A power operation critical edge intelligent early warning system, device and method

Abstract

This invention relates to the field of power early warning technology, and in particular to an intelligent early warning system, device, and method for critical edges of power operations. The system includes a live-line identification and positioning module, an electric field critical voltage zoning module, a live-line distance measurement module, and a power operation critical early warning module. It utilizes an image acquisition unit to acquire real-time image frames of the environment where the worker is located and performs live-line identification and positioning of the worker to obtain the worker's real-time live-line spatial location. Combined with a field strength sensing unit, it performs live-line positioning voltage strength measurement and critical voltage level assessment of the corresponding real-time operation process of the power worker to obtain the critical voltage level zoning of each worker. It uses a radar ranging unit to measure the live-line distance and performs power operation early warning processing through an early warning unit to execute corresponding power operation critical edge safety early warning work. This invention can realize integrated intelligent early warning for critical edges of power operations, effectively improving early warning efficiency.

Description

Technical Field

[0001] This invention relates to the field of power early warning technology, and in particular to an intelligent early warning system, device, and method for power operation critical edge. Background Technology

[0002] In recent years, with the development of the power grid, the number of power transmission and transformation facilities has increased year by year. Safety accidents caused by insufficient critical operation have occurred frequently. The intelligent early warning of critical edge of power operation is facing increasing difficulty and the early warning requirements are becoming higher. However, the protection capabilities of operators are not matched with reality and are not adapted, and safety accidents occur from time to time.

[0003] The requirements for critical edge defense involve all levels of the power system, including the power production and consumption system composed of substations, transmission lines, distribution lines, and consumption lines, as well as the weak current side equipment and facilities, including substations, converter stations, transmission lines, and various detection equipment, communication equipment, safety protection devices, automatic control devices, monitoring automation, and dispatch automation systems. Currently, existing technical means mostly involve measuring necessary critical edges before energization according to relevant regulations and standards, artificially setting up safety passages, safety warning lines, and safety signs in advance, formulating relevant regulations for critical edges, compiling responsibility measures for critical edges, and improving the risk classification and defense mechanism for critical edges, thereby achieving hard defense of critical edges for work sites, personnel, and equipment. However, due to the high difficulty of critical edge defense in power systems, the mismatch and inadequacy of construction units' critical edge defense capabilities with reality, problems such as on-site personnel violating critical edge operations and operating equipment beyond designated distances still exist. Especially when power equipment is in a critical state, it is difficult to accurately predict impending dangers, resulting in low accuracy of early warnings. Summary of the Invention

[0004] Therefore, it is necessary for the present invention to provide an intelligent early warning system, device, or method for critical edges of power operations, in order to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a smart early warning system for critical edges of power operations includes the following modules:

[0006] The live-line identification and positioning module is used to acquire real-time image frames of the environment where the operator is located using the image acquisition unit and transmit them to the corresponding image recognition component in the control center; the image recognition component performs live-line identification and positioning of the operator in the image frames of the environment where the operator is located to obtain the real-time live-line spatial positioning of the operator;

[0007] The electric field critical voltage zoning module is used to measure the electric field strength corresponding to the real-time operation process of the power workers based on the real-time energized spatial positioning of the workers and in conjunction with the field strength sensing unit, so as to obtain the electric field strength corresponding to the positioning of the workers; and to evaluate the critical voltage level based on the electric field strength corresponding to the positioning of the workers, so as to obtain the critical voltage level zoning of each worker, and upload it to the control center.

[0008] The live-line distance measurement module is used to measure the live-line distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, using a radar ranging unit. The result is obtained as the live-line distance between the workers and the live equipment in each voltage level zone and then uploaded to the control center.

[0009] The critical early warning module for power operations is used to set safe early warning distances for each voltage level in the control center according to the critical voltage level of each operator, and to compare the safe early warning distance with the energized distance between the operator and the live equipment to obtain the critical safety comparison result of the operator; the early warning unit performs power operation early warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety early warning work.

[0010] Furthermore, the live-line identification and positioning module includes the following functions:

[0011] The camera component in the image acquisition unit is used to monitor the environment in real time during the actual operation of the power workers, so as to collect image frames of the environment in which the workers are located in real time.

