Electronic computer remote maintenance service evaluation method and system based on cloud platform

Abstract

The invention relates to the technical field of electronic computer remote maintenance cloud platforms, in particular to an electronic computer remote maintenance service evaluation method and system based on a cloud platform. The method comprises the following steps: acquiring a hardware data set of the electronic computer equipment by using a camera and a sensor; acquiring an electronic computer system software data set by using a sensor; performing structure information identification on the hardware data set of the electronic computer equipment to obtain hardware structure data; performing three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure chart; and performing image enhancement on the three-dimensional hardware structure diagram, and constructing a hardware structure three-dimensional enhanced image. According to the invention, through the Internet of Things technology, the three-dimensional mapping technology, the anomaly detection technology and the cloud platform technology, the analysis capability in the aspect of remote maintenance service evaluation hardware state is improved, and the hardware structure can be visually displayed; and real-time monitoring and maintenance service feedback of the electronic computer is realized, and remote maintenance service evaluation can be effectively carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic computer remote maintenance cloud platform, and in particular to an electronic computer remote maintenance service evaluation method and system based on a cloud platform. Background Art

[0002] With the rapid development of information technology, electronic computers have become the infrastructure of all walks of life in modern society. From corporate office to personal entertainment, to scientific research and industrial production, the application of computers is everywhere. However, just like any technical product, computers will encounter various hardware failures during use, such as hard disk damage, memory failure, power supply problems; or software failures, such as operating system crashes, virus attacks, and driver conflicts. If these failures are not solved in time, they will seriously affect the user's work efficiency and quality of life. Traditional computer maintenance services mainly rely on manual door-to-door maintenance or users sending faulty equipment to maintenance centers for processing. This method is not only inefficient, but also has high maintenance time and cost costs, especially when equipment failures occur in remote areas or users do not have professional technical knowledge. In recent years, with the popularization of cloud computing and Internet technology, remote maintenance services have gradually emerged. Through remote connection technology, technicians can diagnose and repair faulty equipment in remote locations, greatly improving maintenance efficiency and service coverage. However, the existing remote maintenance service evaluation has shortcomings in hardware status analysis and lacks the ability to intuitively display hardware structure and status; it is also difficult to monitor maintenance service feedback in real time. When faced with complex and changeable electronic computer problems, it is difficult to achieve effective remote maintenance service evaluation. Summary of the invention

[0003] Based on this, it is necessary to provide a cloud platform-based electronic computer remote maintenance service evaluation method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for evaluating a remote maintenance service of an electronic computer based on a cloud platform is provided, the method comprising the following steps:

[0005] Step S1: using a camera and a sensor to obtain a computer device hardware data set; using a sensor to obtain a computer system software data set;

[0006] Step S2: performing structural information recognition on the electronic computer device hardware data set to obtain hardware structure data; performing three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; performing image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure;

[0007] Step S3: extracting the status information of the electronic computer hardware data set to obtain hardware status data; performing equipment operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate equipment operating temperature data; extracting electrical characteristic information of the electronic computer equipment hardware data set to obtain hardware electrical characteristic data; performing equipment electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate equipment electrical property data; integrating the equipment operating temperature data and the equipment electrical property data to obtain equipment status property data;

[0008] Step S4: extracting system configuration information of the electronic computer system software data set to obtain system configuration data; performing system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extracting log files of the electronic computer system software data set to obtain log file data; performing system network security analysis on the log file data to obtain system network security event data; integrating the system configuration abnormality data and the system network security event data to obtain system abnormality event data;

[0009] Step S5: construct a remote maintenance detection model based on the equipment state attribute data and the system abnormal event data to obtain a remote maintenance detection model; perform equipment fault detection on the equipment state attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set; perform system abnormality detection on the system abnormal event data based on the remote maintenance detection model to obtain system abnormality detection data; perform repair operation feedback on the system abnormal event data through the system abnormality detection data to generate system repair operation feedback data;

[0010] Step S6: Conduct real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, and generate a real-time monitoring and maintenance service feedback report; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute the electronic computer remote maintenance service evaluation task based on the remote maintenance service evaluation cloud platform.

[0011] The present invention realizes the comprehensive collection of electronic computer hardware and software data sets through the use of cameras and sensors, and provides rich original information for subsequent analysis and evaluation. The electronic computer equipment hardware data set is subjected to structural information recognition to obtain hardware structure data; the hardware structure data is subjected to three-dimensional mapping technology to generate a three-dimensional hardware structure diagram; the three-dimensional hardware structure diagram is subjected to image enhancement technology to construct a three-dimensional enhanced image of the hardware structure, and a three-dimensional hardware structure diagram is generated, making the hardware structure more intuitive and easy to understand. The three-dimensional enhanced image of the hardware structure is subjected to equipment working temperature analysis according to the hardware status data to intuitively display the working temperature status of the equipment, thereby generating equipment working temperature data; the electrical characteristic information of the electronic computer equipment hardware data set is extracted by using electrical characteristic processing technology to obtain hardware electrical characteristic data; the three-dimensional enhanced image of the hardware structure is subjected to equipment electrical property parameter analysis according to the hardware electrical characteristic data to intuitively display the electrical properties of the equipment, thereby generating equipment electrical property data; the equipment working temperature data and the equipment electrical property data are integrated to obtain comprehensive equipment status property data. Conducting system anomaly analysis on system configuration data can effectively identify abnormal data in system configuration and timely discover potential configuration errors or inconsistencies, thereby obtaining system configuration anomaly data; using semantic analysis technology to extract log files of electronic computer system software data sets to obtain log file data; conducting system network security analysis on log file data, focusing on discovering and analyzing network security events, and recording network security events to obtain system network security event data; integrating system configuration anomaly data and system network security event data to obtain system anomaly event data, can provide comprehensive system anomaly event data and provide a more comprehensive perspective for system maintenance services. A remote maintenance detection model is constructed based on the equipment status attribute data and the system abnormal event data to obtain a remote maintenance detection model, which can accurately analyze the equipment status attribute data and the system abnormal event data; equipment fault detection is performed on the equipment status attribute data based on the remote maintenance detection model, so that equipment faults can be quickly detected to obtain equipment fault detection data; fault service feedback is performed on the three-dimensional enhanced image of the hardware structure through the equipment fault detection data to generate an equipment fault service feedback image set, which can intuitively display the fault location and status, so that users can quickly locate the problem; system abnormality detection is performed on the system abnormal event data based on the remote maintenance detection model to obtain system abnormality detection data, which can ensure that system-level problems can be discovered in a timely manner; repair operation feedback is performed on the system abnormal event data through the system abnormality detection data, and automatic repair operations are performed on the electronic computer, which improves the efficiency of maintenance work, thereby generating system repair operation feedback data.Through the equipment failure service feedback image set and the system repair operation feedback data, the electronic computer is monitored for real-time maintenance service feedback, and a real-time monitoring maintenance service feedback report is generated, which provides users with instant maintenance service information and improves the response speed and quality of the service; a cloud platform is constructed according to the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform, which realizes the systematic management and evaluation of the electronic computer remote maintenance service; based on the remote maintenance service evaluation cloud platform, the electronic computer remote maintenance service evaluation operation is performed, which significantly improves the efficiency of the remote maintenance service and reduces the need for human intervention and on-site maintenance. Therefore, the present invention improves the analysis ability of the hardware status of the remote maintenance service evaluation through the Internet of Things technology, three-dimensional mapping technology, anomaly detection technology and cloud platform technology, and can intuitively display the hardware structure; and realizes the real-time monitoring maintenance service feedback of the electronic computer, which can effectively perform remote maintenance service evaluation.

[0012] Preferably, step S3 comprises the following steps:

[0013] Step S31: performing state feature recognition on the electronic computer hardware data set to generate hardware state feature information; extracting state data of the hardware state feature information to obtain hardware state data;

[0014] Step S32: monitoring the device operating temperature of the hardware structure three-dimensional enhanced image according to the hardware status data to generate device operating temperature data, wherein the device operating temperature data includes the temperature of the central processing unit and the temperature of the mainboard;

[0015] Step S33: performing electrical characteristic recognition on the electronic computer hardware data set to generate electrical characteristic information; extracting characteristic data of the electrical characteristic information to obtain hardware electrical characteristic data;

[0016] Step S34: performing device electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical property data;

[0017] Step S35: Integrate the device operating temperature data and the device electrical property data to obtain device status property data.

[0018] The present invention can accurately extract hardware status feature information from the hardware data set through the status feature recognition technology, providing accurate basic data for subsequent status monitoring and analysis; the three-dimensional enhanced image of the hardware structure is used to monitor the equipment working temperature according to the hardware status data, especially the temperature monitoring of the central processing unit and the mainboard, so that the temperature state of the key components of the computer can be more detailed, which is helpful to timely discover overheating problems, thereby generating equipment working temperature data. Electrical characteristics recognition is performed on the electronic computer hardware data set, which can accurately identify the electrical characteristics of the electronic computer equipment, thereby generating electrical characteristics information; the characteristic data of the electrical characteristics information is extracted to obtain hardware electrical characteristics data, which provides an important basis for hardware electrical performance analysis. The three-dimensional enhanced image of the hardware structure is used to analyze the equipment electrical property parameters according to the hardware electrical characteristic data, so that the electrical property parameters of the hardware are comprehensively and deeply evaluated, and the equipment electrical property data is generated; the equipment status property data obtained by integrating the operating temperature data and the electrical property data can comprehensively reflect the operating status of the equipment, and provide comprehensive data support for equipment fault maintenance.

[0019] Preferably, step S34 includes the following steps:

[0020] Step S341: performing device interface recognition on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device interface data; performing interface signal strength measurement on the device interface data to obtain device interface signal strength data;

[0021] Step S342: performing device voltage parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device voltage attribute data; continuously recording voltage fluctuations of the device voltage attribute data to obtain device voltage fluctuation data;

[0022] Step S343: performing signal interference frequency feedback on the device interface signal strength data according to the device voltage fluctuation data to obtain interface signal interference data;

[0023] Step S344: performing device current overload analysis on the hardware electrical characteristic data according to the interface signal interference data to generate device current overload data;

[0024] Step S345: performing interface current pulse monitoring on the device interface data through the device current overload data to obtain interface current pulse data;

[0025] Step S346: Integrate the device voltage fluctuation data, the interface signal interference data and the interface current pulse data to obtain the device electrical property data.

