A safe operation management and control system

Abstract

The utility model discloses a safe operation management and control system, including weather unit, weather unit includes weather acquisition device and information acquisition module, target positioning unit, target positioning unit includes personnel position module and target detection module, management and control unit, management and control unit includes data processing module and decision module. The utility model has the beneficial effect that the temperature and humidity sensor, the wind speed and direction sensor, the precipitation sensor and the video monitoring device identify and obtain real -time environmental information, weather data identifies and obtains real -time weather, personnel position module positions personnel position, and target detection module monitors dangerous area through the camera device, and through the electronic fence dynamic calibration, and the above information is arranged and fused through the data processing module, and the decision module executes the judgment of data processing module, and the remote monitoring module revises data processing module result, and the early warning notification module receives the result from data processing module and carries out early warning work.

Description

Technical Field

[0001] This utility model relates to the field of safe operation management and control, and in particular to a safe operation management and control system. Background Technology

[0002] With the acceleration of industrialization, safety issues at various work sites are becoming increasingly prominent, especially in high-risk industries such as power, construction, mining, and chemical industries. Ensuring the safety of personnel in dangerous areas has become an important issue. Traditional safety management relies heavily on manual inspections and static safety signs, lacking dynamic real-time monitoring and automated early warning methods.

[0003] Especially when facing extreme weather conditions (such as lightning, strong winds, and heavy rain), the safety risks to workers increase dramatically. In existing technologies, weather warnings are mostly issued by external meteorological stations. Although effective, there is usually a time lag in the issuance of warnings, and the detailed forecasts for specific work sites are insufficient, making it impossible to provide accurate and timely responses when extreme weather occurs. In terms of personnel detection in dangerous areas, some existing safety management systems usually rely on manual patrols or simple intrusion detection systems to determine whether personnel have entered dangerous areas. This is easily affected by human negligence, misjudgment, or missed detections. For example, insufficient frequency of manual patrols may cause missed danger signals, and comprehensive coverage of all areas cannot be achieved. Traditional safety operation systems rely heavily on manual judgment and reaction. Safety personnel manually determine whether to take protective measures based on real-time changes in the work environment. This method has problems with slow response speed and low efficiency, and is easily affected by subjective factors such as the experience and judgment of managers. In existing systems, many safety warning, personnel detection, and equipment monitoring functions are scattered across different platforms or systems, making it difficult for managers to view all relevant information in a centralized interface, affecting information sharing and decision-making efficiency. Utility Model Content

[0004] Therefore, the technical problem to be solved by this utility model is: real-time monitoring and timely early warning of hazardous working environments, as well as accurate detection of workers.

[0005] The above-mentioned technical problems are solved by the following technical solution: This utility model proposes a safe operation control system, which includes a meteorological unit, wherein the meteorological unit includes a meteorological acquisition device and an information acquisition module.

[0006] The target positioning unit includes a personnel location module and a target detection module.

[0007] The control unit includes a data processing module and a decision-making module. The meteorological data acquisition device, personnel location module, and target detection module transmit the collected data to the data processing module via wired or wireless means. The data processing module transmits the data to the decision-making module via wired or wireless means. The decision-making module triggers early warnings and automated responses based on preset rules.

[0008] In a preferred embodiment of the safety operation control system described in this utility model: the meteorological data acquisition device includes a temperature and humidity sensor, a wind speed and direction sensor, a precipitation sensor, and a video monitoring device to obtain environmental information; the temperature and humidity sensor, wind speed and direction sensor, and precipitation sensor are integrated into a single unit.

[0009] The information acquisition module is used to collect real-time weather information. The information acquisition module includes a first core processor, a first communication interface, a first memory, a data parsing component, a first power supply, and a first input / output interface. The first core processor is electrically connected to the first communication interface, the first memory, the data parsing component, and the first input / output interface, respectively. It controls the first communication interface to acquire real-time weather information from the network, stores the acquired weather information in the first memory, parses the weather data through the data parsing component, and transmits the parsed data to the data processing module through the first input / output interface. The first power supply is electrically connected to the first core processor, the first communication interface, the first memory, the data parsing component, and the first input / output interface, respectively, to provide a stable power supply.

[0010] In a preferred embodiment of the safety operation control system of this utility model: the temperature and humidity sensor, wind speed and direction sensor, precipitation sensor and video monitoring device are connected to the data processing module through RS485 or Ethernet interface, and the information acquisition module is connected to the data processing module through HTTP protocol.

