Safety belt real-time supervision system

Abstract

The invention relates to the technical field of power equipment safety monitoring, and provides a safety belt real-time supervision system, which solves the problem of untimely safety early warning caused by data transmission delay or loss in power equipment safety monitoring, and comprises a safety belt equipment end, a data acquisition mobile terminal, a sensor end, a server end and a background control end, by collecting the wearing state of the safety belt, the behaviors of power maintenance personnel and power equipment data in real time, generating a power equipment image in combination with a digital twin technology and performing feature extraction and data fusion, the system can monitor the positions and behaviors of the maintenance personnel in real time, compare the positions and behaviors with safety limits, generate early warning information when the positions and behaviors exceed the safety limits, and meanwhile, perform early warning. And the system detection unit monitors the operation memory to ensure stable operation of the system, so that the timeliness and accuracy of safety early warning are improved, and the safety of power maintenance operation is enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of power equipment safety monitoring, and in particular to a real-time safety belt monitoring system. Background Art

[0002] The seat belt is designed with an alarm device and an information transmission device including a sensor, a control unit and an alarm. The seat belt sensor is installed on the car seat, near the seat belt buckle, and can detect whether the seat belt is fastened or unfastened. The sensor transmits the status signal to the seat belt real-time monitoring system through wires or other communication media. When receiving a signal that the seat belt is not fastened, the seat belt real-time monitoring system will issue a command to activate the alarm. The alarm can be an audible alarm, a visual alarm or both to remind power workers to fasten their seat belts.

[0003] The real-time seat belt monitoring system has the following technical pain points in actual use. During the application of the installation belt in power scenarios, due to the complexity and diversity of power application scenarios, for example, seat belts are needed during the installation of wind power equipment and during the maintenance of power equipment. In the complex and changeable usage environment, the real-time transmission and processing of data is a problem. The seat belt monitoring operation platform needs to collect and process the seat belt usage data in real time in order to issue alarms or reminders in time, but the delay or loss in the data transmission process will affect the response speed of the system, resulting in untimely safety warnings. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a real-time seat belt monitoring system to solve the problem that in the complex and changeable use environment of the prior art, delays or losses in the data transmission process will affect the response speed of the system, thereby causing untimely safety warnings.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] The seat belt real-time monitoring system provided by the present invention comprises: a seat belt device end, a data acquisition mobile terminal, a sensor end, a server end and a background control end, the server end respectively establishes communication connections with the data acquisition mobile terminal, the seat belt device end, the sensor end and the background control end, the sensor end is installed on the seat belt device end, the sensor end is used to collect data information of the seat belt device end, the data acquisition mobile terminal is used to collect image data of the power maintenance personnel during the work process, the data acquisition mobile terminal and the sensor end transmit the collected data information to the server end, and the server end comprises:

[0007] The data acquisition module acquires the seat belt wearing status data, the behavior data of the power maintenance personnel, the power equipment location data, the power equipment shape data and the system operation data, substitutes the power equipment location data and the power equipment shape data into the preset digital twin model, and generates the digital twin image of the power equipment;

[0008] A data acquisition unit receives power maintenance demand information, the power maintenance demand information includes the location information of the equipment to be repaired and the power maintenance construction procedure information, the power maintenance construction procedure information includes the power maintenance construction safety standard data, the power maintenance construction safety standard data includes the safety area for construction personnel, the safety standard for the natural construction environment, and the safety standard for deformation of power equipment, takes the location information of the equipment to be repaired as the area to be collected, and sends the area to be collected to the data acquisition mobile terminal, the data acquisition mobile terminal adopts an unmanned aerial vehicle device terminal, receives the image data information of the area to be collected collected by the unmanned aerial vehicle device terminal, obtains the image data of the area to be collected, obtains the coordinate information of the digital twin image of the power equipment, configures the coordinates of the image data of the area to be collected based on the coordinate information of the digital twin image of the power equipment, obtains the image data coordinate information of the area to be collected, substitutes the image data coordinate information of the area to be collected into the digital twin image of the power equipment, and obtains the first digital twin image;

[0009] The data processing module extracts features from the first digital twin image to obtain the first digital twin image equipment features, and based on the first digital twin image equipment features, substitutes the construction personnel safety area, the construction natural environment safety standard, and the electric power equipment deformation safety standard into the electric power equipment digital twin image to obtain the second digital twin image, wherein the second digital twin image includes the electric power equipment image data and the electric power equipment maintenance safety boundary image;

[0010] A data judgment unit receives information from a seat belt device. If it is detected whether the seat belt is fastened, the position information of the power maintenance personnel is obtained, the position information of the power maintenance personnel is marked, the position to be inspected is obtained, the position to be inspected is sent to the drone device terminal, the image data of the position to be inspected collected by the drone device terminal is received, feature extraction is performed on the image data of the position to be inspected, and image data features of the position to be inspected are obtained. The image data features of the position to be inspected are matched with the data features in the second digital twin image to obtain distinguishing image features of the image data features of the position to be inspected and the data features in the second digital twin image, and distinguishing image features to be processed are obtained. The distinguishing image features to be processed include features of the power maintenance personnel and features of the shape of the power equipment. The distinguishing image features to be processed are compared with the image of the power equipment maintenance safety limit. If the distinguishing image features to be processed exceed the image of the power equipment maintenance safety limit, a first warning information is generated.

[0011] The system detection unit detects the real-time system operation memory data during the data processing by the data acquisition module, the data collection unit, the data processing module and the data judgment unit. If the real-time system operation memory data is higher than the preset system operation content standard value, a second warning information is generated.

[0012] Furthermore, in the real-time safety belt monitoring system of the present invention, the data acquisition module is also used for:

[0013] Acquire seat belt wearing status data from the seat belt device, where the seat belt wearing status data includes whether the seat belt is fastened and the time when the seat belt is fastened;

[0014] Obtain image data of power maintenance personnel at work from the drone equipment terminal, and the behavior and location movement of the maintenance personnel during the work process;

[0015] Acquire the position and shape data of the electric equipment from the sensor end, and preliminarily process the acquired position and shape data of the electric equipment to obtain the preprocessed position and shape data of the electric equipment;

[0016] The pre-processed location and shape data of the power equipment are substituted into the preset digital twin model, and the digital twin image of the power equipment is generated through the digital twin model;

[0017] The generated digital twin image of the power equipment is integrated with the seat belt wearing status and maintenance personnel behavior, and the integrated data is output.

