Intelligent fully-mechanized coal mining electromechanical equipment cooperative control system

Abstract

The invention provides an intelligent fully-mechanized coal mining electromechanical equipment cooperative control system, and relates to the field of coal mining. The intelligent fully-mechanized coal mine electromechanical equipment cooperative control system comprises a data acquisition module which is used for acquiring operation state data of all electromechanical equipment in a fully-mechanized coal mine in real time; the data processing and analyzing module is used for processing and analyzing the collected data in real time and identifying the running state and potential problems of the equipment; the cooperative control module is used for intelligently adjusting operation parameters of the electromechanical equipment according to an output result of the data processing and analyzing module, so that cooperative work among the equipment is realized; and the safety monitoring module monitors the running state of the equipment in real time, and immediately starts safety protection measures once an abnormal condition is found. The system can detect abnormal conditions in operation of the equipment in real time and give an alarm in time, so that the possibility of accidents is reduced, and when security threats are detected, the system can automatically execute preset emergency response measures.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining, and in particular to an intelligent fully mechanized coal mining electromechanical equipment collaborative control system. Background Art

[0002] The coal mine comprehensive automation system is a system developed in my country that integrates mine monitoring, alarm, and production operations. The system consists of three layers: application layer, network layer, and field equipment layer. The application layer uses a unified data platform and login interface, and users can visit each subsystem. The network layer backbone network uses a Gigabit redundant industrial Ethernet ring network to ensure high bandwidth and reliability of network communication, and can access various systems such as industrial control, video, audio, and communication. The field uses a 100M industrial Ethernet ring network and a variety of mine-use intrinsically safe field industrial buses to ensure that users can access various monitoring systems and equipment; the field equipment layer is composed of various bus communication substations, wireless communication substations, monitoring devices, intelligent instruments, and sensors.

[0003] With the continuous growth of global energy demand and the increasing depletion of mineral resources, the coal mining industry is facing the dual challenges of improving production efficiency and ensuring safe production. Traditional coal mining methods often rely on manual operation and experience judgment, which is not only inefficient but also has high safety risks. With the development of automation and intelligent technology, the coal mining industry is gradually transforming towards intelligence in order to solve the above problems through technical means.

[0004] In the coal mining process, comprehensive mining equipment is the core production tool, and its operating status directly affects the mining efficiency and safety. Traditional comprehensive mining equipment control systems usually lack an effective coordination mechanism. Each device operates independently, making it difficult to achieve efficient collaborative operations. In addition, the collection and analysis of equipment operation data mostly rely on manual work, which is not only time-consuming and labor-intensive, but also prone to human errors, leading to equipment failures and safety hazards. Therefore, technical personnel in this field have provided an intelligent comprehensive mining coal mine electromechanical equipment collaborative control system to solve the problems raised in the above background technology. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent coordinated control system for electromechanical equipment in fully mechanized coal mines, which solves the problem that data collection and analysis during operation of existing equipment mostly rely on manual labor, which is time-consuming, labor-intensive and prone to human errors.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent fully mechanized coal mining electromechanical equipment collaborative control system, comprising:

[0009] The data acquisition module is used to collect the operating status data of each electromechanical equipment in the fully-mechanized coal mine in real time, including but not limited to the equipment location, operating speed, load condition and energy consumption data;

[0010] Data processing and analysis module, which processes and analyzes the collected data in real time to identify the operating status and potential problems of the equipment;

[0011] The collaborative control module intelligently adjusts the operating parameters of each electromechanical device based on the output results of the data processing and analysis module to achieve collaborative work between devices;

[0012] The safety monitoring module monitors the operating status of the equipment in real time and immediately initiates safety protection measures once an abnormal situation is found;

[0013] User interface provides an intuitive user operation interface, allowing operators to monitor equipment status in real time and adjust control strategies.

