obtaining a hardware detection abnormality coefficient of the computer hardware based on the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient;

[0009] Step S4: comparing and analyzing the software operation data of the computer software with the software standard operation data to obtain a software sound abnormality coefficient and a software vibration abnormality coefficient of the computer software, and obtaining a software detection abnormality coefficient of the computer software based on the software sound abnormality coefficient and the software vibration abnormality coefficient;

[0010] Step S5: Analyzing the hardware abnormality coefficient of the computer hardware to obtain the computer hardware abnormality coefficient of the target computer, and analyzing the software abnormality coefficient of the computer software to obtain the computer software abnormality coefficient of the target computer;

[0011] Step S6: obtaining a computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer software abnormality coefficient, and obtaining a usage status abnormality degree of the target computer according to the computer status abnormality coefficient.

[0012] Preferably, step S1 is specifically:

[0013] The computer data includes hardware parameters and hardware types of computer hardware in the target computer, and software parameters and software types of computer software;

[0014] Obtaining hardware standard operating data of the computer hardware according to the hardware parameters and the hardware type, and building a hardware feature library according to the hardware standard operating data of the computer hardware in the target computer;

[0015] Software standard operation data of the computer software is obtained according to the software parameters and software type, and a software feature library is constructed according to the software standard operation data of the computer software in the target computer.

[0016] Preferably, step S2 is specifically:

[0017] The hardware operation data includes hardware operation sound data and hardware operation vibration data of the computer hardware; the software operation data includes software operation sound data and software operation vibration data of the computer software.

[0018] Preferably, the hardware operation data of the computer hardware is compared and analyzed with the hardware standard operation data to obtain the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient of the computer hardware, specifically:

[0019] The hardware operation sound data includes hardware time domain characteristics and hardware spectrum characteristics of the computer hardware;

[0020] Comparing the hardware time domain features with the standard time domain features in the hardware standard operation data to obtain the time domain feature anomaly coefficient of the computer hardware; comparing the hardware spectrum features with the standard spectrum features in the hardware standard operation data to obtain the spectrum feature anomaly coefficient of the computer hardware;

[0021] Setting a hardware time domain weight and a hardware spectrum weight, and obtaining a hardware sound anomaly coefficient of the computer hardware according to the hardware time domain weight and the time domain feature anomaly coefficient, the hardware spectrum weight and the time domain spectrum anomaly coefficient;

[0022] The hardware operation vibration data includes the hardware vibration frequency, hardware vibration amplitude and hardware vibration phase of the computer hardware; obtaining the hardware standard vibration data of the computer hardware in the hardware feature library;

[0023] The hardware operation vibration data of the computer hardware is compared with the hardware standard vibration data to obtain the hardware vibration anomaly coefficient of the computer hardware.

[0024] Preferably, the hardware detection abnormality coefficient of the computer hardware is obtained based on the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient, specifically:

[0025] Set the hardware sound weight and hardware vibration weight of the computer hardware;

[0026] According to the hardware sound weight and the hardware sound abnormality coefficient, the hardware vibration weight and the hardware vibration abnormality coefficient, a hardware detection abnormality coefficient of the computer hardware is obtained.

[0027] Preferably, the software operation data of the computer software is compared and analyzed with the software standard operation data to obtain the software sound abnormality coefficient and the software vibration abnormality coefficient of the computer software, specifically:

[0028] The software running sound data includes the software time domain characteristics and software spectrum characteristics of the computer software;

[0029] Comparing the software time domain feature with the standard time domain feature in the software standard operation data to obtain the time domain feature anomaly coefficient of the computer software; comparing the software spectrum feature with the standard spectrum feature in the software standard operation data to obtain the spectrum feature anomaly coefficient of the computer software;

[0030] Setting a software time domain weight and a software spectrum weight, and obtaining a software sound anomaly coefficient of the computer software according to the software time domain weight and the time domain characteristic anomaly coefficient, the software spectrum weight and the time domain spectrum anomaly coefficient;

[0031] The software running vibration data includes the software vibration frequency, software vibration amplitude and software vibration phase of the computer software; obtaining the software standard vibration data of the computer software in the software feature library;

[0032] The software running vibration data of the computer software is compared with the software standard vibration data to obtain the software vibration abnormality coefficient of the computer software.

