Thermal power plant pulverizing equipment state remote diagnosis system

Abstract

The invention discloses a thermal power plant powder manufacturing equipment state remote diagnosis system, and relates to the technical field of powder manufacturing equipment state diagnosis, and the system comprises a data collection module which obtains the real-time state information of equipment; the edge calculation module is used for carrying out edge real-time analysis and detecting abnormal conditions; the data processing and analyzing module is used for identifying a fault mode and trend and dynamically adjusting a self-adaptive threshold value; the remote monitoring and diagnosis module is used for displaying equipment state, alarm and fault information; the maintenance and decision support module is used for providing maintenance and repair suggestions; and the security and authority management module is used for setting different levels of user authorities and carrying out data encryption. Through data acquisition and edge calculation, the system can monitor the equipment state in real time, identify and alarm abnormity in time, improve the safety and reliability of equipment operation, and utilize deep learning and time sequence analysis, the system can identify potential fault modes, optimize maintenance decisions, and reduce downtime and maintenance cost.

Description

Technical Field

[0001] The invention relates to the technical field of milling equipment status diagnosis, and in particular to a remote diagnosis system for milling equipment status in a thermal power plant. Background Art

[0002] The pulverizing equipment of a thermal power plant is used to crush coal and other fuels into powder of a certain particle size to improve combustion efficiency and calorific value utilization. The pulverizing system usually includes a coal mill, a conveying system, a grading equipment, etc. Common types of coal mills include ball mills, roller mills, and air separation mills. The various types vary according to grinding efficiency, energy consumption, and maintenance requirements. Modern pulverizing equipment uses advanced automated control systems to achieve real-time monitoring and adjustment of the grinding process to ensure that the particle size, humidity and other parameters of the coal powder meet the requirements. The pulverizing equipment needs to be equipped with a dust removal system to reduce dust emissions, and combined with exhaust gas treatment technology to reduce the impact on the environment, use energy-saving technologies such as frequency conversion drives, optimize the operating efficiency of motors and mills, and reduce energy consumption. Technological advances have extended the maintenance cycle of equipment, reduced the failure rate, and improved the overall reliability of operation.

[0003] The existing remote diagnosis system for the status of powder making equipment in thermal power plants is not intelligent enough and cannot perform edge computing, which wastes a lot of energy. In addition, the threshold is relatively fixed and cannot be updated dynamically, resulting in inaccurate diagnostic results. Summary of the invention

[0004] In order to solve the above technical problems, a remote diagnosis system for the status of pulverizing equipment in a thermal power plant is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A remote diagnosis system for the status of pulverizing equipment in a thermal power plant, comprising:

[0007] Data acquisition module: Based on the sensors on the equipment, the real-time status information of the equipment is acquired;

[0008] Edge computing module: The edge computing module is electrically connected to the data acquisition module. The edge computing module is used to perform real-time edge analysis based on the real-time status information of the device, detect abnormal conditions, extract important data, and upload the important data to the cloud;

[0009] Data processing and analysis module: The data processing and analysis module and the edge computing module are electrically connected to the data acquisition module. The data processing and analysis module is used to perform deep learning and historical data analysis on the cloud platform, identify fault modes and trends, and dynamically adjust the adaptive threshold based on the analysis results;

[0010] Remote monitoring and diagnosis module: The remote monitoring and diagnosis module is electrically connected to the data processing and analysis module, the edge computing module and the data acquisition module. The remote monitoring and diagnosis module is used to display the device status, alarm and fault information based on the real-time monitoring dashboard, allowing technicians to remotely view the device status and historical data;

[0011] Maintenance and decision support module: The maintenance and decision support module is electrically connected to the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The maintenance and decision support module is used to provide maintenance and repair suggestions and optimize the maintenance plan based on the real-time status of the equipment and the prediction analysis results;

[0012] Security and authority management module: The security and authority management module is electrically connected to the maintenance and decision support module, the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The security and authority management module is used to set different levels of user authority and perform data encryption.

