A computer management-based operating status monitoring method and system

Abstract

The present invention discloses a method and system for monitoring the operating state based on computer management, which relates to the field of computer monitoring. The operating state monitoring method includes the following steps: Step 1: Obtain the operating environment parameters, hardware performance parameters, running item data, and user operation events of the computer to be detected; Step 2: Introduce the predetermined environment parameters, hardware performance parameters, and running item data within the current operating cycle, and set standard limits. When the collected parameters exceed the standard limits, they are marked as abnormal factors; By capturing key parameters such as the computer operating environment, hardware performance, and user operations in real time, data analysis and Pearson correlation coefficient are used to quantitatively analyze the correlation between user operations and abnormal factors, and step-by-step adjustment and iterative optimization are applied to dynamically adjust the parameter configuration to achieve the best evaluation value, so as to take targeted measures, and then realize the continuous monitoring and timely optimization of the computer.

Description

Technical Field

[0001] The present invention relates to the technical field of computer monitoring, and particularly to a method and system for monitoring the operating status based on computer management. Background Art

[0002] Monitoring the operating status of computer management is of great significance for fault prevention, system performance optimization, extension of hardware life, and ensuring security and stability. By regularly monitoring and maintaining the operating status of the computer, the efficiency and reliability of the computer can be improved, the normal operation of the system can be ensured, and a good user experience can be provided. The system usually comes with monitoring tools to obtain CPU utilization, memory occupancy, disk space, network traffic, etc.

[0003] However, the conventional monitoring methods are always limited to one or two monitoring aspects of software or hardware. Although there are many existing monitoring methods, and using two aspects of tools in combination can overcome some conventional problems, many fault problems are closely related to the wrong operations of the operators. However, the existing technology lacks the analysis and prevention of such abnormal factors. In view of the chain reaction caused by abnormal operations, it is difficult to provide an effective solution to such abnormalities in a timely manner, resulting in difficulties in achieving the expected adjustment of the applied treatment plan when the computer runs into faults. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the above-mentioned drawbacks of the existing technology, the present invention provides a method and system for monitoring the operating status based on computer management, which can effectively solve the problems of the existing technology.

[0006] (II) Technical Solutions

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] The present invention discloses a method for monitoring the operating status based on computer management, including the following steps:

[0009] Step 1: Obtain the operating environment parameters, hardware performance parameters, running item data, and user operation events of the computer to be detected;

[0010] Step 2: Introduce the predetermined environment parameters, hardware performance parameters, and running item data within the current operating cycle, and set standard boundaries. When the collected parameters exceed the standard boundaries, they are marked as abnormal factors;

[0011] Step 3: Calculate the correlation coefficients between different abnormal factors and the corresponding user operation events, and classify the factors based on the correlation coefficients;

[0012] Step 4: Using the classified correlation coefficients as inputs, construct a corresponding adjustment model and perform step-by-step adjustments based on the model;

[0013] Step 5: After each level of adjustment, obtain the evaluation values for comparison, obtain the optimal adjustment result and apply it, and perform actual adjustments to the computer parameters;

[0014] Step 6: Store the collected data and adjustment results in the cloud database, construct the corresponding categories of historical data, and form a reference database as associated reference data;

[0015] Step 7: Based on the reference database, match the operation behavior data of the user in the next cycle and provide associated early warning feedback.

[0016] Furthermore, the operation item data in Step 1 includes: the process ID, memory occupancy, CPU time slice, and response time of a specific application program or service.

[0017] Furthermore, the calculation of the correlation coefficient between different abnormal factors and the corresponding user operation events in Step 3 quantifies the degree of linear relationship between the two through the Pearson correlation coefficient. The calculation formula is:

[0018]

[0019] In the formula, x represents the quantified data set of user operation items, y represents the quantified data set of abnormal factors, n represents the number of data points, and the value range of r is between -1 and 1. r = 1 indicates a perfect positive correlation between the user operation item and the abnormal factor, r = 0 indicates a complete lack of correlation between the user operation item and the abnormal factor, and r = -1 indicates a perfect negative correlation between the user operation item and the abnormal factor.