[0014] Preferably, the data acquisition module comprises the following steps:

[0015] S1. Sensor deployment: sensors are deployed at various key locations in fully mechanized coal mines, including but not limited to position sensors, speed sensors, force sensors, current sensors, electric energy meters, temperature sensors, humidity sensors and gas sensors;

[0016] S2. Data acquisition: sensors monitor the operating status and environmental parameters of the equipment in real time and convert the data into electrical signals;

[0017] S3. Signal transmission, the collected electrical signals are transmitted to a central processing unit or a data acquisition system by wire or wireless means;

[0018] S4. Data preprocessing: preliminary processing of the received data, including denoising, filtering and amplification, to improve the accuracy and reliability of the data;

[0019] S5. Data storage, storing the preprocessed data in a database for subsequent data processing and analysis;

[0020] S6. Data synchronization, ensuring the time synchronization of all data for accurate data analysis and collaborative control.

[0021] Preferably, the data processing and analysis module comprises the following steps:

[0022] S1. Data cleaning, remove noise, outliers and duplicate data, ensure data accuracy and completeness, use filtering technology to smooth data, reduce the impact of random errors, and use Z-score to remove outliers. The formula is:

[0023]

[0024] Where X is the data point, μ is the mean, and σ is the standard deviation;

[0025] S2. Feature extraction, extracting useful features from raw data, using time series analysis methods to identify patterns and trends in the data, where:

[0026] Mean calculation:

[0027]

[0028] Variance calculation:

[0029]

[0030] S3. Pattern recognition, using support vector machine to identify the operation mode of the equipment, using cluster analysis to find similarities and differences in the data, the decision formula of the support vector machine is:

[0031]

[0032] Among them, x i is the training sample, y i is the label, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, b is the bias term;

[0033] S4. Predictive analysis uses linear regression to predict the future state of the equipment and applies the time series prediction model to predict the trend and periodic changes of equipment operation. The linear regression prediction formula is:

[0034]

[0035] in, is the predicted value, β i is the regression coefficient, x i is the eigenvalue;

[0036] S5. Anomaly detection uses statistical methods and machine learning models to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected. The anomaly scoring formula of the isolation forest is used:

[0037]

[0038] Where h(x) is the path length of sample x in the tree, E(h(x)) is the expected path length, and c(n) is the normalization factor of the path length;

[0039] S6. Decision support, generating decision recommendations based on the analysis results, including adjusting equipment parameters, optimizing operation strategies, and using decision trees or rule engines to develop collaborative control strategies.

[0040] Preferably, the collaborative control module comprises the following steps:

[0041] S1. Receive analysis results, receive device status, prediction results and anomaly detection results from the data processing and analysis module;

[0042] S2. Strategy formulation: formulate corresponding collaborative control strategies based on the received analysis results. The strategies may include adjusting equipment operating parameters, optimizing equipment operation sequence, and redistributing loads.

[0043] S3. Control instruction generation: Generate specific control instructions according to the formulated strategy. The control instructions may include start, stop, accelerate, decelerate and change direction;

[0044] S4. Instruction execution: sending control instructions to corresponding devices for execution to ensure the accuracy and timeliness of instruction execution;

[0045] S5. Feedback monitoring, monitoring the state changes of the equipment after executing the control instructions, collecting feedback on the execution results, and using it to evaluate the control effect and adjust the strategy;

[0046] S6. Strategy adjustment: adjust the collaborative control strategy in real time based on the feedback results, continuously optimize the control effect, and ensure stable operation of the system.

[0047] Preferably, the security monitoring module comprises the following steps:

[0048] S1. Data collection: real-time collection of safety-related data from various sensors and devices, including but not limited to temperature, humidity, gas concentration, equipment vibration, equipment current and equipment voltage;

[0049] S2. Data preprocessing: preliminary processing of the collected data, including denoising, filtering, and normalization, to improve the accuracy and reliability of the data;

[0050] S3. Anomaly detection, using Z-score to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected;

[0051] S4. Alarm triggering: when an abnormal situation is detected, the alarm system is immediately triggered to notify the operator. The alarm information includes the abnormality type, the event, and the location;

[0052] S5. Emergency response: Automatically execute corresponding safety measures according to the preset emergency response strategy, which may include equipment shutdown, emergency ventilation and power cut-off;

[0053] S6. Log records, recording all security incidents and corresponding measures for subsequent analysis and audit;

[0054] S7. System maintenance: regularly maintain and update the security monitoring module to ensure its continued effectiveness.