[0033] Preferably, the software detection abnormality coefficient of the computer software is obtained based on the software sound abnormality coefficient and the software vibration abnormality coefficient, specifically:

[0034] Set the software sound weight and software vibration weight of the computer software;

[0035] According to the software sound weight and the software sound abnormality coefficient, the software vibration weight and the software vibration abnormality coefficient, a software detection abnormality coefficient of the computer software is obtained.

[0036] Preferably, step S5 is specifically:

[0037] Obtaining the computer hardware running quantity and computer software running quantity during the target computer running detection process;

[0038] Obtaining a computer hardware anomaly coefficient of the target computer according to the number of computer hardware operations and a hardware detection anomaly coefficient corresponding to the computer hardware;

[0039] The computer software anomaly coefficient of the target computer is obtained according to the number of computer software operations and the software detection anomaly coefficient corresponding to the computer software.

[0040] Preferably, step S6 is specifically:

[0041] Set the computer hardware weight and computer software weight of the target computer;

[0042] Obtaining a computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer hardware weight, the computer software abnormality coefficient and the computer software weight;

[0043] The abnormality degree of the target computer's usage status is positively correlated with the computer status abnormality coefficient.

[0044] In summary, the beneficial effects of the present application are as follows: the present application establishes a hardware feature library and a software feature library based on the computer data of the target computer, wherein the hardware feature library contains the hardware standard operation data of the computer hardware, and the software feature library contains the software standard operation data of the computer software; and performs computer operation detection on the target computer to obtain the hardware operation data of the computer hardware and the software operation data of the computer software in the target computer; by comparing and analyzing the hardware operation data of the computer hardware with the hardware standard operation data, the hardware sound anomaly coefficient and the hardware vibration anomaly coefficient of the computer hardware are obtained, and the hardware detection anomaly coefficient of the computer hardware is obtained based on the hardware sound anomaly coefficient and the hardware vibration anomaly coefficient; by comparing and analyzing the software operation data of the computer software with the software standard operation data, the hardware detection anomaly coefficient of the computer hardware is obtained. The software sound anomaly coefficient and software vibration anomaly coefficient of the computer software are obtained, and the software detection anomaly coefficient of the computer software is obtained based on the software sound anomaly coefficient and the software vibration anomaly coefficient; in addition, the computer hardware anomaly coefficient of the target computer is obtained based on the hardware detection anomaly coefficient of the computer hardware, and the computer software anomaly coefficient of the target computer is obtained based on the software detection anomaly coefficient of the computer software; finally, the computer status anomaly coefficient of the target computer is obtained based on the computer hardware anomaly coefficient and the computer software anomaly coefficient, and the abnormal degree of the use status of the target computer is obtained based on the computer status anomaly coefficient, so as to realize comprehensive and accurate status detection of the hardware and software during the use of the computer according to the actual parameters of the computer, thereby improving the comprehensiveness and accuracy of the computer use status detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, some of the drawings in the embodiments of the present application will be briefly described below. It should be understood that the following drawings only show some embodiments of the present application and therefore should not be considered as limiting the scope of the present application.

[0046] Figure 1 This is a flowchart of a method for evaluating and managing computer usage status detection provided in this application. DETAILED DESCRIPTION

[0047] Below is a combination of the embodiments and Figure 1 The present application will be described in further detail, but the implementation methods of the present application are not limited thereto.

[0048] Reference Figure 1 1 is a flow chart of an evaluation and management method for detecting computer usage status provided in an embodiment of the present application.

[0049] A method for evaluating and managing computer usage status detection includes the following steps:

[0050] Step S1: Obtain computer data of the target computer and establish a hardware feature library and a software feature library based on the computer data; the hardware feature library contains hardware standard operation data of the computer hardware, and the software feature library contains software standard operation data of the computer software;

[0051] Step S2: performing a computer operation test on the target computer to obtain hardware operation data of the computer hardware and software operation data of the computer software in the target computer;

[0052] Step S3: comparing and analyzing the hardware operation data of the computer hardware with the hardware standard operation data to obtain the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient of the computer hardware, and obtaining the hardware detection abnormality coefficient of the computer hardware based on the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient;

[0053] Step S4: comparing and analyzing the software operation data of the computer software with the software standard operation data to obtain a software sound abnormality coefficient and a software vibration abnormality coefficient of the computer software, and obtaining a software detection abnormality coefficient of the computer software based on the software sound abnormality coefficient and the software vibration abnormality coefficient;

[0054] Step S5: Analyzing the hardware abnormality coefficient of the target computer based on the hardware detection abnormality coefficient of the computer hardware, and analyzing the software abnormality coefficient of the target computer based on the software detection abnormality coefficient of the computer software;

[0055] Step S6: obtaining the computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer software abnormality coefficient, and obtaining the abnormality degree of the target computer's usage status according to the computer status abnormality coefficient.

