Thermal power plant safety monitoring system and method based on 5G network

Abstract

The invention relates to the field of power systems, in particular to a thermal power plant safety monitoring system and method based on a 5G network. The system comprises a self-driven detection vehicle, an equipment nameplate, monitoring equipment and a control module. The detection vehicle moves along a preset track to approach equipment in the thermal power plant; a data memory and a signal transmitter are arranged in the equipment nameplate, and information is provided for the detection vehicle; the monitoring device integrates a camera, a temperature sensor and a 5G communication module and uploads data to the control module. The control module compares the data with preset security data, and gives an alarm when the data does not meet the standard. The method comprises the steps of controlling the detection vehicle to move along the track, receiving equipment information, detecting temperature and shooting environment, uploading data, comparing a result, and deciding whether to give an alarm or not according to the comparison result, so that efficient and safe monitoring of thermal power plant equipment is realized. According to the invention, the inspection efficiency is improved, and the safety risk caused by insufficient obstacle avoidance capability of the robot in a complex environment is reduced.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a 5G network-based thermal power plant safety monitoring system and method. Background Art

[0002] In the field of safety monitoring of thermal power plants, traditional monitoring methods mainly rely on manual inspections and robots walking on the ground. Manual inspections are inefficient, and in extreme environments such as high temperature and high pressure, manual inspections are extremely risky and can easily miss safety hazards. Manual inspections not only consume a lot of human resources, but also in specific areas, such as near high-temperature equipment or high-voltage electrical facilities, workers may face safety threats such as burns and electric shocks. In addition, manual inspections are greatly affected by subjective factors, and fatigue or negligence may lead to unstable inspection quality, thereby increasing the risk of equipment failure and accidents.

[0003] In order to improve inspection efficiency and safety, existing technologies have begun to explore the use of robots for automated inspections. For example, patent CN115237131A proposes an inspection control method for an inspection robot, which plans the inspection path by receiving the original route map of the power plant and the predetermined inspection points, thereby reducing the inspection workload and improving the inspection efficiency. This method uses the robot to automatically inspect according to the predetermined inspection path, reducing human intervention and improving the continuity and reliability of the inspection. However, this method is prone to frequent alarms when the robot deviates from the predetermined inspection path, affecting the inspection efficiency, and has limitations in the handling of obstacles and path updates. In the complex environment of a thermal power plant, the robot may encounter unexpected obstacles or path changes, and it is necessary to replan the path or wait for human intervention, which limits the robot's autonomy and adaptability in complex environments.

[0004] In addition, patent CN118051054A describes an outdoor autonomous inspection robot for substations based on collaborative optimization, which aims to improve the inspection capability under multiple tasks. This technology improves the inspection efficiency to a certain extent through the collaborative work of multiple robots, especially when multiple inspection tasks need to be performed simultaneously, the overall inspection time can be reduced by task allocation and path planning optimization. However, in practical applications, the navigation and positioning system of the robot may still be interfered by environmental factors, such as the metal structure in the thermal power plant may have a shielding effect on the wireless signal, affecting the communication quality between the robot and the control center. In addition, the equipment layout in the thermal power plant is complex, and the robot needs precise positioning and obstacle avoidance capabilities when performing inspection tasks to avoid collisions with equipment or structures, and ensure the safety and effectiveness of the inspection process. In this complex and changeable environment, how to improve the mobility efficiency of the robot, reduce safety hazards, and real-time data communication has become a technical problem to be solved urgently. In response to the above problems, this application proposes a thermal power plant safety monitoring system and method based on a 5G network. Summary of the invention

[0005] The purpose of the present invention is to provide a thermal power plant safety monitoring system and method based on 5G network to solve the problems of low mobility and many safety hazards of monitoring robots in existing monitoring technologies.

[0006] To achieve the above purpose, the following technical solution is adopted.

[0007] A thermal power plant safety monitoring system based on 5G network, including:

[0008] At least one self-propelled inspection vehicle, moving along a preset track, for approaching various equipment in the thermal power plant;

[0009] Each device is equipped with a nameplate, which has a built-in data storage device and a signal transmitter for providing device information to the inspection vehicle;

[0010] The self-driving inspection vehicle is equipped with a monitoring device, which includes a camera for shooting, a temperature sensor, a receiver for receiving device information sent by a nameplate, and a 5G communication module for network information interaction;

[0011] The monitoring device uploads the acquired data to the control module through the 5G communication module;

[0012] The control module is used to compare the received data with the preset safety data in the database to determine whether it meets the safety standards. According to the comparison result, if it does not meet the safety standards, an alarm is issued.