[0012] The image transmission component in the image acquisition unit transmits the image frames of the environment where the workers are located to the corresponding image recognition component in the control center. The image recognition component then uses the received image frames of the environment where the workers are located to perform live hazard segmentation and screening to identify and screen the image frames corresponding to live objects such as metal tools held by the power workers or live edges near high places, thereby generating live hazard segments for each worker.

[0013] The frames of live electrical hazards for each worker are time-synchronized and sorted to generate a sequence of live electrical hazard image frames for the workers.

[0014] The image recognition component is used to identify and locate the worker in live electrical positions by analyzing the sequence of images showing the worker in danger of being energized, so as to obtain the real-time spatial location of the worker in live electrical positions.

[0015] Furthermore, the step of using an image recognition component to identify and locate workers in a live-lined hazardous image frame sequence includes:

[0016] The image recognition component uses Canny edge detection to perform contour topology recognition between the worker and the live equipment in each image frame within the sequence of images of workers in danger of being energized, so as to obtain the contour spatial position and connection relationship between the worker and the live equipment within the image frame sequence.

[0017] Based on the contour spatial position and connection relationship between the worker and the live equipment within the image frame sequence, the live relative trajectory analysis is performed on each image frame in the image frame sequence of the worker's live hazard image to obtain the dynamic trajectory between the relative positions of the worker and the live equipment within the image frame sequence.

[0018] The relative spatial position between the operator and the live equipment is obtained by the dynamic trajectory between the relative positions of the operator and the live equipment within the image frame sequence, and the relative spatial position between the operator and the live equipment is reconstructed by three-dimensional spatial projection to generate a live projection mesh of the operator containing the spatial coordinates.

[0019] Based on the live-line projection grid containing the corresponding spatial coordinates of the operator, the relative spatial position between the operator and the live equipment is located in real time by live-line projection positioning, so as to obtain the real-time live-line spatial positioning of the operator.

[0020] Furthermore, the electric field critical voltage partitioning module includes the following functions:

[0021] Based on the real-time energized spatial positioning of the operator and combined with the field strength sensing unit, the electric field strength of the operator is measured in real time during the real-time operation process. The electric field strength of the operator in the corresponding real-time energized positioning environment is measured in real time by the field strength sensing unit to obtain the electric field strength corresponding to the operator's positioning.

[0022] The electric field intensity distribution gradient is calculated based on the electric field intensity corresponding to the location of the operator, and the electric field intensity distribution gradient between the locations of the operator is obtained.

[0023] The critical voltage level is determined by assessing the gradient of electric field intensity distribution between the positions of the workers, so as to obtain the critical voltage level zone of each worker and upload it to the control center.

[0024] Furthermore, the calculation of the electric field intensity distribution gradient based on the electric field intensity corresponding to the operator's location includes:

[0025] The electric field distance between any two positions is determined by the positions of the operators.

[0026] The electric field strength difference between each pair of positions of the workers is calculated to obtain the electric field strength difference between each pair of positions.

[0027] The electric field intensity distribution gradient between the two locations is calculated based on the electric field distance between them, thus obtaining the electric field intensity distribution gradient between the locations of the workers.

[0028] Furthermore, the critical voltage level zones for each operator are specifically determined based on the distribution range corresponding to the electric field intensity distribution gradient, classifying them into safe voltage level, low voltage level, medium voltage level, high voltage level, ultra-high voltage level, and extra-high voltage level. The electric field intensity distribution gradient for the safe voltage level is 0-0.05V / m. 2 The corresponding critical voltage levels are 6V-42V; the electric field intensity distribution gradient at low voltage levels is 0.05-0.2V / m. 2 The corresponding critical voltage levels are 220V-380V; the electric field intensity distribution gradient for medium voltage levels is 0.2-2V / m. 2 The corresponding critical voltage levels are 3.6kV-10kV; the electric field intensity distribution gradient of the high-voltage level is 2-10V / m. 2 The corresponding critical voltage level is 110kV-220kV; the electric field intensity distribution gradient of the ultra-high voltage level is 10-30V / m. 2 The corresponding critical voltage level is 330kV-750kV; the electric field intensity distribution gradient of the ultra-high voltage level is greater than 30V / m. 2 The corresponding critical voltage levels are 1000kV and above AC and ±800kV and above DC.