[0018] Furthermore, in the real-time safety belt monitoring system of the present invention, the data acquisition unit is also used for:

[0019] Extracting coordinate information from the generated digital twin image of the power equipment, wherein the coordinate information extracted from the digital twin image of the power equipment includes the position, direction and size of the power equipment;

[0020] Preprocess the image data of the area to be collected collected by the drone equipment terminal, match the image data of the area to be collected with the coordinate information of the digital twin image of the power equipment, and convert the pixel coordinates in the image data of the area to be collected into three-dimensional coordinates consistent with the digital twin model;

[0021] Use feature matching algorithms to establish the correspondence between the image data of the area to be collected and the digital twin image of the power equipment. According to the matching results, coordinate configuration is performed on the image data of the area to be collected, and each pixel or feature point in the image data is associated with the corresponding coordinates of the digital twin image.

[0022] The image data of the area to be collected with configured coordinates is substituted into the digital twin image of the power equipment, and the image data is applied as texture to the corresponding position of the digital twin model to obtain the digital twin image of the power equipment that integrates the image data of the area to be collected, that is, the first digital twin image.

[0023] Furthermore, in the real-time safety belt monitoring system of the present invention, the data processing module is also used for:

[0024] Using image processing technology and computer vision algorithms, extracting features from the first digital twin image, wherein the feature extraction from the first digital twin image includes edge detection, corner point detection, and texture analysis;

[0025] According to the safety area standard for construction personnel in the power maintenance construction procedure information, a corresponding safety area is delineated in the first digital twin image, and the delineation of the corresponding safety area in the first digital twin image includes marking the safety area that the construction personnel should maintain on the image;

[0026] According to the deformation safety standards of power equipment, analyze the deformation of the equipment in the first digital twin image and compare it with the image of the equipment in the design or normal state to detect abnormal deformation of the equipment;

[0027] The extracted first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard data are fused, and the first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard are displayed in the digital twin model using a data fusion algorithm;

[0028] Based on the fused data, the digital twin image of the power equipment is updated. The updating of the digital twin image of the power equipment includes adding a safety area for construction personnel to the image, modifying the safety area for construction personnel, the safety standards of the natural construction environment, and the safety standards of the deformation of power equipment. After data fusion and model updating, a second digital twin image is generated.

[0029] Furthermore, in the real-time safety belt monitoring system of the present invention, the data judgment unit is also used for:

[0030] Use feature extraction algorithms to extract features from the image data of the inspection location collected by the UAV equipment terminal to obtain the appearance features, action features, position features of the power maintenance personnel, local shape features, color features and texture features of the power equipment;

[0031] Extracting corresponding data features from the second digital twin image, wherein the corresponding data features extracted from the second digital twin image correspond to the features extracted from the image data of the position to be inspected;

[0032] A feature matching algorithm is used to preliminarily match the features in the image data of the position to be inspected with the features in the second digital twin image, and the feature matching result is obtained by comparing the position, direction and size of the feature points;

[0033] Calculate the difference between the feature matching result and the feature of the second digital twin image data to obtain a difference calculation result, where the difference calculation result includes a position offset result, a shape change result, and a color difference result;

[0034] According to the calculated difference calculation result, the distinguishing image features between the image data features of the position to be inspected and the image data features of the second digital twin are extracted to form distinguishing image features to be processed;

[0035] Mark the distinguishing image features to be processed and annotate the difference areas on the images.

[0036] Furthermore, in the real-time safety belt monitoring system of the present invention, the data judgment unit is also used for:

[0037] Extract and integrate distinguishing image features between the image data features of the location to be inspected and the image data features of the second digital twin, where the distinguishing image features include features of power maintenance personnel and features of the shape of power equipment;

[0038] Compare the features of the power maintenance personnel and the shape features of the power equipment in the distinguished image features to be processed with the power equipment maintenance safety boundary image one by one to check whether the maintenance personnel are in the safe area and whether the shape of the equipment exceeds the safety limit;

[0039] During the comparison process, if the features of the distinguished image to be processed exceed the safety range defined by the power equipment maintenance safety boundary image, it is considered unsafe;

[0040] When it is determined that the distinguishing image features to be processed exceed the safety limit image of the power equipment maintenance, the condition of generating the first warning information is triggered;

[0041] According to the exceeding situation, a first warning information is constructed, the first warning information includes the position of the maintenance personnel exceeding the safety limit and the abnormal shape of the equipment, and the first warning information is sent to the background control terminal.

[0042] Furthermore, in the real-time safety belt monitoring system of the present invention, the system detection unit is also used for:

[0043] Receive the system running memory standard value and monitor the real-time system running memory data. The real-time system running memory data includes the used memory, available memory and memory occupancy rate;

[0044] Compare the acquired real-time system running memory data with the preset system running memory standard value to determine whether the memory usage rate exceeds the preset system running memory standard value;

[0045] According to the comparison result, it is determined whether the real-time system running memory data is higher than the preset system running memory standard value. If it is exceeded, the system is in a high load state;

[0046] When the real-time system running memory data is higher than the preset standard value, the condition for generating the second warning information is triggered.

[0047] Beneficial effects of the present invention:

[0048] Through real-time data collection and transmission, the system of the present invention can quickly obtain the working status of power maintenance personnel, the wearing status of safety belts, and the location and shape data of power equipment, providing a basis for immediate safety analysis and early warning, and significantly improving the timeliness of safety early warning.

[0049] Using digital twin technology and image processing algorithms, the system can generate digital twin images of power equipment, extract features and fuse data from the images. The system can accurately identify the status of power equipment, the behavior of maintenance personnel, and potential safety hazards, thereby enhancing the accuracy of safety supervision.

[0050] The system adopts modular design, and each module works together to improve data processing efficiency. At the same time, the system detection unit can monitor the system's running memory data in real time, so that the system can generate early warning information in time under high load conditions, avoid system crashes or data loss, and improve the system's response speed and stability.

[0051] By real-time monitoring and analysis of the system's operating status, system administrators can promptly understand the system's resource usage and optimize or expand resources as needed, helping to avoid resource waste and reduce operation and maintenance costs.

[0052] The present invention provides comprehensive safety supervision and early warning support for power maintenance operations, which helps reduce the occurrence of safety accidents and protect the lives of maintenance personnel. At the same time, through digital and intelligent supervision methods, the system can improve the efficiency and quality of maintenance operations.