[0013] Optionally, the control module includes:

[0014] A data processing unit, used for preprocessing and feature extraction of images captured by the camera;

[0015] A personnel activity analysis module, connected to the data processing unit, for analyzing personnel activities in the images captured by the camera to determine the number and location of on-site personnel;

[0016] The temperature data analysis module is connected to the data processing unit and is used to analyze the data collected by the temperature sensor and monitor whether the external temperature of the equipment exceeds a preset safety threshold.

[0017] Optionally, the self-driving inspection vehicle further includes a movement control unit for controlling the movement of the inspection vehicle along a preset track so that the self-driving inspection vehicle can accurately approach various equipment in the thermal power plant;

[0018] A positioning system, working in conjunction with a receiver on the self-propelled inspection vehicle, is used to determine the precise position of the inspection vehicle on the track.

[0019] Optionally, the control module is arranged on the self-driving inspection vehicle.

[0020] Optionally, the track includes a surround track and a passing track, the surround track surrounds each device to be detected in turn, the passing track is separately provided and connected with the surround track near each device to be detected, and also includes a track switching mechanism, the track switching mechanism is provided at the connection between the surround track and the passing track, the track switching mechanism is electrically connected to the control module, and is used to receive instructions from the control module to perform track switching.

[0021] A thermal power plant safety monitoring method based on 5G network includes the following steps:

[0022] Using the tracks laid at a safe distance along the equipment in the thermal power plant, the self-driving inspection vehicle is controlled to move along the tracks and approach the equipment;

[0023] The monitoring device on the self-driving inspection vehicle receives the information transmitted by the equipment nameplate, wherein the equipment nameplate is preset with a data storage device and a signal transmitter, and the signal transmission distance covers the laid track;

[0024] Use the temperature sensor on the self-driving inspection vehicle to detect the external temperature of the equipment and obtain temperature data;

[0025] Use the camera on the self-driving inspection vehicle to shoot the environment in the equipment area to obtain the external status of the equipment and the data shot by the on-site personnel;

[0026] The acquired temperature data and shooting data are uploaded to the control module using the 5G communication module;

[0027] The control module compares the received temperature data and shooting data with the preset safety data in the database to obtain a comparison result;

[0028] If the comparison result meets the safety value, no alarm command is issued, and the self-driving inspection vehicle continues to move along the track to conduct safety monitoring on the next device;

[0029] If the comparison result does not meet the safety value, the safety item category that does not meet the safety value and the alarm command will be sent to the monitoring terminal, and the self-driving inspection vehicle will continue to move along the track to perform safety monitoring on the next device.

[0030] Optionally, the step of the control module comparing the received temperature data and the photographed data with the preset safety data in the database specifically includes:

[0031] The control module compares the received temperature data with the preset safety temperature value in the database, and analyzes the camera data through the preset neural network model to obtain the number of staff and equipment status, compares the number of staff obtained with the number of people required for production in the database, and compares the obtained equipment status with the normal equipment status in the database.

[0032] Optionally, the specific steps of analyzing the camera data by a preset neural network model to obtain the number of staff members include:

[0033] Import the image processing library and drawing library, where the image processing library is used for image processing and the drawing library is used for image display;

[0034] Use the image processing library to read camera data;

[0035] Performing grayscale processing on the read camera data to obtain a grayscale image;

[0036] Detect faces in grayscale images using the face recognition algorithm in the image processing library to obtain a list of face position data;

[0037] Calculate the length of the face position data list to get the number of faces;

[0038] Output the number of faces as the number of staff members.

[0039] Optionally, the following steps are also included: when the self-driving inspection vehicle enters the signal transmission range of the current equipment nameplate, the information transmitted by the equipment nameplate is received by the receiver. From then until the self-driving inspection vehicle leaves the signal transmission range of the current equipment nameplate, the external temperature of the current equipment is detected by the temperature sensor at multiple frequencies and multiple angles to obtain temperature data; the environment in the equipment area is photographed by the camera at multiple frequencies and multiple angles to obtain shooting data.

[0040] Optionally, the following steps are also included: when the self-driving inspection vehicle enters the signal transmission range of the nameplate of the current equipment, the control module determines whether the current equipment needs to be inspected. If inspection is required, the track switching mechanism switches the passing track to the surrounding track, and the self-driving inspection vehicle enters the surrounding track to perform a full range of external temperature detection on the current equipment and take a comprehensive picture of the environment in the current equipment area; when the control module determines that the temperature data and the shooting data are complete, the track switching mechanism is controlled to switch the surrounding track to the passing track;