[0020] Furthermore, the process of comparing the evaluation values in Step 5 is as follows:

[0021] Step a: Define the evaluation index;

[0022] Step b: Obtain the adjustment plan for each level of adjustment. After the simulation ends, calculate the comprehensive evaluation value according to the predefined evaluation index;

[0023] Step c: Use a search algorithm to analyze the parameter space and calculate the parameter configuration of the best evaluation value;

[0024] Step d: Iteratively execute Step b several times, and adjust the parameters of the adjustment model according to the comprehensive evaluation value. The comprehensive evaluation value serves as a feedback signal to guide the direction and amplitude of parameter adjustment until the parameter configuration of the best evaluation value is hit;

[0025] Step e: Determine the parameter configuration corresponding to the highest evaluation value obtained in all simulations.

[0026] An operation status monitoring system based on computer management, comprising:

[0027] A control module for overall control of global functional units and functional modules and intervening in the computer group to obtain read and write permissions;

[0028] An acquisition unit for deploying sensors and monitoring tools to collect specified data;

[0029] The acquisition unit is deployed with an environment acquisition module, a performance acquisition module and a data acquisition module, wherein:

[0030] The environment acquisition module is used to detect and obtain the regional temperature, humidity, network supply parameters and power supply parameters set by the computer group, as well as the real-time working temperature, humidity, network supply parameters and power supply parameters of the detected functional components within all targets to be detected;

[0031] The performance acquisition module is used to detect and obtain the CPU usage rate, memory utilization rate, disk I / O speed and network bandwidth parameters of the detection target;

[0032] The data acquisition module is used to obtain the software operation logs, process information and program operation fluctuation parameters of the current running project of the detection target;

[0033] An operation behavior capture module for tracking the operation steps of users to form a set of reference factors;

[0034] A pattern recognition unit for identifying and classifying the current operation status based on the data submitted by the acquisition unit and the operation behavior capture module;

[0035] The pattern recognition unit is deployed with a definition module and an association module. The definition module is connected to the acquisition unit, the operation behavior capture module and the association module through wireless network interaction, wherein:

[0036] The definition module is used to analyze the data collected by the acquisition unit, identify the data points exceeding the predetermined threshold, mark and extract the specified information according to the preset template to form a second set of reference factors;

[0037] The association module is used to extract the parameters of the first set of reference factors and the second set of reference factors, calculate the correlation coefficient and classify them;

[0038] A preview module for constructing an adjustment model, inputting the classified correlation coefficient, obtaining a simulated performance adjustment plan, conducting an effect evaluation, and based on the evaluation value, performing step-by-step parameter adjustment and plan iteration until the evaluation value hits the optimal solution interval and outputting the optimal adjustment plan. The association module is connected to the preview module through wireless network interaction;

[0039] An adjustment module, configured to actually adjust computer parameters by applying the optimal adjustment scheme values obtained by the preview module;

[0040] A behavior warning module, configured to perform comprehensive analysis based on a set of reference factors of the user in the previous cycle and the optimal adjustment scheme values, generate a warning template including several abnormal factors, and actively provide warning information feedback when detecting operation data that may cause the detection target to be abnormal in the set of reference factors of a certain type collected in the next cycle.

[0041] Furthermore, the control module and the acquisition unit are connected through wireless network interaction, the operation behavior capture module and the behavior warning module are connected through wireless network interaction, and the preview module is connected to the behavior warning module and the adjustment module through wireless network interaction.

[0042] Furthermore, the operation step attributes of the operation behavior capture module for the user include: mouse click, keyboard input, program startup, shutdown, and setting change.

[0043] Furthermore, the definition module is connected to a factor extraction module through wireless network interaction. After the factor extraction module is actively triggered, it allows the administrator to customize restrictions on the extractable factors according to the custom factor extraction priority.