[0053] In summary, the present invention significantly improves the safety supervision level of power maintenance operations through real-time data acquisition and transmission, application of digital twin technology, modular design, and system status monitoring, and provides a strong guarantee for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0055] Figure 1 A schematic diagram of a unit module of a seat belt real-time monitoring system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0057] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0058] The seat belt real-time monitoring system provided by the present invention comprises: a seat belt device end, a data acquisition mobile terminal, a sensor end, a server end and a background control end, the server end respectively establishes communication connections with the data acquisition mobile terminal, the seat belt device end, the sensor end and the background control end, the sensor end is installed on the seat belt device end, the sensor end is used to collect data information of the seat belt device end, the data acquisition mobile terminal is used to collect image data of the power maintenance personnel during the work process, the data acquisition mobile terminal and the sensor end transmit the collected data information to the server end, and the server end comprises:

[0059] The data acquisition module acquires the seat belt wearing status data, the behavior data of the power maintenance personnel, the power equipment location data, the power equipment shape data and the system operation data, substitutes the power equipment location data and the power equipment shape data into the preset digital twin model, and generates the digital twin image of the power equipment;

[0060] The seat belt wearing status data is used to monitor in real time whether the seat belt is worn correctly through the sensor installed on the seat belt device end;

[0061] The behavior data of power maintenance personnel are collected through data collection mobile terminals, and the behavior data of power maintenance personnel include image data of power maintenance personnel during work;

[0062] Perform preliminary processing on the collected data, such as verifying the integrity of the data, performing necessary format conversion, etc. Perform special processing on the location and shape data of the power equipment so that they can be used by the digital twin model in the subsequent steps. Substitute the processed location and shape data of the power equipment into the preset digital twin model. The digital twin model is a virtual, highly accurate replica of the power equipment that can simulate and predict the actual behavior and performance of the power equipment.

[0063] Through the processing of the digital twin model, a digital twin image of the power equipment is generated. The digital twin image of the power equipment not only contains the physical location and shape of the power equipment, but also contains other information related to the equipment status and maintenance. The generation of digital twin images is crucial for the real-time seat belt monitoring system because it provides the system with a real-time and accurate view of the power equipment status. This view can be used by subsequent data processing modules, data judgment units, etc. to support various safety supervision and analysis functions, such as anomaly detection, warning generation, etc.

[0064] In general, the data acquisition module provides a solid foundation for the real-time seat belt supervision system by efficiently and accurately collecting and processing various types of data, enabling the system to monitor and manage the safety status of power equipment in real time and accurately.

[0065] A data acquisition unit receives power maintenance demand information, the power maintenance demand information includes the location information of the equipment to be repaired and the power maintenance construction procedure information, the power maintenance construction procedure information includes the power maintenance construction safety standard data, the power maintenance construction safety standard data includes the safety area for construction personnel, the safety standard for the natural construction environment, and the safety standard for deformation of power equipment, takes the location information of the equipment to be repaired as the area to be collected, and sends the area to be collected to the data acquisition mobile terminal, the data acquisition mobile terminal adopts an unmanned aerial vehicle device terminal, receives the image data information of the area to be collected collected by the unmanned aerial vehicle device terminal, obtains the image data of the area to be collected, obtains the coordinate information of the digital twin image of the power equipment, configures the coordinates of the image data of the area to be collected based on the coordinate information of the digital twin image of the power equipment, obtains the image data coordinate information of the area to be collected, substitutes the image data coordinate information of the area to be collected into the digital twin image of the power equipment, and obtains the first digital twin image;

[0066] The data acquisition unit first receives the power maintenance demand information, which includes the location information of the equipment to be maintained and the power maintenance construction procedure information. The location information of the equipment to be maintained indicates the exact location of the power equipment that needs maintenance and overhaul. The power maintenance construction procedure information includes the safety standards and operating procedures that must be followed when performing maintenance tasks.

[0067] The power maintenance construction safety standard data is extracted from the power maintenance construction procedure information. The power maintenance construction safety standard data specifies in detail the safety areas for construction personnel, the safety standards for the construction natural environment, and the safety standards for deformation of power equipment.

[0068] The construction personnel safety zone defines the safe area that maintenance personnel should maintain when performing maintenance tasks.

[0069] The natural construction environment safety standards take into account the impact of natural factors in the construction environment (such as wind speed, temperature, etc.) on construction safety.

[0070] The power equipment deformation safety standard is used to monitor whether abnormal deformation occurs in power equipment during maintenance, so as to promptly discover and deal with potential safety hazards.

[0071] The location information of the equipment to be repaired is used as the area to be collected, which is the target area where the drone equipment terminal needs to collect image data. The information of the area to be collected is sent to the data collection mobile terminal (in this case, the drone equipment terminal), instructing it to go to the designated area for image data collection.

[0072] After receiving the acquisition instruction, the UAV equipment terminal will collect image data for the area to be collected and transmit the collected image data information back to the data acquisition unit. The data acquisition unit receives the image data information and performs necessary preprocessing (such as denoising, enhancement, etc.) to improve the quality and availability of the data.

[0073] Obtain the coordinate information of the previously generated digital twin image of the power equipment, which is crucial for matching the image data with the actual position of the power equipment. The image data of the area to be collected is configured based on the coordinate information of the digital twin image of the power equipment, that is, the corresponding position of each pixel or feature point in the image data on the actual power equipment is determined.

[0074] Substitute the image data of the area to be collected with the configured coordinates into the digital twin image of the power equipment to achieve the fusion of image data and digital twin model. This process generates the first digital twin image, which combines the accuracy of actual image data and digital twin model, providing strong support for subsequent safety analysis and early warning.

[0075] In summary, the data acquisition unit receives power maintenance demand information, parses safety standard data, coordinates the drone equipment terminal to collect image data, and processes this data to generate the first digital twin image that integrates actual images and digital twin model information, providing an important data basis for safety analysis and early warning of the real-time seat belt supervision system.

[0076] The data processing module extracts features from the first digital twin image to obtain the first digital twin image equipment features, and based on the first digital twin image equipment features, substitutes the construction personnel safety area, the construction natural environment safety standard, and the electric power equipment deformation safety standard into the electric power equipment digital twin image to obtain the second digital twin image, wherein the second digital twin image includes the electric power equipment image data and the electric power equipment maintenance safety boundary image;

[0077] The data processing module extracts features from the first digital twin image and uses image processing technology and computer vision algorithms to identify and extract information from the image. Feature extraction includes edge detection, corner detection, texture analysis and other technologies, aiming to capture detailed information on the power equipment and the surrounding environment from the image.

[0078] Through feature extraction, the data processing module can obtain the equipment features in the first digital twin image. The equipment features in the first digital twin image include the outline, size, shape, etc. of the power equipment.