[0011] In a preferred embodiment of the safety operation control system of this utility model: the personnel location module includes a Wi-Fi access point and a mobile terminal device. The mobile terminal device communicates with the Wi-Fi access point through a Wi-Fi signal to realize personnel location positioning.

[0012] In a preferred embodiment of the safety operation control system of this utility model: the target detection module includes at least one camera device and is installed above the hazardous area, and the camera device is connected to the data processing module through an IP network.

[0013] In a preferred embodiment of the safety operation control system of this utility model: the data processing module includes a second core processor, a second memory, a second communication device, a data processing component, a second input / output interface, and a second power supply. The second core processor is electrically connected to the second memory, the second communication device, the data processing component, and the second input / output interface, respectively, and is used to coordinate the work of each component and perform data processing and logic control.

[0014] The second communication device is electrically connected to the second core processor and is used to receive data from the meteorological unit, the target positioning unit, and the target detection module, and transmit the processed data to the decision module.

[0015] The second memory is electrically connected to the second core processor and is used to store the received data as well as intermediate and final results during the processing.

[0016] The data processing component is electrically connected to the second core processor and is used to preprocess, fuse, and analyze the received data.

[0017] The second input / output interface is electrically connected to the second core processor and is used for data interaction with other modules;

[0018] The second power supply is electrically connected to the second core processor, the second memory, the second communication device, the data processing unit, and the second input / output interface, respectively, and is used to provide a stable power supply to each component of the data processing module.

[0019] In a preferred embodiment of the safety operation control system of this utility model: the decision module includes a third core processor, a third memory, a third communication device, a logic control device, a third power supply, and a third input / output interface. The third core processor is electrically connected to the third memory, the third communication device, the logic control device, and the third input / output interface, respectively, and is used to coordinate the work of each component and execute decision logic and control instructions.

[0020] The third memory is electrically connected to the third core processor and is used to store preset decision rules, intermediate results during processing, and final decision results.

[0021] The third communication device is electrically connected to the third core processor and is used to receive the processing results from the data processing module and transmit the decision results to the early warning notification module and the remote monitoring module.

[0022] The logic control component is electrically connected to the third core processor and is used to generate decision instructions based on preset rules and real-time data.

[0023] The third input / output interface is electrically connected to the third core processor and is used for data interaction and control signal transmission with other modules.

[0024] The third power supply is electrically connected to the third core processor, the third memory, the third communication device, the logic control device, and the third input / output interface, respectively, and is used to provide stable power to each component of the decision module.

[0025] In a preferred embodiment of the safety operation control system of this utility model: the control unit further includes a remote monitoring module and an early warning notification module, which are connected to the decision module via wired or wireless means.

[0026] In a preferred embodiment of the safety operation control system of this utility model: the remote monitoring module includes a visualization platform, which is connected to the third communication device via wired or wireless means to display real-time weather, personnel location, and status of dangerous areas.

[0027] In a preferred embodiment of the safety operation control system of this utility model: the early warning notification module includes an audible and visual alarm, a loudspeaker, and an LED warning screen, all of which are connected to the third communication device via wired or wireless means.

[0028] The beneficial effects of this utility model are as follows: the temperature and humidity sensor, wind speed and direction sensor, precipitation sensor and video monitoring device identify and obtain real-time environmental information; meteorological data identification obtains real-time weather; personnel location module locates personnel positions; target detection module monitors dangerous areas through camera device; dynamic marking is achieved through electronic fence; the above information is sorted and integrated by data processing module; decision module executes the judgment of data processing module; remote monitoring module can revise the results of data processing module; and early warning notification module receives the results from data processing module and performs early warning work. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this utility model, the accompanying drawings of the embodiments of this utility model will be briefly described below. Obviously, the drawings described below only relate to some embodiments of this utility model and are not intended to limit the scope of this utility model. Wherein:

[0030] Figure 1 A structural diagram of a safe operation control system is shown;

[0031] Figure 2 The diagram shows the structure of the meteorological unit and the control unit;

[0032] Figure 3The structural diagram of the target positioning unit and the control unit is shown;

[0033] Figure 4 A structural diagram of the control unit is shown;

[0034] Figure 5 The diagram shows the structure of the information acquisition module;

[0035] Figure 6 The diagram shows the structure of the data processing module;

[0036] Figure 7 A structural diagram of the decision-making unit is shown. Detailed Implementation

[0037] To enable those skilled in the art to better understand this utility model, the present utility model will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0038] The terminology used in this invention refers to those general terms currently widely used in the art in consideration of the functionality of this invention; however, these terms may vary according to the intent, precedent, or new technology of those skilled in the art. Furthermore, specific terms may be chosen by the applicant, and in such cases, their detailed meanings will be described in the detailed description of this invention. Therefore, the terminology used in this specification should not be construed as simple names, but rather based on the meaning of the terms and the overall description of this invention.