[0055] Preferably, the user interface comprises the following steps:

[0056] S1. Interface design: design a user-friendly interface layout, including the instrument panel, control panel and alarm information display area, and use graphical elements to intuitively display the equipment status and control options;

[0057] S2. Data display: real-time display of equipment operation data and environmental parameters obtained from the data acquisition module, using charts and dashboards to display data trends and current values;

[0058] S3. Control function, providing control buttons and regulators, allowing operators to adjust the operating parameters of the equipment, and realize one-button start, stop, speed adjustment and direction change;

[0059] S4. Alarm processing: When the security monitoring module detects an abnormality, the user interface immediately displays an alarm message, which includes the type, severity and location of the abnormality, and provides corresponding processing suggestions;

[0060] S5. Log recording and query, record all operation logs and alarm events, and provide query function, allowing operators to view historical data and event records for subsequent analysis and audit;

[0061] S6. User authority management: restrict access to certain control functions and data according to the authority levels of different users, and realize user login, authority allocation and audit tracking functions;

[0062] S7. System maintenance and update, providing a system maintenance and update interface, allowing technicians to perform system configuration and software updates, and realize remote diagnosis and troubleshooting functions.

[0063] (III) Beneficial effects

[0064] The present invention provides an intelligent fully mechanized coal mining electromechanical equipment coordinated control system, which has the following beneficial effects:

[0065] 1. In the present invention, the system can monitor the operating status of the equipment in real time and intelligently adjust the equipment parameters according to the analysis results to ensure that the equipment always operates in the best state. Through the coordinated control between devices, the operation process can be optimized, the equipment idle time can be reduced, and the overall operation efficiency can be improved.

[0066] 2. In the present invention, the system can detect abnormal conditions in the operation of the equipment in real time and give an alarm in time to reduce the possibility of accidents. When a security threat is detected, the system can automatically execute preset emergency response measures.

[0067] 3. In the present invention, the system analyzes a large amount of collected data to provide support for decision-making, help optimize resource allocation and work planning, and through the analysis of equipment operation data, predict possible equipment failures, arrange maintenance in advance, and reduce unplanned downtime.

[0068] 4. In the present invention, by providing an intuitive user interface, operators can easily monitor the status of the equipment, adjust the control strategy, support remote monitoring and control, and operators can operate the equipment without having to be on site. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the overall flow of the system in the present invention;

[0070] Figure 2 It is a flow chart of the data acquisition module in the present invention;

[0071] Figure 3 It is a flow chart of the data processing and analysis module in the present invention;

[0072] Figure 4 It is a flowchart of the collaborative control module in the present invention;

[0073] Figure 5 It is a schematic diagram of the process of the safety monitoring module in the present invention;

[0074] Figure 6 It is a flowchart of the user interface in the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Embodiment 1:

[0077] like Figure 1-6 As shown, the embodiment of the present invention provides an intelligent fully mechanized coal mining electromechanical equipment collaborative control system, including:

[0078] The data acquisition module is used to collect the operating status data of each electromechanical equipment in the fully-mechanized coal mine in real time, including but not limited to the equipment location, operating speed, load condition and energy consumption data;

[0079] Data processing and analysis module, which processes and analyzes the collected data in real time to identify the operating status and potential problems of the equipment;

[0080] The collaborative control module intelligently adjusts the operating parameters of each electromechanical device based on the output results of the data processing and analysis module to achieve collaborative work between devices;

[0081] The safety monitoring module monitors the operating status of the equipment in real time and immediately initiates safety protection measures once an abnormal situation is found;

[0082] User interface provides an intuitive user operation interface, allowing operators to monitor equipment status in real time and adjust control strategies.