[0079] After extracting the equipment features of the first digital twin image, the data processing module fuses these features with data such as the safety zone for construction personnel, the safety standards for the natural construction environment, and the safety standards for deformation of power equipment. Using the data fusion algorithm, data from multiple sources are integrated and displayed in the digital twin model.

[0080] The construction worker safety zone standard defines the safe distance and area that power maintenance personnel should maintain during work, and the data is integrated into the digital twin image to monitor the safety status of maintenance personnel in real time.

[0081] The construction natural environment safety standards take into account the natural environmental factors of the construction site, including wind speed, temperature, humidity, etc. The natural environmental factors of the construction site have an impact on the operation and maintenance of power equipment. Integrating these standards into digital twin images will help evaluate the impact of the natural environment on construction safety.

[0082] The deformation safety standard of power equipment is used to detect abnormal deformation of power equipment. By comparing the deformation safety standard with the equipment characteristics in the digital twin image, the safety hazards of the equipment can be discovered in time.

[0083] After data fusion, the data processing module generates a second digital twin image. The second digital twin image not only contains the original image data of the power equipment, but also superimposes information such as the safety area for construction personnel, safety standards of the natural construction environment, and deformation safety standards of power equipment. The second digital twin image thus becomes a virtual model that comprehensively reflects the safety status and maintenance needs of power equipment.

[0084] In the second digital twin image, you can see the image data of the power equipment and the safety boundary images surrounding the equipment. The safety boundary images are automatically generated based on the construction personnel safety area standards and deformation safety standards, providing an intuitive visual reference for real-time monitoring and early warning.

[0085] In summary, the data processing module generates a second digital twin image containing rich safety information by extracting features and fusing data from the first digital twin image. This process not only improves the data processing and analysis capabilities of the real-time seat belt monitoring system, but also provides strong support for subsequent safety judgments and early warnings.

[0086] A data judgment unit receives information from a seat belt device. If it is detected whether the seat belt is fastened, the position information of the power maintenance personnel is obtained, the position information of the power maintenance personnel is marked, the position to be inspected is obtained, the position to be inspected is sent to the drone device terminal, the image data of the position to be inspected collected by the drone device terminal is received, feature extraction is performed on the image data of the position to be inspected, and image data features of the position to be inspected are obtained. The image data features of the position to be inspected are matched with the data features in the second digital twin image to obtain distinguishing image features of the image data features of the position to be inspected and the data features in the second digital twin image, and distinguishing image features to be processed are obtained. The distinguishing image features to be processed include features of the power maintenance personnel and features of the shape of the power equipment. The distinguishing image features to be processed are compared with the image of the power equipment maintenance safety limit. If the distinguishing image features to be processed exceed the image of the power equipment maintenance safety limit, a first warning information is generated.

[0087] The data judgment unit first receives information from the seat belt device, including whether the seat belt is fastened. If it is detected that the seat belt is not fastened, the data judgment unit will immediately start the subsequent safety inspection and early warning process. When the seat belt is not fastened, the data judgment unit will further obtain the location information of the current power maintenance personnel. The location information will be marked as a location to be inspected, that is, the system believes that there is a safety hazard at this location and further inspection and confirmation are required.

[0088] The data judgment unit sends the information of the location to be inspected to the drone device terminal, instructing it to go to the location to collect image data. After receiving the instruction, the drone device terminal will collect image data of the location to be inspected and transmit the data back to the data judgment unit.

[0089] After the data judgment unit receives the image data of the location to be inspected collected by the drone equipment terminal, it uses a feature extraction algorithm to extract features from the image data. The features extracted from the image data include the appearance features, action features, and position features of the power maintenance personnel, as well as the local shape features, color features, and texture features of the power equipment.

[0090] The extracted image data features of the location to be inspected will be matched with the data features of the previously generated second digital twin image (including information such as power equipment image data, maintenance personnel safety area, construction natural environment safety standards, and power equipment deformation safety standards). Through matching, the system can identify the difference between the image data of the location to be inspected and the second digital twin image, that is, distinguish the image features.

[0091] The data judgment unit compares the extracted distinguishing image features to be processed with the power equipment maintenance safety boundary image. If the distinguishing image features to be processed exceed the safety range defined by the power equipment maintenance safety boundary image (for example, the maintenance personnel are not in the safe area, or the equipment shape is abnormally deformed, etc.), the system considers that there is a safety hazard.

[0092] The data judgment unit triggers the conditions for generating the first warning information and constructs the warning information according to the specific situation of exceeding the safety limit. The warning information usually includes detailed information such as the location of the maintenance personnel who exceed the safety limit, abnormal equipment shape, etc., so that the backend control end can take corresponding measures in time.

[0093] To summarize, the data judgment unit realizes the real-time monitoring and early warning functions of the safety status of power maintenance personnel and power equipment through a series of operations such as receiving information from the seat belt device, obtaining and marking the location of maintenance personnel, sending inspection instructions, receiving and processing image data, performing feature extraction and data matching, and comparing and early warning safety limits.

[0094] The system detection unit detects the real-time system operation memory data during the data processing by the data acquisition module, the data collection unit, the data processing module and the data judgment unit. If the real-time system operation memory data is higher than the preset system operation content standard value, a second warning information is generated.

[0095] The system detection unit continuously monitors the real-time system running memory data, which includes indicators such as used memory, available memory, and memory occupancy rate. These data reflect the current resource usage and load status of the system.

[0096] The system detection unit compares the acquired real-time system running memory data with the preset system running memory standard value. The preset standard value is set according to the performance requirements and resource limitations of the normal operation of the system, and is used to determine whether the current system load is within an acceptable range.

[0097] By comparing the real-time system running memory data with the preset standard value, the system detection unit can determine whether the system is currently in a high load state. If the real-time system running memory data is higher than the preset system running memory standard value, it means that the system resource usage has approached or exceeded the threshold, resulting in system performance degradation or other problems.

[0098] When the system detection unit determines that the system is in a high load state, it triggers the condition for generating the second warning information. The second warning information is used to remind the system administrator or relevant personnel to pay attention to the current high load state of the system and take corresponding measures to optimize system performance, increase resources or adjust system configuration.

[0099] The system detection unit helps ensure system stability and reliability by timely monitoring and warning of high-load conditions of the system. It can help system administrators to promptly discover and resolve potential performance bottlenecks or resource limitations, thus avoiding serious consequences such as system crashes or data loss.