[0039] Reference Figure 1 This embodiment provides a safe operation management system, including a meteorological unit 1. The meteorological unit 1 includes a meteorological data acquisition device 11 and an information acquisition module 12. The meteorological data acquisition device 11 acquires real-time weather information, and the information acquisition module 12 acquires real-time network weather status.

[0040] Target positioning unit 2 includes a personnel location module 21 and a target detection module 22. The personnel location module 21 mainly collects personnel location data, while the target detection module 22 is used to determine whether personnel within the monitored area are in dangerous zones and to identify the personnel.

[0041] Control unit 3 includes a data processing module 31 and a decision-making module 32. Meteorological data acquisition module 11, personnel location module 21, and target detection module 22 transmit the collected data to the data processing module 31 via wired or wireless means for fusion. The data processing module 31 is the central hub of the control system, performing spatiotemporal alignment and feature fusion on heterogeneous data such as temperature and humidity, wind speed and direction, precipitation sensors, video surveillance, external meteorological data, Wi-Fi positioning coordinates, and YOLO target detection boxes. The data processing module 31 transmits the data to the decision-making module 32 via wired or wireless means. The decision-making module 32 triggers early warnings and automated responses based on preset rules. The decision-making module 32 converts the analysis results into executable instructions, including the threshold for triggering early warnings. The specific scenarios preset by this system are as follows: when the wind speed is ≥10m / s, it is a Level 1 warning, which sends SMS notifications, broadcast alarms, and displays on the monitoring screen; when the rainfall within 1 hour is ≥30mm, it is a Level 1 warning, which sends SMS notifications, broadcast alarms, and displays on the monitoring screen; when the temperature is ≥40℃ or ≤-10℃, it is a Level 2 warning, which sends SMS notifications, broadcast alarms, and displays on the monitoring screen. At the same time, the system assesses the risk based on personnel behavior (such as staying, approaching dangerous equipment, etc.). When personnel enter the dangerous area or severe weather occurs, the system notifies the operators and managers through multiple channels (mobile phone, broadcast, and screen display). The dangerous area is the area defined by the electronic fence. The electronic fence setting range is based on existing technology, and the specific setting method is not described in this embodiment.

[0042] Reference Figures 1-2 As an optional embodiment, the meteorological data acquisition device 11 includes a temperature and humidity sensor 111, a wind speed and direction sensor 112, a precipitation sensor 113, and a video monitoring device 114 to obtain environmental information. The temperature and humidity sensor 111, wind speed and direction sensor 112, and precipitation sensor 113 are integrated. The information acquisition module 12 collects real-time weather information. In this embodiment, the temperature and humidity sensor 111, wind speed and direction sensor 112, and precipitation sensor 113 can be ordinary sensor models, which are existing technologies. The specific details of how the above devices collect information are not described here. The data collected by the temperature and humidity sensor 111, wind speed and direction sensor 112, and precipitation sensor 113 are transmitted to the data processing module 31 via wired or wireless means. The specific transmission method is existing technology. The video monitoring device 114 is a high-definition monitoring probe, which allows real-time weather conditions to be observed from the video.