[0013] Preferably, the data acquisition module specifically includes:

[0014] Sensor unit: monitors the temperature status of the equipment, pressure changes in the system, equipment vibration, material flow rate and operating parameters of electrical equipment;

[0015] Data acquisition unit: converts sensor signals into digital data and performs filtering;

[0016] Communication unit: transmits data to the edge computing module.

[0017] Preferably, the edge computing module specifically includes:

[0018] Data receiving unit: receives data from sensors, cleans and preprocesses the raw data, and uses moving average to remove noise;

[0019] Real-time analysis unit: performs real-time analysis on data, and determines data anomalies and whether data exceeds the threshold by calculating the standard deviation of the data. The standard deviation calculation formula is:

[0020]

[0021] In the formula, Z is the standard score of the data, x is the original data, μ is the mean of the original data, and σ is the standard deviation;

[0022] Important data extraction unit: extracts important features based on anomaly detection results and real-time analysis data;

[0023] Data upload unit: Safely upload the extracted important data to the cloud for storage.

[0024] Preferably, the data processing and analysis module specifically includes:

[0025] Data collection unit: collects real-time data and historical data from edge devices and outputs structured data sets;

[0026] Data analysis unit: Identify fault modes based on CNN deep learning model and perform real-time data analysis;

[0027] Data trend identification unit: Based on structured data sets, use time series analysis methods to identify data trends;

[0028] Dynamic threshold adjustment unit: dynamically adjusts the threshold according to the statistical characteristics of historical data.

[0029] Preferably, the data analysis unit specifically includes:

[0030] Divide the data set into training set, validation set and test set, and build a CNN model;

[0031] Calculate the model loss function, where the model loss function calculation formula is:

[0032]

[0033] In the formula, L is the value of the loss function, N is the total number of samples, and y i is the true label of the i-th sample, is the predicted probability of the kth sample

[0034] Use the training set to train the model, and based on the model's loss function, adjust the weights to minimize the loss function;

[0035] Input real-time data into the trained CNN model for fault pattern recognition;

[0036] Output recognition results and visualize them to generate reports.

[0037] Preferably, the data trend identification unit specifically includes:

[0038] Collect timestamped structured data, draw time series graphs, and make preliminary observations of data trends;

[0039] Use units based on testing the stationarity of the data, if the data is non-stationary, perform differencing, and use seasonal decomposition to extract trend and seasonality;

[0040] A data trend identification model is established based on the ARIMA time series model, where the formula of the data trend identification model is:

[0041]

[0042] In the formula, H t is the observed value at the current time point t, is an autoregressive polynomial, B is a lag operator, θ(B) is a moving average polynomial, θ is a moving average parameter, and ε is a white noise sequence;

[0043] Based on the data trend recognition model, the data trend is output.

[0044] Preferably, the dynamic threshold adjustment unit specifically includes:

[0045] Based on historical data, obtain the initial threshold;

[0046] According to the new data input, the statistical features are updated regularly and the threshold is adjusted. The threshold adjustment formula is:

[0047] T=μ+M·σ

[0048] Where: T is the dynamically adjusted threshold, μ is the mean in the current window, σ is the standard deviation in the current window, and σ is the parameter for controlling the threshold sensitivity;

[0049] Apply new thresholds for real-time monitoring.

[0050] Preferably, the remote monitoring and diagnosis module specifically includes:

[0051] Real-time monitoring dashboard unit: displays equipment status including operating status and load, visualizes charts, and displays key performance indicators;

[0052] Alarm system unit: set alarm thresholds, monitor equipment anomalies in real time, and send alarm notifications;

[0053] Fault information management unit: records and classifies faults, provides fault descriptions, troubleshooting suggestions and solutions;

[0054] Historical data storage and playback unit: stores historical data, allowing technicians to query and playback historical data and conduct trend analysis;

[0055] Data analysis and report generation unit: automatically generates equipment operation reports, summarizing key indicators and abnormal events

[0056] Remote control unit: technicians can remotely adjust equipment parameters and perform maintenance tasks.