[0100] Specifically, in the real-time safety belt monitoring system of the present invention, the data acquisition module is also used for:

[0101] Acquire seat belt wearing status data from the seat belt device, where the seat belt wearing status data includes whether the seat belt is fastened and the time when the seat belt is fastened;

[0102] Obtain image data of power maintenance personnel at work from the drone equipment terminal, and the behavior and location movement of the maintenance personnel during the work process;

[0103] Acquire the position and shape data of the electric equipment from the sensor end, and preliminarily process the acquired position and shape data of the electric equipment to obtain the preprocessed position and shape data of the electric equipment;

[0104] The pre-processed location and shape data of the power equipment are substituted into the preset digital twin model, and the digital twin image of the power equipment is generated through the digital twin model;

[0105] The generated digital twin image of the power equipment is integrated with the seat belt wearing status and maintenance personnel behavior, and the integrated data is output.

[0106] The data acquisition module establishes a communication connection with the seat belt device to obtain the seat belt wearing status data in real time. This data includes whether the seat belt is correctly fastened and the time when the seat belt is fastened. By monitoring the wearing status of the seat belt, the system can ensure that power maintenance personnel always wear seat belts during the operation, thereby improving the safety of the operation.

[0107] The data acquisition module also obtains image data of the power maintenance personnel during their work from the drone equipment terminal. These image data record the maintenance personnel's behavior and position movement. By analyzing the image data, the system can monitor the maintenance personnel's operating status, promptly detect and correct irregular behaviors, and ensure that the operating process meets safety standards. The data acquisition module communicates with the sensor terminal installed on the power equipment to obtain the position and shape data of the power equipment in real time.

[0108] After acquiring the raw data, the data acquisition module will perform preliminary processing on it. For example, the location and shape data of the power equipment are cleaned, denoised, and formatted to ensure the accuracy and availability of the data. The preprocessed location and shape data of the power equipment are substituted into the preset digital twin model. The digital twin model is a highly realistic virtual model that can reflect the actual status and behavior of the power equipment. Through the digital twin model, the system can generate digital twin images of the power equipment. These images not only contain the appearance information of the equipment, but also include information such as the operating status and working environment of the equipment.

[0109] Finally, the data acquisition module integrates the generated digital twin image of the power equipment with the acquired seat belt wearing status and maintenance personnel behavior. The integrated data contains all the information on the work site, providing comprehensive data support for the subsequent processing and analysis of the system. The integrated data is output to other modules of the system for further processing and analysis, such as the data acquisition unit, data processing module, and data judgment unit.

[0110] Specifically, in the real-time safety belt monitoring system of the present invention, the data acquisition unit is further used for:

[0111] Extracting coordinate information from the generated digital twin image of the power equipment, wherein the coordinate information extracted from the digital twin image of the power equipment includes the position, direction and size of the power equipment;

[0112] Preprocess the image data of the area to be collected collected by the drone equipment terminal, match the image data of the area to be collected with the coordinate information of the digital twin image of the power equipment, and convert the pixel coordinates in the image data of the area to be collected into three-dimensional coordinates consistent with the digital twin model;

[0113] Use feature matching algorithms to establish the correspondence between the image data of the area to be collected and the digital twin image of the power equipment. According to the matching results, coordinate configuration is performed on the image data of the area to be collected, and each pixel or feature point in the image data is associated with the corresponding coordinates of the digital twin image.

[0114] The image data of the area to be collected with configured coordinates is substituted into the digital twin image of the power equipment, and the image data is applied as texture to the corresponding position of the digital twin model to obtain the digital twin image of the power equipment that integrates the image data of the area to be collected, that is, the first digital twin image.

[0115] The data acquisition unit extracts coordinate information from the generated digital twin image of the power equipment. These coordinate information include parameters such as the position, direction, and size of the power equipment, which describe the precise position and posture of the power equipment in three-dimensional space. The data acquisition unit preprocesses the image data of the area to be collected by the drone equipment terminal. The preprocessing steps include image denoising, enhancement, cropping, etc. to improve the quality and usability of the image data.

[0116] The data acquisition unit matches the preprocessed image data of the area to be acquired with the coordinate information of the digital twin image of the power equipment. The purpose of this step is to convert the pixel coordinates in the image data into three-dimensional coordinates consistent with the digital twin model, thereby achieving spatial alignment between the image data and the digital twin model.

[0117] Using the feature matching algorithm, the data acquisition unit further analyzes the correspondence between the image data of the area to be collected and the digital twin image of the power equipment. According to the matching results, the coordinates of the image data of the area to be collected are configured, and each pixel or feature point in the image data is associated with the corresponding coordinates of the digital twin image. In this way, the image data is accurately mapped to the digital twin model.

[0118] The data acquisition unit substitutes the image data of the area to be collected with the configured coordinates into the digital twin image of the power equipment. These image data are applied as textures to the corresponding positions of the digital twin model, thereby generating a digital twin image of the power equipment that integrates the image data of the area to be collected, that is, the first digital twin image. The first digital twin image not only contains the geometric shape and location information of the power equipment, but also contains the on-site image data collected from the drone, providing richer and more comprehensive information for the subsequent processing and analysis of the system.

[0119] Specifically, in the real-time safety belt monitoring system of the present invention, the data processing module is also used for:

[0120] Using image processing technology and computer vision algorithms, extracting features from the first digital twin image, wherein the feature extraction from the first digital twin image includes edge detection, corner point detection, and texture analysis;

[0121] According to the safety area standard for construction personnel in the power maintenance construction procedure information, a corresponding safety area is delineated in the first digital twin image, and the delineation of the corresponding safety area in the first digital twin image includes marking the safety area that the construction personnel should maintain on the image;

[0122] According to the deformation safety standards of power equipment, analyze the deformation of the equipment in the first digital twin image and compare it with the image of the equipment in the design or normal state to detect abnormal deformation of the equipment;

[0123] The extracted first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard data are fused, and the first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard are displayed in the digital twin model using a data fusion algorithm;

[0124] Based on the fused data, the digital twin image of the power equipment is updated. The updating of the digital twin image of the power equipment includes adding a safety area for construction personnel to the image, modifying the safety area for construction personnel, the safety standards of the natural construction environment, and the safety standards of the deformation of power equipment. After data fusion and model updating, a second digital twin image is generated.

[0125] The data processing module uses advanced image processing technology and computer vision algorithms to perform detailed feature extraction on the first digital twin image. The detailed feature extraction process for the first digital twin image includes steps such as edge detection, corner detection, and texture analysis, aiming to accurately identify the edges, corners, and surface texture features of the power equipment from the image.

[0126] According to the safety area standards for construction workers in the power maintenance construction regulations, the data processing module delineates the corresponding safety areas in the first digital twin image. This step provides an intuitive visual reference for safety supervision by clearly marking the safety areas that construction workers should maintain on the image.