[0043] The information acquisition module 12 is used to collect real-time weather information. The information acquisition module 12 includes a first core processor 121, a first communication interface 122, a first memory 123, a data parsing unit 124, a first power supply 125, and a first input / output interface 126. The first core processor 121 is a microcontroller (MCU), such as STM32 or Arduino, used for network communication and data processing. The first communication interface 122 includes a WiFi module, such as ESP8266 or ESP32, for wireless network communication; an Ethernet interface, such as W5500 or ENC28J60, for wired network communication; and a 4G / 5G module, such as SIM800C or SIM76. 00, used for cellular network communication; the first memory 123 is RAM (Random Access Memory): used for temporary storage of data being processed; Flash memory: used to store fixed program code and preset rules; the data parsing unit 124 is used to parse weather data obtained from the network, usually in JSON, XML, or CSV format, including software parsing libraries: such as the JSON parsing library (ArduinoJson) and the XML parsing library (TinyXML); the first power supply 125 includes a power chip: used to convert external power to a voltage suitable for use inside the module; a voltage regulator: to ensure the stability of the power supply voltage; the first input / output interface 126 includes GPIO (General Purpose Input / Output)... Output interface: used to connect external devices; serial port interface: such as UART, used to communicate with data processing module 31; the first core processor 121 is electrically connected to the first communication interface 122, the first memory 123, the data parsing unit 124, and the first input / output interface 126, respectively, to control the first communication interface 122 to obtain real-time weather information from the network, store the obtained weather information in the first memory 123, parse the weather data through the data parsing unit 124, and transmit the parsed data to the data processing module 31 through the first input / output interface 126; the first power supply 125 is connected to the first core processor 121, the first communication interface 122, the first memory 123, the data parsing unit 124, and the first input / output interface 126, respectively. The analysis component 124 and the first input / output interface 126 are electrically connected to provide a stable power supply. The first core processor 121 is used to handle network communication, data parsing, and data transmission. The first communication interface 122 is used to obtain weather information from the network and transmit the data to the data processing module 31. The first memory 123 is used to store the weather data obtained from the network, as well as preset communication protocols and parsing rules. The data parsing component 124 is used to parse the weather data obtained from the network, usually in JSON, XML, or CSV format. The first power supply 125 provides a stable power supply to the meteorological module. The first input / output interface 126 is used to receive network data and send the parsed data to the data processing module 31.

[0044] Furthermore, the temperature and humidity sensor 111, wind speed and direction sensor 112, precipitation sensor 113, and video monitoring device 114 are connected to the data processing module 31 via RS485 or Ethernet interfaces. The information measured by the temperature and humidity sensor 111, wind speed and direction sensor 112, and precipitation sensor 113 is used as data input, while the video monitoring device 114 is used as image input. Meteorological data is connected to the data processing module 31 via the HTTP protocol. The two modalities of data input and image input complement each other to achieve accurate weather classification and prediction. The sensor data and image data are preprocessed and feature extracted respectively. The sensor data is standardized and weather-related features are extracted through a network connection. The image data is used to extract visual features through a convolutional neural network. The two types of data features are input into a multimodal model. The sensor data and image data are cross-fused at different levels of the model to finally output a weather classification prediction.

[0045] Furthermore, sensor data and image data employ a three-layer cross-fusion strategy: early fusion projects sensor features onto the image space for channel stitching and convolutional fusion, guiding visual feature extraction; mid-stage fusion generates attention weights through sensor features, dynamically adjusting the saliency of image features; late-stage fusion stitches together high-level features from both modalities before classification, achieving information complementarity through a fully connected layer, ultimately forming a comprehensive weather classification prediction. This model utilizes a deep learning multimodal model trained with a large amount of historically measured real-time weather data to generate predictions of future weather conditions, including weather, temperature, humidity, wind speed and direction, and rainfall. In this embodiment, the multimodal weather prediction model is existing technology; therefore, the architectural details, image feature extraction network, three-layer cross-fusion mechanism, and training and optimization details are not described in detail here. The weather information is uniformly sent to the data processing module 31 and the decision-making module 32 for processing. In order to facilitate managers to understand the weather conditions in real time, the remote monitoring module 33 visualizes the results of data fusion and displays them by overlaying real-time video images with meteorological data using monitoring information, which helps decision-makers assess the weather conditions from an intuitive perspective. After the weather forecast information is connected to the system, the decision-making module 32 will process the data in real time according to the preset weather warning rules. If the forecast shows that the future weather may lead to extreme weather events (such as rainstorms, typhoons, strong winds, etc.), the system will automatically trigger a warning according to its severity. The warning level is usually divided into multiple levels (such as blue, yellow, orange, red, etc.). The higher the warning level, the greater the weather risk. The system will adjust the response strategy according to the warning level. The above training model is existing technology.

[0046] Reference Figures 1-2As an optional embodiment, the personnel location module 21 includes a Wi-Fi access point and a mobile terminal device. The Wi-Fi access point transmits wireless signals to form a coverage area. The mobile terminal device receives the AP signal and transmits back the RSSI value. The mobile terminal device communicates with the Wi-Fi access point through the Wi-Fi signal to realize personnel location positioning. The above-mentioned personnel positioning through communication with the Wi-Fi access point via Wi-Fi signal is the prior art.