[0083] The data acquisition module includes the following steps:

[0084] S1. Sensor deployment: sensors are deployed at various key locations in fully mechanized coal mines, including but not limited to position sensors, speed sensors, force sensors, current sensors, electric energy meters, temperature sensors, humidity sensors and gas sensors;

[0085] S2. Data acquisition: sensors monitor the operating status and environmental parameters of the equipment in real time and convert the data into electrical signals;

[0086] S3. Signal transmission, the collected electrical signals are transmitted to a central processing unit or a data acquisition system by wire or wireless means;

[0087] S4. Data preprocessing: preliminary processing of the received data, including denoising, filtering and amplification, to improve the accuracy and reliability of the data;

[0088] S5. Data storage, storing the preprocessed data in a database for subsequent data processing and analysis;

[0089] S6. Data synchronization, ensuring the time synchronization of all data for accurate data analysis and collaborative control.

[0090] The data processing and analysis module includes the following steps:

[0091] S1. Data cleaning, remove noise, outliers and duplicate data, ensure data accuracy and completeness, use filtering technology to smooth data, reduce the impact of random errors, and use Z-score to remove outliers. The formula is:

[0092]

[0093] Where X is the data point, μ is the mean, and σ is the standard deviation;

[0094] S2. Feature extraction, extracting useful features from raw data, using time series analysis methods to identify patterns and trends in the data, where:

[0095] Mean calculation:

[0096]

[0097] Variance calculation:

[0098]

[0099] S3. Pattern recognition, using support vector machine to identify the operation mode of the equipment, using cluster analysis to find similarities and differences in the data, the decision formula of the support vector machine is:

[0100]

[0101] Among them, x i is the training sample, y i is the label, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, b is the bias term;

[0102] S4. Predictive analysis uses linear regression to predict the future state of the equipment and applies the time series prediction model to predict the trend and periodic changes of equipment operation. The linear regression prediction formula is:

[0103]

[0104] in, is the predicted value, β i is the regression coefficient, x i is the eigenvalue;

[0105] S5. Anomaly detection uses statistical methods and machine learning models to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected. The anomaly scoring formula of the isolation forest is used:

[0106]

[0107] Where h(x) is the path length of sample x in the tree, E(h(x)) is the expected path length, and c(n) is the normalization factor of the path length;

[0108] S6. Decision support, generating decision recommendations based on the analysis results, including adjusting equipment parameters, optimizing operation strategies, and using decision trees or rule engines to develop collaborative control strategies.

[0109] The collaborative control module includes the following steps:

[0110] S1. Receive analysis results, receive device status, prediction results and anomaly detection results from the data processing and analysis module;

[0111] S2. Strategy formulation: formulate corresponding collaborative control strategies based on the received analysis results. The strategies may include adjusting equipment operating parameters, optimizing equipment operation sequence, and redistributing loads.

[0112] S3. Control instruction generation: Generate specific control instructions according to the formulated strategy. The control instructions may include start, stop, accelerate, decelerate and change direction;

[0113] S4. Instruction execution: sending control instructions to corresponding devices for execution to ensure the accuracy and timeliness of instruction execution;

[0114] S5. Feedback monitoring, monitoring the state changes of the equipment after executing the control instructions, collecting feedback on the execution results, and using it to evaluate the control effect and adjust the strategy;

[0115] S6. Strategy adjustment: adjust the collaborative control strategy in real time based on the feedback results, continuously optimize the control effect, and ensure stable operation of the system.

[0116] The security monitoring module includes the following steps:

[0117] S1. Data collection: real-time collection of safety-related data from various sensors and devices, including but not limited to temperature, humidity, gas concentration, equipment vibration, equipment current and equipment voltage;

[0118] S2. Data preprocessing: preliminary processing of the collected data, including denoising, filtering, and normalization, to improve the accuracy and reliability of the data;

[0119] S3. Anomaly detection, using Z-score to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected;

[0120] S4. Alarm triggering: when an abnormal situation is detected, the alarm system is immediately triggered to notify the operator. The alarm information includes the abnormality type, the event, and the location;

[0121] S5. Emergency response: Automatically execute corresponding safety measures according to the preset emergency response strategy, which may include equipment shutdown, emergency ventilation and power cut-off;

[0122] S6. Log records, recording all security incidents and corresponding measures for subsequent analysis and audit;

[0123] S7. System maintenance: regularly maintain and update the security monitoring module to ensure its continued effectiveness.