[0057] Preferably, the maintenance and decision support module specifically includes:

[0058] Real-time status monitoring unit: continuously monitors the operating parameters and status of the equipment to ensure the timeliness and accuracy of the data;

[0059] Maintenance suggestion generation unit: automatically generates maintenance and overhaul suggestions based on real-time data and prediction results, including the time and content of the suggestions;

[0060] Optimize maintenance plans: Combine equipment usage, historical failures, and predicted results to optimize maintenance and overhaul priorities and schedules;

[0061] Task management and scheduling: Provides a management interface for maintenance tasks, including task allocation, progress tracking, and resource allocation;

[0062] Reporting and performance evaluation unit: Generate reports on maintenance activities, evaluate maintenance effects and equipment performance, and assist in decision making;

[0063] User Feedback and Learning Unit: Collect feedback from maintainers.

[0064] Preferably, the security and rights management module specifically includes:

[0065] User identity authentication unit: supports multiple authentication methods to ensure the authenticity of user identity;

[0066] Permission management unit: defines different user roles and permission levels, and controls access scope and operation permissions;

[0067] User registration and management unit: provides a management interface for user registration, editing, deletion, and activation / disable functions;

[0068] Data encryption unit: encrypts the storage and transmission of sensitive data to ensure data security;

[0069] Access log recording unit: records user access and operation logs;

[0070] Customized security policies: Allow users to configure security policies and access control rules as needed.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention proposes that through data collection and edge computing, the system can monitor the equipment status in real time, promptly identify and alarm abnormalities, improve the safety and reliability of equipment operation, and use deep learning and time series analysis to identify potential failure modes, optimize maintenance decisions, and reduce downtime and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is the internal system framework diagram of the present invention. DETAILED DESCRIPTION

[0074] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0075] Reference Figure 1 As shown, a remote diagnosis system for the status of pulverizing equipment in a thermal power plant comprises:

[0076] Data acquisition module: Based on the sensors on the equipment, the real-time status information of the equipment is acquired;

[0077] Edge computing module: The edge computing module is electrically connected to the data acquisition module. The edge computing module is used to perform real-time edge analysis based on the real-time status information of the device, detect abnormal conditions, extract important data, and upload the important data to the cloud;

[0078] Data processing and analysis module: The data processing and analysis module and the edge computing module are electrically connected to the data acquisition module. The data processing and analysis module is used to perform deep learning and historical data analysis on the cloud platform, identify fault modes and trends, and dynamically adjust the adaptive threshold based on the analysis results;

[0079] Remote monitoring and diagnosis module: The remote monitoring and diagnosis module is electrically connected to the data processing and analysis module, the edge computing module and the data acquisition module. The remote monitoring and diagnosis module is used to display the device status, alarm and fault information based on the real-time monitoring dashboard, allowing technicians to remotely view the device status and historical data;

[0080] Maintenance and decision support module: The maintenance and decision support module is electrically connected to the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The maintenance and decision support module is used to provide maintenance and repair suggestions and optimize the maintenance plan based on the real-time status of the equipment and the prediction analysis results;

[0081] Security and authority management module: The security and authority management module is electrically connected to the maintenance and decision support module, the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The security and authority management module is used to set different levels of user authority and perform data encryption.

[0082] Furthermore, the data acquisition module specifically includes:

[0083] Sensor unit: monitors the temperature status of the equipment, pressure changes in the system, equipment vibration, material flow rate and operating parameters of electrical equipment;

[0084] Data acquisition unit: converts sensor signals into digital data and performs filtering;

[0085] Communication unit: transmits data to the edge computing module.

[0086] Furthermore, the edge computing module specifically includes:

[0087] Data receiving unit: receives data from sensors, cleans and preprocesses the raw data, and uses moving average to remove noise;

[0088] Real-time analysis unit: performs real-time analysis on data, and determines data anomalies and whether data exceeds the threshold by calculating the standard deviation of the data. The standard deviation calculation formula is:

[0089]

[0090] In the formula, Z is the standard score of the data, x is the original data, μ is the mean of the original data, and σ is the standard deviation;

[0091] Important data extraction unit: extracts important features based on anomaly detection results and real-time analysis data;

[0092] Data upload unit: Safely upload the extracted important data to the cloud for storage.