[0041] When the control module determines that the current device does not need to be inspected, the self-driving inspection vehicle keeps moving on the pass track.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention uses a self-driving inspection vehicle to move along a preset track, replacing a ground walking robot, thereby improving inspection efficiency. Since the inspection vehicle runs on the track, the problem of slow movement efficiency caused by changeable ground conditions is reduced, and the safety risk caused by the robot's insufficient obstacle avoidance ability in a complex environment is also reduced. The present invention avoids the problem that the ground walking robot cannot continue to perform tasks when encountering obstacles through the application of a self-driving inspection vehicle and a track system, and reduces inspection interruptions and potential safety hazards caused by obstacles. The positioning system of the self-driving inspection vehicle in the present invention works in conjunction with the signal transmitter of the equipment nameplate to improve the positioning accuracy of the inspection vehicle on the track. This mechanism is more reliable than the navigation and positioning system of the ground walking robot because it reduces the interference of environmental factors and improves the inspection effect and safety. The nameplate installed on each device has a built-in data storage device and a signal transmitter, so that the self-driving inspection vehicle can efficiently receive equipment information without close scanning or contact like a ground walking robot, thereby improving the efficiency and accuracy of information acquisition. The self-driving inspection vehicle uploads the acquired data to the control module in real time through the 5G communication module, and uses the high speed and low latency characteristics of the 5G network to ensure the real-time and accuracy of data transmission, which is crucial for quickly responding to potential safety issues. Through the design of the track system, the self-driving inspection vehicle can conduct inspections according to preset paths and plans, reducing the inefficiency caused by improper path planning of ground walking robots, and also reducing the frequency of re-planning paths due to environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a module schematic diagram of an embodiment of a thermal power plant safety monitoring system based on a 5G network according to the present invention.

[0045] Figure 2 It is a schematic diagram of the step flow of an embodiment of a thermal power plant safety monitoring method based on a 5G network according to the present invention. DETAILED DESCRIPTION

[0046] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0047] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0048] Example 1

[0049] like Figure 1 As shown in the figure, a thermal power plant safety monitoring system based on 5G network mainly includes a self-driving inspection vehicle, equipment nameplate, monitoring equipment, 5G communication module, control module and alarm module. The self-driving inspection vehicle is the core part of the system, moving along the preset track in the thermal power plant to approach and monitor various equipment in the thermal power plant. A nameplate is installed on each device, which has a built-in data storage device and signal transmitter for storing basic information of the equipment and providing this information to the self-driving inspection vehicle.

[0050] The monitoring equipment installed on the self-driving inspection vehicle includes a camera and a temperature sensor for real-time monitoring of the appearance status and external temperature of the equipment. In addition, the monitoring equipment also includes a receiver for receiving equipment information sent by the signal transmitter on the equipment nameplate, and a 5G communication module for uploading the data obtained by the monitoring equipment to the control module.

[0051] The 5G communication module uses the high speed, large bandwidth and low latency characteristics of the 5G network to ensure that the monitoring data can be transmitted to the control module in real time and accurately. The control module is the data processing center of the system. It compares the received data with the preset safety data in the database to determine whether the equipment meets the safety standards. If the comparison results show that the equipment does not meet the safety standards, the control module will issue an alarm through the alarm module to prompt relevant personnel to take appropriate safety measures.

[0052] In order to realize the specific implementation of the above monitoring system, the self-driving inspection vehicle can move along a preset track through a wheeled or tracked moving mechanism, and the track can be a ground track or a suspended track to adapt to different thermal power plant environments. The data storage device of the equipment nameplate can store information such as the model, location, maintenance history, etc. of the equipment, and the signal transmitter uses wireless communication technology, such as RFID or NFC, to wirelessly send this information to the inspection vehicle.

[0053] The camera in the monitoring device can be a high-definition camera with night vision and zoom function to adapt to different lighting conditions and monitoring distances. The temperature sensor can be an infrared temperature sensor that can measure the temperature of the device surface without contact. The receiver can be a wireless communication module that works in pair with the signal transmitter on the device nameplate to ensure accurate reception of information.

[0054] The 5G communication module can be integrated into the self-driving inspection vehicle and communicate with the 5G base station in the thermal power plant to achieve wireless data upload. The control module can be a high-performance computer system with powerful data processing capabilities, which can quickly compare data and make decisions. The preset safety data in the database can include the safety operation parameters of various equipment, such as temperature thresholds, pressure thresholds, etc.

[0055] The alarm module may include an audible and visual alarm and / or a remote notification system, such as SMS, email or mobile application push notification, to ensure that relevant personnel can receive the alarm information in a timely manner. Through this specific implementation, the thermal power plant safety monitoring system can achieve real-time and comprehensive monitoring of thermal power plant equipment, timely discover safety hazards, and improve the safety management level of the thermal power plant.