[0044] Furthermore, the association module is connected to a sharing module through wireless network interaction. The sharing module is used to record and analyze the historical data obtained by the preview module, generate corresponding categories, and construct a reference database as the index reference for the association module.

[0045] Furthermore, the behavior warning module is connected to a threshold module through wireless network interaction. The threshold module is used to customize the adjustment of factor indicators for the warning templates of several abnormal factors generated by the behavior warning module.

[0046] (III) Beneficial effects

[0047] Adopting the technical solution provided by the present invention, compared with the known prior art, it has the following beneficial effects:

[0048] 1. By real-time capturing key parameters such as the computer running environment, hardware performance, and user operations, using data analysis and Pearson correlation coefficient to quantitatively analyze the association between user operations and abnormal factors, and applying step-by-step adjustment and iterative optimization to dynamically adjust parameter configuration to achieve the best evaluation value, so as to take targeted measures, and then realize continuous monitoring and timely optimization of the computer, enabling the computer system to ensure its own operation in the best state in the face of various operating environments and user behaviors.

[0049] 2. By storing the collected data and adjustment results in the cloud database and establishing a historical data reference library, the correlation between user behavior and system anomalies can be predicted, and early warning feedback can be given in advance to achieve preventive maintenance of faults. By combining user operation behavior data, the system can provide personalized configuration suggestions for users, thereby improving the work efficiency of users and the overall performance of the system.

[0050] 3. By real-time monitoring of the hardware performance parameters and running project data of the computer and making timely adjustments, it helps to allocate and use limited computing resources more effectively, avoid resource waste. It can not only automatically collect data and adjust parameters, but also continuously learn from historical data, continuously optimize the adjustment model, and improve the accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 It is a flowchart of the operation status monitoring method in the present invention;

[0053] Figure 2 It is a framework diagram of the operation status monitoring system in the present invention;

[0054] Figure 3 It is an architecture demonstration diagram of the present invention;

[0055] Figure 4 It is a flowchart of the process of comparing the evaluation values in the present invention.

[0056] The reference numerals in the figure respectively represent: 1, control module; 2, acquisition unit; 201, environment acquisition module; 202, performance acquisition module; 203, data acquisition module; 3, operation behavior capture module; 4, pattern recognition unit; 401, definition module; 4011, factor extraction module; 402, association module; 4021, sharing module; 5, preview module; 6, adjustment module; 7, behavior early warning module; 71, threshold module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] The following further describes the present invention with reference to embodiments.

[0059] Embodiment 1

[0060] A method for monitoring the operating state based on computer management in this embodiment, as Figure 1 and Figure 3 shown, includes the following steps:

[0061] Step 1: Obtain the operating environment parameters, hardware performance parameters, running project data, and user operation events of the computer to be detected. The running project data includes: the process ID, memory occupancy, CPU time slice, and response time of a specific application or service;

[0062] Step 2: Introduce the predetermined environment parameters, hardware performance parameters, and running project data in the current running cycle, and set standard boundaries. When the collected parameters exceed the standard boundaries, they are marked as abnormal factors;

[0063] Step 3: Calculate the correlation coefficients between different abnormal factors and the corresponding user operation events, and classify the factors based on the correlation coefficients;

[0064] Step 4: Use the classified correlation coefficients as inputs to construct a corresponding adjustment model, and perform step-by-step adjustments based on the model;

[0065] Step 5: After each level of adjustment, obtain the evaluation values for comparison, obtain the optimal adjustment result and apply it to actually adjust the computer parameters;

[0066] Step 6: Store the collected data and adjustment results in the cloud database, construct the corresponding categories of historical data, and form a reference database as associated reference data;

[0067] Step 7: Based on the reference database, match the operation behavior data of the user in the next cycle and provide associated warning feedback.