[0029] Furthermore, the live-line distance measurement module includes the following functions:

[0030] The radar ranging unit uses the radar transmitting component to transmit corresponding ultrasonic beams between the metal tools and live equipment of the power workers in the critical voltage level zone where each worker is located. The ultrasonic beams are received in the radar receiving component, and the data analysis component in the control center monitors and determines the corresponding duration between transmission and reception.

[0031] The difference in electric field strength distribution between the workers and the live equipment is determined by the metal tools and live equipment held by the workers in the critical voltage level zone where each worker is located.

[0032] Based on the difference in electric field intensity distribution between the operator and the live equipment, the transmission influence of the emitted ultrasonic beam is evaluated using the electric field influence assessment calculation formula, and the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam is obtained.

[0033] The specific formula for calculating the impact of the electric field is as follows:

[0034]

[0035] In the formula, ε c denoted as the electric field difference transmission influence coefficient, V as the electric field range between the operator and the live equipment, r as the electric field spatial position parameter, E(r) as the difference in electric field intensity distribution between the operator and the live equipment at position r, ρ(r) as the ultrasonic propagation frequency of the ultrasonic beam at position r, α as the ultrasonic propagation energy attenuation coefficient of the ultrasonic beam within the corresponding electric field range, and d(r) as the propagation distance of the ultrasonic beam from position r to the ultrasonic propagation target.

[0036] Based on the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam, the transmission rate corresponding to the ultrasonic beam is corrected for the transmission influence, and the ultrasonic transmission correction rate is obtained.

[0037] Based on the ultrasonic transmission correction rate and combined with the corresponding time between transmission and reception, the live distance between the metal tools held by the power workers and the live equipment in each critical voltage level zone is measured to obtain the live distance between the workers and the live equipment in each voltage level zone, and then uploaded to the control center.

[0038] Furthermore, the critical early warning module for power operation includes the following functions:

[0039] By setting safe warning distances for each voltage level in the control center according to the critical voltage level where each operator is located;

[0040] The critical safety comparison results for the operator are obtained by comparing the safety warning distance with the corresponding energized distance between the operator and the energized equipment. This includes the comparison results corresponding to whether the energized distance is greater than, equal to or less than the safety warning distance.

[0041] By analyzing the critical safety comparison results of the workers on the corresponding data analysis component in the control center, if the energized distance is greater than the safety warning distance, the corresponding comparison result is stored in the control center. If the energized distance is equal to the safety warning distance, the corresponding audible warning control signal is generated and uploaded to the audible warning component via the signal transmission component of the warning unit to start the corresponding audible alarm for critical safety of the power operation. If the energized distance is less than the safety warning distance, the corresponding audible and photoelectric warning control signals are generated and uploaded to the audible and photoelectric warning components via the signal transmission component of the warning unit to simultaneously execute the corresponding audible and visual warning for critical safety of the power operation.

[0042] Furthermore, the present invention also provides an intelligent early warning device for critical edges of power operations, used to execute the intelligent early warning system for critical edges of power operations as described above. The intelligent early warning device for critical edges of power operations includes a housing, the housing being a cylindrical shell structure. A plurality of image acquisition units are arranged on the outer circumference of the cylindrical shell structure. The plurality of image acquisition units on the housing are arranged at equal angles. A radar ranging unit is arranged on the upper side of each image acquisition unit. Inside the housing are a field strength sensing unit, an early warning unit, and a control center. The control center has built-in image recognition components and data analysis components. The image acquisition unit, the radar ranging unit, the field strength sensing unit, and the... All the aforementioned early warning units are electrically connected to the control center. The image acquisition unit includes a camera component and an image transmission component. The camera component is electrically connected to the image transmission component, and the image transmission component is electrically connected to the image recognition component. The radar ranging unit includes a radar transmitting component and a radar receiving component. The radar transmitting component is electrically connected to the radar receiving component, and the radar receiving component is electrically connected to the data analysis component. The early warning unit includes an audio early warning component, a photoelectric early warning component, and a signal transmission component. The audio early warning component and the photoelectric early warning component are both electrically connected to the signal transmission component, and the signal transmission component is electrically connected to the control center.