[0056] As a specific example of the above example, the detailed composition and functions of the control module are as follows:

[0057] The control module is the core data processing center of the system, responsible for receiving the data uploaded by the self-driving inspection vehicle through the 5G communication module, and conducting in-depth analysis and processing. The control module consists of three main submodules: data processing unit, personnel activity analysis module and temperature data analysis module.

[0058] The data processing unit is responsible for preprocessing the images captured by the camera, including denoising, contrast enhancement, color correction, etc., to improve image quality and provide clear image data for subsequent feature extraction and analysis.

[0059] Feature extraction is the process of using algorithms to identify specific patterns and objects in images, such as the outline of equipment, specific safety signs, potential safety hazards, etc. These features will be converted into quantifiable data for further analysis.

[0060] The personnel activity analysis module is connected to the data processing unit, receives the processed image data, and analyzes the personnel activities in the image. This module uses computer vision techniques such as motion detection and behavior recognition to determine the number and location of on-site personnel.

[0061] This module can use deep learning algorithms, AI algorithms, especially convolutional neural networks (CNN), to identify and track people and assess whether their behavior complies with safety regulations. By analyzing the location and activities of people, the system can promptly detect potential safety risks, such as people mistakenly entering dangerous areas or operating in an irregular manner.

[0062] The temperature data analysis module is also connected to the data processing unit, receives data from the temperature sensor, and analyzes whether the temperature outside the device exceeds a preset safety threshold.

[0063] The module uses statistical and machine learning techniques to identify abnormal patterns in temperature data, such as a sudden increase in temperature or a sustained abnormally low temperature. By setting different thresholds, the system can respond to different types of equipment and environmental conditions to ensure that the equipment operates within a safe temperature range.

[0064] In a specific implementation, the control module can be deployed on a cloud server or a local server, or the control module can be a server to take advantage of its powerful computing power. The server will run specialized software that integrates the above three modules and is able to process and analyze a large amount of data from the self-driving inspection vehicle.

[0065] In order to achieve real-time monitoring and rapid response, the control module will continuously receive and process data and compare it with the preset safety data in the database. If the analysis results show any safety hazards, such as abnormal personnel activities or equipment temperature exceeding the threshold, the control module will immediately issue an alarm through the alarm module, which can be an audible and visual alarm, a text message notification system or an automatic call to the security person in charge.

[0066] In addition, the control module can also provide a user interface that allows operators to monitor real-time data, historical records and alarm logs, as well as manually adjust preset safety thresholds and parameters. In this way, the control module can not only automatically process and analyze data, but also provide interactive monitoring and management functions to meet the complex needs of thermal power plant safety management.

[0067] As a specific example of the above example, the specific structure and functions of the self-driving inspection vehicle are as follows:

[0068] The self-driving inspection vehicle is a key component of the system, responsible for moving along the preset track and approaching various equipment in the thermal power plant for monitoring. The specific implementation method includes two key parts: a mobile control unit and a positioning system.

[0069] The mobile control unit is the core control component of the self-driving inspection vehicle, responsible for controlling the movement of the inspection vehicle along the preset track. The unit receives instructions from the control module and accurately controls the start, stop, acceleration and deceleration of the inspection vehicle.

[0070] The mobile control unit integrates motor drives and sensors that can adjust the speed and direction of the inspection vehicle according to the slope and curvature of the track, ensuring smooth and precise approach to each device.

[0071] The unit also has fault diagnosis and self-repair capabilities, which can monitor the movement status of the inspection vehicle in real time and automatically adjust or stop operation when an abnormality is detected to prevent potential damage.

[0072] The positioning system works with the receiver on the self-driving inspection vehicle to determine the precise position of the inspection vehicle on the track. The system can use a variety of positioning technologies such as GPS, RFID or vision positioning system.

[0073] For example, by placing RFID tags on the track, the inspection vehicle determines its position on the track by reading these tags. Alternatively, by installing cameras and visual recognition software, the system can identify specific markings or landmarks on the track to determine the location of the inspection vehicle.

[0074] The data of the positioning system is transmitted to the control module in real time, so that the control module can monitor the position of the inspection vehicle in real time and adjust the monitoring plan or issue instructions according to the position information.

[0075] In specific implementation, the self-driving inspection vehicle can be designed as a multifunctional platform that can carry different monitoring equipment according to different monitoring needs. For example, in addition to basic cameras and temperature sensors, gas sensors, vibration sensors, etc. can also be installed as needed.

[0076] The software of the mobile control unit and the positioning system can be integrated into the control system of the inspection vehicle, which can be a high-performance computing platform based on an embedded computer or industrial PC. The software platform is capable of processing inputs from sensors, executing control algorithms, and communicating with the control module.