[0068] As a preferred implementation in this embodiment, the calculation of the correlation coefficients between different abnormal factors and the corresponding user operation events in Step 3 quantifies the degree of linear relationship between the two through the Pearson correlation coefficient, and its calculation formula is:

[0069]

[0070] In the formula, x represents the quantitative data set of user operation items, y represents the quantitative data set of abnormal factors, n represents the number of data points, and the value range of r is between -1 and 1. When r = 1, it means that the user operation item is completely positively correlated with the abnormal factor; when r = 0, it means that the user operation item has no correlation with the abnormal factor; when r = -1, it means that the user operation item is completely negatively correlated with the abnormal factor. The detailed information of user operations and abnormal occurrences, including operation types, frequencies, abnormal types, and occurrence times, is then used to calculate and interpret the results by applying relevant data analysis or statistical software.

[0071] Compared with the prior art, the prior art usually only focuses on certain aspects of software or hardware, while this method provides a comprehensive monitoring framework, including real-time capture and analysis of operating environment parameters, hardware performance parameters, running project data, and user operation events. This all-round monitoring can capture more factors related to system performance and stability;

[0072] The prior art often ignores the impact of user operations on the system running state. By calculating the correlation coefficient between different abnormal factors and user operation events, this method can quantitatively analyze the correlation between user operations and system anomalies, and perform effective prevention and response, which is an obvious improvement in the prior art. It helps to identify and prevent potential problems caused by user operations. Using the cloud database to store historical data and adjustment results to form a reference database, it can match the operation behavior data of users based on historical data, provide associated early warning feedback. This data-driven method improves the prediction ability for future potential anomalies and allows for more precise preventive maintenance.

[0073] Embodiment 2

[0074] On other levels, this embodiment also provides a running state monitoring system based on computer management, as Figure 2 shown, including:

[0075] The control module 1 is used to overall control the global functional units and functional modules and intervene to obtain read and write permissions for the computer group;

[0076] The acquisition unit 2 is used to deploy sensors and monitoring tools to collect specified data;

[0077] The acquisition unit 2 is deployed with an environment acquisition module 201, a performance acquisition module 202, and a data acquisition module 203, where:

[0078] The environment acquisition module 201 is used to detect and obtain the regional temperature, humidity, network supply parameters, and power supply parameters set for the computer group, as well as the real-time working temperature, humidity, network supply parameters, and power supply parameters of the detected functional components within all targets to be detected;

[0079] The performance collection module 202 is used to detect and obtain the CPU usage rate, memory utilization rate, disk I / O speed, and network bandwidth parameters of the detection target;

[0080] The data collection module 203 is used to obtain the software operation logs, process information, and program operation fluctuation parameters of the currently running project of the detection target;

[0081] The operation behavior capture module 3 is used to track the operation steps of the user, constituting a set of reference factors. The attributes of the user's operation steps include: mouse click, keyboard input, program startup, shutdown, and setting change;

[0082] The pattern recognition unit 4 is used to identify and classify the current running state based on the data submitted by the collection unit 2 and the operation behavior capture module 3;

[0083] The pattern recognition unit 4 is deployed with a definition module 401 and an association module 402. The definition module 401 is connected to the collection unit 2, the operation behavior capture module 3, and the association module 402 through wireless network interaction. Among them:

[0084] The definition module 401 is used to analyze the data collected by the collection unit 2, identify the data points that exceed the predetermined threshold, mark and extract the specified information according to the preset template, constituting a set of secondary reference factors. The definition module 401 is connected to a factor extraction module 4011 through wireless network interaction. After the factor extraction module 4011 is actively triggered, it allows the administrator to customize the restrictions on the extractable factors according to the custom factor extraction priority;

[0085] The association module 402 is used to extract the parameters of the first set of reference factors and the second set of reference factors, calculate the correlation coefficient, and classify them. The association module 402 is connected to a sharing module 4021 through wireless network interaction. The sharing module 4021 is used to record and analyze the historical data obtained by the preview module 5, generate corresponding categories, and construct a reference database as the index reference of the association module 402;