[0043] Furthermore, the present invention also provides an intelligent early warning method for critical edges of power operations. This method is implemented based on the intelligent early warning system for critical edges of power operations described above. The intelligent early warning method for critical edges of power operations includes:

[0044] The image acquisition unit acquires real-time image frames of the environment where the workers are located and transmits them to the corresponding image recognition component in the control center; the image recognition component then uses the image frames of the environment where the workers are located to identify and locate the workers while they are in power, so as to obtain the real-time live spatial location of the workers.

[0045] Based on the real-time energized spatial positioning of the workers and combined with the field strength sensing unit, the electric field strength of the workers is measured in real time to obtain the electric field strength corresponding to the positioning of the workers; the critical voltage level is evaluated based on the electric field strength corresponding to the positioning of the workers to obtain the critical voltage level zone of each worker, and the data is uploaded to the control center.

[0046] The radar ranging unit is used to measure the energized distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, so as to obtain the energized distance between the workers and the live equipment in each voltage level zone, and then upload it to the control center.

[0047] By setting safety warning distances for each voltage level in the control center according to the critical voltage level of each operator, and comparing the safety warning distance with the energized distance between the operator and the live equipment, the critical safety comparison result of the operator is obtained. The warning unit performs power operation warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety warning work.

[0048] The beneficial effects of this invention are:

[0049] 1. The intelligent early warning system for critical edges of power operations proposed in this invention is composed of a live-line identification and positioning module, an electric field critical voltage partitioning module, a live-line distance measurement module, and a critical early warning module for power operations. Compared with the prior art, the beneficial effect of this application is that by using image acquisition units set at equal angles to collect image frames of the environment in which the operator is located in real time and transmitting them to the image recognition component in the control center, it can effectively identify and locate the operator. The greatest advantage of this process is that it can achieve real-time monitoring, accurately identify whether the operator is in a live environment, and promptly locate their specific spatial position. By combining image recognition technology, the system can quickly and accurately determine whether the operator has entered a high-voltage area or other dangerous environment, thereby providing data support for subsequent safety warnings and power operation protection. It can not only improve the safety of the work site, but also reduce human error, provide automated detection methods, and update the status and position of the operator in real time. This means that the working status of the operator in a dangerous environment can be fully monitored, thereby accurately predicting the dangers that are about to occur. Secondly, by combining real-time energized spatial positioning of workers with field strength sensing units to measure the electric field strength of the environment in which workers are located, this process is of great significance for ensuring the safety of workers. By monitoring the electric field strength at the location of workers in real time, the system can accurately assess the risk level of electrical work and evaluate the critical voltage level based on the electric field strength. This assessment can not only determine the voltage level zone where workers are located, but also further understand the degree of electrical hazard in the work environment, thus providing a scientific basis for subsequent early warning and safety measures. Through accurate electric field strength measurement, workers and managers can promptly understand the potential risks of the work environment, realize risk prediction for electrical work, and take appropriate safety measures to ensure the safety of workers' lives. Then, the radar ranging unit measures the live distance between the worker and the live equipment. This is a very important safety measure in power operations. Through real-time live distance measurement, the system can determine whether the safe distance between the worker and the live equipment meets the safety standards for power operations. Especially for workers using handheld metal tools, the measurement of live distance is crucial. The application of radar technology can monitor the changes in distance between the worker and the live equipment in real time with high precision, providing accurate data for managers. In this way, workers can know in a timely manner whether they are within the safe range and make necessary adjustments. Combined with the voltage level zone where the worker is located, the system can provide personalized safety distance warnings for each worker, greatly improving the safety of power operations and reducing electric shock accidents caused by excessively close proximity.Finally, the control center performs a critical safety comparison based on the voltage level zone and safety warning distance of the workers, ensuring that every aspect of the power operation is within safe control. This allows for a comparison between the actual energized distance between the workers and the live equipment and the preset safety warning distance, assessing whether the workers are in a potentially dangerous state. Through the power operation early warning unit, the system can promptly issue a warning signal when it detects that the workers are on the edge of safety, notifying the workers and managers to take emergency measures to avoid danger. This process not only improves the safety assurance capabilities of the workers but also provides comprehensive and real-time safety monitoring for the power operation site, thereby improving the accuracy and efficiency of power operation early warning.