[0077] In addition, the design of the self-driving inspection vehicle also takes into account the special environment of thermal power plants, such as high temperature, high humidity, dust, etc. Therefore, the material and structural design of the inspection vehicle must be able to withstand these environmental factors to ensure long-term stable operation.

[0078] The self-driving inspection vehicle can move accurately and reliably within the thermal power plant to perform safety monitoring tasks, while ensuring the accuracy and real-time nature of the monitoring data, providing strong support for the safe operation of the thermal power plant.

[0079] As a preferred example, the control module is arranged on the self-driving detection vehicle, as follows:

[0080] The control module is directly integrated into the self-driving inspection vehicle, which can reduce external wiring, reduce system complexity, and improve the real-time performance of data transmission. The control module includes a data processing unit, a personnel activity analysis module, and a temperature data analysis module. These modules work together to achieve real-time processing and analysis of monitoring data. In order to protect the instruments and equipment in the control module from damage due to bumps and vibrations during vehicle driving, the control module is fixed with a vibration reduction device to avoid damage to the instruments due to bumps and vibrations during transportation.

[0081] Considering the high temperature in thermal power plants, the control module is designed with a powerful heat dissipation function. The heat sink is embedded in the front of the machine body, and the heat is dissipated inside the machine body by drawing in outside air. At the same time, the filter cover and swing head structure are designed to clean the large amount of soot adhering to the filter cover through multiple impacts by the rebound effect of the impact ball, ensuring that the temperature inside the monitoring system is suitable for normal operation.

[0082] Example 2

[0083] like Figure 2 As shown, a thermal power plant safety monitoring method based on 5G network has the following steps:

[0084] Using the track laid along the safety distance set for each device in the thermal power plant, the mobile control unit accurately controls the self-driving inspection vehicle to move along the track and approach each device. The mobile control unit receives instructions from the control module and adjusts the speed and direction of the inspection vehicle to ensure that the inspection vehicle approaches each device smoothly.

[0085] The monitoring equipment on the self-driving inspection vehicle includes a receiver for receiving information transmitted by the equipment nameplate. The equipment nameplate has a built-in data storage and signal transmitter, and the signal transmission distance covers the laid track. The receiver receives data from the nameplate through wireless communication technology (such as RFID, NFC or Wi-Fi), which includes information such as the model, location, and maintenance history of the equipment.

[0086] The temperature sensor on the self-driving inspection vehicle is used to detect the external temperature of the equipment to obtain temperature data. The temperature sensor can be an infrared sensor or a thermocouple, which can measure the temperature of the surface of the equipment non-contactly or contact-wise, and transmit the data to the control module in real time.

[0087] Use the camera on the self-driving inspection vehicle to shoot the environment in the equipment area to obtain the external status of the equipment and the shooting data of the on-site personnel. The camera can be a high-definition camera with night vision and zoom functions to adapt to different lighting conditions and monitoring distances.

[0088] The acquired temperature data and shooting data are uploaded to the control module using the 5G communication module. The 5G communication module is responsible for processing and forwarding data, and uses the high speed and low latency characteristics of the 5G network to ensure real-time data transmission.

[0089] The control module compares the received temperature data and shooting data with the preset safety data in the database to obtain a comparison result. The preset safety data in the database includes the safety operation parameters of various devices, such as temperature thresholds, pressure thresholds, etc.

[0090] If the comparison result meets the safety value, the control module does not issue an alarm command, and the self-driving inspection vehicle continues to move along the track to perform safety monitoring on the next device.

[0091] If the comparison result does not meet the safety value, the control module will send the safety item category that does not meet the safety value and the alarm command to the monitoring terminal. The monitoring terminal can be the central control room of the thermal power plant or the mobile device of the security personnel. At the same time, the self-driving inspection vehicle continues to move along the track to conduct safety monitoring on the next device.

[0092] Throughout the monitoring process, the control module monitors the data in real time and adjusts the monitoring plan or issues instructions as needed. For example, if the temperature of a device continues to rise, the control module can instruct the self-driving inspection vehicle to return to the device for further monitoring.

[0093] As a specific example, the detailed steps of the control module processing the monitoring data and comparing it with the preset safety data in the database are as follows:

[0094] The control module receives temperature sensor data from the self-driving test vehicle. This data includes real-time temperature readings outside the device.

[0095] The control module compares these real-time temperature data with the preset safety temperature values ​​for each device in the database. The safety temperature values ​​in the database are set based on the specifications of the equipment manufacturer and the historical operating data of the thermal power plant to ensure that the equipment operates within a safe temperature range.

[0096] The control module receives video stream or image data captured by a camera of the self-driving inspection vehicle.

[0097] The image data is analyzed by a preset neural network model, which is specially trained for thermal power plant safety monitoring and can identify and classify objects and activities in the image.