[0086] The preview module 5 is used to construct an adjustment model, input the classified correlation coefficient, obtain the simulated performance adjustment plan, conduct an effect evaluation, and based on the evaluation value, perform step-by-step parameter adjustment and plan iteration until the evaluation value hits the optimal solution interval, and output the optimal adjustment plan. The association module 402 is connected to the preview module 5 through wireless network interaction;

[0087] The adjustment module 6 is used to actually adjust the computer parameters by applying the optimal adjustment plan value obtained by the preview module 5;

[0088] The behavior warning module 7 is used to comprehensively analyze based on a set of reference factors and the optimal adjustment scheme value in the previous cycle of the user, generate a warning template containing several abnormal factors, and actively provide warning information feedback when detecting operation data that may cause abnormalities in the set of reference factors collected in the next cycle. The behavior warning module 7 is connected to a threshold module 71 through wireless network interaction. The threshold module 71 is used to custom-adjust the factor indicators of the warning template of several abnormal factors generated by the behavior warning module 7.

[0089] As a preferred implementation manner in this embodiment, as Figure 2 shown, the control module 1 and the acquisition unit 2 are connected through wireless network interaction, the operation behavior capture module 3 and the behavior warning module 7 are connected through wireless network interaction, and the preview module 5 is connected to the behavior warning module 7 and the adjustment module 6 through wireless network interaction.

[0090] In this embodiment, the control module 1 is used to overall control the whole system. The acquisition unit 2 is used to collect the environmental parameters and computer hardware performance parameters of the computer group in real time to ensure that all indicators are within the safe and optimal operating range. The operation behavior capture module 3 can track the user's operation steps, which helps to analyze the user behavior that causes problems, so as to adjust and prevent potential wrong operations in time. The definition module 401 and the association module 402 are used to analyze the collected data, identify the data points that exceed the predetermined threshold, and calculate the correlation coefficient to achieve accurate identification and classification of the operating state;

[0091] The preview module 5 is used to build an adjustment model, input the classified correlation coefficient, simulate the performance adjustment scheme, and perform step-by-step parameter adjustment until the optimal solution is reached and sent to the adjustment module 6 to ensure that the computer runs in the best state. The behavior warning module 7 generates a warning template containing several abnormal factors, which can actively provide warning information when detecting operation data that may cause abnormalities, improving the security of the system. Managers can customize the restrictions on the extractable factors through the factor extraction module 4011 according to actual needs, and can customize and adjust the factor indicators in the warning template through the threshold module 71 to provide personalized monitoring solutions for different operation and maintenance environments;

[0092] The sharing module 4021 records historical data, analyzes and generates corresponding categories based on this, and constructs a reference database, which helps to continuously improve the monitoring accuracy and adjustment strategy, and effectively reduce the occurrence of future similar problems.

[0093] Embodiment 3

[0094] In this embodiment, a measure for screening and comparing evaluation values is provided, as Figure 4 shown, the process of comparing evaluation values is as follows:

[0095] Step a: Define evaluation metrics;

[0096] Step b: Obtain the adjustment plan for each level of adjustment. After the simulation ends, calculate the comprehensive evaluation value according to the pre-defined evaluation metrics;

[0097] Step c: Use a search algorithm to analyze the parameter space and calculate the parameter configuration of the best evaluation value;

[0098] Step d: Iteratively execute Step b several times, and adjust the model parameters according to the comprehensive evaluation value. The comprehensive evaluation value serves as a feedback signal to guide the direction and amplitude of parameter adjustment until the parameter configuration of the best evaluation value is hit;

[0099] Step e: Determine the parameter configuration corresponding to the highest evaluation value obtained in all simulations.