[0050] 2. The intelligent early warning device for critical edges of power operations proposed in this invention can highly integrate camera components through image acquisition units, with a modular optional mode. It collects, identifies, analyzes, and confirms power facilities according to the requirements of critical edges in power operations. Through a radar ranging unit using ultrasonic radar components, the data analysis component can perform planned analysis on ultrasonic radar ranging data and distance. Through a field strength sensing unit, the field strength measurement component measures the electric field strength, and data analysis can overcome the influence and interference of complex field strengths to accurately judge the field strength. Through an early warning unit, under corresponding field strengths, when approaching power facilities beyond the safe distance, the sound warning component issues an alarm, the photoelectric warning component emits a red warning light, and the signal transmission component has signal reading and storage functions. Finally, placed on the worker's protective safety helmet, it can realize integrated intelligent early warning for critical edges of power operations, effectively reducing the operational complexity of cable channel status early warning, improving early warning efficiency, and reducing accidents caused by insufficient safe distance. Attached Figure Description

[0051] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0052] Figure 1 This is a schematic diagram of the modules of the intelligent early warning system for critical edges of power operations according to the present invention;

[0053] Figure 2 This is a three-dimensional schematic diagram of the intelligent early warning device for critical edges of power operations according to the present invention after it is installed on a safety helmet;

[0054] Figure 2 The markings are: 1. Safety helmet; 2. Intelligent early warning device for critical edge of power operation;

[0055] Figure 3 This is a three-dimensional schematic diagram of the intelligent early warning device for critical edges of power operations according to the present invention;

[0056] Figure 3 The following are marked as follows: 201, Early Warning Unit; 202, Image Acquisition Unit; 203, Radar Ranging Unit;

[0057] Figure 4 This is a schematic diagram showing that the image acquisition unit, the radar ranging unit, the field strength sensing unit, and the early warning unit of the present invention are all electrically connected to the control center. Detailed Implementation

[0058] The technical system of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0059] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0060] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0061] To achieve the above objectives, please refer to Figures 1 to 4 This invention provides an intelligent early warning system for critical edges of power operations, the system comprising the following modules:

[0062] The live-line identification and positioning module is used to acquire real-time image frames of the environment where the operator is located using the image acquisition unit 202 and transmit them to the corresponding image recognition component in the control center; the image recognition component performs live-line identification and positioning of the operator's environment image frames to obtain the real-time live-line spatial positioning of the operator;

[0063] The electric field critical voltage zoning module is used to measure the electric field strength corresponding to the real-time operation process of the power workers based on the real-time energized spatial positioning of the workers and in conjunction with the field strength sensing unit, so as to obtain the electric field strength corresponding to the positioning of the workers; and to evaluate the critical voltage level based on the electric field strength corresponding to the positioning of the workers, so as to obtain the critical voltage level zoning of each worker, and upload it to the control center.

[0064] The live-line distance measurement module is used to measure the live-line distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, using the radar ranging unit 203, so as to obtain the live-line distance between the workers and the live equipment in each voltage level zone and upload it to the control center.

[0065] The critical safety warning module for power operations is used to set a safety warning distance for each voltage level in the control center according to the critical voltage level of each operator, and to compare the safety warning distance with the energized distance between the operator and the live equipment to obtain the critical safety comparison result of the operator; the warning unit 201 performs power operation warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety warning work.

[0066] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a schematic of the modules of the intelligent early warning system for critical edges of power operations according to the present invention. In this example, it is applied to the intelligent early warning device 2 for critical edges of power operations, which is fixed to the upper part of the safety helmet 1 (e.g., Figure 2 As shown in the figure, the intelligent early warning system for critical edges of power operations includes the following modules:

[0067] S1: Live-line identification and positioning module, used to acquire real-time image frames of the environment where the operator is located using the image acquisition unit 202 and transmit them to the corresponding image recognition component in the control center; the image recognition component performs live-line identification and positioning of the operator's environment image frames to obtain the real-time live-line spatial positioning of the operator;