[0047] Furthermore, the personnel location module 21 uses RSSI and fingerprint positioning to locate personnel. It converts RSSI into physical coordinates. The RSSI-to-physical coordinate conversion and fingerprint positioning methods are existing technologies and will not be detailed here. Using RSSI technology, it employs fingerprint positioning (in a known environment, the Wi-Fi signal strength of each area is first scanned, and its characteristic values ​​are recorded to establish a fingerprint database; during real-time positioning, the location is estimated by comparing the current Wi-Fi signal strength with the data in the fingerprint database) to achieve accurate personnel location. Simultaneously, a free space path loss model is used to correct the RSSI value. This model considers the signal attenuation characteristics in free space, and the formula is:

[0048]

[0049] Where PL(d) is the path loss at distance d, PL(d0) is the path loss at a reference distance d0, and n is the path loss exponent, representing the rate of signal attenuation. This model can be used to correct the acquired RSSI values, making them closer to the true values. The corrected RSSI values ​​are then used as feature values ​​and stored in a fingerprint database. The fingerprint database uses the coordinates of the sampling points as indices, with each sampling point corresponding to an RSSI feature vector. The structure of the fingerprint database is as follows:

[0050] Fingerprint Database = {(x i ,y i ):RSSI i}

[0051] Where, x i ,y i is the coordinate of the sampling point, and RSSIi is the RSSI feature vector of that point.

[0052] Preferably, the target detection module 22 includes at least one camera device installed above the hazardous area. The camera device is connected to the data processing module 31 via an IP network. The technical solution for achieving accurate human detection and real-time positioning in hazardous areas is to combine Wi-Fi positioning technology with YOLO target detection to construct a hazardous area human detection and real-time positioning solution. Visual target detection uses YOLOv8, a deep learning-based target detection algorithm. YOLOv8 is a version of the YOLO algorithm; it is an anchor-free method that does not require complex anchor presets and post-processing, achieving a good balance between processing speed and accuracy. It is suitable for target detection in real-time video streams. The input to YOLOv8 is a real-time captured video stream. After processing by the neural network model, the system can accurately identify all objects in the image. The system focuses on targets (in this system, the emphasis is on personnel, equipment, and hazardous areas). Identified targets are marked with bounding boxes. Bounding box annotation data for these three categories of targets are collected for YOLOv8 training. YOLOv8 learns how to identify personnel, equipment, and hazardous areas in images based on these bounding boxes. The system's data fusion preprocessing module simply matches personnel location with their bounding boxes, adding personnel information to the boxes. Bounding box annotation data for three categories of targets—personnel (5000 images with different poses), equipment (3000 images with different angles and distances), and hazardous areas (2000 images, including dangerous scenarios such as fires, leaks, and high-voltage areas)—are collected for YOLOv8 training. YOLOv8 learns how to identify personnel, equipment, and hazardous areas in images based on these bounding boxes. This system uses the default model structure of YOLOv8, but it has been optimized and adjusted. For example, the backbone network has been improved by adding a depthwise separable convolution module. While maintaining model performance, the computational complexity has been significantly reduced and the detection speed has been improved.

[0053] The neck network incorporates a Feature Pyramid Network (FPN) structure, enhancing the model's ability to detect targets at multiple scales, particularly small targets. The head network employs an anchor-free detection head, avoiding the complexity of anchor design in traditional anchor-based methods while improving detection accuracy and speed. The system's data fusion preprocessing module simply matches personnel locations with their bounding boxes, adding personnel information to the bounding boxes. This data, along with the bounding boxes for equipment and hazardous areas, is then sent to the data processing module 31. Once personnel are detected entering a hazardous area, or anomalies (such as fire or leaks) are detected in the hazardous area, the decision module 32 immediately issues an alarm. The alarm includes information such as the personnel's location, the specific location of the hazardous area, and the degree of danger, specifically referring to the preset thresholds in Example 1.

[0054] Furthermore, dangerous areas are dynamically marked using electronic fences, which can be adjusted according to weather conditions (such as strong winds and lightning). Electronic fences are existing technology and will not be described in detail here.