[0093] Furthermore, the data processing and analysis module specifically includes:

[0094] Data collection unit: collects real-time data and historical data from edge devices and outputs structured data sets;

[0095] Data analysis unit: Identify fault modes based on CNN deep learning model and perform real-time data analysis;

[0096] Data trend identification unit: Based on structured data sets, use time series analysis methods to identify data trends;

[0097] Dynamic threshold adjustment unit: dynamically adjusts the threshold according to the statistical characteristics of historical data.

[0098] Furthermore, the data analysis unit specifically includes:

[0099] Divide the data set into training set, validation set and test set, and build a CNN model;

[0100] Calculate the model loss function, where the model loss function calculation formula is:

[0101]

[0102] In the formula, L is the value of the loss function, N is the total number of samples, and y i is the true label of the i-th sample, is the predicted probability of the kth sample

[0103] Use the training set to train the model, and based on the model's loss function, adjust the weights to minimize the loss function;

[0104] Input real-time data into the trained CNN model for fault pattern recognition;

[0105] Output recognition results and visualize them to generate reports.

[0106] Furthermore, the data trend identification unit specifically includes:

[0107] Collect timestamped structured data, draw time series graphs, and make preliminary observations of data trends;

[0108] Use units based on testing the stationarity of the data, if the data is non-stationary, perform differencing, and use seasonal decomposition to extract trend and seasonality;

[0109] A data trend identification model is established based on the ARIMA time series model, where the formula of the data trend identification model is:

[0110]

[0111] In the formula, H t is the observed value at the current time point t, is an autoregressive polynomial, B is a lag operator, θ(B) is a moving average polynomial, θ is a moving average parameter, and ε is a white noise sequence;

[0112] Based on the data trend recognition model, the data trend is output.

[0113] Furthermore, the dynamic threshold adjustment unit specifically includes:

[0114] Based on historical data, obtain the initial threshold;

[0115] According to the new data input, the statistical features are updated regularly and the threshold is adjusted. The threshold adjustment formula is:

[0116] T=μ+M·σ

[0117] Where: T is the dynamically adjusted threshold, μ is the mean in the current window, σ is the standard deviation in the current window, and σ is the parameter for controlling the threshold sensitivity;

[0118] Apply new thresholds for real-time monitoring.

[0119] Furthermore, the remote monitoring and diagnosis module specifically includes:

[0120] Real-time monitoring dashboard unit: displays equipment status including operating status and load, visualizes charts, and displays key performance indicators;

[0121] Alarm system unit: set alarm thresholds, monitor equipment anomalies in real time, and send alarm notifications;

[0122] Fault information management unit: records and classifies faults, provides fault descriptions, troubleshooting suggestions and solutions;

[0123] Historical data storage and playback unit: stores historical data, allowing technicians to query and playback historical data and conduct trend analysis;

[0124] Data analysis and report generation unit: automatically generates equipment operation reports, summarizing key indicators and abnormal events

[0125] Remote control unit: technicians can remotely adjust equipment parameters and perform maintenance tasks.

[0126] Furthermore, the maintenance and decision support module specifically includes:

[0127] Real-time status monitoring unit: continuously monitors the operating parameters and status of the equipment to ensure the timeliness and accuracy of the data;

[0128] Maintenance suggestion generation unit: automatically generates maintenance and overhaul suggestions based on real-time data and prediction results, including the time and content of the suggestions;

[0129] Optimize maintenance plans: Combine equipment usage, historical failures, and predicted results to optimize maintenance and overhaul priorities and schedules;

[0130] Task management and scheduling: Provides a management interface for maintenance tasks, including task allocation, progress tracking, and resource allocation;

[0131] Reporting and performance evaluation unit: Generate reports on maintenance activities, evaluate maintenance effects and equipment performance, and assist in decision making;

[0132] User Feedback and Learning Unit: Collect feedback from maintainers.