[0026] The present invention uses hardware electrical characteristic data to identify the device interface of the three-dimensional enhanced image and generate device interface data, which provides accurate interface information for further electrical characteristic analysis; measures the interface signal strength of the device interface data to obtain device interface signal strength data, which provides quantitative data for evaluating the performance of the device interface and helps to identify signal transmission problems. The device voltage attribute data continuously records voltage fluctuations to obtain device voltage fluctuation data, which enables a deeper understanding of voltage changes; the device interface signal strength data is fed back with signal interference frequency according to the device voltage fluctuation data to obtain interface signal interference data, which helps to identify and locate the signal interference source. The hardware electrical characteristic data is analyzed for device current overload according to the interface signal interference data to generate device current overload data, which can diagnose current overload problems and help with subsequent maintenance detection feedback. The device interface data is monitored for interface current pulses through the device current overload data, so that the interface current state can be monitored in real time, and current anomalies can be discovered in time, thereby obtaining interface current pulse data. The voltage fluctuation, signal interference and current pulse data are integrated to obtain device electrical attribute data, which provides a comprehensive evaluation of the overall electrical performance of the device.

[0027] Preferably, step S4 comprises the following steps:

[0028] Step S41: performing system feature recognition on the electronic computer system software data set to generate system feature information; extracting configuration data of the system feature information to obtain system configuration data;

[0029] Step S42: recording the system version of the system configuration data to obtain the system version data; performing system version update anomaly analysis on the system configuration data according to the system version data to obtain system version anomaly data;

[0030] Step S43: performing application configuration monitoring on the system configuration data to obtain system application data; performing application abnormality association on the system application data according to the system version abnormality data to obtain system version-program abnormality association data; performing crash type classification on the system version-program abnormality association data to obtain application crash type data; performing abnormal crash data mapping on the system version-program abnormality association data through the application crash type data to obtain system application abnormality data;

[0031] Step S44: Integrate the system version exception data and the system application exception data to obtain the system configuration exception data;

[0032] Step S45: performing log feature recognition on the electronic computer system software data set to generate log feature information; extracting file data of the log feature information to obtain log file data;

[0033] Step S46: Conduct system network security analysis on the log file data to obtain system network security event data;

[0034] Step S47: Integrate the system configuration abnormality data and the system network security event data to obtain the system abnormal event data.

[0035] The present invention performs system feature recognition on the electronic computer system software data set to generate system feature information; extracts the configuration data of the system feature information to obtain the system configuration data, so as to ensure a better understanding of the basic attributes and configuration information of the system; performs system version recording on the system configuration data to obtain the system version data; performs system version update anomaly analysis on the system configuration data according to the system version data to obtain the system version anomaly data, which can provide an accurate information basis for the tracking and management of the subsequent system configuration. The system configuration data is subjected to application configuration monitoring to obtain the system application data, which is helpful to identify the problems in the application settings; the system application data is associated with the application anomaly situation according to the system version anomaly data, and the mutual influence between different components is deeply understood, so as to obtain the system version-program anomaly association data; the system version-program anomaly association data is classified into crash types, so as to quickly identify the type of application crash, so as to obtain the application crash type data; the system version-program anomaly association data is mapped with abnormal crash data through the application crash type data, so as to obtain the system application anomaly data, so as to make the positioning of the system application anomaly problem more accurate, which is helpful to respond and solve the problem quickly in the future. The integration of the system version anomaly data and the system application anomaly data can provide a comprehensive system configuration anomaly view, which is helpful to comprehensively evaluate the system configuration anomaly situation. Extract the file data of log feature information to obtain log file data, so that the detailed log records of system operation can be effectively used; conduct system network security analysis on log file data to help timely discover network security threats, thereby obtaining system network security event data. Integrate system configuration abnormality data and system network security event data to obtain system abnormal event data. Ensure the maintenance and detection of subsequent system abnormal events.

[0036] Preferably, step S46 includes the following steps:

[0037] Step S461: Perform system network feature recognition on the log file data to generate system network feature information; extract flow data of the system network feature information to obtain network flow data; perform abnormal flow pattern analysis on the network flow data to obtain abnormal flow event data; perform network access source and frequency recognition on the abnormal flow event data to obtain abnormal flow source frequency data;

[0038] Step S462: performing system access feature recognition on the log file data to generate system access feature information; extracting access permission data of the system access feature information to obtain access permission data; performing access rule matching on the access permission data according to the abnormal traffic source frequency data to obtain abnormal traffic access permission data; performing unauthorized access event analysis on the abnormal traffic access permission data to obtain unauthorized access event data;

[0039] Step S463: performing system resource feature recognition on the log file data to generate system resource feature information; extracting resource usage data from the system resource feature information to obtain resource usage data; performing high occupancy statistics on the resource usage data to obtain resource high occupancy event data;

[0040] Step S464: Integrate abnormal traffic event data, unauthorized access event data and high resource usage event data to obtain system network security event data.

[0041] The present invention performs system network feature recognition on log file data, generates system network feature information, extracts flow data of system network feature information, obtains network flow data, realizes refined monitoring of network flow, and helps to timely discover abnormal flow patterns; performs abnormal flow pattern analysis on network flow data, can accurately identify potential malicious flow and abnormal flow patterns, obtains abnormal flow event data, and provides a basis for network security protection; performs network access source and frequency recognition on abnormal flow event data, obtains abnormal flow source frequency data, and helps to gain insight into potential network attack behaviors. Performs system access feature recognition on log file data, clarifies the identification of system access features, and thus generates system access feature information; extracts access permission data of system access feature information, obtains access permission data, and ensures that the access permission information of the system is mastered; performs permission rule matching on access permission data according to abnormal flow source frequency data, clarifies the rule management of system access permission, and can timely discover abnormal flow access permission, thereby obtaining abnormal flow access permission data; through the analysis of abnormal flow access permission data, unauthorized access events can be discovered in time, thereby obtaining unauthorized access event data. The identification of system resource feature information and the extraction of resource usage data enable comprehensive monitoring of system resource usage; statistics and analysis of high resource usage help to timely discover resource overuse problems, thereby obtaining high resource usage event data. The integration of abnormal traffic, unauthorized access and high resource usage event data provides a comprehensive network security event data, which helps to comprehensively evaluate the security status of the system in the future.

[0042] Preferably, step S5 comprises the following steps:

[0043] Step S51: constructing a remote maintenance detection model according to the device state attribute data and the system abnormal event data to obtain a remote maintenance detection pre-model, and training the remote maintenance detection pre-model using the device state attribute data and the system abnormal event data to obtain a remote maintenance detection training model;

[0044] Step S52: performing model cross-validation evaluation on the remote maintenance detection training model to obtain remote maintenance detection model evaluation data; adjusting model parameters of the remote maintenance detection training model using the remote maintenance detection model evaluation data to obtain a remote maintenance detection model;

[0045] Step S53: performing equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; performing fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set;

[0046] Step S54: performing system abnormality detection on the system abnormality event data based on the remote maintenance detection model to obtain system abnormality detection data; performing repair operation feedback on the system abnormality event data through the system abnormality detection data to generate system repair operation feedback data.

[0047] The present invention constructs a remote maintenance detection model based on the device state attribute data and the system abnormal event data to obtain a remote maintenance detection pre-model, which lays a foundation for further fault detection; by using the device state attribute data and the system abnormal event data to train the pre-model, a more accurate remote maintenance detection training model is obtained, which enhances the adaptability and detection ability of the model to practical problems. Through model cross-validation evaluation, the evaluation data of the remote maintenance detection model is obtained, which provides a basis for the optimization of the model; the model parameters are adjusted according to the evaluation results, which further improves the accuracy and reliability of the model and ensures the high efficiency of fault detection. Based on the remote maintenance detection model, the device state attribute data is used for device fault detection to obtain device fault detection data; according to the device fault detection data, fault service feedback is performed on the three-dimensional enhanced image of the hardware structure, and an intuitive device fault service feedback image set is generated, which is convenient for rapid identification and location of problems. Based on the remote maintenance detection model, the system abnormal event data is used for system abnormality detection to obtain system abnormality detection data; according to the system abnormality detection data, targeted repair operations are performed, and system repair operation feedback data is generated, which provides a clear solution for system abnormalities.

[0048] Preferably, step S53 includes the following steps:

[0049] Step S531: Performing device operating temperature fault detection on the device status attribute data based on the remote maintenance detection model, if the device operating temperature is higher than the preset device operating temperature, generating device operating temperature fault data, wherein the device operating temperature fault data includes central processing unit temperature fault data and mainboard temperature fault data;

[0050] Step S532: performing fault service feedback on the hardware structure 3D enhanced image through the device working temperature fault data. If the device working temperature fault data is the CPU temperature fault data, then performing CPU component image identification on the hardware structure 3D enhanced image to obtain the CPU temperature fault service image; if the device working temperature fault data is the motherboard temperature fault data, then performing motherboard component image identification on the hardware structure 3D enhanced image to obtain the motherboard temperature fault service image; integrating the CPU temperature fault service image and the motherboard temperature fault service image to obtain the device working temperature fault service image set;

[0051] Step S533: performing equipment electrical fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment electrical fault detection data, wherein the equipment electrical fault detection data includes equipment voltage fluctuation fault data, interface signal interference fault data and interface current pulse fault data;

[0052] Step S534: performing fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment electrical fault detection data. If the equipment electrical fault detection data is equipment voltage fluctuation fault data, the voltage stabilizing device image is marked on the three-dimensional enhanced image of the hardware structure to generate an equipment voltage fault service image; if the equipment electrical fault detection data is interface signal interference fault data, the electromagnetic filter image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface signal interference fault service image; if the equipment electrical fault detection data is interface current pulse fault data, the power supply image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface current pulse fault service image; the equipment voltage fault maintenance image, the interface signal interference fault service image and the interface current pulse fault service image are integrated to obtain an equipment electrical fault service image set;

[0053] Step S535: Integrate the equipment operating temperature fault service image set and the equipment electrical fault service image set to obtain an equipment fault service feedback image set.