[0055] Reference Figure 6As an optional embodiment, the data processing module 31 includes a second core processor 311, a second memory 312, a second communication device 313, a data processing unit 314, a second input / output interface 315, and a second power supply 316. The second core processor 311 is electrically connected to the second memory 312, the second communication device 313, the data processing unit 314, and the second input / output interface 315, respectively, for coordinating the work of each component and performing data processing and logic control. The second core processor 311 includes a CPU / GPU for performing complex computing tasks such as data fusion, feature extraction, and model inference. For example, Intel Core series processors or NVIDIA GPUs, FPGAs (Field-Programmable Gate Arrays): used to accelerate specific computing tasks, especially real-time data processing and signal processing; DSPs (Digital Signal Processors): used to process sensor data, such as filtering, analog-to-digital conversion, etc.; the second memory 312 includes RAM (Random Access Memory): used to temporarily store data being processed; ROM (Read-Only Memory): used to store fixed program code; hard disks (HDD / SSD): used for long-term storage of large amounts of historical data and model parameters; the second communication device 313 includes an Ethernet interface: used for wired network communication; and wireless communication modules: such as Wi-Fi, 4G / 5G modules, used for wireless networks. Communication, RS485 / RS232 interface: for connecting sensors or other devices; data processing unit 314 includes a data fusion chip: for spatiotemporal alignment and feature fusion of data from different sources; AI accelerator: such as NVIDIA Jetson series, for accelerating inference of deep learning models; FPGA coprocessor: for parallel processing tasks to improve data processing efficiency; second input / output interface 315 includes an ADC (analog-to-digital converter): for converting analog signals from sensors into digital signals; GPIO (general purpose input / output interface): for connecting external devices; second power supply 316 includes a power chip: for converting external power supply into a voltage suitable for use inside the module; voltage regulator: to ensure the stability of the power supply voltage.

[0056] The second communication device 313 is electrically connected to the second core processor 311 and is used to receive data from the meteorological unit 1, the target positioning unit 2 and the target detection module 22, and transmit the processed data to the decision module 32. The second core processor 311, as the main control unit of the data processing module 31, is connected to all other components and is responsible for coordinating and controlling the operation of the entire module.

[0057] The second memory 312 is electrically connected to the second core processor 311 and is used to store the received data as well as intermediate and final results during the processing.

[0058] The data processing unit 314 is electrically connected to the second core processor 311 and is used to preprocess, fuse and analyze the received data.

[0059] The second input / output interface 315 is electrically connected to the second core processor 311 and is used for data interaction with other modules;

[0060] The second power supply 316 is electrically connected to the second core processor 311, the second memory 312, the second communication device 313, the data processing device 314, and the second input / output interface 315, respectively, and is used to provide a stable power supply to each component of the data processing module 31.

[0061] Reference Figure 7 As an optional embodiment, the decision module 32 includes a third core processor 321, a third memory 322, a third communication device 323, a logic control unit 324, a third power supply 325, and a third input / output interface 326. The third core processor 321 is electrically connected to the third memory 322, the third communication device 323, the logic control unit 324, and the third input / output interface 326, respectively, for coordinating the work of each component and executing decision logic and control instructions. The third core processor 321 includes a CPU / GPU for executing complex logical judgments and decision algorithms. For example, Intel... Core series processors or NVIDIA GPUs, FPGAs (Field-Programmable Gate Arrays): used to accelerate specific logic judgment tasks, especially real-time decision-making; DSPs (Digital Signal Processors): used to process real-time data streams and support fast response; third memory 322 includes RAM (Random Access Memory): used for temporary storage of data being processed; ROM (Read-Only Memory): used to store fixed program code and preset rules; hard disks (HDD / SSD): used for long-term storage of large amounts of historical data and decision records; third communication components 323 include an Ethernet interface: used for wired network communication; wireless communication modules: such as Wi-Fi, 4G / 5G modules, used for wireless network communication; RS485 / RS232 interfaces: used to connect other modules or devices. USB interface: used to connect external storage devices or debugging tools; Logic control unit 324 includes a microcontroller (MCU): used to implement logic control functions, such as the STM32 series; PLC (Programmable Logic Controller): used for logic control in industrial environments; FPGA coprocessor: used for parallel processing of tasks to improve decision-making efficiency; Third power supply 325 includes a power chip: used to convert external power to a voltage suitable for use inside the module; Voltage regulator: ensures the stability of the power supply voltage; Third input / output interface 326 includes GPIO (General Purpose Input / Output Interface): used to connect external devices; Relay module: used to control external devices (such as audible and visual alarms, loudspeakers, etc.); Communication protocol conversion chip: used to convert internal signals into communication protocols suitable for external devices.

[0062] The third memory 322 is electrically connected to the third core processor 321 and is used to store preset decision rules, intermediate results during processing, and final decision results.

[0063] The third communication device 323 is electrically connected to the third core processor 321 and is used to receive the processing results from the data processing module 31 and transmit the decision results to the early warning notification module 34 and the remote monitoring module 33.

