Station real-time monitoring equipment and monitoring method based on edge calculation

Abstract

The invention relates to the field of data monitoring, in particular to a station real-time monitoring device and method based on edge computing, and the device comprises a data collection module, an edge computing module, a device control module, an alarm notification module, a cloud processing platform and a user interaction interface. Compared with a centralized monitoring system which is commonly adopted in the prior art and depends on a remote data center to carry out data processing and analysis, the system has the defects that data processing delay is high, bandwidth occupation is large, data security risks are increased, and system reliability is affected by network stability; according to the system, real-time processing and analysis of data at the edge of equipment are realized by adopting an edge computing technology; the delay and bandwidth cost of data transmission are remarkably reduced, the real-time response capability of the system is improved, and the local security of data and the distributed fault-tolerant capability of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of data monitoring, and in particular to a real-time monitoring device and method for field stations based on edge computing. Background Technology

[0002] Real-time monitoring equipment for a facility refers to devices and systems installed inside the facility for real-time monitoring, data acquisition, processing, and analysis of the facility's operational status. These devices typically include sensors, cameras, data acquisition units, edge computing units, and data processing servers. They are connected via wired or wireless means to form a complete monitoring system, which is an indispensable tool in modern facility management. By integrating various advanced technologies and equipment, they achieve real-time monitoring, data analysis, and anomaly alarms for various operating parameters within the facility, providing strong support for the safe, stable, and efficient operation of the facility. The centralized monitoring systems commonly used in existing technologies often rely on remote data centers for data processing and analysis, which suffers from drawbacks such as high data processing latency, large bandwidth consumption, increased data security risks, and system reliability being affected by network stability. Summary of the Invention

[0003] To overcome the shortcomings of the centralized monitoring systems commonly used in existing technologies, which often rely on remote data centers for data processing and analysis, resulting in high data processing latency, large bandwidth consumption, increased data security risks, and system reliability being affected by network stability.

[0004] The technical solution of this invention is: a real-time monitoring device for a field station based on edge computing, comprising: The data acquisition module is used to collect data in real time using various data source acquisition devices installed in the site. These data source acquisition devices include sensors, cameras, and equipment control systems. The edge computing module is used to perform preliminary processing and analysis on the data collected by the data acquisition module through edge computing. The equipment control module is used to receive control commands from the edge computing module and the cloud processing module to remotely control and adjust the equipment in the site. The alarm notification module is used to trigger the alarm function when the edge computing module detects an abnormal situation, and notify the staff through sound, light, SMS and email or a combination of these notification methods. The cloud processing platform is used to back up and store the data collected by the data acquisition module in the cloud, and to perform further processing and analysis. User interface, used to provide a user-friendly interface and interaction method.

[0005] Preferably, the data acquisition module includes sensor devices, camera devices, data reading devices, smart meter devices, and a data acquisition terminal. The sensor devices are used to collect environmental parameters and equipment status information within the site using various sensor technologies. The camera devices are used to collect real-time images within the site and record video footage. The data reading devices are used to collect and identify data about objects using radio frequency technology. The smart meter devices are used to collect energy usage data in real time. The data acquisition terminal is used to aggregate, process, and forward the data collected by the sensor devices, camera devices, data reading devices, and smart meter devices.

[0006] Preferably, the sensor devices include temperature sensors, humidity sensors, pressure sensors, and vibration sensors; the camera devices include high-definition cameras, infrared cameras, and panoramic cameras; the data reading devices include RFID readers; and the smart meter devices include smart meters, smart water meters, and smart gas meters.

[0007] Preferably, when the edge computing module performs preliminary processing and analysis on the data collected by the data acquisition module using edge computing, it includes the following steps: S11: The data acquisition terminal transmits data to the edge computing module, and the edge computing module reads the data transmitted by the data acquisition terminal; S12: After receiving the data, the edge computing module first performs preprocessing operations to improve the data quality. The preprocessing operations include data cleaning, noise reduction, and deduplication. S13: The edge computing module uses built-in analysis algorithms to perform in-depth analysis of data, extract useful information and features, and identify anomalies and potential problems; S14: The edge computing module generates corresponding control commands and alarm information based on the analysis results; S15: The edge computing module feeds back the analysis results and control commands to the control system or equipment in the site in real time.