[0127] The data processing module also analyzes the equipment deformation in the first digital twin image according to the deformation safety standards of power equipment. By comparing it with the image of the equipment in the design or normal state, the module can accurately detect abnormal deformation of the equipment, thereby promptly discovering potential safety hazards.

[0128] After completing feature extraction, safety zone demarcation and equipment deformation detection, the data processing module deeply integrates the extracted first digital twin image equipment features, construction personnel safety zone, construction natural environment safety standards and power equipment deformation safety standards data. By using advanced data fusion algorithms, the module can ensure that this information is accurately displayed in the digital twin model.

[0129] Based on the fused data, the data processing module updates the digital twin image of the power equipment in real time. This update process includes adding new safety areas for construction personnel to the image, modifying existing safety areas, adjusting the safety standards for the natural environment of construction, and updating the deformation safety standard information of the power equipment. After data fusion and model update, the module finally generates a second digital twin image. This image not only contains the geometric shape and location information of the power equipment, but also incorporates comprehensive safety supervision data, providing strong support for safety supervision decisions.

[0130] Specifically, in the real-time seat belt monitoring system of the present invention, the data judgment unit is further used for:

[0131] Use feature extraction algorithms to extract features from the image data of the inspection location collected by the UAV equipment terminal to obtain the appearance features, action features, position features of the power maintenance personnel, local shape features, color features and texture features of the power equipment;

[0132] Extracting corresponding data features from the second digital twin image, wherein the corresponding data features extracted from the second digital twin image correspond to the features extracted from the image data of the position to be inspected;

[0133] A feature matching algorithm is used to preliminarily match the features in the image data of the position to be inspected with the features in the second digital twin image, and the feature matching result is obtained by comparing the position, direction and size of the feature points;

[0134] Calculate the difference between the feature matching result and the feature of the second digital twin image data to obtain a difference calculation result, where the difference calculation result includes a position offset result, a shape change result, and a color difference result;

[0135] According to the calculated difference calculation result, the distinguishing image features between the image data features of the position to be inspected and the image data features of the second digital twin are extracted to form distinguishing image features to be processed;

[0136] Mark the distinguishing image features to be processed and annotate the difference areas on the images.

[0137] The data judgment unit first uses advanced feature extraction algorithms to extract features from the image data of the location to be inspected collected by the drone equipment terminal. These features include but are not limited to the appearance features, action features, and location features of power maintenance personnel, as well as the local shape features, color features, and texture features of power equipment. These features provide a basis for subsequent data analysis and comparison.

[0138] The data judgment unit extracts data features corresponding to the image data of the position to be inspected from the second digital twin image. These features should be directly comparable to the features in the image data of the position to be inspected. The feature matching algorithm is used to preliminarily match the features in the image data of the position to be inspected with the features in the second digital twin image. This process evaluates the similarities and differences between the two by comparing the attributes of the feature points such as position, direction, and size.

[0139] After completing the feature matching, the data judgment unit calculates the difference between the feature matching result and the second digital twin image data feature, including position offset, shape change, color difference, etc. By quantifying these differences, the degree of deviation between the position to be inspected and the preset safety standard can be more accurately evaluated.

[0140] Based on the calculated difference calculation results, the data judgment unit will extract the distinguishing image features between the image data features of the position to be inspected and the image data features of the second digital twin. These features represent the significant differences between the two and are also an intuitive reflection of potential safety hazards.

[0141] The distinguishing features of the images to be processed are marked, and the difference areas are clearly marked on the images. This helps safety supervisors to quickly locate the problem and take appropriate measures to deal with it.

[0142] Through the above steps, the data judgment unit can efficiently process and analyze the image data of the inspection location, timely discover potential safety hazards, and provide strong decision-making support for safety supervisors. This function is of great significance for improving the work safety of power maintenance personnel and reducing the occurrence of safety accidents.

[0143] Specifically, in the real-time seat belt monitoring system of the present invention, the data judgment unit is further used for:

[0144] Extract and integrate distinguishing image features between the image data features of the location to be inspected and the image data features of the second digital twin, where the distinguishing image features include features of power maintenance personnel and features of the shape of power equipment;

[0145] Compare the features of the power maintenance personnel and the shape features of the power equipment in the distinguished image features to be processed with the power equipment maintenance safety boundary image one by one to check whether the maintenance personnel are in the safe area and whether the shape of the equipment exceeds the safety limit;

[0146] During the comparison process, if the features of the distinguished image to be processed exceed the safety range defined by the power equipment maintenance safety boundary image, it is considered unsafe;

[0147] When it is determined that the distinguishing image features to be processed exceed the safety limit image of the power equipment maintenance, the condition of generating the first warning information is triggered;

[0148] According to the exceeding situation, a first warning information is constructed, the first warning information includes the position of the maintenance personnel exceeding the safety limit and the abnormal shape of the equipment, and the first warning information is sent to the background control terminal.

[0149] The data judgment unit in the real-time seat belt monitoring system is responsible for in-depth analysis of the collected data and comparison with the preset safety standards to determine whether there are safety hazards and generate early warning information in a timely manner. Specifically, the data judgment unit in the real-time seat belt monitoring system of the present invention also has the following detailed functions and operation procedures:

[0150] The data judgment unit first extracts and integrates the distinguishing image features between the image data features of the location to be inspected and the image data features of the second digital twin. These distinguishing image features mainly include the features of the power maintenance personnel (such as position, action, etc.) and the shape features of the power equipment. These features are an important basis for judging whether there are safety hazards.

[0151] The data judgment unit will compare the power maintenance personnel features and power equipment shape features in the to-be-processed distinguished image features with the preset power equipment maintenance safety limit images one by one. The purpose of this step is to check whether the maintenance personnel are in the safe area and whether the equipment shape exceeds the safety limit.

[0152] During the comparison process, if the difference image features to be processed exceed the safety range defined by the power equipment maintenance safety boundary image, the data judgment unit will immediately judge it as an unsafe state. This means that there is a potential safety hazard and immediate measures need to be taken to deal with it.

[0153] When it is determined that the distinguishing image features to be processed exceed the safety limit, the data judgment unit will trigger the conditions for generating the first warning information. According to the specific circumstances of the exceedance (such as the location of the maintenance personnel, abnormal equipment shape, etc.), the data judgment unit will construct a detailed first warning information. The warning information will include information such as the location of the maintenance personnel and abnormal equipment shape that exceed the safety limit, so that safety supervisors can quickly understand and deal with safety hazards. Finally, the data judgment unit will send the warning information to the background control end so that relevant personnel can respond and take measures in time.