[0054] The present invention monitors the working temperature of the equipment in real time through a remote maintenance detection model to ensure that the fault can be detected immediately when the temperature exceeds the preset threshold, including the temperature fault data of the central processing unit and the mainboard. By marking the faulty components on the three-dimensional enhanced image of the hardware structure, intuitive visual feedback of the fault location and nature is provided, which is convenient for users to quickly identify the problem. The fault service images of the central processing unit and the mainboard are integrated to form a complete set of equipment working temperature fault service image sets, which provides a comprehensive visual reference for maintenance decisions. Based on the remote maintenance detection model, the equipment voltage fluctuation, interface signal interference and interface current pulse faults are comprehensively detected to ensure the comprehensiveness of fault detection. According to different types of electrical fault detection data, the corresponding equipment electrical fault service image is marked on the three-dimensional enhanced image of the hardware structure, such as the voltage stabilizing device, the electromagnetic filter and the power supply; the fault service images of the voltage fault, the signal interference and the current pulse are integrated to form a comprehensive set of equipment electrical fault service image sets, which provides targeted fault feedback for maintenance work. The service image sets of the working temperature and the electrical fault are integrated to form a comprehensive equipment fault service feedback image set, which provides comprehensive fault service information from hardware to the electrical system.

[0055] Preferably, step S54 includes the following steps:

[0056] Step S541: performing system configuration anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system configuration anomaly detection data, wherein the system configuration anomaly detection data includes system version anomaly data and system application anomaly data;

[0057] Step S542: Performing repair operation feedback on the system configuration abnormality data through the system abnormality detection data. If the system configuration fault detection data is system version abnormality data, then updating the system patch to generate version abnormality repair data; if the system configuration fault detection data is system application abnormality data, then uninstalling and reinstalling the application to generate application abnormality repair data; integrating the version abnormality repair data and the application abnormality repair data to obtain the system configuration abnormality repair data;

[0058] Step S543: Performing system network security abnormal event detection on the system abnormal event data based on the remote maintenance detection model to obtain network security abnormal event detection data, wherein the network security abnormal event detection data includes abnormal traffic event data, unauthorized access abnormal data and resource high occupancy abnormal data;

[0059] Step S544: Performing repair operation feedback on the system network security event data through the network security abnormal event detection data. If the network security abnormal event detection data is abnormal traffic event data, the IP address and port of the abnormal traffic are blocked, and the information of the abnormal traffic is updated into the firewall rules to generate abnormal traffic event repair data; if the network security abnormal event detection data is unauthorized access abnormal data, the unauthorized access is isolated and processed to generate unauthorized access abnormal repair data; if the network security abnormal event detection data is high resource occupancy abnormal data, the abnormal resource high occupancy service process is closed to generate high resource occupancy abnormal repair data; the abnormal traffic event repair data, the unauthorized access abnormal repair data and the high resource occupancy abnormal repair data are integrated to obtain the network security abnormal event repair data;

[0060] Step S545: Integrate the system configuration anomaly repair data and the network security anomaly event repair data to obtain system repair operation feedback data.

[0061] The present invention utilizes a remote maintenance detection model to quickly identify abnormal situations in system configuration, including system version and application exceptions, and provides a data basis for further repair operations. According to the specific situation of system version exceptions and application exceptions, take corresponding measures to update patches or reinstall applications to ensure that the system configuration is restored to a normal state; integrate version exception repair data and application exception repair data to obtain system configuration exception repair data, and provide repair data for system configuration exceptions. Through the remote maintenance detection model, network security abnormal events are comprehensively detected, covering multiple aspects such as abnormal traffic, unauthorized access and high resource occupancy; for the detected network security anomalies, measures such as blocking IP addresses and ports, updating firewall rules, isolating unauthorized access and closing abnormal service processes are taken, which can effectively prevent the spread of security threats; integrate repair data for different network security anomalies, form a set of systematic network security abnormal event repair data, and improve the efficiency of network security management. The system configuration abnormality repair data and the network security abnormality event repair data are integrated to form comprehensive system repair operation feedback data, which provides important information for the stable operation and continuous maintenance of the system.

[0062] Preferably, step S6 comprises the following steps:

[0063] Step S61: Performing real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data to obtain real-time monitoring data and real-time maintenance service feedback data;

[0064] Step S62: Integrate the real-time monitoring data and the real-time maintenance service feedback data to obtain a real-time monitoring maintenance service feedback report;

[0065] Step S63: A cloud platform is constructed based on the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform, wherein the remote maintenance service evaluation cloud platform construction includes the equipment real-time monitoring maintenance service evaluation interface construction and the system real-time monitoring maintenance service evaluation interface construction to perform electronic computer remote maintenance service evaluation operations.

[0066] The present invention realizes real-time monitoring and maintenance service feedback of electronic computers through equipment failure service feedback image sets and system repair operation feedback data, ensuring that failures and abnormal conditions can be quickly discovered and responded to; the obtained real-time monitoring data and real-time maintenance service feedback data provide data support for users, making maintenance feedback more based on facts and analysis. The real-time monitoring and maintenance service feedback data are integrated to form a comprehensive real-time monitoring maintenance service feedback report, providing users with systematic maintenance service feedback. A cloud platform is constructed based on the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform. The construction of the cloud platform provides a centralized working environment, so that remote maintenance services can be executed more efficiently, and the response speed and processing capacity of maintenance services are improved. The construction of the remote maintenance service evaluation cloud platform includes the construction of a real-time monitoring maintenance service evaluation interface for equipment and a real-time monitoring maintenance service evaluation interface for system to perform remote maintenance service evaluation operations for electronic computers.

[0067] In this specification, a cloud platform-based electronic computer remote maintenance service evaluation system is provided, which is used to execute the above-mentioned cloud platform-based electronic computer remote maintenance service evaluation method. The cloud platform-based electronic computer remote maintenance service evaluation system includes:

[0068] The data acquisition module is used to acquire the electronic computer equipment hardware data set using the camera and the sensor; and to acquire the electronic computer system software data set using the sensor;

[0069] The hardware structure three-dimensional image module is used to identify the structural information of the electronic computer equipment hardware data set to obtain the hardware structure data; perform three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; perform image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure;

[0070] The device status attribute module is used to extract the status information of the electronic computer hardware data set to obtain the hardware status data; perform device operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate device operating temperature data; extract the electrical characteristic information of the electronic computer device hardware data set to obtain hardware electrical characteristic data; perform device electrical attribute parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical attribute data; integrate the device operating temperature data and the device electrical attribute data to obtain device status attribute data;

[0071] The system abnormal event module is used to extract the system configuration information of the electronic computer system software data set to obtain the system configuration data; perform system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extract the log file of the electronic computer system software data set to obtain the log file data; perform system network security analysis on the log file data to obtain system network security event data; integrate the system configuration abnormality data and the system network security event data to obtain the system abnormality event data;

[0072] The remote maintenance detection model module is used to construct a remote maintenance detection model based on the equipment status attribute data and the system abnormal event data to obtain the remote maintenance detection model; perform equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment fault detection data to generate an equipment fault service feedback image set; perform system anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system anomaly detection data; perform repair operation feedback on the system abnormal event data through the system anomaly detection data to generate system repair operation feedback data;

[0073] The cloud platform module is used to perform real-time monitoring and maintenance service feedback on electronic computers through equipment failure service feedback image sets and system repair operation feedback data, and generate real-time monitoring and maintenance service feedback reports; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute electronic computer remote maintenance service evaluation operations based on the remote maintenance service evaluation cloud platform.

[0074] The data acquisition module of the present invention is used to realize the comprehensive collection of electronic computer hardware and software data sets through the use of cameras and sensors, and provides rich original information for subsequent analysis and evaluation. The device state attribute module is used to perform structural information recognition on the electronic computer device hardware data set through structural information recognition to obtain hardware structure data; perform three-dimensional mapping on the hardware structure data through three-dimensional mapping technology to generate a three-dimensional hardware structure diagram; perform image enhancement on the three-dimensional hardware structure diagram through image enhancement technology, construct a three-dimensional enhanced image of the hardware structure, and generate a three-dimensional hardware structure diagram, so that the hardware structure is more intuitive and easy to understand. According to the hardware state data, the three-dimensional enhanced image of the hardware structure is analyzed for the device working temperature to intuitively display the working temperature state of the device, thereby generating the device working temperature data; the electrical feature information of the electronic computer device hardware data set is extracted by using the electrical feature processing technology to obtain the hardware electrical feature data; according to the hardware electrical feature data, the three-dimensional enhanced image of the hardware structure is analyzed for the device electrical property parameters to intuitively display the electrical properties of the device, thereby generating the device electrical property data; the device working temperature data and the device electrical property data are integrated to obtain comprehensive device state attribute data. The system abnormal event module is used to perform system abnormality analysis on system configuration data. It can effectively identify abnormal data in system configuration and promptly discover potential configuration errors or inconsistencies, thereby obtaining system configuration abnormality data; use semantic analysis technology to extract log files of electronic computer system software data sets to obtain log file data; perform system network security analysis on log file data, focus on discovering and analyzing network security events, and record network security events to obtain system network security event data; integrate system configuration abnormality data and system network security event data to obtain system abnormal event data, which can provide comprehensive system abnormal event data and provide a more comprehensive perspective for system maintenance services. The remote maintenance detection model module is used to construct a remote maintenance detection model based on the equipment status attribute data and the system abnormal event data to obtain a remote maintenance detection model, which can accurately analyze the equipment status attribute data and the system abnormal event data; based on the remote maintenance detection model, the equipment status attribute data is used for equipment fault detection, which can quickly detect the equipment fault and obtain equipment fault detection data; through the equipment fault detection data, the three-dimensional enhanced image of the hardware structure is used for fault service feedback, and a set of equipment fault service feedback images is generated, which can intuitively display the fault location and status, so that users can quickly locate the problem; based on the remote maintenance detection model, the system abnormal event data is used for system abnormality detection, and the system abnormality detection data is obtained, which can ensure that the problems at the system level can be discovered in time; the system abnormal event data is used for repair operation feedback through the system abnormality detection data, and the electronic computer is automatically repaired, which improves the efficiency of maintenance work, thereby generating system repair operation feedback data.The cloud platform module is used to perform real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, generate a real-time monitoring and maintenance service feedback report, provide users with instant maintenance service information, and improve the response speed and quality of the service; according to the remote maintenance detection model and the real-time monitoring and maintenance service feedback report, the cloud platform is constructed to obtain a remote maintenance service evaluation cloud platform, which realizes the systematic management and evaluation of the remote maintenance service of the electronic computer; based on the remote maintenance service evaluation cloud platform, the electronic computer remote maintenance service evaluation operation is performed, which significantly improves the efficiency of the remote maintenance service and reduces the need for human intervention and on-site maintenance. Therefore, the present invention improves the analysis ability of the hardware status of the remote maintenance service evaluation through the Internet of Things technology, three-dimensional mapping technology, anomaly detection technology and cloud platform technology, and can intuitively display the hardware structure; and realizes the real-time monitoring and maintenance service feedback of the electronic computer, which can effectively perform remote maintenance service evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of the steps of a cloud platform-based electronic computer remote maintenance service evaluation method;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S5;