[0008] Preferably, the built-in algorithms of the edge computing module include: A11: Data preprocessing algorithms are used to preprocess data to improve data quality; A12: Time series analysis algorithm used to process time series data; A13: Machine learning algorithms used for pattern recognition, classification, and prediction tasks; A14: Feature extraction algorithms are used to extract key features from raw data for subsequent analysis and identification; A15: Rule engine algorithm, used to judge real-time data according to preset rules and trigger corresponding control commands; A16: Optimization algorithms used to solve resource allocation, path planning, and other optimization problems; A17: Data encryption algorithm, used to encrypt sensitive data to ensure the security of data transmission and storage; A18: Intelligent recognition algorithm, used to intelligently identify different items in different scenarios.

[0009] Preferably, the equipment control module includes: The control command receiving unit is responsible for receiving control commands sent from the edge computing module or the cloud server. The device interface unit is responsible for converting received control commands into signals and commands that the device can recognize, ensuring the accurate transmission and execution of control commands; The control logic processing unit is used to parse and process the received control commands and generate control signals for the equipment according to preset control logic and rules. The feedback monitoring unit is used to monitor the operating status and control effect of the equipment in real time, and transmit the feedback information to the edge computing module or cloud server for further adjustment and optimization.

[0010] Preferably, the equipment control module also includes: Programmable logic controllers (PLCs) use programmable memory to store instructions for performing logical operations, sequential control, timing, counting, and arithmetic operations, and control various types of mechanical equipment or production processes through digital and analog inputs and outputs. Embedded systems are used to process and control devices in scenarios requiring real-time processing and control. The remote terminal unit is used to connect to various sensors and actuators to realize remote data acquisition, processing and control; Intelligent actuators are used to receive control signals and automatically adjust their output to control the controlled object.

[0011] Preferably, the system also includes an intermediate computing module, which is signal-connected to multiple edge computing modules. The intermediate computing module is used to perform further real-time analysis on the data received by the edge computing modules when the edge computing modules detect an anomaly.

[0012] Preferably, when the intermediate computing module performs further real-time analysis on the data received by the edge computing module, it includes the following steps: S21: The intermediate computing module first receives data from each edge computing module; S22: Perform real-time analysis on the received data to quickly identify potential problems. The analysis algorithms used include data aggregation, pattern recognition, and anomaly detection. S23: Based on the results of real-time analysis, the intermediate computing module makes decisions and generates control commands; S24: The intermediate computing module distributes the analyzed and processed control commands to the relevant edge computing modules.

[0013] Preferably, by performing further analysis and processing of data in the intermediate computing module, unnecessary data transmission to the cloud or more distant processing centers can be reduced. This helps reduce data transmission costs and latency, improves the overall performance of the system, and the intermediate computing module, by centrally processing data from multiple edge computing modules, can gain a more comprehensive understanding of the site's operational status. At the same time, its powerful computing capabilities ensure the accuracy and efficiency of the analysis, helping to identify and resolve problems in a timely manner. Furthermore, as a bridge connecting edge computing modules and higher-level system components, the intermediate computing module can be flexibly configured and expanded according to actual needs. This flexibility allows the system to better adapt to the needs of different sites and scenarios.

[0014] The edge computing-based real-time monitoring method for power stations includes the following steps: S31: Deploy edge computing devices with data acquisition, processing, and communication capabilities at key locations within the site; S32: Configure intermediate computing modules in the station control center or a suitable location, and establish signal connections between intermediate computing modules and multiple edge computing modules to ensure that data can be transmitted and shared in real time; S33: After the alarm function is triggered, the status of equipment, personnel and items in the site is maintained.

[0015] The beneficial effects of this invention are: 1. Compared to the centralized monitoring systems commonly used in existing technologies, which often rely on remote data centers for data processing and analysis, this system suffers from drawbacks such as high data processing latency, large bandwidth consumption, increased data security risks, and system reliability being affected by network stability. This system, by employing edge computing technology, enables real-time data processing and analysis at the device edge. This innovation not only significantly reduces data transmission latency and bandwidth costs and improves the system's real-time response capability, but also enhances local data security and the system's distributed fault tolerance. Therefore, the edge computing-based real-time monitoring system for field stations demonstrates significant advantages in improving monitoring efficiency, ensuring data security, optimizing resource utilization, and enhancing system stability. 2. Edge computing-based real-time monitoring systems for power stations have become an important support for modern power station management due to their high-efficiency data processing capabilities, near real-time response speed, enhanced data security, significant bandwidth cost savings, and high system reliability. By deploying edge computing devices within the power station, the system enables local data acquisition, preprocessing, and real-time analysis, effectively reducing data transmission latency and bandwidth consumption, while improving data security and system stability, providing strong support for the safe operation and efficient management of power stations. 3. By performing further analysis and processing on data in the intermediate computing module, unnecessary data transmission to the cloud or more distant processing centers can be reduced. This helps reduce data transmission costs and latency, improves overall system performance, and allows for a more comprehensive understanding of the site's operational status through centralized processing of data from multiple edge computing modules. Simultaneously, its powerful computing capabilities ensure the accuracy and efficiency of the analysis, facilitating timely problem identification and resolution. Furthermore, as a bridge connecting edge computing modules and higher-level system components, the intermediate computing module can be flexibly configured and expanded according to actual needs. This flexibility enables the system to better adapt to the requirements of different sites and scenarios. Attached Figure Description