[0124] The user interface consists of the following steps:

[0125] S1. Interface design: design a user-friendly interface layout, including the instrument panel, control panel and alarm information display area, and use graphical elements to intuitively display the equipment status and control options;

[0126] S2. Data display: real-time display of equipment operation data and environmental parameters obtained from the data acquisition module, using charts and dashboards to display data trends and current values;

[0127] S3. Control function, providing control buttons and regulators, allowing operators to adjust the operating parameters of the equipment, and realize one-button start, stop, speed adjustment and direction change;

[0128] S4. Alarm processing: When the security monitoring module detects an abnormality, the user interface immediately displays an alarm message, which includes the type, severity and location of the abnormality, and provides corresponding processing suggestions;

[0129] S5. Log recording and query, record all operation logs and alarm events, and provide query function, allowing operators to view historical data and event records for subsequent analysis and audit;

[0130] S6. User authority management: restrict access to certain control functions and data according to the authority levels of different users, and realize user login, authority allocation and audit tracking functions;

[0131] S7. System maintenance and update, providing a system maintenance and update interface, allowing technicians to perform system configuration and software updates, and realize remote diagnosis and troubleshooting functions.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent fully mechanized coal mining electromechanical equipment coordinated control system, characterized by: include: The data acquisition module is used to collect the operating status data of each electromechanical equipment in the fully-mechanized coal mine in real time, including but not limited to the equipment location, operating speed, load condition and energy consumption data; Data processing and analysis module, which processes and analyzes the collected data in real time to identify the operating status and potential problems of the equipment; The collaborative control module intelligently adjusts the operating parameters of each electromechanical device based on the output results of the data processing and analysis module to achieve collaborative work between devices; The safety monitoring module monitors the operating status of the equipment in real time and immediately initiates safety protection measures once an abnormal situation is found; User interface provides an intuitive user operation interface, allowing operators to monitor equipment status in real time and adjust control strategies.

2. The intelligent fully mechanized coal mining electromechanical equipment coordinated control system according to claim 1 is characterized in that: The data acquisition module comprises the following steps: S1. Sensor deployment: sensors are deployed at various key locations in fully mechanized coal mines, including but not limited to position sensors, speed sensors, force sensors, current sensors, electric energy meters, temperature sensors, humidity sensors and gas sensors; S2. Data acquisition: sensors monitor the operating status and environmental parameters of the equipment in real time and convert the data into electrical signals; S3. Signal transmission, the collected electrical signals are transmitted to a central processing unit or a data acquisition system by wire or wireless means; S4. Data preprocessing: preliminary processing of the received data, including denoising, filtering and amplification, to improve the accuracy and reliability of the data; S5. Data storage, storing the preprocessed data in a database for subsequent data processing and analysis; S6. Data synchronization, ensuring the time synchronization of all data for accurate data analysis and collaborative control.

3. The intelligent fully mechanized coal mining electromechanical equipment coordinated control system according to claim 1 is characterized in that: The data processing and analysis module includes the following steps: S1. Data cleaning, remove noise, outliers and duplicate data, ensure data accuracy and completeness, use filtering technology to smooth data, reduce the impact of random errors, and use Z-score to remove outliers. The formula is: Where X is the data point, μ is the mean, and σ is the standard deviation; S2. Feature extraction, extracting useful features from raw data, using time series analysis methods to identify patterns and trends in the data, where: Mean calculation: Variance calculation: S3. Pattern recognition, using support vector machine to identify the operation mode of the equipment, using cluster analysis to find similarities and differences in the data, the decision formula of the support vector machine is: Among them, x i is the training sample, y i is the label, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, b is the bias term; S4. Predictive analysis uses linear regression to predict the future state of the equipment and applies the time series prediction model to predict the trend and periodic changes of equipment operation. The linear regression prediction formula is: in, is the predicted value, β i is the regression coefficient, x i is the eigenvalue; S5. Anomaly detection uses statistical methods and machine learning models to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected. The anomaly scoring formula of the isolation forest is used: Where h(x) is the path length of sample x in the tree, E(h(x)) is the expected path length, and c(n) is the normalization factor of the path length; S6. Decision support, generating decision recommendations based on the analysis results, including adjusting equipment parameters, optimizing operation strategies, and using decision trees or rule engines to develop collaborative control strategies.