[0064] The logic control unit 324 is electrically connected to the third core processor 321 and is used to generate decision instructions based on preset rules and real-time data.

[0065] The third input / output interface 326 is electrically connected to the third core processor 321 and is used for data interaction and control signal transmission with other modules.

[0066] The third power supply 325 is electrically connected to the third core processor 321, the third memory 322, the third communication device 323, the logic control device 324, and the third input / output interface 326, respectively, and is used to provide stable power to each component of the decision module 32.

[0067] Reference Figures 1-2 As an optional embodiment, it also includes a remote monitoring module 33 and an early warning notification module 34. The remote monitoring module 33 and the early warning notification module 34 are connected to the third communication device 323 of the decision module 32 via wired or wireless means. The remote monitoring module 33 receives the "global situation screen" pushed by the decision module 32, and can also input manual commands back to the decision module 32, such as the "clear alarm" command. The early warning notification module 34 receives the "early warning level + action command" generated by the decision module 32 and completes actions such as sound and light / broadcast / power off. The priority of the automatic response of the decision module 32 and the linkage timing of actions such as sound and light / broadcast / power off are settings of the prior art and will not be described in detail here.

[0068] Preferably, the remote monitoring module 33 includes a visualization platform for displaying real-time weather, personnel location, and dangerous area status. That is, it obtains four types of information after fusion from the decision module 32: weather information, real-time personnel trajectory, dangerous area, and equipment information. The remote monitoring module 33 can also modify the warning level or cancel the alarm through manual operation.

[0069] Preferably, the early warning notification module 34 includes an audible and visual alarm, a loudspeaker, and an LED warning screen. The audible and visual alarm, the loudspeaker, and the LED warning screen are all connected through the decision module 32 to achieve linkage alarm. That is, the early warning notification module 34 realizes the result of the decision module 32 through physical actions such as sound and light, broadcasting, and power failure.

[0070] The specific process of this safety operation control system is as follows: Temperature and humidity sensor 111, wind speed and direction sensor 112, and precipitation sensor 113 are integrated and installed; video monitoring device 114 is set up at multiple points; environmental information is obtained from temperature and humidity sensor 111, wind speed and direction sensor 112, precipitation sensor 113, and video monitoring device 114; information acquisition module 12 collects real-time weather information; data processing module 31 performs data analysis based on the information transmitted by the above devices; two types of data features are input into a model; sensor data and image data are cross-fused at different levels of the model; finally, the model is trained to output weather classification predictions; personnel location module 21 serves as a Wi-Fi access point and mobile terminal device; the mobile terminal device connects to Wi-Fi via Wi-Fi signal. Access point communication uses RSSI and fingerprint positioning to locate personnel. The target detection module 22 includes at least one camera installed above the danger zone, which is dynamically marked by an electronic fence. The camera transmits the collected information to the data acquisition module 31, and the decision module 32 analyzes the weather information, personnel location, and danger zone according to the YOLOv8 model. For example, when the wind speed is ≥10m / s, personnel enter the danger zone, and the decision module 32 issues an early warning based on the YOLOv8 model analysis results. The early warning information is delivered through audible and visual alarms, loudspeakers, and LED warning screens. The remote monitoring module 33 is connected to a visualization platform, where monitoring personnel can monitor the situation and manually modify the warning level or cancel the alarm.

[0071] Finally, it should be noted that the methods and devices described in detail above are merely embodiments, and those skilled in the art can modify these embodiments in different ways as long as they do not depart from the scope of this utility model.