[0016] Figure 1 The diagram shown is a schematic representation of the structure of the edge computing-based real-time monitoring device for a field station according to the present invention. Figure 2 The diagram shown is a flowchart of the real-time monitoring method for power stations based on edge computing according to the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Please see Figure 1 The present invention provides an embodiment of a real-time monitoring device for a field station based on edge computing, comprising: The data acquisition module is used to collect data in real time using various data source acquisition devices installed in the site. These data source acquisition devices include sensors, cameras, and equipment control systems. The edge computing module is used to perform preliminary processing and analysis on the data collected by the data acquisition module through edge computing. The equipment control module is used to receive control commands from the edge computing module and the cloud processing module to remotely control and adjust the equipment in the site. The alarm notification module is used to trigger the alarm function when the edge computing module detects an abnormal situation, and notify the staff through sound, light, SMS and email or a combination of these notification methods. The cloud processing platform is used to back up and store the data collected by the data acquisition module in the cloud, and to perform further processing and analysis. User interface, used to provide a user-friendly interface and interaction method.

[0019] Preferably, the edge computing-based real-time monitoring system for power stations, with its efficient data processing capabilities, near real-time response speed, enhanced data security, significant bandwidth cost savings, and high system reliability, has become an important support for modern power station management. By deploying edge computing devices within the power station, the system enables local data acquisition, preprocessing, and real-time analysis, effectively reducing data transmission latency and bandwidth consumption, while improving data security and system stability, providing strong support for the safe operation and efficient management of the power station.

[0020] Preferably, the data acquisition module includes sensor devices, camera devices, data reading devices, smart meter devices, and a data acquisition terminal. The sensor devices are used to collect environmental parameters and equipment status information within the site using various sensor technologies. The camera devices are used to collect real-time images within the site and record video footage. The data reading devices are used to collect and identify data about objects using radio frequency technology. The smart meter devices are used to collect energy usage data in real time. The data acquisition terminal is used to aggregate, process, and forward the data collected by the sensor devices, camera devices, data reading devices, and smart meter devices.

[0021] Preferably, the sensor devices include temperature sensors, humidity sensors, pressure sensors, and vibration sensors; the camera devices include high-definition cameras, infrared cameras, and panoramic cameras; the data reading devices include RFID readers; and the smart meter devices include smart meters, smart water meters, and smart gas meters.

[0022] Preferably, when the edge computing module performs preliminary processing and analysis on the data collected by the data acquisition module using edge computing, it includes the following steps: S11: The data acquisition terminal transmits data to the edge computing module, and the edge computing module reads the data transmitted by the data acquisition terminal; S12: After receiving the data, the edge computing module first performs preprocessing operations to improve the data quality. The preprocessing operations include data cleaning, noise reduction, and deduplication. S13: The edge computing module uses built-in analysis algorithms to perform in-depth analysis of data, extract useful information and features, and identify anomalies and potential problems; S14: The edge computing module generates corresponding control commands and alarm information based on the analysis results; S15: The edge computing module feeds back the analysis results and control commands to the control system or equipment in the site in real time.

[0023] Preferably, the built-in algorithms of the edge computing module include: A11: Data preprocessing algorithms are used to preprocess data to improve data quality; A12: Time series analysis algorithm used to process time series data; A13: Machine learning algorithms used for pattern recognition, classification, and prediction tasks; A14: Feature extraction algorithms are used to extract key features from raw data for subsequent analysis and identification; A15: Rule engine algorithm, used to judge real-time data according to preset rules and trigger corresponding control commands; A16: Optimization algorithms used to solve resource allocation, path planning, and other optimization problems; A17: Data encryption algorithm, used to encrypt sensitive data to ensure the security of data transmission and storage; A18: Intelligent recognition algorithm, used to intelligently identify different items in different scenarios.