4. The intelligent fully mechanized coal mining electromechanical equipment coordinated control system according to claim 1 is characterized in that: The collaborative control module includes the following steps: S1. Receive analysis results, receive device status, prediction results and anomaly detection results from the data processing and analysis module; S2. Strategy formulation: formulate corresponding collaborative control strategies based on the received analysis results. The strategies may include adjusting equipment operating parameters, optimizing equipment operation sequence, and redistributing loads. S3. Control instruction generation: Generate specific control instructions according to the formulated strategy. The control instructions may include start, stop, accelerate, decelerate and change direction; S4. Instruction execution: sending control instructions to corresponding devices for execution to ensure the accuracy and timeliness of instruction execution; S5. Feedback monitoring, monitoring the state changes of the equipment after executing the control instructions, collecting feedback on the execution results, and using it to evaluate the control effect and adjust the strategy; S6. Strategy adjustment: adjust the collaborative control strategy in real time based on the feedback results, continuously optimize the control effect, and ensure stable operation of the system.

5. The intelligent fully mechanized coal mining electromechanical equipment coordinated control system according to claim 1 is characterized in that: The security monitoring module includes the following steps: S1. Data collection: real-time collection of safety-related data from various sensors and devices, including but not limited to temperature, humidity, gas concentration, equipment vibration, equipment current and equipment voltage; S2. Data preprocessing: preliminary processing of the collected data, including denoising, filtering, and normalization, to improve the accuracy and reliability of the data; S3. Anomaly detection, using Z-score to detect abnormal behavior, monitor device status in real time, and immediately trigger alarms and corresponding mechanisms once an anomaly is detected; S4. Alarm triggering: when an abnormal situation is detected, the alarm system is immediately triggered to notify the operator. The alarm information includes the abnormality type, the event, and the location; S5. Emergency response: Automatically execute corresponding safety measures according to the preset emergency response strategy, which may include equipment shutdown, emergency ventilation and power cut-off; S6. Log records, recording all security incidents and corresponding measures for subsequent analysis and audit; S7. System maintenance: regularly maintain and update the security monitoring module to ensure its continued effectiveness.

6. The intelligent fully mechanized coal mining electromechanical equipment coordinated control system according to claim 1 is characterized by: The user interface comprises the following steps: S1. Interface design: design a user-friendly interface layout, including the instrument panel, control panel and alarm information display area, and use graphical elements to intuitively display the equipment status and control options; S2. Data display: real-time display of equipment operation data and environmental parameters obtained from the data acquisition module, using charts and dashboards to display data trends and current values; S3. Control function, providing control buttons and regulators, allowing operators to adjust the operating parameters of the equipment, and realize one-button start, stop, speed adjustment and direction change; S4. Alarm processing: When the security monitoring module detects an abnormality, the user interface immediately displays an alarm message, which includes the type, severity and location of the abnormality, and provides corresponding processing suggestions; S5. Log recording and query, record all operation logs and alarm events, and provide query function, allowing operators to view historical data and event records for subsequent analysis and audit; S6. User authority management: restrict access to certain control functions and data according to the authority levels of different users, and realize user login, authority allocation and audit tracking functions; S7. System maintenance and update, providing a system maintenance and update interface, allowing technicians to perform system configuration and software updates, and realize remote diagnosis and troubleshooting functions.

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