Claims

1. A safe operation control system, characterized in that: include, Meteorological unit (1), which includes a meteorological acquisition device (11) and an information acquisition module (12). The target positioning unit (2) includes a personnel location module (21) and a target detection module (22). The personnel location module (21) includes a Wi-Fi access point and a mobile terminal device. The mobile terminal device communicates with the Wi-Fi access point via Wi-Fi signals to achieve personnel location positioning. The target detection module (22) includes at least one camera device installed above the danger zone. The danger zone is dynamically marked by an electronic fence, and its range can be adjusted according to weather conditions. The camera device is connected to the data processing module (31) via an IP network. The control unit (3) includes a data processing module (31) and a decision-making module (32). The meteorological data acquisition device (11), personnel location module (21), and target detection module (22) transmit the collected data to the data processing module (31) via wired or wireless means. The data processing module (31) transmits the data to the decision-making module (32) via wired or wireless means. The decision-making module (32) triggers early warning and automatic response based on preset rules. The data processing module (31) includes a second core processor (311), a second memory (312), a second communication device (313), a data processing component (314), a second input / output interface (315), and a second power supply (316). The second core processor (311) is electrically connected to the second memory (312), the second communication device (313), the data processing component (314), and the second input / output interface (315) respectively, and is used to coordinate the work of each component and perform data processing and logic control. The second communication device (313) is electrically connected to the second core processor (311) and is used to receive data from the meteorological unit (1), the target positioning unit (2) and the target detection module (22), and transmit the processed data to the decision module (32). The second memory (312) is electrically connected to the second core processor (311) and is used to store the received data as well as intermediate and final results during the processing. The data processing component (314) is electrically connected to the second core processor (311) and is used to preprocess, fuse and analyze the received data; The second input / output interface (315) is electrically connected to the second core processor (311) and is used for data interaction with other modules; The second power supply (316) is electrically connected to the second core processor (311), the second memory (312), the second communication device (313), the data processing unit (314), and the second input / output interface (315), respectively, to provide a stable power supply for each component of the data processing module (31). The decision module (32) includes a third core processor (321), a third memory (322), a third communication device (323), a logic control unit (324), a third power supply (325), and a third input / output interface (326). The third core processor (321) is electrically connected to the third memory (322), the third communication device (323), the logic control unit (324), and the third input / output interface (326) respectively, and is used to coordinate the work of each component and execute decision logic and control instructions.

2. The safety operation control system according to claim 1, characterized in that: The meteorological data acquisition device (11) includes a temperature and humidity sensor (111), a wind speed and direction sensor (112), a precipitation sensor (113), and a video monitoring device (114) to obtain environmental information. The temperature and humidity sensor (111), the wind speed and direction sensor (112), and the precipitation sensor (113) are integrated into a single unit. The information acquisition module (12) is used to acquire real-time weather information. The information acquisition module (12) includes a first core processor (121), a first communication interface (122), a first memory (123), a data parsing component (124), a first power supply (125), and a first input / output interface (126). The first core processor (121) is electrically connected to the first communication interface (122), the first memory (123), the data parsing component (124), and the first input / output interface (126) respectively. It is used to control the first communication interface (122) to acquire real-time weather information from the network, store the acquired weather information in the first memory (123), parse the weather data through the data parsing component (124), and transmit the parsed data to the data processing module (31) through the first input / output interface (126). The first power supply (125) is electrically connected to the first core processor (121), the first communication interface (122), the first memory (123), the data parsing component (124), and the first input / output interface (126) respectively. It is used to provide a stable power supply.

3. The safe operation control system according to claim 2, characterized in that: The temperature and humidity sensor (111), wind speed and direction sensor (112), precipitation sensor (113) and video monitoring device (114) are connected to the data processing module (31) via RS485 or Ethernet interface, and the information acquisition module (12) is connected to the data processing module (31) via HTTP protocol.

4. The safe operation control system according to claim 1, characterized in that: The third memory (322) is electrically connected to the third core processor (321) and is used to store preset decision rules, intermediate results during processing, and final decision results; The third communication device (323) is electrically connected to the third core processor (321) and is used to receive the processing results from the data processing module (31) and transmit the decision results to the early warning notification module (34) and the remote monitoring module (33). The logic control unit (324) is electrically connected to the third core processor (321) and is used to generate decision instructions according to preset rules and real-time data; The third input / output interface (326) is electrically connected to the third core processor (321) and is used for data interaction and control signal transmission with other modules; The third power supply (325) is electrically connected to the third core processor (321), the third memory (322), the third communication device (323), the logic control device (324), and the third input / output interface (326) respectively, and is used to provide a stable power supply for each component of the decision module (32).

5. The safe operation control system according to claim 1, characterized in that: The control unit (3) further includes a remote monitoring module (33) and an early warning notification module (34), which are connected to the decision module (32) via wired or wireless means.

6. The safe operation control system according to claim 5, characterized in that: The remote monitoring module (33) includes a visualization platform, which is connected to a third communication device (323) via wired or wireless means to display real-time weather, personnel location, and status of dangerous areas.

7. The safe operation control system according to claim 6, characterized in that: The warning notification module (34) includes an audible and visual alarm, a loudspeaker, and an LED warning screen. The audible and visual alarm, the loudspeaker, and the LED warning screen are all connected to a third communication device (323) via wired or wireless means.

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