[0024] Preferably, the equipment control module includes: The control command receiving unit is responsible for receiving control commands sent from the edge computing module or the cloud server. The device interface unit is responsible for converting received control commands into signals and commands that the device can recognize, ensuring the accurate transmission and execution of control commands; The control logic processing unit is used to parse and process the received control commands and generate control signals for the equipment according to preset control logic and rules. The feedback monitoring unit is used to monitor the operating status and control effect of the equipment in real time, and transmit the feedback information to the edge computing module or cloud server for further adjustment and optimization.

[0025] Preferably, the equipment control module also includes: Programmable logic controllers (PLCs) use programmable memory to store instructions for performing logical operations, sequential control, timing, counting, and arithmetic operations, and control various types of mechanical equipment or production processes through digital and analog inputs and outputs. Embedded systems are used to process and control devices in scenarios requiring real-time processing and control. The remote terminal unit is used to connect to various sensors and actuators to realize remote data acquisition, processing and control; Intelligent actuators are used to receive control signals and automatically adjust their output to control the controlled object.

[0026] Preferably, the system also includes an intermediate computing module, which is signal-connected to multiple edge computing modules. The intermediate computing module is used to perform further real-time analysis on the data received by the edge computing modules when the edge computing modules detect an anomaly.

[0027] Preferably, when the intermediate computing module performs further real-time analysis on the data received by the edge computing module, it includes the following steps: S21: The intermediate computing module first receives data from each edge computing module; S22: Perform real-time analysis on the received data to quickly identify potential problems. The analysis algorithms used include data aggregation, pattern recognition, and anomaly detection. S23: Based on the results of real-time analysis, the intermediate computing module makes decisions and generates control commands; S24: The intermediate computing module distributes the analyzed and processed control commands to the relevant edge computing modules.

[0028] Preferably, by performing further analysis and processing of data in the intermediate computing module, unnecessary data transmission to the cloud or more distant processing centers can be reduced. This helps reduce data transmission costs and latency, improves the overall performance of the system, and the intermediate computing module, by centrally processing data from multiple edge computing modules, can gain a more comprehensive understanding of the site's operational status. At the same time, its powerful computing capabilities ensure the accuracy and efficiency of the analysis, helping to identify and resolve problems in a timely manner. Furthermore, as a bridge connecting edge computing modules and higher-level system components, the intermediate computing module can be flexibly configured and expanded according to actual needs. This flexibility allows the system to better adapt to the needs of different sites and scenarios.

[0029] Please see Figure 2 The present invention provides an embodiment of a real-time monitoring method for power stations based on edge computing, comprising the following steps: S31: Deploy edge computing devices with data acquisition, processing, and communication capabilities at key locations within the site; S32: Configure intermediate computing modules in the station control center or a suitable location, and establish signal connections between intermediate computing modules and multiple edge computing modules to ensure that data can be transmitted and shared in real time; S33: After the alarm function is triggered, the status of equipment, personnel and items in the site is maintained.

[0030] Example 1 In industrial equipment remote monitoring systems, equipment collects operational data in real time through sensors and controllers, including voltage input / output data, current input / output data, equipment temperature data, and equipment vibration data. The edge computing module utilizes built-in data preprocessing algorithms, time series algorithms, machine learning algorithms, and rule engine algorithms to preprocess and initially analyze the collected data, identifying anomalies or faults. The pre-processed data is then transmitted via network to the cloud or remote monitoring center for further analysis and processing. Finally, the monitoring center sends control commands to the equipment based on the analysis results, enabling remote control and fault repair. Specifically, edge computing-based real-time monitoring equipment and methods can be applied to real-time monitoring in production workshops. By deploying sensor networks and surveillance cameras, the operating status of production equipment and workshop environmental parameters can be monitored in real time. When equipment malfunctions or environmental parameters become abnormal, the edge computing gateway immediately triggers an alarm mechanism, notifying maintenance personnel for timely handling. Simultaneously, through the data analysis capabilities of the cloud server, production data can be deeply mined and analyzed, providing strong support for production optimization.