[0154] Specifically, in the real-time safety belt monitoring system of the present invention, the system detection unit is further used for:

[0155] Receive the system running memory standard value and monitor the real-time system running memory data. The real-time system running memory data includes the used memory, available memory and memory occupancy rate;

[0156] Compare the acquired real-time system running memory data with the preset system running memory standard value to determine whether the memory usage rate exceeds the preset system running memory standard value;

[0157] According to the comparison result, it is determined whether the real-time system running memory data is higher than the preset system running memory standard value. If it is exceeded, the system is in a high load state;

[0158] When the real-time system running memory data is higher than the preset standard value, the condition for generating the second warning information is triggered.

[0159] The system detection unit is responsible for monitoring the operating status of the system in the real-time seat belt monitoring system, so that the system can operate under safe and stable conditions. Specifically, the system detection unit in the real-time seat belt monitoring system of the present invention also has the following detailed functions and operation procedures:

[0160] The system detection unit first receives the preset system running memory standard value. This standard value is determined based on the system's hardware configuration, software requirements, and expected workload, and is used to measure the reasonable range of system memory usage.

[0161] The system detection unit monitors the real-time system's running memory data in real time. These data include indicators such as used memory, available memory, and memory occupancy rate, which can fully reflect the current memory usage of the system.

[0162] The system detection unit compares the acquired real-time system running memory data with the preset system running memory standard value. Through this step, it can be determined whether the current system memory usage exceeds the preset standard value.

[0163] Based on the comparison results, the system detection unit will determine whether the real-time system operating memory data is higher than the preset system operating memory standard value. If the memory usage exceeds the standard value, the system detection unit will determine that the system is currently in a high load state. This means that the system is processing a large amount of data or running complex tasks, and its operating status needs to be closely monitored.

[0164] When the real-time system running memory data is higher than the preset standard value, the system detection unit will trigger the condition for generating the second warning information. The warning information will indicate that the system is currently under high load and contain some additional suggestions or measures to help administrators or operators take timely measures to optimize system performance or increase resources. Finally, the system detection unit will send the warning information to the background control terminal or relevant managers so that they can understand the system status in time and take corresponding actions.

[0165] The present invention solves the problem that in the prior art, in a complex and changeable use environment, delays or losses in the data transmission process may affect the response speed of the system, thereby causing untimely security warnings through the following technical solutions:

[0166] Use a data collection mobile terminal (such as a drone device terminal) to collect image data of power maintenance personnel during work and transmit it to the server in real time. The sensor is installed on the seat belt device to collect seat belt wearing status data in real time and transmit it to the server.

[0167] The server side is equipped with a data acquisition module that can quickly obtain seat belt wearing status data, power maintenance personnel behavior data, power equipment location and shape data, etc., and substitute the power equipment location and shape data into the preset digital twin model to generate a digital twin image of the power equipment, providing a basis for subsequent data processing and analysis.

[0168] Image processing technology and computer vision algorithms are used to extract features from the collected image data, including edge detection, corner detection, and texture analysis, to obtain the characteristics of power equipment and maintenance personnel. The extracted feature data is integrated with data such as the safety area for construction personnel, safety standards for the natural construction environment, and safety standards for deformation of power equipment to generate a second digital twin image containing comprehensive safety information.

[0169] Receive information from the seat belt device. If it is detected that the seat belt is not fastened, obtain the location information of the power maintenance personnel and compare it with the second digital twin image. If the location or behavior of the maintenance personnel exceeds the safety limit, generate the first warning information immediately. During the data processing process, monitor the system running memory data in real time. If the memory usage rate exceeds the preset standard value, generate the second warning information, indicating that the system is in a high load state and needs to be optimized or expanded in time. Reduce the risk of data transmission delay and loss through efficient data collection tools such as drone equipment terminals.

[0170] To summarize, the present invention effectively solves the problem of untimely security warning caused by data transmission delay or loss in complex and changeable usage environments in the prior art through technical means such as real-time data collection and transmission, efficient server-side processing, feature extraction and data fusion, real-time security judgment and warning, and optimization of data transmission and processing procedures.

Claims

1. The real-time seat belt monitoring system is characterized by: include: The seat belt device side, the data acquisition mobile terminal, the sensor side, the server side and the background control side respectively establish communication connections with the data acquisition mobile terminal, the seat belt device side, the sensor side and the background control side. The sensor side is installed on the seat belt device side. The sensor side is used to collect data information from the seat belt device side. The data acquisition mobile terminal is used to collect image data during the work of power maintenance personnel. The data acquisition mobile terminal and the sensor side transmit the collected data information to the server side. The server side includes: The data acquisition module acquires the seat belt wearing status data, the behavior data of the power maintenance personnel, the power equipment location data, the power equipment shape data and the system operation data, substitutes the power equipment location data and the power equipment shape data into the preset digital twin model, and generates the digital twin image of the power equipment; A data acquisition unit receives power maintenance demand information, the power maintenance demand information includes the location information of the equipment to be repaired and the power maintenance construction procedure information, the power maintenance construction procedure information includes the power maintenance construction safety standard data, the power maintenance construction safety standard data includes the safety area for construction personnel, the safety standard for the natural construction environment, and the safety standard for deformation of power equipment, takes the location information of the equipment to be repaired as the area to be collected, and sends the area to be collected to the data acquisition mobile terminal, the data acquisition mobile terminal adopts an unmanned aerial vehicle device terminal, receives the image data information of the area to be collected collected by the unmanned aerial vehicle device terminal, obtains the image data of the area to be collected, obtains the coordinate information of the digital twin image of the power equipment, configures the coordinates of the image data of the area to be collected based on the coordinate information of the digital twin image of the power equipment, obtains the image data coordinate information of the area to be collected, substitutes the image data coordinate information of the area to be collected into the digital twin image of the power equipment, and obtains the first digital twin image; The data processing module extracts features from the first digital twin image to obtain the first digital twin image equipment features, and based on the first digital twin image equipment features, substitutes the construction personnel safety area, the construction natural environment safety standard, and the electric power equipment deformation safety standard into the electric power equipment digital twin image to obtain the second digital twin image, wherein the second digital twin image includes the electric power equipment image data and the electric power equipment maintenance safety boundary image; A data judgment unit receives information from a seat belt device. If it is detected whether the seat belt is fastened, the position information of the power maintenance personnel is obtained, the position information of the power maintenance personnel is marked, the position to be inspected is obtained, the position to be inspected is sent to the drone device terminal, the image data of the position to be inspected collected by the drone device terminal is received, feature extraction is performed on the image data of the position to be inspected, and image data features of the position to be inspected are obtained. The image data features of the position to be inspected are matched with the data features in the second digital twin image to obtain distinguishing image features of the image data features of the position to be inspected and the data features in the second digital twin image, and distinguishing image features to be processed are obtained. The distinguishing image features to be processed include features of the power maintenance personnel and features of the shape of the power equipment. The distinguishing image features to be processed are compared with the image of the power equipment maintenance safety limit. If the distinguishing image features to be processed exceed the image of the power equipment maintenance safety limit, a first warning information is generated. The system detection unit detects the real-time system operation memory data during the data processing by the data acquisition module, the data collection unit, the data processing module and the data judgment unit. If the real-time system operation memory data is higher than the preset system operation content standard value, a second warning information is generated.