[0100] Compared with the prior art, by using a search algorithm and iterative optimization to guide parameter adjustment, combined with intelligent data analysis and machine learning techniques, this method provides a more efficient and intelligent decision support system. Compared with the prior art that usually only provides static exception handling solutions, this method introduces a dynamic adjustment model that allows step-by-step adjustment according to real-time data and evaluation values until the optimal adjustment result is found. This dynamic and adaptive adjustment strategy greatly improves the effectiveness and predictability of the handling solution and can provide more accurate and effective solutions.

[0101] In summary, the present invention quantitatively evaluates the correlation between user activities and abnormal factors through real-time monitoring of key indicators such as the computer operating environment, hardware performance, and user activities, and by using data analysis methods and Pearson correlation coefficients. The present invention adopts a hierarchical adjustment and iterative optimization strategy to dynamically adjust system parameters, aiming to obtain the optimal evaluation result. Accordingly, corresponding measures can be taken targeted to achieve continuous monitoring and instant optimization of the computer system. This strategy ensures that the computer system can maintain the best operating state under various operation scenarios and user behavior patterns;

[0102] By uploading the collected data and adjustment results to the cloud database and constructing a historical database, this solution can predict the correlation between user behavior and system anomalies and issue early warnings in advance to achieve preventive maintenance of faults. Combining user operation data, the system can provide personalized configuration suggestions to further improve the user's work efficiency and the overall performance of the system. By real-time monitoring and timely adjustment of the computer's hardware performance parameters and running item data, the present invention helps to more effectively configure and utilize limited computing resources and avoid resource waste. This system not only automates the data collection and parameter adjustment processes, but also can continuously optimize the adjustment strategy through learning from historical data to improve decision-making accuracy.

[0103] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring operating status based on computer management, characterized in that: The following steps are involved: Step 1: Obtain the operating environment parameters, hardware performance parameters, operating project data and user operation events of the computer being tested; Step 2: Introduce the preset environmental parameters, hardware performance parameters and operation project data in the current operation cycle, and set standard limits. When the collected parameters exceed the standard limits, they are marked as abnormal factors; Step 3: Calculate the correlation coefficients between different abnormal factors and the corresponding user operation events, and classify the correlation coefficients; Step 4: Use the classified correlation coefficient as input to build a corresponding adjustment model, and make adjustments step by step based on the model; Step 5: After each level of adjustment, obtain the evaluation value for comparison, obtain the optimal adjustment result and apply it to make actual adjustments to the computer parameters; Step 6: Store the collected data and adjustment results in the cloud database, construct the corresponding categories of historical data, and form a reference database as associated reference data; Step 7: Based on the reference database, match the user's operation behavior data for the next cycle and provide related warning feedback; The calculation of the correlation coefficient between the different abnormal factors and the corresponding user operation events in step 3 is performed by quantifying the degree of linear relationship between the two through the Pearson correlation coefficient, and the calculation formula is: In the formula, x represents the quantitative data set of user operation items, y represents the quantitative data set of abnormal factors, n represents the number of data points, and the value range of r is between -1 and 1. r = 1 means that the user operation items are completely positively correlated with the abnormal factors, r = 0 means that the user operation items are completely unrelated to the abnormal factors, and r = -1 means that the user operation items are completely negatively correlated with the abnormal factors. The process of comparing the evaluation values ​​in step 5 is as follows: Step a: Define evaluation metrics; Step b: Obtain the adjustment plan for each level of adjustment, and after the simulation is completed, calculate the comprehensive evaluation value according to the pre-defined evaluation indicators; Step c: Use a search algorithm to analyze the parameter space and calculate the parameter configuration with the best evaluation value; Step d: Execute step b several times in iterations, and adjust the model parameters according to the comprehensive evaluation value. The comprehensive evaluation value serves as a feedback signal to guide the direction and amplitude of parameter adjustment until the parameter configuration with the best evaluation value is reached. Step e: Determine the parameter configuration corresponding to the highest evaluation value obtained in all simulations.

2. The method for monitoring the operation status based on computer management according to claim 1, characterized in that: The running project data in step 1 includes: process ID, memory usage, CPU time slice and response time of a specific application or service.