[0056] Step S1 is specifically as follows:

[0057] Computer data includes hardware parameters and hardware types of computer hardware in the target computer, and software parameters and software types of computer software;

[0058] Obtaining hardware standard operation data of the computer hardware according to the hardware parameters and the hardware type, and building a hardware feature library according to the hardware standard operation data of the computer hardware in the target computer;

[0059] The software standard operation data of the computer software is obtained according to the software parameters and the software type, and a software feature library is constructed according to the software standard operation data of the computer software in the target computer.

[0060] In some embodiments, after obtaining the hardware parameters and hardware type of the computer hardware, the hardware standard operation data of the computer hardware during normal operation can be obtained based on big data or historical computer usage data;

[0061] Similarly, after obtaining the software parameters and software type of the computer software, the software standard operation data of the computer software during normal operation can be obtained based on big data or historical computer usage data.

[0062] Step S2 is specifically as follows:

[0063] The hardware operation data includes the hardware operation sound data and the hardware operation vibration data of the computer hardware; the software operation data includes the software operation sound data and the software operation vibration data of the computer software.

[0064] In some embodiments, audio acquisition sensors and voiceprint recognition technology can be used to obtain hardware operation sound data and software operation sound data of computer hardware and computer software during operation respectively; in addition, vibration sensors can be used to obtain hardware operation vibration data and software operation vibration data of computer hardware and computer software during operation respectively; different sensors have high sensitivity and specificity to their corresponding physical quantities, and can independently obtain relevant information about sound and vibration.

[0065] Compare and analyze the hardware operation data of the computer hardware with the hardware standard operation data to obtain the hardware sound abnormality coefficient and hardware vibration abnormality coefficient of the computer hardware, specifically:

[0066] The hardware operation sound data includes the hardware time domain characteristics and hardware spectrum characteristics of the computer hardware;

[0067] Comparing the hardware time domain features with the standard time domain features in the hardware standard operation data to obtain the time domain feature anomaly coefficient of the computer hardware; comparing the hardware spectrum features with the standard spectrum features in the hardware standard operation data to obtain the spectrum feature anomaly coefficient of the computer hardware;

[0068] Setting a hardware time domain weight and a hardware spectrum weight, and obtaining a hardware sound anomaly coefficient of the computer hardware according to the hardware time domain weight and the time domain feature anomaly coefficient, the hardware spectrum weight and the time domain spectrum anomaly coefficient;

[0069] The hardware operation vibration data includes the hardware vibration frequency, hardware vibration amplitude and hardware vibration phase of the computer hardware; the hardware standard vibration data of the computer hardware in the hardware feature library is obtained;

[0070] The hardware operation vibration data of the computer hardware is compared with the hardware standard vibration data to obtain the hardware vibration anomaly coefficient of the computer hardware.

[0071] In some embodiments, the hardware time domain characteristics of the computer hardware are specifically the amplitude, duration, and zero-crossing rate of the operating sound of the computer hardware during operation; the hardware spectrum characteristics of the computer hardware are specifically the spectrum, resonance peak, and harmonics of the operating sound of the computer hardware during operation;

[0072] The time domain feature anomaly coefficient is specifically the absolute value of the corresponding deviation values of the operating sound amplitude, operating sound duration and operating sound zero-crossing rate of the computer hardware during operation and the standard sound amplitude, standard sound duration and standard sound zero-crossing rate of the computer hardware; the spectrum feature anomaly coefficient is specifically the absolute value of the corresponding deviation values of the operating sound spectrum, operating sound resonance peak and operating sound harmonics of the computer hardware during operation and the standard sound spectrum, standard sound resonance peak and standard sound harmonics of the computer hardware;

[0073] The specific value of the hardware sound anomaly coefficient of computer hardware can be calculated by using the calculation function: hardware sound anomaly coefficient = hardware time domain weight * time domain feature anomaly coefficient + hardware spectrum weight * time domain spectrum anomaly coefficient, where the hardware time domain weight and hardware spectrum weight are the weights of the influence of the time domain feature anomaly coefficient and the time domain spectrum anomaly coefficient on the hardware sound anomaly coefficient, respectively.