[0098] The neural network model uses deep learning techniques, such as convolutional neural networks (CNNs), to identify the status of people and equipment in the image. This includes but is not limited to the location and number of people and the operating status of equipment.

[0099] After analyzing the image data, the neural network model is able to identify and count the people in the image to obtain the number of on-site staff.

[0100] The control module compares the number of people obtained with the number of people required for production recorded in the database. This step is used to ensure that the number of on-site staff meets safety regulations and production needs, and to prevent safety risks caused by too many or too few people.

[0101] The neural network model can also assess the external condition of the equipment, such as the integrity of the equipment and whether there are obvious signs of damage.

[0102] The control module compares the acquired device status with the normal device status recorded in the database. This step is used to ensure that the device is in good operating condition and to detect any possible faults or abnormalities in a timely manner.

[0103] Based on the comparison results, the control module makes a safety decision. If the temperature data, number of personnel and equipment status meet the preset safety standards, the control module will not issue an alarm and allow the self-driving inspection vehicle to continue to perform the monitoring task.

[0104] If any comparison result exceeds the preset safety threshold, the control module will trigger an alarm. The alarm information will include the specific category of items that do not meet the safety value and will be sent to the monitoring terminal via the 5G network, such as the central control room of the thermal power plant or the mobile device of the security personnel, so that appropriate safety measures can be taken.

[0105] The control module continuously monitors the status of all equipment and updates the safety thresholds in the database based on real-time data to adapt to changes in the operating conditions of the thermal power plant.

[0106] The control module can also optimize the accuracy and efficiency of the neural network model based on historical data and monitoring results, thereby improving the intelligence level of safety monitoring.

[0107] As a specific example, import the image processing library cv2 and the drawing library matplotlib, where cv2 is used for image processing and matplotlib is used for image display;

[0108] Read the captured live picture, use the cv2.imread() method to read the picture, and store it in the variable image.

[0109] Perform grayscale processing on the read live picture, use the cv2.cvtColor() method to convert the image from BGR to a grayscale image, and store it in the variable gray.

[0110] Detect faces and use the face_cascade.detectMultiScale() method to detect faces in an image. This method requires setting multiple parameters, including image, scale, minimum number of neighbors, and minimum size. After using this method, a list faces containing the face locations will be returned.

[0111] Calculate the number of detected faces and use the len() function to calculate the length of the list faces, that is, the number of detected faces.

[0112] To output the results, use the print() function.

[0113] As a preferred example, a 5G network-based thermal power plant safety monitoring method also includes the following steps: when the self-driving inspection vehicle enters the current equipment nameplate signal transmission range, the information transmitted by the equipment nameplate is received by the receiver on the vehicle. The equipment nameplate has a built-in data storage device and a signal transmitter, which can cover the laid track.

[0114] The self-driving inspection vehicle is equipped with a high-precision temperature sensor. When it enters the signal transmission range of the equipment nameplate until it leaves, it performs multi-frequency and multi-angle detection of the current equipment's external temperature. This step ensures the comprehensiveness and accuracy of the temperature data, and can capture the temperature distribution of the equipment from different angles, thereby obtaining more accurate temperature data.

[0115] At the same time, the high-definition camera on the self-driving inspection vehicle takes multiple shots of the environment in the equipment area at multiple frequencies and angles to obtain the external status of the equipment and the shooting data of the on-site personnel. This shooting method can provide more comprehensive on-site information, including the immediate status of the equipment and the surrounding environment, providing rich materials for subsequent data analysis.

[0116] Through the above multi-frequency and multi-angle detection and shooting, the self-driving inspection vehicle collects a large amount of temperature data and image data. These data will be transmitted to the control module for further processing and analysis.

[0117] The collected temperature data and shooting data are uploaded to the control module in real time using the 5G communication module. The high speed and low latency of the 5G network ensure the efficiency and real-time nature of data transmission, which is crucial for the safety monitoring of thermal power plants.

[0118] The control module compares the received temperature data and shooting data with the preset safety data in the database to evaluate the safety status of the equipment. This step is the key in the safety monitoring method, which can detect potential safety problems in time and take corresponding measures.

[0119] According to the comparison results, if the data meets the safety value, the system will not issue an alarm, and the self-driving inspection vehicle will continue to move along the track to the next device for monitoring. If the data does not meet the safety value, the system will issue an alarm and take appropriate safety measures, while the self-driving inspection vehicle continues to perform the monitoring task.

[0120] Example 3

[0121] The design and functions of a track system for a thermal power plant safety monitoring system based on a 5G network are as follows:

[0122] The track includes two types: a surrounding track and a passing track. The surrounding track surrounds each device to be inspected in turn to ensure that the self-driving inspection vehicle can perform all-round monitoring of each device. The passing track is set separately and connected to the surrounding track near each device to be inspected, so as to allow the self-driving inspection vehicle to pass quickly when not in a monitoring state.