3. A computer-managed operation status monitoring system, the system being a system equipped with a computer-managed operation status monitoring method according to any one of claims 1 to 2, characterized in that: include: The control module (1) is used to control the global functional units and functional modules and intervene in the computer group to obtain read and write permissions; A collection unit (2), used to deploy sensors and monitoring tools to collect designated data; The acquisition unit (2) is equipped with an environment acquisition module (201), a performance acquisition module (202) and a data acquisition module (203), wherein: The environment acquisition module (201) is used to detect and obtain the regional temperature, humidity, network supply parameters and power supply parameters set by the computer group, as well as the real-time working temperature, humidity, network supply parameters and power supply parameters of the functional components to be detected in all the targets to be detected; The performance acquisition module (202) is used to detect and obtain the CPU usage rate, memory utilization rate, disk I / O speed and network bandwidth parameters of the detection target; A data acquisition module (203) is used to obtain the software operation log, process information and program operation fluctuation parameters of the currently running project of the detection target; The operation behavior capture module (3) is used to track the user's operation steps and form a reference factor set; A pattern recognition unit (4) is used to identify and classify the current operating state based on the data submitted by the acquisition unit (2) and the operation behavior capture module (3); The pattern recognition unit (4) is equipped with a definition module (401) and an association module (402), and the definition module (401) is interactively connected with the collection unit (2), the operation behavior capture module (3) and the association module (402) via a wireless network, wherein: A definition module (401) is used to analyze the data collected by the collection unit (2), identify data points exceeding a predetermined threshold, mark and extract designated information according to a preset template, and form a set of two reference factors; A correlation module (402) is used to extract parameters of the first reference factor set and the second reference factor set, calculate correlation coefficients, and classify them; A preview module (5) is used to construct an adjustment model, input the classified correlation coefficient, obtain a simulation performance adjustment plan, perform effect evaluation, and perform step-by-step parameter adjustment and plan iteration based on the evaluation value until the evaluation value hits the optimal solution interval, and output the optimal adjustment plan. The correlation module (402) is interactively connected with the preview module (5) via a wireless network; An adjustment module (6) is used to apply the optimal adjustment solution value obtained by the preview module (5) to actually adjust the computer parameters; The behavior warning module (7) is used to conduct a comprehensive analysis based on a set of reference factors and the optimal adjustment solution values ​​of the user in the previous cycle, generate a warning template containing a number of abnormal factors, and actively provide warning information feedback when it is detected that the set of reference factors collected in the next cycle has operation data that causes abnormality in the detection target.

4. The computer-managed operation status monitoring system according to claim 3, characterized in that: The control module (1) is interactively connected to the collection unit (2) via a wireless network, the operation behavior capture module (3) is interactively connected to the behavior warning module (7) via a wireless network, and the preview module (5) is interactively connected to the behavior warning module (7) and the adjustment module (6) via a wireless network.

5. The computer-managed operation status monitoring system according to claim 3, characterized in that: The operation step attributes of the user of the operation behavior capture module (3) include: mouse click, keyboard input, program start, close and setting change.

6. The computer-managed operation status monitoring system according to claim 3, characterized in that: The definition module (401) is interactively connected to the factor extraction module (4011) via a wireless network. After the factor extraction module (4011) is actively triggered, it allows the administrator to customize the restrictions on the extractable factors and extract the priority according to the customized factors.

7. The computer-managed operation status monitoring system according to claim 3, characterized in that: The association module (402) is interactively connected to a sharing module (4021) via a wireless network. The sharing module (4021) is used to record and analyze historical data obtained by the preview module (5), generate corresponding categories, and construct a reference database as an index reference for the association module (402).

8. The computer-managed operation status monitoring system according to claim 3, characterized in that: The behavior warning module (7) is interactively connected to a threshold module (71) via a wireless network, and the threshold module (71) is used to perform custom adjustment of factor indicators in warning templates of several abnormal factors generated by the behavior warning module (7).

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