[0077] Figure 3 for Figure 2 Detailed implementation steps of step S53;

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 , a method for evaluating a remote maintenance service of an electronic computer based on a cloud platform, the method comprising the following steps:

[0083] Step S1: using a camera and a sensor to obtain a computer device hardware data set; using a sensor to obtain a computer system software data set;

[0084] Step S2: performing structural information recognition on the electronic computer device hardware data set to obtain hardware structure data; performing three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; performing image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure;

[0085] Step S3: extracting the status information of the electronic computer hardware data set to obtain hardware status data; performing equipment operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate equipment operating temperature data; extracting electrical characteristic information of the electronic computer equipment hardware data set to obtain hardware electrical characteristic data; performing equipment electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate equipment electrical property data; integrating the equipment operating temperature data and the equipment electrical property data to obtain equipment status property data;

[0086] Step S4: extracting system configuration information of the electronic computer system software data set to obtain system configuration data; performing system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extracting log files of the electronic computer system software data set to obtain log file data; performing system network security analysis on the log file data to obtain system network security event data; integrating the system configuration abnormality data and the system network security event data to obtain system abnormality event data;

[0087] Step S5: construct a remote maintenance detection model based on the equipment state attribute data and the system abnormal event data to obtain a remote maintenance detection model; perform equipment fault detection on the equipment state attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set; perform system abnormality detection on the system abnormal event data based on the remote maintenance detection model to obtain system abnormality detection data; perform repair operation feedback on the system abnormal event data through the system abnormality detection data to generate system repair operation feedback data;

[0088] Step S6: Conduct real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, and generate a real-time monitoring and maintenance service feedback report; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute the electronic computer remote maintenance service evaluation task based on the remote maintenance service evaluation cloud platform.

[0089] The present invention realizes the comprehensive collection of electronic computer hardware and software data sets through the use of cameras and sensors, and provides rich original information for subsequent analysis and evaluation. The electronic computer equipment hardware data set is subjected to structural information recognition to obtain hardware structure data; the hardware structure data is subjected to three-dimensional mapping technology to generate a three-dimensional hardware structure diagram; the three-dimensional hardware structure diagram is subjected to image enhancement technology to construct a three-dimensional enhanced image of the hardware structure, and a three-dimensional hardware structure diagram is generated, making the hardware structure more intuitive and easy to understand. The three-dimensional enhanced image of the hardware structure is subjected to equipment working temperature analysis according to the hardware status data to intuitively display the working temperature status of the equipment, thereby generating equipment working temperature data; the electrical characteristic information of the electronic computer equipment hardware data set is extracted by using electrical characteristic processing technology to obtain hardware electrical characteristic data; the three-dimensional enhanced image of the hardware structure is subjected to equipment electrical property parameter analysis according to the hardware electrical characteristic data to intuitively display the electrical properties of the equipment, thereby generating equipment electrical property data; the equipment working temperature data and the equipment electrical property data are integrated to obtain comprehensive equipment status property data. Conducting system anomaly analysis on system configuration data can effectively identify abnormal data in system configuration and timely discover potential configuration errors or inconsistencies, thereby obtaining system configuration anomaly data; using semantic analysis technology to extract log files of electronic computer system software data sets to obtain log file data; conducting system network security analysis on log file data, focusing on discovering and analyzing network security events, and recording network security events to obtain system network security event data; integrating system configuration anomaly data and system network security event data to obtain system anomaly event data, can provide comprehensive system anomaly event data and provide a more comprehensive perspective for system maintenance services. A remote maintenance detection model is constructed based on the equipment status attribute data and the system abnormal event data to obtain a remote maintenance detection model, which can accurately analyze the equipment status attribute data and the system abnormal event data; equipment fault detection is performed on the equipment status attribute data based on the remote maintenance detection model, so that equipment faults can be quickly detected to obtain equipment fault detection data; fault service feedback is performed on the three-dimensional enhanced image of the hardware structure through the equipment fault detection data to generate an equipment fault service feedback image set, which can intuitively display the fault location and status, so that users can quickly locate the problem; system abnormality detection is performed on the system abnormal event data based on the remote maintenance detection model to obtain system abnormality detection data, which can ensure that system-level problems can be discovered in a timely manner; repair operation feedback is performed on the system abnormal event data through the system abnormality detection data, and automatic repair operations are performed on the electronic computer, which improves the efficiency of maintenance work, thereby generating system repair operation feedback data.Through the equipment failure service feedback image set and the system repair operation feedback data, the electronic computer is monitored for real-time maintenance service feedback, and a real-time monitoring maintenance service feedback report is generated, which provides users with instant maintenance service information and improves the response speed and quality of the service; a cloud platform is constructed according to the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform, which realizes the systematic management and evaluation of the electronic computer remote maintenance service; based on the remote maintenance service evaluation cloud platform, the electronic computer remote maintenance service evaluation operation is performed, which significantly improves the efficiency of the remote maintenance service and reduces the need for human intervention and on-site maintenance. Therefore, the present invention improves the analysis ability of the hardware status of the remote maintenance service evaluation through the Internet of Things technology, three-dimensional mapping technology, anomaly detection technology and cloud platform technology, and can intuitively display the hardware structure; and realizes the real-time monitoring maintenance service feedback of the electronic computer, which can effectively perform remote maintenance service evaluation.

[0090] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a cloud platform-based electronic computer remote maintenance service evaluation method of the present invention. In this example, the cloud platform-based electronic computer remote maintenance service evaluation method includes the following steps:

[0091] Step S1: using a camera and a sensor to obtain a computer device hardware data set; using a sensor to obtain a computer system software data set;

[0092] In the embodiment of the present invention, the computer hardware is visually scanned by a camera to capture the image information of the hardware components, thereby obtaining the electronic computer equipment hardware data set; and various types of sensors are deployed to collect hardware status data, the temperature sensor is used to monitor the temperature of the central processing unit and the key components of the motherboard; the voltage sensor detects the power supply; and the current sensor is used to measure the current passing through the hardware components. The electronic computer system software data set is obtained through the monitoring software installed on the computer, the configuration information of the operating system, the list of installed applications, the system version and the update history information are collected, and the system log file analysis tool is used to capture the log data generated during the operation of the system, wherein the log data includes system events, error reports and performance indicator information.

[0093] Step S2: performing structural information recognition on the electronic computer device hardware data set to obtain hardware structure data; performing three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; performing image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure;

[0094] In the embodiment of the present invention, the computer vision recognition technology is used to analyze the structural information of the electronic computer equipment hardware data set, identify and classify the hardware components, such as hard disk, central processing unit, motherboard and power supply, and extract the geometric features and spatial positions of the hardware components to obtain the hardware structure data. The hardware structure data is three-dimensionally mapped to the actual hardware component size and shape using three-dimensional modeling technology to generate a three-dimensional hardware structure diagram; the three-dimensional hardware structure diagram is enhanced using graphic enhancement technology, wherein the image enhancement includes adjusting the brightness, adjusting the contrast and applying a sharpening filter operation, so that the surface features of the three-dimensional model are closer to the appearance of the real hardware, thereby constructing a three-dimensional enhanced image of the hardware structure.

[0095] Step S3: extracting the status information of the electronic computer hardware data set to obtain hardware status data; performing equipment operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate equipment operating temperature data; extracting electrical characteristic information of the electronic computer equipment hardware data set to obtain hardware electrical characteristic data; performing equipment electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate equipment electrical property data; integrating the equipment operating temperature data and the equipment electrical property data to obtain equipment status property data;

[0096] In an embodiment of the present invention, hardware status data is obtained by extracting status information of an electronic computer hardware data set; the three-dimensional enhanced image of the hardware structure is analyzed for device operating temperature based on the hardware status data to generate a temperature map showing the temperature distribution of the hardware components, thereby generating device operating temperature data, which can be highlighted in the three-dimensional image to intuitively identify components in overheated areas. Electrical characteristic information, including voltage, current, and interface signal parameters, is extracted from the hardware data set to obtain hardware electrical characteristic data; the three-dimensional enhanced image of the hardware structure is analyzed for device electrical property parameters based on the hardware electrical characteristic data, and by monitoring the transmission of electrical signals in the hardware, potential electrical problems such as voltage fluctuations and current anomalies can be identified, thereby generating device electrical property data. The device operating temperature data and the device electrical property data are integrated to form a comprehensive device status property data set.