[0074] In some embodiments, the hardware vibration anomaly coefficient is specifically the absolute value of the corresponding deviation data value between the hardware vibration frequency, hardware vibration amplitude and hardware vibration phase of the computer hardware and the standard vibration frequency, standard vibration amplitude and standard vibration phase of the computer hardware.

[0075] And according to the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient, the hardware detection abnormality coefficient of the computer hardware is obtained, specifically:

[0076] Set the hardware sound weight and hardware vibration weight of the computer hardware;

[0077] The hardware detection anomaly coefficient of the computer hardware is obtained according to the hardware sound weight and the hardware sound anomaly coefficient, the hardware vibration weight and the hardware vibration anomaly coefficient.

[0078] In some embodiments, the specific value of the hardware detection anomaly coefficient of the computer hardware can be calculated by the calculation function: hardware detection anomaly coefficient = hardware sound weight * hardware sound anomaly coefficient + hardware vibration weight * hardware vibration anomaly coefficient, where the hardware sound weight and hardware vibration weight are respectively the weights of the influence of the hardware sound anomaly coefficient and the hardware vibration anomaly coefficient on the hardware detection anomaly coefficient.

[0079] Compare and analyze the software operation data of the computer software with the software standard operation data to obtain the software sound abnormality coefficient and software vibration abnormality coefficient of the computer software, specifically:

[0080] Software running sound data includes the software time domain characteristics and software spectrum characteristics of computer software;

[0081] Comparing the software time domain features with the standard time domain features in the software standard operation data to obtain the time domain feature anomaly coefficient of the computer software; comparing the software spectrum features with the standard spectrum features in the software standard operation data to obtain the spectrum feature anomaly coefficient of the computer software;

[0082] Setting a software time domain weight and a software spectrum weight, and obtaining a software sound anomaly coefficient of the computer software according to the software time domain weight and the time domain characteristic anomaly coefficient, the software spectrum weight and the time domain spectrum anomaly coefficient;

[0083] The software running vibration data includes the software vibration frequency, software vibration amplitude and software vibration phase of the computer software; the software standard vibration data of the computer software in the software feature library is obtained;

[0084] The software running vibration data of the computer software is compared with the software standard vibration data to obtain the software vibration abnormality coefficient of the computer software.

[0085] In some embodiments, the time domain features of the computer software are specifically the amplitude, duration, and zero-crossing rate of the running sound of the computer software during operation; the frequency spectrum features of the computer software are specifically the frequency spectrum, resonance peak, and harmonics of the running sound of the computer software during operation;

[0086] The time domain feature anomaly coefficient is specifically the absolute value of the corresponding deviation values of the running sound amplitude, running sound duration and running sound zero-crossing rate of the computer software during the running process and the standard sound amplitude, standard sound duration and standard sound zero-crossing rate of the computer software; the spectrum feature anomaly coefficient is specifically the absolute value of the corresponding deviation values of the running sound spectrum, running sound resonance peak and running sound harmonics of the computer software during the running process and the standard sound spectrum, standard sound resonance peak and standard sound harmonics of the computer software;

[0087] The specific value of the software sound anomaly coefficient of computer software can be calculated by using the calculation function: software sound anomaly coefficient = software time domain weight * time domain feature anomaly coefficient + software spectrum weight * time domain spectrum anomaly coefficient, where the software time domain weight and software spectrum weight are the weights of the influence of the time domain feature anomaly coefficient and the time domain spectrum anomaly coefficient on the software sound anomaly coefficient, respectively.

[0088] In some embodiments, the software vibration anomaly coefficient is specifically the absolute value of the corresponding deviation data values of the software vibration frequency, software vibration amplitude and software vibration phase of the computer software and the standard vibration frequency, standard vibration amplitude and standard vibration phase of the computer software.

[0089] And according to the software sound abnormality coefficient and the software vibration abnormality coefficient, the software detection abnormality coefficient of the computer software is obtained, which is specifically:

[0090] Set the software sound weight and software vibration weight of the computer software;

[0091] The software detection anomaly coefficient of the computer software is obtained according to the software sound weight and the software sound anomaly coefficient, the software vibration weight and the software vibration anomaly coefficient.