[0133] Furthermore, the security and rights management module specifically includes:

[0134] User identity authentication unit: supports multiple authentication methods to ensure the authenticity of user identity;

[0135] Permission management unit: defines different user roles and permission levels, and controls access scope and operation permissions;

[0136] User registration and management unit: provides a management interface for user registration, editing, deletion, and activation / disable functions;

[0137] Data encryption unit: encrypts the storage and transmission of sensitive data to ensure data security;

[0138] Access log recording unit: records user access and operation logs;

[0139] Customized security policies: Allow users to configure security policies and access control rules as needed.

[0140] In summary, the advantages of the present invention are:

[0141] Through data collection and edge computing, the system can monitor the status of equipment in real time, identify and alert abnormalities in a timely manner, and improve the safety and reliability of equipment operation;

[0142] Using deep learning and time series analysis, the system can identify potential failure modes, optimize maintenance decisions, and reduce downtime and repair costs;

[0143] The security and rights management module ensures the encrypted storage and transmission of sensitive data, while protecting the system from unauthorized access through multi-level rights management;

[0144] Based on real-time and historical data, the system can automatically generate maintenance recommendations, optimize maintenance plans, improve equipment efficiency and extend its life;

[0145] Technicians can remotely view equipment status and historical data, allowing for quick response to faults and improving overall operation and maintenance efficiency.

[0146] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant, characterized in that: include: Data acquisition module: Based on the sensors on the equipment, the real-time status information of the equipment is acquired; Edge computing module: The edge computing module is electrically connected to the data acquisition module. The edge computing module is used to perform real-time edge analysis based on the real-time status information of the device, detect abnormal conditions, extract important data, and upload the important data to the cloud; Data processing and analysis module: The data processing and analysis module and the edge computing module are electrically connected to the data acquisition module. The data processing and analysis module is used to perform deep learning and historical data analysis on the cloud platform, identify fault modes and trends, and dynamically adjust the adaptive threshold based on the analysis results; Remote monitoring and diagnosis module: The remote monitoring and diagnosis module is electrically connected to the data processing and analysis module, the edge computing module and the data acquisition module. The remote monitoring and diagnosis module is used to display the device status, alarm and fault information based on the real-time monitoring dashboard, allowing technicians to remotely view the device status and historical data; Maintenance and decision support module: The maintenance and decision support module is electrically connected to the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The maintenance and decision support module is used to provide maintenance and repair suggestions and optimize the maintenance plan based on the real-time status of the equipment and the prediction analysis results; Security and authority management module: The security and authority management module is electrically connected to the maintenance and decision support module, the remote monitoring and diagnosis module, the data processing and analysis module, the edge computing module and the data acquisition module. The security and authority management module is used to set different levels of user authority and perform data encryption.

2. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 1, characterized in that: The data acquisition module specifically includes: Sensor unit: monitors the temperature status of the equipment, pressure changes in the system, equipment vibration, material flow rate and operating parameters of electrical equipment; Data acquisition unit: converts sensor signals into digital data and performs filtering; Communication unit: transmits data to the edge computing module.

3. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 2, characterized in that: The edge computing module specifically includes: Data receiving unit: receives data from sensors, cleans and preprocesses the raw data, and uses moving average to remove noise; Real-time analysis unit: performs real-time analysis on data, and determines data anomalies and whether data exceeds the threshold by calculating the standard deviation of the data. The standard deviation calculation formula is: In the formula, Z is the standard score of the data, x is the original data, μ is the mean of the original data, and σ is the standard deviation; Important data extraction unit: extracts important features based on anomaly detection results and real-time analysis data; Data upload unit: Safely upload the extracted important data to the cloud for storage.

4. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 3, characterized in that: The data processing and analysis module specifically includes: Data collection unit: collects real-time data and historical data from edge devices and outputs structured data sets; Data analysis unit: Identify fault modes based on CNN deep learning model and perform real-time data analysis; Data trend identification unit: Based on structured data sets, use time series analysis methods to identify data trends; Dynamic threshold adjustment unit: dynamically adjusts the threshold according to the statistical characteristics of historical data.

5. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 4, characterized in that: The data analysis unit specifically includes: Divide the data set into training set, validation set and test set, and build a CNN model; Calculate the model loss function, where the model loss function calculation formula is: In the formula, L is the value of the loss function, N is the total number of samples, and y i is the true label of the i-th sample, is the predicted probability of the kth sample Use the training set to train the model, and based on the model's loss function, adjust the weights to minimize the loss function; Input real-time data into the trained CNN model for fault pattern recognition; Output recognition results and visualize them to generate reports.

6. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 5, characterized in that: The data trend identification unit specifically includes: Collect timestamped structured data, draw time series graphs, and make preliminary observations of data trends; Use units based on testing the stationarity of the data, if the data is non-stationary, perform differencing, and use seasonal decomposition to extract trend and seasonality; A data trend identification model is established based on the ARIMA time series model, where the formula of the data trend identification model is: In the formula, H t is the observed value at the current time point t, is an autoregressive polynomial, B is a lag operator, θ(B) is a moving average polynomial, θ is a moving average parameter, and ε is a white noise sequence; Based on the data trend recognition model, the data trend is output.

7. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 6, characterized in that: The dynamic threshold adjustment unit specifically includes: Based on historical data, obtain the initial threshold; According to the new data input, the statistical features are updated regularly and the threshold is adjusted. The threshold adjustment formula is: T=μ+M·σ Where: T is the dynamically adjusted threshold, μ is the mean in the current window, σ is the standard deviation in the current window, and σ is the parameter for controlling the threshold sensitivity; Apply new thresholds for real-time monitoring.

8. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 7, characterized in that: The remote monitoring and diagnosis module specifically includes: Real-time monitoring dashboard unit: displays equipment status including operating status and load, visualizes charts, and displays key performance indicators; Alarm system unit: set alarm thresholds, monitor equipment anomalies in real time, and send alarm notifications; Fault information management unit: records and classifies faults, provides fault descriptions, troubleshooting suggestions and solutions; Historical data storage and playback unit: stores historical data, allowing technicians to query and playback historical data and conduct trend analysis; Data analysis and report generation unit: automatically generates equipment operation reports, summarizing key indicators and abnormal events Remote control unit: technicians can remotely adjust equipment parameters and perform maintenance tasks.

9. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 8, characterized in that: The maintenance and decision support module specifically includes: Real-time status monitoring unit: continuously monitors the operating parameters and status of the equipment to ensure the timeliness and accuracy of the data; Maintenance suggestion generation unit: automatically generates maintenance and overhaul suggestions based on real-time data and prediction results, including the time and content of the suggestions; Optimize maintenance plans: Combine equipment usage, historical failures, and predicted results to optimize maintenance and overhaul priorities and schedules; Task management and scheduling: Provides a management interface for maintenance tasks, including task allocation, progress tracking, and resource allocation; Reporting and performance evaluation unit: Generate reports on maintenance activities, evaluate maintenance effects and equipment performance, and assist in decision making; User Feedback and Learning Unit: Collect feedback from maintainers.

10. A remote diagnosis system for the status of pulverizing equipment in a thermal power plant according to claim 9, characterized in that: The security and rights management module specifically includes: User identity authentication unit: supports multiple authentication methods to ensure the authenticity of user identity; Permission management unit: defines different user roles and permission levels, and controls access scope and operation permissions; User registration and management unit: provides a management interface for user registration, editing, deletion, and activation / disable functions; Data encryption unit: encrypts the storage and transmission of sensitive data to ensure data security; Access log recording unit: records user access and operation logs; Customized security policies: Allow users to configure security policies and access control rules as needed.

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