[0123] The system also includes a track switching mechanism, which is arranged at the connection between the surrounding track and the passing track. The core function of the track switching mechanism is to realize the switching of the self-driving inspection vehicle between different tracks so as to switch between the monitoring mode and the fast passing mode.

[0124] The track switching mechanism is electrically connected to the control module on the self-driving inspection vehicle and is used to receive instructions from the control module to switch tracks. This design allows remote or automatic control of track switching, improving the flexibility and response speed of the system.

[0125] The track switching mechanism may include symmetrically arranged fixed tracks, between which a moving frame is slidably mounted. A straight track and a curved track for switching the route of the self-driving inspection vehicle are arranged at the bottom of the moving frame. A first end plate and a second end plate are respectively installed at both ends of the fixed track, and a driving motor is installed on the first end plate, which is transmission-connected to the moving frame and is used to control the movement of the moving frame.

[0126] When the control module receives a monitoring task, it will send a command to the track switching mechanism, causing the mobile frame to move to the side of the first end plate, the main track to connect with the curved track, and the self-driving inspection vehicle to enter the surrounding track for monitoring. After the task is completed, the control module sends another command, the mobile frame moves to the side of the second end plate, the main track to connect with the straight track, and the self-driving inspection vehicle switches to the pass track to continue to perform other tasks or pass quickly.

[0127] A thermal power plant safety monitoring method based on 5G network also includes the following steps:

[0128] When the self-driving inspection vehicle enters the current equipment nameplate signal transmission range, the control module first receives the information transmitted by the equipment nameplate through the receiver on the vehicle. This information includes the equipment's identification, location, and possible maintenance records.

[0129] The control module determines whether it is necessary to detect the current device based on the received information and the preset detection logic (for example, the maintenance cycle of the device, historical fault records, current operating status, etc.).

[0130] If the control module determines that the current device needs to be tested, it will send a command to the track switching mechanism to activate the track switching mechanism and switch the passing track to the surrounding track.

[0131] The self-driving inspection vehicle then enters the orbit to conduct a full range of external temperature detection and environmental photography of the current equipment. This step involves using the temperature sensors and cameras on the vehicle to collect detailed data on the equipment from different angles and positions.

[0132] During the comprehensive inspection process, the temperature sensor of the self-driving inspection vehicle will collect temperature data outside the equipment at multiple frequencies, while the camera will capture images of the equipment and its surrounding environment from multiple angles.

[0133] These data will be transmitted to the control module in real time for further processing and analysis.

[0134] The control module checks the integrity and validity of the collected temperature data and shooting data to ensure that the collected data can fully reflect the safety status of the equipment.

[0135] Once the control module determines that the data is complete and valid, it will send instructions to the track switching mechanism again, controlling the track switching mechanism to switch the surrounding track back to the passing track so that the self-driving inspection vehicle can continue to move along the passing track to the next inspection point.

[0136] If the control module determines that the current device does not need to be inspected, the self-driving inspection vehicle will not switch to the surrounding track, but will remain on the passing track and continue to move to the next device along the preset route.

[0137] During the entire process, the control module continuously monitors the monitoring status of all equipment and updates the monitoring plan and track switching instructions based on real-time data to achieve continuous monitoring and real-time response.

[0138] If any abnormality is found during data collection or analysis, the control module will immediately trigger an alarm and take corresponding safety measures, such as stopping equipment operation and notifying maintenance personnel.

[0139] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A thermal power plant safety monitoring system based on 5G network, characterized in that: include, At least one self-propelled inspection vehicle, moving along a preset track, for approaching various equipment in the thermal power plant; Each device is equipped with a nameplate, which has a built-in data storage device and a signal transmitter for providing device information to the inspection vehicle; The self-driving inspection vehicle is equipped with a monitoring device, which includes a camera for shooting, a temperature sensor, a receiver for receiving device information sent by a nameplate, and a 5G communication module for network information interaction; The monitoring device uploads the acquired data to the control module through the 5G communication module; The control module is used to compare the received data with the preset safety data in the database to determine whether it meets the safety standards. According to the comparison result, if it does not meet the safety standards, an alarm is issued.

2. According to claim 1, a thermal power plant safety monitoring system based on 5G network is characterized in that: The control module comprises: A data processing unit, used for preprocessing and feature extraction of images captured by the camera; A personnel activity analysis module, connected to the data processing unit, for analyzing personnel activities in the images captured by the camera to determine the number and location of on-site personnel; The temperature data analysis module is connected to the data processing unit and is used to analyze the data collected by the temperature sensor and monitor whether the external temperature of the equipment exceeds a preset safety threshold.