[0092] In some embodiments, the specific value of the software detection anomaly coefficient of the computer software can be calculated by the calculation function: software detection anomaly coefficient = software sound weight * software sound anomaly coefficient + software vibration weight * software vibration anomaly coefficient, where the software sound weight and software vibration weight are respectively the weights of the influence of the software sound anomaly coefficient and the software vibration anomaly coefficient on the software detection anomaly coefficient.

[0093] Step S5 is specifically as follows:

[0094] Obtaining the computer hardware running quantity and computer software running quantity during the target computer running detection process;

[0095] Obtaining a computer hardware anomaly coefficient of the target computer according to the number of computer hardware operations and a hardware detection anomaly coefficient corresponding to the computer hardware;

[0096] The computer software anomaly coefficient of the target computer is obtained according to the number of computer software operations and the software detection anomaly coefficient corresponding to the computer software.

[0097] In some embodiments, the computer hardware anomaly coefficient of the target computer is obtained by summing the coefficients of the hardware monitoring anomaly coefficients in the target computer according to the number of computer hardware running, and the computer software anomaly coefficient of the target computer is obtained by summing the coefficients of the software monitoring anomaly coefficients in the target computer according to the number of computer software running.

[0098] Step S6 is specifically as follows:

[0099] Set the computer hardware weight and computer software weight of the target computer;

[0100] Obtaining a computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer hardware weight, the computer software abnormality coefficient and the computer software weight;

[0101] The abnormal degree of the target computer's usage status is positively correlated with the computer status abnormality coefficient.

[0102] In some embodiments, the specific value of the computer state abnormality coefficient of the target computer can be calculated by calculating the function: computer state abnormality coefficient = computer hardware abnormality coefficient * computer hardware weight + computer software abnormality coefficient * computer software weight, wherein the computer hardware weight and the computer software weight are respectively the weights of the influence of the computer hardware abnormality coefficient and the computer software abnormality coefficient on the computer state abnormality coefficient;

[0103] The abnormal degree of the target computer's usage state is positively correlated with the computer state abnormality coefficient, which means that the greater the computer state abnormality coefficient of the target computer is, the more serious the abnormal degree of its usage state is.

[0104] The above are only preferred embodiments of the present application. The scope of protection of the present application is not limited to the above embodiments. All technical solutions based on the concept of the present application are within the scope of protection of the present application. It should be noted that for those skilled in the art, certain improvements and modifications that do not depart from the principles of the present application should also be considered within the scope of protection of the present application.

Claims

1. A method for evaluating and managing computer usage status detection, characterized in that: include: Step S1: obtaining computer data of a target computer, and establishing a hardware feature library and a software feature library based on the computer data; The hardware feature library contains hardware standard operation data of computer hardware, and the software feature library contains software standard operation data of computer software; Step S2: performing a computer operation test on the target computer to obtain hardware operation data of the computer hardware and software operation data of the computer software in the target computer; Step S3: comparing and analyzing the hardware operation data of the computer hardware with the hardware standard operation data to obtain a hardware sound abnormality coefficient and a hardware vibration abnormality coefficient of the computer hardware, and obtaining a hardware detection abnormality coefficient of the computer hardware based on the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient; Step S4: comparing and analyzing the software operation data of the computer software with the software standard operation data to obtain a software sound abnormality coefficient and a software vibration abnormality coefficient of the computer software, and obtaining a software detection abnormality coefficient of the computer software based on the software sound abnormality coefficient and the software vibration abnormality coefficient; Step S5: Analyzing the hardware abnormality coefficient of the computer hardware to obtain the computer hardware abnormality coefficient of the target computer, and analyzing the software abnormality coefficient of the computer software to obtain the computer software abnormality coefficient of the target computer; Step S6: obtaining a computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer software abnormality coefficient, and obtaining a usage status abnormality degree of the target computer according to the computer status abnormality coefficient.

2. The method for evaluating and managing computer usage status detection according to claim 1, characterized in that: Step S1 is specifically as follows: The computer data includes hardware parameters and hardware types of computer hardware in the target computer, and software parameters and software types of computer software; Obtaining hardware standard operating data of the computer hardware according to the hardware parameters and the hardware type, and building a hardware feature library according to the hardware standard operating data of the computer hardware in the target computer; Software standard operation data of the computer software is obtained according to the software parameters and software type, and a software feature library is constructed according to the software standard operation data of the computer software in the target computer.