2. The real-time seat belt monitoring system according to claim 1, characterized in that: The data acquisition module is further used for: Acquire seat belt wearing status data from the seat belt device, where the seat belt wearing status data includes whether the seat belt is fastened and the time when the seat belt is fastened; Obtain image data of power maintenance personnel during their work from the drone equipment terminal, and the behavior and location movement of the maintenance personnel during their work; Acquire the position and shape data of the electric equipment from the sensor end, and preliminarily process the acquired position and shape data of the electric equipment to obtain the preprocessed position and shape data of the electric equipment; The pre-processed location and shape data of the power equipment are substituted into the preset digital twin model, and the digital twin image of the power equipment is generated through the digital twin model; The generated digital twin image of the power equipment is integrated with the seat belt wearing status and maintenance personnel behavior, and the integrated data is output.

3. The real-time seat belt monitoring system according to claim 1, characterized in that: The data acquisition unit is also used for: Extracting coordinate information from the generated digital twin image of the power equipment, wherein the coordinate information extracted from the digital twin image of the power equipment includes the position, direction and size of the power equipment; Preprocess the image data of the area to be collected by the drone equipment terminal, match the image data of the area to be collected with the coordinate information of the digital twin image of the power equipment, and convert the pixel coordinates in the image data of the area to be collected into three-dimensional coordinates consistent with the digital twin model; Use feature matching algorithms to establish the correspondence between the image data of the area to be collected and the digital twin image of the power equipment. According to the matching results, coordinate configuration is performed on the image data of the area to be collected, and each pixel or feature point in the image data is associated with the corresponding coordinates of the digital twin image. The image data of the area to be collected with configured coordinates is substituted into the digital twin image of the power equipment, and the image data is applied as texture to the corresponding position of the digital twin model to obtain the digital twin image of the power equipment that integrates the image data of the area to be collected, that is, the first digital twin image.

4. The real-time seat belt monitoring system according to claim 1, characterized in that: The data processing module is further used for: Using image processing technology and computer vision algorithms, extracting features from the first digital twin image, wherein the feature extraction from the first digital twin image includes edge detection, corner point detection, and texture analysis; According to the safety area standard for construction personnel in the power maintenance construction procedure information, a corresponding safety area is delineated in the first digital twin image, and the delineation of the corresponding safety area in the first digital twin image includes marking the safety area that the construction personnel should maintain on the image; According to the deformation safety standards of power equipment, analyze the deformation of the equipment in the first digital twin image and compare it with the image of the equipment in the design or normal state to detect abnormal deformation of the equipment; The extracted first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard data are fused, and the first digital twin image equipment features, construction personnel safety area, construction natural environment safety standard and power equipment deformation safety standard are displayed in the digital twin model using a data fusion algorithm; Based on the fused data, the digital twin image of the power equipment is updated. The updating of the digital twin image of the power equipment includes adding a safety area for construction personnel to the image, modifying the safety area for construction personnel, the safety standards of the natural construction environment, and the safety standards of the deformation of power equipment. After data fusion and model updating, a second digital twin image is generated.

5. The real-time seat belt monitoring system according to claim 1, characterized in that: The data judgment unit is further used for: Use feature extraction algorithms to extract features from the image data of the inspection location collected by the UAV equipment terminal to obtain the appearance features, action features, position features of the power maintenance personnel, local shape features, color features and texture features of the power equipment; Extracting corresponding data features from the second digital twin image, wherein the corresponding data features extracted from the second digital twin image correspond to the features extracted from the image data of the position to be inspected; A feature matching algorithm is used to preliminarily match the features in the image data of the position to be inspected with the features in the second digital twin image, and the feature matching result is obtained by comparing the position, direction and size of the feature points; Calculate the difference between the feature matching result and the second digital twin image data feature to obtain a difference calculation result, where the difference calculation result includes a position offset result, a shape change result, and a color difference result; According to the calculated difference calculation result, the distinguishing image features between the image data features of the position to be inspected and the image data features of the second digital twin are extracted to form distinguishing image features to be processed; Mark the distinguishing image features to be processed and annotate the difference areas on the images.

6. The real-time seat belt monitoring system according to claim 5, characterized in that: The data judgment unit is further used for: Extract and integrate distinguishing image features between the image data features of the location to be inspected and the image data features of the second digital twin, where the distinguishing image features include features of power maintenance personnel and features of the shape of power equipment; Compare the features of the power maintenance personnel and the shape features of the power equipment in the distinguished image features to be processed with the power equipment maintenance safety boundary image one by one to check whether the maintenance personnel are in the safe area and whether the shape of the equipment exceeds the safety limit; During the comparison process, if the features of the distinguished image to be processed exceed the safety range defined by the power equipment maintenance safety boundary image, it is considered unsafe; When it is determined that the distinguishing image features to be processed exceed the safety limit image of the power equipment maintenance, the condition of generating the first warning information is triggered; According to the exceeding situation, a first warning information is constructed, the first warning information includes the position of the maintenance personnel exceeding the safety limit and the abnormal shape of the equipment, and the first warning information is sent to the background control terminal.

7. The real-time seat belt monitoring system according to claim 1, characterized in that: The system detection unit is further used for: Receive the system running memory standard value and monitor the real-time system running memory data. The real-time system running memory data includes the used memory, available memory and memory occupancy rate; Compare the acquired real-time system running memory data with the preset system running memory standard value to determine whether the memory usage rate exceeds the preset system running memory standard value; According to the comparison result, it is determined whether the real-time system running memory data is higher than the preset system running memory standard value. If it is exceeded, the system is in a high load state; When the real-time system running memory data is higher than the preset standard value, the condition for generating the second warning information is triggered.

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