[0097] Step S4: extracting system configuration information of the electronic computer system software data set to obtain system configuration data; performing system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extracting log files of the electronic computer system software data set to obtain log file data; performing system network security analysis on the log file data to obtain system network security event data; integrating the system configuration abnormality data and the system network security event data to obtain system abnormality event data;

[0098] In the embodiment of the present invention, the system configuration information of the electronic computer system software data set is extracted through the system built-in collection tool to obtain the system configuration data, wherein the system configuration data includes the system version, system setting parameters, system resource allocation information and application configuration information. The system configuration data is subjected to system anomaly analysis using an anomaly detection algorithm, and the system anomaly analysis includes checking the deviation, missing and potential configuration error information between the system configuration and the default configuration, thereby obtaining the system configuration anomaly data; the log file of the electronic computer system software data set is extracted using a log analysis tool to obtain the log file data; the network security analysis technology is used to conduct system network security analysis on the log file data to obtain the system network security event data.

[0099] Step S5: construct a remote maintenance detection model based on the equipment state attribute data and the system abnormal event data to obtain a remote maintenance detection model; perform equipment fault detection on the equipment state attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set; perform system abnormality detection on the system abnormal event data based on the remote maintenance detection model to obtain system abnormality detection data; perform repair operation feedback on the system abnormal event data through the system abnormality detection data to generate system repair operation feedback data;

[0100] In an embodiment of the present invention, by integrating device state attribute data and system abnormal event data, a remote maintenance detection model is constructed using a machine learning algorithm to obtain a remote maintenance detection model; based on the remote maintenance detection model, device fault detection is performed on the device state attribute data, wherein the device fault detection includes the device fault type and the location where the fault occurs, thereby obtaining device fault detection data; and fault service feedback is performed in combination with the three-dimensional enhanced image of the hardware structure to generate a device fault service feedback image set. Based on the remote maintenance detection model, system abnormality detection is performed on the system abnormal event data to identify abnormal behaviors and security issues in the operating system and application programs to obtain system abnormality detection data; and based on the system abnormality detection data, repair operation feedback is performed to generate system repair operation feedback data.

[0101] Step S6: Conduct real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, and generate a real-time monitoring and maintenance service feedback report; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute the electronic computer remote maintenance service evaluation task based on the remote maintenance service evaluation cloud platform.

[0102] In the embodiment of the present invention, through the equipment fault service feedback image set and the system repair operation feedback data, the real-time monitoring technology is used to perform real-time monitoring and maintenance service feedback on the electronic computer, thereby generating a real-time monitoring and maintenance service feedback report; using the cloud platform technology, a cloud platform is constructed according to the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; based on the remote maintenance service evaluation cloud platform, an electronic computer remote maintenance service evaluation operation is performed, wherein the remote maintenance service evaluation operation includes real-time monitoring and evaluation of the status of hardware and software, automatic detection of potential faults, and feedback of maintenance service measures for the faults, thereby improving the flexibility and efficiency of the remote maintenance service.

[0103] Preferably, step S3 comprises the following steps:

[0104] Step S31: performing state feature recognition on the electronic computer hardware data set to generate hardware state feature information; extracting state data of the hardware state feature information to obtain hardware state data;

[0105] Step S32: monitoring the device operating temperature of the hardware structure three-dimensional enhanced image according to the hardware status data to generate device operating temperature data, wherein the device operating temperature data includes the temperature of the central processing unit and the temperature of the mainboard;

[0106] Step S33: performing electrical characteristic recognition on the electronic computer hardware data set to generate electrical characteristic information; extracting characteristic data of the electrical characteristic information to obtain hardware electrical characteristic data;

[0107] Step S34: performing device electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical property data;

[0108] Step S35: Integrate the device operating temperature data and the device electrical property data to obtain device status property data.

[0109] In an embodiment of the present invention, feature recognition technology is used to identify the state features of an electronic computer hardware data set to generate hardware state feature information; the state data of the hardware state feature information is extracted to obtain hardware state data, wherein the hardware state data includes the type, temperature and load of the hardware components. The hardware state data is used to monitor the operating temperature of the hardware structure three-dimensional enhanced image, to achieve temperature monitoring of the central processing unit and mainboard hardware components, and to generate equipment operating temperature data. Electrical characteristics of an electronic computer hardware data set are identified to generate electrical characteristics information; feature data of the electrical characteristics information is extracted to obtain hardware electrical characteristics data; the hardware electrical characteristics data is used to analyze the electrical property parameters of the hardware structure three-dimensional enhanced image, and the electrical property parameter analysis includes analyzing the specific values ​​of voltage, current and signal electrical parameters, thereby generating equipment electrical property data.

[0110] Preferably, step S34 includes the following steps:

[0111] Step S341: performing device interface recognition on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device interface data; performing interface signal strength measurement on the device interface data to obtain device interface signal strength data;

[0112] Step S342: performing device voltage parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device voltage attribute data; continuously recording voltage fluctuations of the device voltage attribute data to obtain device voltage fluctuation data;

[0113] Step S343: performing signal interference frequency feedback on the device interface signal strength data according to the device voltage fluctuation data to obtain interface signal interference data;

[0114] Step S344: performing device current overload analysis on the hardware electrical characteristic data according to the interface signal interference data to generate device current overload data;

[0115] Step S345: performing interface current pulse monitoring on the device interface data through the device current overload data to obtain interface current pulse data;

[0116] Step S346: Integrate the device voltage fluctuation data, the interface signal interference data and the interface current pulse data to obtain the device electrical property data.

[0117] In an embodiment of the present invention, the device interface is identified on the three-dimensional enhanced image of the hardware structure through the hardware electrical characteristic data, and the electrical connection points, ports and interface characteristics are analyzed to generate device interface data; the interface signal strength is measured on the device interface data to obtain the device interface signal strength data. The device voltage attribute data is generated by analyzing the voltage stability and consistency, and the device voltage attribute data is generated; then it is used to record the continuous fluctuation of the voltage to obtain the device voltage fluctuation data. The device interface signal strength is evaluated by the device voltage fluctuation data, and the impact of the voltage fluctuation on the signal quality is further analyzed to determine the frequency of the signal interference, thereby obtaining the interface signal interference data. The device interface data is monitored for abnormal peak value and duration of the current through the interface signal interference data to generate the device current overload data; the device interface data is monitored for interface current pulse through the device current overload data, and the interface current pulse monitoring includes the transient change of the current and the pulse width, thereby obtaining the interface current pulse data.

[0118] Preferably, step S4 comprises the following steps:

[0119] Step S41: performing system feature recognition on the electronic computer system software data set to generate system feature information; extracting configuration data of the system feature information to obtain system configuration data;

[0120] Step S42: recording the system version of the system configuration data to obtain the system version data; performing system version update anomaly analysis on the system configuration data according to the system version data to obtain system version anomaly data;

[0121] Step S43: performing application configuration monitoring on the system configuration data to obtain system application data; performing application abnormality association on the system application data according to the system version abnormality data to obtain system version-program abnormality association data; performing crash type classification on the system version-program abnormality association data to obtain application crash type data; performing abnormal crash data mapping on the system version-program abnormality association data through the application crash type data to obtain system application abnormality data;

[0122] Step S44: Integrate the system version exception data and the system application exception data to obtain the system configuration exception data;

[0123] Step S45: performing log feature recognition on the electronic computer system software data set to generate log feature information; extracting file data of the log feature information to obtain log file data;

[0124] Step S46: Conduct system network security analysis on the log file data to obtain system network security event data;

[0125] Step S47: Integrate the system configuration abnormality data and the system network security event data to obtain the system abnormal event data.

[0126] In the embodiment of the present invention, the system feature recognition is performed on the electronic computer system software data set through the feature recognition technology to generate system feature information; the configuration data of the system feature information is extracted to obtain the system configuration data, wherein the system configuration data includes the configuration options and status of the system. The system version is recorded on the system configuration data to obtain the system version data; by comparing the current system version data with the latest official release version, the system version update exception analysis is performed to identify the outdated or unexpected system version configuration to obtain the system version exception data. The application data is analyzed through the system version exception data to find the application configuration that is incompatible with the system version or has known problems to obtain the system version-program exception association data. The system version-program exception association data is crash type classified to obtain the application crash type data; the system version-program exception association data is abnormally mapped to the crash data through the application crash type data to obtain the system application exception data. The log feature recognition is performed on the electronic computer system software data set to generate the log feature information; the file data of the log feature information is extracted to obtain the log file data, wherein the log file data includes the event type, timestamp, user activity and system message. By analyzing security events, access patterns, and abnormal behaviors in log file data, potential network security threats can be identified and system network security event data can be obtained.

[0127] Preferably, step S46 includes the following steps:

[0128] Step S461: Perform system network feature recognition on the log file data to generate system network feature information; extract flow data of the system network feature information to obtain network flow data; perform abnormal flow pattern analysis on the network flow data to obtain abnormal flow event data; perform network access source and frequency recognition on the abnormal flow event data to obtain abnormal flow source frequency data;

[0129] Step S462: performing system access feature recognition on the log file data to generate system access feature information; extracting access permission data from the system access feature information to obtain access permission data; performing access permission rule matching on the access permission data according to the abnormal traffic source frequency data to obtain abnormal traffic access permission data; performing unauthorized access event analysis on the abnormal traffic access permission data to obtain unauthorized access event data;

[0130] Step S463: performing system resource feature recognition on the log file data to generate system resource feature information; extracting resource usage data from the system resource feature information to obtain resource usage data; performing high occupancy statistics on the resource usage data to obtain resource high occupancy event data;

[0131] Step S464: Integrate abnormal traffic event data, unauthorized access event data and high resource usage event data to obtain system network security event data.