3. The evaluation and management method for computer usage status detection according to claim 2, characterized in that: Step S2 is specifically as follows: The hardware operation data includes hardware operation sound data and hardware operation vibration data of the computer hardware; the software operation data includes software operation sound data and software operation vibration data of the computer software.

4. The method for evaluating and managing computer usage status detection according to claim 3, characterized in that: The hardware operation data of the computer hardware is compared and analyzed with the hardware standard operation data to obtain the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient of the computer hardware, specifically: The hardware operation sound data includes hardware time domain characteristics and hardware spectrum characteristics of the computer hardware; Comparing the hardware time domain features with the standard time domain features in the hardware standard operation data to obtain the time domain feature anomaly coefficient of the computer hardware; comparing the hardware spectrum features with the standard spectrum features in the hardware standard operation data to obtain the spectrum feature anomaly coefficient of the computer hardware; Setting a hardware time domain weight and a hardware spectrum weight, and obtaining a hardware sound anomaly coefficient of the computer hardware according to the hardware time domain weight and the time domain feature anomaly coefficient, the hardware spectrum weight and the time domain spectrum anomaly coefficient; The hardware operation vibration data includes the hardware vibration frequency, hardware vibration amplitude and hardware vibration phase of the computer hardware; obtaining the hardware standard vibration data of the computer hardware in the hardware feature library; The hardware operation vibration data of the computer hardware is compared with the hardware standard vibration data to obtain the hardware vibration anomaly coefficient of the computer hardware.

5. The method for evaluating and managing computer usage status detection according to claim 4, characterized in that: And according to the hardware sound abnormality coefficient and the hardware vibration abnormality coefficient, the hardware detection abnormality coefficient of the computer hardware is obtained, specifically: Set the hardware sound weight and hardware vibration weight of the computer hardware; According to the hardware sound weight and the hardware sound abnormality coefficient, the hardware vibration weight and the hardware vibration abnormality coefficient, a hardware detection abnormality coefficient of the computer hardware is obtained.

6. The method for evaluating and managing computer usage status detection according to claim 5, characterized in that: The software operation data of the computer software is compared and analyzed with the software standard operation data to obtain the software sound abnormality coefficient and the software vibration abnormality coefficient of the computer software, specifically: The software running sound data includes the software time domain characteristics and software spectrum characteristics of the computer software; Comparing the software time domain feature with the standard time domain feature in the software standard operation data to obtain the time domain feature anomaly coefficient of the computer software; comparing the software spectrum feature with the standard spectrum feature in the software standard operation data to obtain the spectrum feature anomaly coefficient of the computer software; Setting a software time domain weight and a software spectrum weight, and obtaining a software sound anomaly coefficient of the computer software according to the software time domain weight and the time domain characteristic anomaly coefficient, the software spectrum weight and the time domain spectrum anomaly coefficient; The software running vibration data includes the software vibration frequency, software vibration amplitude and software vibration phase of the computer software; obtaining the software standard vibration data of the computer software in the software feature library; The software running vibration data of the computer software is compared with the software standard vibration data to obtain the software vibration abnormality coefficient of the computer software.

7. The method for evaluating and managing computer usage status detection according to claim 6, characterized in that: And according to the software sound abnormality coefficient and the software vibration abnormality coefficient, the software detection abnormality coefficient of the computer software is obtained, which is specifically: Set the software sound weight and software vibration weight of the computer software; According to the software sound weight and the software sound abnormality coefficient, the software vibration weight and the software vibration abnormality coefficient, a software detection abnormality coefficient of the computer software is obtained.

8. The method for evaluating and managing computer usage status detection according to claim 7, characterized in that: Step S5 is specifically as follows: Obtaining the computer hardware running quantity and computer software running quantity during the target computer running detection process; Obtaining a computer hardware anomaly coefficient of the target computer according to the number of computer hardware operations and a hardware detection anomaly coefficient corresponding to the computer hardware; The computer software anomaly coefficient of the target computer is obtained according to the number of computer software operations and the software detection anomaly coefficient corresponding to the computer software.

9. The method for evaluating and managing computer usage status detection according to claim 8, characterized in that: Step S6 is specifically as follows: Set the computer hardware weight and computer software weight of the target computer; Obtaining a computer status abnormality coefficient of the target computer according to the computer hardware abnormality coefficient and the computer hardware weight, the computer software abnormality coefficient and the computer software weight; The abnormality degree of the target computer's usage status is positively correlated with the computer status abnormality coefficient.

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