[0031] Example 2 In intelligent security systems, cameras collect video data in real time in public places and residential areas. Edge computing modules utilize built-in video processing algorithms, facial recognition algorithms, behavior recognition algorithms, and rule engine algorithms to preprocess and analyze the video data in real time. For example, facial recognition and behavior recognition can detect abnormal behavior or suspicious persons, triggering an alarm mechanism immediately and sending relevant information to the monitoring center or security personnel's mobile phones. The monitoring center or security personnel can then respond and handle the situation quickly based on the alarm information. In smart communities and smart campuses, edge computing technology can enable real-time monitoring and management. By deploying edge computing devices and cameras at key locations such as community entrances and campuses, video images and personnel information can be collected in real time and intelligently analyzed and processed. This helps improve the security management level of communities and campuses, enabling timely detection and handling of abnormal situations. At the same time, edge computing can also support intelligent management functions in smart communities and smart campuses, such as intelligent access control and intelligent parking.

[0032] Example 3 In autonomous driving systems, onboard sensors such as radar, lidar, and cameras collect data about the vehicle's surrounding environment in real time. Edge computing modules utilize built-in deep learning algorithms, computer vision algorithms, path planning algorithms, and decision and control algorithms to process and analyze the collected data in real time, such as object detection, tracking, and path planning. Based on the analysis results, the edge computing module sends control commands to the vehicle control system to achieve autonomous driving. At the same time, the edge computing module can also upload some data to a cloud server for further analysis and optimization.

[0033] Example 4 In smart city construction, edge computing modules can be applied to multiple fields such as traffic monitoring, environmental monitoring, and public safety. The specific principle is as follows: various sensors and cameras collect urban operational data in real time. The edge computing module utilizes built-in time series analysis, machine learning algorithms, decision and control algorithms, traffic flow prediction algorithms, and environmental monitoring data analysis algorithms to process and analyze the collected data in real time, such as traffic flow analysis and environmental monitoring data parsing. Based on the analysis results, the edge computing module sends control commands or early warning information to relevant management systems. The management systems then perform corresponding scheduling and processing based on the commands or early warning information. For example, intelligent transportation is one of the important application scenarios of edge computing technology. By deploying edge computing devices at key locations such as traffic intersections and highways, traffic information (such as traffic flow, vehicle speed, and road conditions) can be perceived in real time and analyzed and processed in real time. This helps optimize traffic scheduling, alleviate urban traffic congestion, and improve traffic safety. In addition, edge computing can also support the intelligent monitoring and early warning functions of intelligent transportation systems, enabling timely detection and handling of emergencies such as traffic accidents.

[0034] Through the steps described above, compared to the centralized monitoring systems commonly used in existing technologies, which often rely on remote data centers for data processing and analysis, this system suffers from drawbacks such as high data processing latency, large bandwidth consumption, increased data security risks, and system reliability being affected by network stability. By employing edge computing technology, this system achieves real-time data processing and analysis at the device edge. This innovation not only significantly reduces data transmission latency and bandwidth costs and improves the system's real-time response capability, but also enhances local data security and the system's distributed fault tolerance. Therefore, the edge computing-based real-time monitoring system for field stations demonstrates significant advantages in improving monitoring efficiency, ensuring data security, optimizing resource utilization, and enhancing system stability.

[0035] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A real-time monitoring device for a field station based on edge computing; characterized in that: Including: The data acquisition module is used to collect data in real time using various data source acquisition devices installed in the site. These data source acquisition devices include sensors, cameras, and equipment control systems. The edge computing module is used to perform preliminary processing and analysis on the data collected by the data acquisition module through edge computing. The equipment control module is used to receive control commands from the edge computing module and the cloud processing module to remotely control and adjust the equipment in the site. The alarm notification module is used to trigger the alarm function when the edge computing module detects an abnormal situation, and notify the staff through sound, light, SMS and email or a combination of these notification methods. The cloud processing platform is used to back up and store the data collected by the data acquisition module in the cloud, and to perform further processing and analysis. User interface, used to provide a user-friendly interface and interaction method.

2. The real-time monitoring equipment for a field station based on edge computing according to claim 1, characterized in that: The data acquisition module includes sensor devices, camera devices, data reading devices, smart meter devices, and a data acquisition terminal. The sensor devices are used to collect environmental parameters and equipment status information within the site using various sensor technologies. The camera devices are used to collect real-time images within the site and record video footage. The data reading devices are used to collect and identify data from objects using radio frequency technology. The smart meter devices are used to collect energy usage data in real time. The data acquisition terminal is used to aggregate, process, and forward the data collected by the sensor devices, camera devices, data reading devices, and smart meter devices.