[0132] In an embodiment of the present invention, the system network feature recognition is performed on the log file data, wherein the system network feature recognition includes identifying the network communication mode, protocol type and flow characteristics, generating the system network feature information; extracting the flow data of the system network feature information, obtaining the network flow data; performing abnormal flow pattern analysis on the network flow data, obtaining the abnormal flow event data; performing network access source and frequency recognition on the abnormal flow event data, obtaining the abnormal flow source frequency data. The access permission data is matched with the abnormal flow source frequency data to identify the permission configuration related to the abnormal flow, and obtain the abnormal flow access permission data. The abnormal flow access permission data is further analyzed for unauthorized access events to determine whether there is an unauthorized access attempt or a behavior that violates the security policy, thereby obtaining the unauthorized access event data. The system resource feature recognition is performed on the log file data, and the central processing unit, memory, storage and network resource features are identified, thereby generating the system resource feature information; the high occupancy situation statistics are performed on the resource usage data, and the situation where the resource usage rate is higher than the normal threshold is counted, and the resource high occupancy event data is obtained.

[0133] As an example of the present invention, refer to Figure 2 As shown, in this example, step S5 includes:

[0134] Step S51: constructing a remote maintenance detection model according to the device state attribute data and the system abnormal event data to obtain a remote maintenance detection pre-model, and training the remote maintenance detection pre-model using the device state attribute data and the system abnormal event data to obtain a remote maintenance detection training model;

[0135] Step S52: performing model cross-validation evaluation on the remote maintenance detection training model to obtain remote maintenance detection model evaluation data; adjusting model parameters of the remote maintenance detection training model using the remote maintenance detection model evaluation data to obtain a remote maintenance detection model;

[0136] Step S53: performing equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; performing fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set;

[0137] Step S54: performing system abnormality detection on the system abnormality event data based on the remote maintenance detection model to obtain system abnormality detection data; performing repair operation feedback on the system abnormality event data through the system abnormality detection data to generate system repair operation feedback data.

[0138] In the embodiment of the present invention, supervised learning algorithm and regression algorithm are used to construct a remote maintenance detection model according to the equipment state attribute data and the system abnormal event data, and a remote maintenance detection pre-model is obtained, wherein the remote maintenance detection pre-model selects a support vector machine (SVM) classifier, sets the kernel function to the radial basis function (RBF), sets the initial parameter C (penalty parameter) to 1.0, and sets γ (kernel function coefficient) to 0.01; and the remote maintenance detection pre-model is trained using the equipment state attribute data and the system abnormal event data. During the training process, the mean square error (MSE) is used as the loss function, and the model weight is adjusted by the stochastic gradient descent (SGD) optimization algorithm to obtain the remote maintenance detection training model. The remote maintenance detection training model is subjected to model cross-validation evaluation, and 5-fold cross-validation is used to evaluate the stability and accuracy of the model. In each iteration, adjust the model parameters, record the accuracy, precision, recall and F1 score, and obtain the remote maintenance detection model evaluation data; adjust the model parameters of the remote maintenance detection training model through the remote maintenance detection model evaluation data. If the model is found to be underfitting, increase the parameter C to 10.0; if it is overfitting, reduce the value of C or increase the value of γ to obtain the remote maintenance detection model. Based on the remote maintenance detection model, perform equipment fault detection on the equipment status attribute data to obtain equipment fault detection data; use the equipment fault detection data to annotate the fault component area of ​​the hardware structure three-dimensional enhanced image to generate an equipment fault service feedback image set. Based on the remote maintenance detection model, perform system anomaly detection on the system abnormal event data to obtain system anomaly detection data; use the system anomaly detection data to provide repair operation feedback on the system abnormal event data to guide repair operations, such as updates, patch applications and configuration adjustments, and generate system repair operation feedback data.

[0139] As an example of the present invention, refer to Figure 3 As shown, in this example, step S53 includes:

[0140] Step S531: Performing device operating temperature fault detection on the device status attribute data based on the remote maintenance detection model, if the device operating temperature is higher than the preset device operating temperature, generating device operating temperature fault data, wherein the device operating temperature fault data includes central processing unit temperature fault data and mainboard temperature fault data;

[0141] Step S532: performing fault service feedback on the hardware structure 3D enhanced image through the device working temperature fault data. If the device working temperature fault data is the CPU temperature fault data, then performing CPU component image identification on the hardware structure 3D enhanced image to obtain the CPU temperature fault service image; if the device working temperature fault data is the motherboard temperature fault data, then performing motherboard component image identification on the hardware structure 3D enhanced image to obtain the motherboard temperature fault service image; integrating the CPU temperature fault service image and the motherboard temperature fault service image to obtain the device working temperature fault service image set;

[0142] Step S533: performing equipment electrical fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment electrical fault detection data, wherein the equipment electrical fault detection data includes equipment voltage fluctuation fault data, interface signal interference fault data and interface current pulse fault data;

[0143] Step S534: performing fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment electrical fault detection data. If the equipment electrical fault detection data is equipment voltage fluctuation fault data, the voltage stabilizing device image is marked on the three-dimensional enhanced image of the hardware structure to generate an equipment voltage fault service image; if the equipment electrical fault detection data is interface signal interference fault data, the electromagnetic filter image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface signal interference fault service image; if the equipment electrical fault detection data is interface current pulse fault data, the power supply image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface current pulse fault service image; the equipment voltage fault maintenance image, the interface signal interference fault service image and the interface current pulse fault service image are integrated to obtain an equipment electrical fault service image set;

[0144] Step S535: Integrate the equipment operating temperature fault service image set and the equipment electrical fault service image set to obtain an equipment fault service feedback image set.

[0145] In the embodiment of the present invention, the remote maintenance detection model is used to detect the device operating temperature fault of the device state attribute data, generate the device operating temperature fault data, and clearly distinguish the CPU temperature fault and the mainboard temperature fault. Based on the device operating temperature fault data, the hardware structure three-dimensional enhanced image is given fault service feedback. When the CPU temperature fault is detected, the CPU component is correspondingly identified on the hardware structure three-dimensional enhanced image to generate the CPU temperature fault service image. Similarly, if the mainboard temperature fault is detected, the mainboard component is identified on the hardware structure three-dimensional enhanced image to generate the mainboard temperature fault service image. Finally, these two types of fault service images are integrated together to form a device operating temperature fault service image set. The remote maintenance detection model is also applied to analyze the electrical fault of the device. The model will detect the electrical problems of voltage fluctuation, interface signal interference and interface current pulse, and record the detection results as the device electrical fault detection data; based on the device electrical fault detection data, the hardware structure three-dimensional enhanced image is given fault service feedback. For the voltage fluctuation fault, the voltage stabilizing device is identified on the hardware structure three-dimensional enhanced image to generate the device voltage fault service image. For the interface signal interference fault, the electromagnetic filter is identified on the hardware structure three-dimensional enhanced image to generate the interface signal interference fault service image. For interface current pulse faults, the power supply is marked on the 3D enhanced image of the hardware structure to generate an interface current pulse fault service image. These three types of electrical fault service images are then integrated into a set of equipment electrical fault service images.

[0146] Preferably, step S54 includes the following steps:

[0147] Step S541: performing system configuration anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system configuration anomaly detection data, wherein the system configuration anomaly detection data includes system version anomaly data and system application anomaly data;

[0148] Step S542: Performing repair operation feedback on the system configuration abnormality data through the system abnormality detection data. If the system configuration fault detection data is system version abnormality data, then updating the system patch to generate version abnormality repair data; if the system configuration fault detection data is system application abnormality data, then uninstalling and reinstalling the application to generate application abnormality repair data; integrating the version abnormality repair data and the application abnormality repair data to obtain the system configuration abnormality repair data;

[0149] Step S543: Performing system network security abnormal event detection on the system abnormal event data based on the remote maintenance detection model to obtain network security abnormal event detection data, wherein the network security abnormal event detection data includes abnormal traffic event data, unauthorized access abnormal data and resource high occupancy abnormal data;

[0150] Step S544: Performing repair operation feedback on the system network security event data through the network security abnormal event detection data. If the network security abnormal event detection data is abnormal traffic event data, the IP address and port of the abnormal traffic are blocked, and the information of the abnormal traffic is updated into the firewall rules to generate abnormal traffic event repair data; if the network security abnormal event detection data is unauthorized access abnormal data, the unauthorized access is isolated and processed to generate unauthorized access abnormal repair data; if the network security abnormal event detection data is high resource occupancy abnormal data, the abnormal resource high occupancy service process is closed to generate high resource occupancy abnormal repair data; the abnormal traffic event repair data, the unauthorized access abnormal repair data and the high resource occupancy abnormal repair data are integrated to obtain the network security abnormal event repair data;

[0151] Step S545: Integrate the system configuration anomaly repair data and the network security anomaly event repair data to obtain system repair operation feedback data.

[0152] In an embodiment of the present invention, the system abnormal event data is analyzed by a remote maintenance detection model to identify abnormalities in the system configuration and generate system configuration abnormality detection data. Specific repair operations are performed according to the system configuration abnormality detection data. If the system version is abnormal, the latest system patch will be installed through an automated script or a manual update process to generate version abnormality repair data. If the application is abnormal, the operation of uninstalling and reinstalling the application will be performed to generate application abnormality repair data. The version abnormality repair data and the application abnormality repair data are then integrated into the system configuration abnormality repair data. The network security abnormal event data is analyzed by a remote maintenance detection model to identify network security abnormal events and generate network security abnormal event detection data. Repair operations are performed on the detected network security issues according to the network security abnormal event detection data. For abnormal traffic events, the IP address and port of the abnormal traffic are blocked, and future abnormal traffic is blocked by updating firewall rules to generate abnormal traffic event repair data. For unauthorized access anomalies, the affected system parts will be identified and isolated to generate unauthorized access anomaly repair data. For high resource usage anomalies, the service processes that cause resource consumption abnormalities will be identified and closed to generate high resource usage anomaly repair data. The abnormal traffic event repair data, unauthorized access abnormality repair data and high resource usage abnormality repair data are then integrated into network security abnormality event repair data.

[0153] Preferably, step S6 comprises the following steps:

[0154] Step S61: Performing real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data to obtain real-time monitoring data and real-time maintenance service feedback data;

[0155] Step S62: Integrate the real-time monitoring data and the real-time maintenance service feedback data to obtain a real-time monitoring maintenance service feedback report;

[0156] Step S63: A cloud platform is constructed based on the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform, wherein the remote maintenance service evaluation cloud platform construction includes the equipment real-time monitoring maintenance service evaluation interface construction and the system real-time monitoring maintenance service evaluation interface construction to perform electronic computer remote maintenance service evaluation operations.