3. According to a 5G network-based thermal power plant safety monitoring system according to claim 1, it is characterized in that: The self-driving inspection vehicle further includes a movement control unit for controlling the movement of the inspection vehicle along a preset track so that the self-driving inspection vehicle can accurately approach various equipment in the thermal power plant; A positioning system, working in conjunction with a receiver on the self-propelled inspection vehicle, is used to determine the precise position of the inspection vehicle on the track.

4. According to claim 1, a thermal power plant safety monitoring system based on 5G network is characterized in that: The control module is arranged on the self-driving detection vehicle.

5. According to a 5G network-based thermal power plant safety monitoring system according to claim 1, it is characterized in that: The track includes a surround track and a passing track. The surround track surrounds each device to be detected in turn. The passing track is separately arranged and connected with the surround track near each device to be detected. It also includes a track switching mechanism. The track switching mechanism is arranged at the connection between the surround track and the passing track. The track switching mechanism is electrically connected to the control module and is used to receive instructions from the control module to perform track switching.

6. A 5G network-based thermal power plant safety monitoring method, based on a 5G network-based thermal power plant safety monitoring system according to any one of claims 1 to 5, characterized in that: The following steps are included: Using the tracks laid at a safe distance along the equipment in the thermal power plant, the self-driving inspection vehicle is controlled to move along the tracks and approach the equipment; The monitoring device on the self-driving inspection vehicle receives the information transmitted by the equipment nameplate, wherein the equipment nameplate is preset with a data storage device and a signal transmitter, and the signal transmission distance covers the laid track; Use the temperature sensor on the self-driving inspection vehicle to detect the external temperature of the equipment and obtain temperature data; Use the camera on the self-driving inspection vehicle to shoot the environment in the equipment area to obtain the external status of the equipment and the data shot by the on-site personnel; The acquired temperature data and shooting data are uploaded to the control module using the 5G communication module; The control module compares the received temperature data and shooting data with the preset safety data in the database to obtain a comparison result; If the comparison result meets the safety value, no alarm command is issued, and the self-driving inspection vehicle continues to move along the track to conduct safety monitoring on the next device; If the comparison result does not meet the safety value, the safety item category that does not meet the safety value and the alarm command will be sent to the monitoring terminal, and the self-driving inspection vehicle will continue to move along the track to perform safety monitoring on the next device.

7. A thermal power plant safety monitoring method based on 5G network according to claim 6, characterized in that: The control module compares the received temperature data and shooting data with the preset safety data in the database, specifically including: The control module compares the received temperature data with the preset safety temperature value in the database, and analyzes the camera data through the preset neural network model to obtain the number of staff and equipment status, compares the number of staff obtained with the number of people required for production in the database, and compares the obtained equipment status with the normal equipment status in the database.

8. A thermal power plant safety monitoring method based on 5G network according to claim 7, characterized in that: The specific steps of analyzing the camera data through the preset neural network model and obtaining the number of staff members include: Import the image processing library and drawing library, where the image processing library is used for image processing and the drawing library is used for image display; Use the image processing library to read camera data; Performing grayscale processing on the read camera data to obtain a grayscale image; Detect faces in grayscale images using the face recognition algorithm in the image processing library to obtain a list of face position data; Calculate the length of the face position data list to get the number of faces; Output the number of faces as the number of staff members.

9. A thermal power plant safety monitoring method based on 5G network according to claim 6, characterized in that: It also includes the following steps: when the self-driving inspection vehicle enters the signal transmission range of the current equipment nameplate, the information transmitted by the equipment nameplate is received by the receiver. From this time until the self-driving inspection vehicle leaves the signal transmission range of the current equipment nameplate, the external temperature of the current equipment is detected by the temperature sensor at multiple frequencies and multiple angles to obtain temperature data; the environment in the equipment area is photographed by the camera at multiple frequencies and multiple angles to obtain photographing data.

10. A thermal power plant safety monitoring method based on 5G network according to claim 6, characterized in that: The following steps are also included: when the self-driving inspection vehicle enters the signal transmission range of the nameplate of the current equipment, the control module determines whether the current equipment needs to be inspected. If inspection is required, the track switching mechanism switches the passing track to the surrounding track, and the self-driving inspection vehicle enters the surrounding track to perform a full range of external temperature inspection on the current equipment and take a comprehensive picture of the environment in the current equipment area; when the control module determines that the temperature data and the shooting data are complete, the track switching mechanism is controlled to switch the surrounding track to the passing track; When the control module determines that the current device does not need to be inspected, the self-driving inspection vehicle keeps moving on the pass track.

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