3. The real-time monitoring equipment for a field station based on edge computing according to claim 2, characterized in that: Sensor devices include temperature sensors, humidity sensors, pressure sensors, and vibration sensors; camera devices include high-definition cameras, infrared cameras, and panoramic cameras; data reading devices include RFID readers; and smart meter devices include smart electricity meters, smart water meters, and smart gas meters.

4. The real-time monitoring equipment for a field station based on edge computing according to claim 3, characterized in that: When the edge computing module performs preliminary processing and analysis on the data collected by the data acquisition module using edge computing, it includes the following steps: S11: The data acquisition terminal transmits data to the edge computing module, and the edge computing module reads the data transmitted by the data acquisition terminal; S12: After receiving the data, the edge computing module first performs preprocessing operations to improve the data quality. The preprocessing operations include data cleaning, noise reduction, and deduplication. S13: The edge computing module uses built-in analysis algorithms to perform in-depth analysis of data, extract useful information and features, and identify anomalies and potential problems; S14: The edge computing module generates corresponding control commands and alarm information based on the analysis results; S15: The edge computing module feeds back the analysis results and control commands to the control system or equipment in the site in real time.

5. The real-time monitoring equipment for a field station based on edge computing according to claim 4, characterized in that: The built-in algorithms of the edge computing module include: A11: Data preprocessing algorithms are used to preprocess data to improve data quality; A12: Time series analysis algorithm used to process time series data; A13: Machine learning algorithms used for pattern recognition, classification, and prediction tasks; A14: Feature extraction algorithms are used to extract key features from raw data for subsequent analysis and identification; A15: Rule engine algorithm, used to judge real-time data according to preset rules and trigger corresponding control commands; A16: Optimization algorithms used to solve resource allocation, path planning, and other optimization problems; A17: Data encryption algorithm, used to encrypt sensitive data to ensure the security of data transmission and storage; A18: Intelligent recognition algorithm, used to intelligently identify different items in different scenarios.

6. The real-time monitoring equipment for a field station based on edge computing according to claim 5, characterized in that: The equipment control module includes: The control command receiving unit is responsible for receiving control commands sent from the edge computing module or the cloud server. The device interface unit is responsible for converting received control commands into signals and commands that the device can recognize, ensuring the accurate transmission and execution of control commands; The control logic processing unit is used to parse and process the received control commands and generate control signals for the equipment according to preset control logic and rules. The feedback monitoring unit is used to monitor the operating status and control effect of the equipment in real time, and transmit the feedback information to the edge computing module or cloud server for further adjustment and optimization.

7. The real-time monitoring equipment for a field station based on edge computing according to claim 6, characterized in that: The equipment control module also includes: Programmable logic controllers (PLCs) use programmable memory to store instructions for performing logical operations, sequential control, timing, counting, and arithmetic operations, and control various types of mechanical equipment or production processes through digital and analog inputs and outputs. Embedded systems are used to process and control devices in scenarios requiring real-time processing and control. The remote terminal unit is used to connect to various sensors and actuators to realize remote data acquisition, processing and control; Intelligent actuators are used to receive control signals and automatically adjust their output to control the controlled object.

8. The real-time monitoring equipment for a field station based on edge computing according to claim 7, characterized in that: It also includes an intermediate computing module, which is connected to multiple edge computing modules. The intermediate computing module is used to perform further real-time analysis on the data received by the edge computing modules when the edge computing modules detect anomalies.

9. The real-time monitoring equipment for a field station based on edge computing according to claim 8, characterized in that: When the intermediate computing module performs further real-time analysis on the data received by the edge computing module, it includes the following steps: S21: The intermediate computing module first receives data from each edge computing module; S22: Perform real-time analysis on the received data to quickly identify potential problems. The analysis algorithms used include data aggregation, pattern recognition, and anomaly detection. S23: Based on the results of real-time analysis, the intermediate computing module makes decisions and generates control commands; S24: The intermediate computing module distributes the analyzed and processed control commands to the relevant edge computing modules.

10. A real-time monitoring method for power stations based on edge computing, characterized in that: It includes the following steps: S31: Deploy edge computing devices with data acquisition, processing, and communication capabilities at key locations within the site; S32: Configure intermediate computing modules in the station control center or a suitable location, and establish signal connections between intermediate computing modules and multiple edge computing modules to ensure that data can be transmitted and shared in real time; S33: After the alarm function is triggered, the status of equipment, personnel and items in the site is maintained.

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