[0157] In the embodiment of the present invention, the electronic computer is monitored and maintained in real time based on the equipment fault service feedback image set and the system repair operation feedback data through the automated real-time monitoring technology, thereby generating a real-time monitoring and maintenance service feedback report. The real-time monitoring data and the real-time maintenance service feedback data are integrated by using the data fusion technology to obtain the real-time monitoring and maintenance service feedback report. The cloud computing and virtualization technologies are used to construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; wherein the remote maintenance service evaluation cloud platform construction includes the construction of the equipment real-time monitoring and maintenance service evaluation interface and the construction of the system real-time monitoring and maintenance service evaluation interface. The equipment real-time monitoring and maintenance service evaluation interface provides a real-time status view of the equipment, including real-time data and fault feedback images of the hardware component status and hardware electronic parameter attributes; the system real-time monitoring and maintenance service evaluation interface provides a real-time view of the system status, including system configuration, application status, network security events and executed repair measures. The electronic computer remote maintenance service evaluation operation is performed based on the remote maintenance service evaluation cloud platform.

[0158] In this specification, a cloud platform-based electronic computer remote maintenance service evaluation system is provided, which is used to execute the above-mentioned cloud platform-based electronic computer remote maintenance service evaluation method. The cloud platform-based electronic computer remote maintenance service evaluation system includes:

[0159] A data acquisition module is used to acquire a data set of electronic computer equipment hardware using a camera and a sensor; and to acquire a data set of electronic computer system software using a sensor;

[0160] The hardware structure three-dimensional image module is used to identify the structural information of the electronic computer equipment hardware data set to obtain the hardware structure data; perform three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; perform image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure;

[0161] The device status attribute module is used to extract the status information of the electronic computer hardware data set to obtain the hardware status data; perform device operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate device operating temperature data; extract the electrical characteristic information of the electronic computer device hardware data set to obtain hardware electrical characteristic data; perform device electrical attribute parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical attribute data; integrate the device operating temperature data and the device electrical attribute data to obtain device status attribute data;

[0162] The system abnormal event module is used to extract the system configuration information of the electronic computer system software data set to obtain the system configuration data; perform system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extract the log file of the electronic computer system software data set to obtain the log file data; perform system network security analysis on the log file data to obtain system network security event data; integrate the system configuration abnormality data and the system network security event data to obtain the system abnormality event data;

[0163] The remote maintenance detection model module is used to construct a remote maintenance detection model based on the equipment status attribute data and the system abnormal event data to obtain the remote maintenance detection model; perform equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment fault detection data to generate an equipment fault service feedback image set; perform system anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system anomaly detection data; perform repair operation feedback on the system abnormal event data through the system anomaly detection data to generate system repair operation feedback data;

[0164] The cloud platform module is used to perform real-time monitoring and maintenance service feedback on electronic computers through equipment failure service feedback image sets and system repair operation feedback data, and generate real-time monitoring and maintenance service feedback reports; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute electronic computer remote maintenance service evaluation operations based on the remote maintenance service evaluation cloud platform.

[0165] The data acquisition module of the present invention is used to realize the comprehensive collection of electronic computer hardware and software data sets through the use of cameras and sensors, and provides rich original information for subsequent analysis and evaluation. The device state attribute module is used to perform structural information recognition on the electronic computer device hardware data set through structural information recognition to obtain hardware structure data; perform three-dimensional mapping on the hardware structure data through three-dimensional mapping technology to generate a three-dimensional hardware structure diagram; perform image enhancement on the three-dimensional hardware structure diagram through image enhancement technology, construct a three-dimensional enhanced image of the hardware structure, and generate a three-dimensional hardware structure diagram, so that the hardware structure is more intuitive and easy to understand. According to the hardware state data, the three-dimensional enhanced image of the hardware structure is analyzed for the device working temperature to intuitively display the working temperature state of the device, thereby generating the device working temperature data; the electrical feature information of the electronic computer device hardware data set is extracted by using the electrical feature processing technology to obtain the hardware electrical feature data; according to the hardware electrical feature data, the three-dimensional enhanced image of the hardware structure is analyzed for the device electrical property parameters to intuitively display the electrical properties of the device, thereby generating the device electrical property data; the device working temperature data and the device electrical property data are integrated to obtain comprehensive device state attribute data. The system abnormal event module is used to perform system abnormality analysis on system configuration data. It can effectively identify abnormal data in system configuration and promptly discover potential configuration errors or inconsistencies, thereby obtaining system configuration abnormality data; use semantic analysis technology to extract log files of electronic computer system software data sets to obtain log file data; perform system network security analysis on log file data, focus on discovering and analyzing network security events, and record network security events to obtain system network security event data; integrate system configuration abnormality data and system network security event data to obtain system abnormal event data, which can provide comprehensive system abnormal event data and provide a more comprehensive perspective for system maintenance services. The remote maintenance detection model module is used to construct a remote maintenance detection model based on the equipment status attribute data and the system abnormal event data to obtain a remote maintenance detection model, which can accurately analyze the equipment status attribute data and the system abnormal event data; based on the remote maintenance detection model, the equipment status attribute data is used for equipment fault detection, which can quickly detect the equipment fault and obtain equipment fault detection data; through the equipment fault detection data, the three-dimensional enhanced image of the hardware structure is used for fault service feedback, and a set of equipment fault service feedback images is generated, which can intuitively display the fault location and status, so that users can quickly locate the problem; based on the remote maintenance detection model, the system abnormal event data is used for system abnormality detection, and the system abnormality detection data is obtained, which can ensure that the problems at the system level can be discovered in time; the system abnormal event data is used for repair operation feedback through the system abnormality detection data, and the electronic computer is automatically repaired, which improves the efficiency of maintenance work, thereby generating system repair operation feedback data.The cloud platform module is used to perform real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, generate a real-time monitoring and maintenance service feedback report, provide users with instant maintenance service information, and improve the response speed and quality of the service; according to the remote maintenance detection model and the real-time monitoring and maintenance service feedback report, the cloud platform is constructed to obtain a remote maintenance service evaluation cloud platform, which realizes the systematic management and evaluation of the remote maintenance service of the electronic computer; based on the remote maintenance service evaluation cloud platform, the electronic computer remote maintenance service evaluation operation is performed, which significantly improves the efficiency of the remote maintenance service and reduces the need for human intervention and on-site maintenance. Therefore, the present invention improves the analysis ability of the hardware status of the remote maintenance service evaluation through the Internet of Things technology, three-dimensional mapping technology, anomaly detection technology and cloud platform technology, and can intuitively display the hardware structure; and realizes the real-time monitoring and maintenance service feedback of the electronic computer, which can effectively perform remote maintenance service evaluation.

[0166] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0167] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for evaluating electronic computer remote maintenance services based on a cloud platform, characterized in that: The following steps are involved: Step S1: using a camera and a sensor to obtain a computer device hardware data set; using a sensor to obtain a computer system software data set; Step S2: performing structural information recognition on the electronic computer device hardware data set to obtain hardware structure data; performing three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; performing image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure; Step S3: extracting the status information of the electronic computer hardware data set to obtain hardware status data; Perform equipment operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate equipment operating temperature data; extract electrical characteristic information of the electronic computer equipment hardware data set to obtain hardware electrical characteristic data; Perform equipment electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate equipment electrical property data; integrate equipment operating temperature data and equipment electrical property data to obtain equipment status property data; Step S4: extracting system configuration information of the electronic computer system software data set to obtain system configuration data; performing system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extracting log files of the electronic computer system software data set to obtain log file data; performing system network security analysis on the log file data to obtain system network security event data; integrating the system configuration abnormality data and the system network security event data to obtain system abnormality event data; Step S5: constructing a remote maintenance detection model according to the device state attribute data and the system abnormal event data to obtain a remote maintenance detection model; performing device fault detection on the device state attribute data based on the remote maintenance detection model to obtain device fault detection data; Perform fault service feedback on the hardware structure three-dimensional enhanced image through equipment fault detection data to generate an equipment fault service feedback image set; Perform system anomaly detection on system abnormal event data based on the remote maintenance detection model to obtain system anomaly detection data; Performing repair operation feedback on system abnormal event data through system abnormality detection data to generate system repair operation feedback data; Step S6: Conduct real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data, and generate a real-time monitoring and maintenance service feedback report; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute the electronic computer remote maintenance service evaluation task based on the remote maintenance service evaluation cloud platform.

2. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing state feature recognition on the electronic computer hardware data set to generate hardware state feature information; extracting state data of the hardware state feature information to obtain hardware state data; Step S32: monitoring the device operating temperature of the hardware structure three-dimensional enhanced image according to the hardware status data to generate device operating temperature data, wherein the device operating temperature data includes the temperature of the central processing unit and the temperature of the mainboard; Step S33: performing electrical characteristic recognition on the electronic computer hardware data set to generate electrical characteristic information; extracting characteristic data of the electrical characteristic information to obtain hardware electrical characteristic data; Step S34: performing device electrical property parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical property data; Step S35: Integrate the device operating temperature data and the device electrical property data to obtain device status property data.

3. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 2 is characterized in that: Step S34 includes the following steps: Step S341: performing device interface recognition on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device interface data; performing interface signal strength measurement on the device interface data to obtain device interface signal strength data; Step S342: performing device voltage parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device voltage attribute data; continuously recording voltage fluctuations of the device voltage attribute data to obtain device voltage fluctuation data; Step S343: performing signal interference frequency feedback on the device interface signal strength data according to the device voltage fluctuation data to obtain interface signal interference data; Step S344: performing device current overload analysis on the hardware electrical characteristic data according to the interface signal interference data to generate device current overload data; Step S345: performing interface current pulse monitoring on the device interface data through the device current overload data to obtain interface current pulse data; Step S346: Integrate the device voltage fluctuation data, the interface signal interference data and the interface current pulse data to obtain the device electrical property data.

4. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing system feature recognition on the electronic computer system software data set to generate system feature information; extracting configuration data of the system feature information to obtain system configuration data; Step S42: recording the system version of the system configuration data to obtain the system version data; performing system version update anomaly analysis on the system configuration data according to the system version data to obtain system version anomaly data; Step S43: performing application configuration monitoring on the system configuration data to obtain system application data; performing application abnormality association on the system application data according to the system version abnormality data to obtain system version-program abnormality association data; performing crash type classification on the system version-program abnormality association data to obtain application crash type data; performing abnormal crash data mapping on the system version-program abnormality association data through the application crash type data to obtain system application abnormality data; Step S44: Integrate the system version exception data and the system application exception data to obtain the system configuration exception data; Step S45: performing log feature recognition on the electronic computer system software data set to generate log feature information; extracting file data of the log feature information to obtain log file data; Step S46: Conduct system network security analysis on the log file data to obtain system network security event data; Step S47: Integrate the system configuration abnormality data and the system network security event data to obtain the system abnormal event data.

5. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 4 is characterized in that: Step S46 includes the following steps: Step S461: Perform system network feature recognition on the log file data to generate system network feature information; extract flow data of the system network feature information to obtain network flow data; perform abnormal flow pattern analysis on the network flow data to obtain abnormal flow event data; perform network access source and frequency recognition on the abnormal flow event data to obtain abnormal flow source frequency data; Step S462: performing system access feature recognition on the log file data to generate system access feature information; extracting access permission data of the system access feature information to obtain access permission data; performing access permission rule matching on the access permission data according to the abnormal traffic source frequency data to obtain abnormal traffic access permission data; performing unauthorized access event analysis on the abnormal traffic access permission data to obtain unauthorized access event data; Step S463: performing system resource feature recognition on the log file data to generate system resource feature information; extracting resource usage data from the system resource feature information to obtain resource usage data; performing high occupancy statistics on the resource usage data to obtain resource high occupancy event data; Step S464: Integrate abnormal traffic event data, unauthorized access event data and high resource usage event data to obtain system network security event data.

6. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: constructing a remote maintenance detection model according to the device state attribute data and the system abnormal event data to obtain a remote maintenance detection pre-model, and training the remote maintenance detection pre-model using the device state attribute data and the system abnormal event data to obtain a remote maintenance detection training model; Step S52: performing model cross-validation evaluation on the remote maintenance detection training model to obtain remote maintenance detection model evaluation data; adjusting model parameters of the remote maintenance detection training model using the remote maintenance detection model evaluation data to obtain a remote maintenance detection model; Step S53: performing equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; performing fault service feedback on the hardware structure three-dimensional enhanced image through the equipment fault detection data to generate an equipment fault service feedback image set; Step S54: performing system abnormality detection on the system abnormality event data based on the remote maintenance detection model to obtain system abnormality detection data; performing repair operation feedback on the system abnormality event data through the system abnormality detection data to generate system repair operation feedback data.

7. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 6, characterized in that: Step S53 includes the following steps: Step S531: Performing device operating temperature fault detection on the device status attribute data based on the remote maintenance detection model, if the device operating temperature is higher than the preset device operating temperature, generating device operating temperature fault data, wherein the device operating temperature fault data includes central processing unit temperature fault data and mainboard temperature fault data; Step S532: performing fault service feedback on the hardware structure 3D enhanced image through the device working temperature fault data. If the device working temperature fault data is the CPU temperature fault data, then performing CPU component image identification on the hardware structure 3D enhanced image to obtain the CPU temperature fault service image; if the device working temperature fault data is the motherboard temperature fault data, then performing motherboard component image identification on the hardware structure 3D enhanced image to obtain the motherboard temperature fault service image; integrating the CPU temperature fault service image and the motherboard temperature fault service image to obtain the device working temperature fault service image set; Step S533: performing equipment electrical fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment electrical fault detection data, wherein the equipment electrical fault detection data includes equipment voltage fluctuation fault data, interface signal interference fault data and interface current pulse fault data; Step S534: performing fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment electrical fault detection data. If the equipment electrical fault detection data is equipment voltage fluctuation fault data, the voltage stabilizing device image is marked on the three-dimensional enhanced image of the hardware structure to generate an equipment voltage fault service image; if the equipment electrical fault detection data is interface signal interference fault data, the electromagnetic filter image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface signal interference fault service image; if the equipment electrical fault detection data is interface current pulse fault data, the power supply image is marked on the three-dimensional enhanced image of the hardware structure to generate an interface current pulse fault service image; the equipment voltage fault maintenance image, the interface signal interference fault service image and the interface current pulse fault service image are integrated to obtain an equipment electrical fault service image set; Step S535: Integrate the equipment operating temperature fault service image set and the equipment electrical fault service image set to obtain an equipment fault service feedback image set.

8. The cloud platform-based electronic computer remote maintenance service evaluation method according to claim 6, characterized in that: Step S54 includes the following steps: Step S541: performing system configuration anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system configuration anomaly detection data, wherein the system configuration anomaly detection data includes system version anomaly data and system application anomaly data; Step S542: Performing repair operation feedback on the system configuration abnormality data through the system abnormality detection data. If the system configuration fault detection data is system version abnormality data, then updating the system patch to generate version abnormality repair data; if the system configuration fault detection data is system application abnormality data, then uninstalling and reinstalling the application to generate application abnormality repair data; integrating the version abnormality repair data and the application abnormality repair data to obtain the system configuration abnormality repair data; Step S543: Performing system network security abnormal event detection on the system abnormal event data based on the remote maintenance detection model to obtain network security abnormal event detection data, wherein the network security abnormal event detection data includes abnormal traffic event data, unauthorized access abnormal data and high resource occupancy abnormal data; Step S544: Performing repair operation feedback on the system network security event data through the network security abnormal event detection data. If the network security abnormal event detection data is abnormal traffic event data, the IP address and port of the abnormal traffic are blocked, and the information of the abnormal traffic is updated into the firewall rules to generate abnormal traffic event repair data; if the network security abnormal event detection data is unauthorized access abnormal data, the unauthorized access is isolated and processed to generate unauthorized access abnormal repair data; if the network security abnormal event detection data is high resource occupancy abnormal data, the abnormal resource high occupancy service process is closed to generate high resource occupancy abnormal repair data; the abnormal traffic event repair data, the unauthorized access abnormal repair data and the high resource occupancy abnormal repair data are integrated to obtain the network security abnormal event repair data; Step S545: Integrate the system configuration anomaly repair data and the network security anomaly event repair data to obtain system repair operation feedback data.

9. The method for evaluating a remote computer maintenance service based on a cloud platform according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Performing real-time monitoring and maintenance service feedback on the electronic computer through the equipment failure service feedback image set and the system repair operation feedback data to obtain real-time monitoring data and real-time maintenance service feedback data; Step S62: Integrate the real-time monitoring data and the real-time maintenance service feedback data to obtain a real-time monitoring maintenance service feedback report; Step S63: A cloud platform is constructed based on the remote maintenance detection model and the real-time monitoring maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform, wherein the remote maintenance service evaluation cloud platform construction includes the equipment real-time monitoring maintenance service evaluation interface construction and the system real-time monitoring maintenance service evaluation interface construction to perform electronic computer remote maintenance service evaluation operations.

10. A cloud platform-based electronic computer remote maintenance service evaluation system, characterized in that: For executing the electronic computer remote maintenance service evaluation method based on a cloud platform as claimed in claim 1, the electronic computer remote maintenance service evaluation system based on a cloud platform comprises: The data acquisition module is used to acquire the electronic computer equipment hardware data set using the camera and the sensor; and to acquire the electronic computer system software data set using the sensor; The hardware structure three-dimensional image module is used to identify the structural information of the electronic computer equipment hardware data set to obtain the hardware structure data; perform three-dimensional mapping on the hardware structure data to generate a three-dimensional hardware structure diagram; perform image enhancement on the three-dimensional hardware structure diagram to construct a three-dimensional enhanced image of the hardware structure; The device status attribute module is used to extract the status information of the electronic computer hardware data set to obtain the hardware status data; perform device operating temperature analysis on the three-dimensional enhanced image of the hardware structure according to the hardware status data to generate device operating temperature data; extract the electrical characteristic information of the electronic computer device hardware data set to obtain hardware electrical characteristic data; perform device electrical attribute parameter analysis on the three-dimensional enhanced image of the hardware structure according to the hardware electrical characteristic data to generate device electrical attribute data; integrate the device operating temperature data and the device electrical attribute data to obtain device status attribute data; The system abnormal event module is used to extract the system configuration information of the electronic computer system software data set to obtain the system configuration data; perform system abnormality analysis on the system configuration data to obtain system configuration abnormality data; extract the log file of the electronic computer system software data set to obtain the log file data; perform system network security analysis on the log file data to obtain system network security event data; integrate the system configuration abnormality data and the system network security event data to obtain the system abnormality event data; The remote maintenance detection model module is used to construct a remote maintenance detection model based on the equipment status attribute data and the system abnormal event data to obtain the remote maintenance detection model; perform equipment fault detection on the equipment status attribute data based on the remote maintenance detection model to obtain equipment fault detection data; perform fault service feedback on the three-dimensional enhanced image of the hardware structure through the equipment fault detection data to generate an equipment fault service feedback image set; perform system anomaly detection on the system abnormal event data based on the remote maintenance detection model to obtain system anomaly detection data; perform repair operation feedback on the system abnormal event data through the system anomaly detection data to generate system repair operation feedback data; The cloud platform module is used to perform real-time monitoring and maintenance service feedback on electronic computers through equipment failure service feedback image sets and system repair operation feedback data, and generate real-time monitoring and maintenance service feedback reports; construct a cloud platform based on the remote maintenance detection model and the real-time monitoring and maintenance service feedback report to obtain a remote maintenance service evaluation cloud platform; and execute electronic computer remote maintenance service evaluation operations based on the remote maintenance service evaluation cloud platform.

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