[0068] In this embodiment of the invention, at the power work site, the intelligent early warning device 2 on the upper part of the safety helmet 1 worn by the worker starts working. Three image acquisition units 202, evenly distributed at equal angles on the outer circumference of the device, immediately activate. Each unit's camera component uses a corresponding image sensor with 12 megapixels, performing real-time monitoring of the 360° environment around the worker at a frame rate of 30 frames per second and a resolution of 1920×1080. For example, during equipment maintenance work at a 110kV substation, the camera component captures an image of a worker holding a metal tool approaching the busbar. After converting the analog video signal into a digital signal, it is transmitted to the image transmission component. The image transmission component uses a low-power Wi-Fi module and transmits the image using the 802.11n protocol. Image frames are sent in real time to the image recognition component in the control center in the form of UDP packets. The image recognition component runs on the processor and uses an improved YOLOv5 algorithm. This algorithm has been trained on a dataset containing 150,000 images of power operation scenes. After receiving the image frame, the algorithm identifies the workers, live equipment and metal tools within 0.06 seconds. By calculating the centroid coordinates of the target contour and combining them with the pre-calibrated camera parameters, the algorithm uses triangulation to determine the position of the workers in three-dimensional space, thus obtaining the real-time live spatial positioning of the workers. For example, the coordinates of the workers are determined to be (3, 2, 1.8) meters. This positioning information, along with the job number, timestamp, etc., is stored in the "Worker Positioning Table" of the embedded database (SQLite) in the control center.

[0069] S2: Electric field critical voltage partitioning module, used to measure the electric field strength of the electric worker's real-time operation process based on the real-time electric field positioning of the worker and combined with the field strength sensing unit to obtain the electric field strength corresponding to the worker's location; to evaluate the critical voltage level based on the electric field strength corresponding to the worker's location to obtain the critical voltage level partition of each worker, and upload it to the control center.

[0070] In this embodiment of the invention, after the control center obtains the real-time energized spatial location of the worker, the field strength sensing unit (using a corresponding electric field sensor with a detection range of 0-500kV / m and a resolution of 0.01kV / m) begins to measure the electric field strength at the worker's location in real time. In the aforementioned substation operation, the sensor converts the sensed electric field signal into an analog voltage signal, which is then converted into a digital signal by a 16-bit ADC at a sampling frequency of 100Hz. This digital signal is transmitted to the control center via the SPI communication protocol. The data analysis component of the control center, based on the corresponding processor, uses an IIR filter to remove signal noise and converts the digital signal into an actual electric field strength value according to the sensor calibration parameters, such as a measured electric field strength of 18kV / m at a certain moment. Next, a critical voltage level assessment is performed according to a pre-set rule corresponding to electric field strength and voltage level: the electric field strength distribution gradient for a safe voltage level is 0-0.05V / m. 2 The corresponding critical voltage levels are 6V-42V; the electric field intensity distribution gradient at low voltage levels is 0.05-0.2V / m. 2 The corresponding critical voltage level is 220V-380V; the electric field intensity distribution gradient for the medium voltage level is 0.2-2V / m. 2 The corresponding critical voltage levels are 3.6kV-10kV; the electric field intensity distribution gradient of the high-voltage level is 2-10V / m. 2 The corresponding critical voltage level is 110kV-220kV; the electric field intensity distribution gradient of the ultra-high voltage level is 10-30V / m. 2 The corresponding critical voltage level is 330kV-750kV; the electric field intensity distribution gradient of the ultra-high voltage level is greater than 30V / m. 2 Furthermore, the corresponding critical voltage levels are 1000kV and above AC and ±800kV and above DC. Taking the electric field intensity distribution gradient data of worker D as an example, if the calculated gradient value over a certain period of time is 1.2V / m... 2 According to the rules, the area where the operator was located during this period was determined to be a medium-voltage zone. The data analysis component compared and evaluated all the gradient data of the operator throughout the entire operation process, determined the critical voltage level zone where each operator was located, and uploaded the corresponding critical voltage level zone of the operator to the control center.

[0071] S3: The live distance measurement module is used to measure the live distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located using the radar ranging unit 203, so as to obtain the live distance between the workers and the live equipment in each voltage level zone and upload it to the control center.

[0072] In this embodiment of the invention, after the control center obtains the critical voltage level zone where the worker is located, the corresponding radar transmitting component (piezoelectric ultrasonic transducer, operating frequency 40kHz) in the radar ranging unit 203 is activated to transmit an ultrasonic beam between the metal tool held by the worker and the live equipment. The radar receiving component receives the reflected beam, and the data analysis component of the control center records the transmission and reception durations through a high-precision timer, such as 400μs in a certain measurement. Simultaneously, the electric field strength at different locations of the worker and the live equipment is acquired, the electric field strength distribution difference is calculated, and the electric field difference transmission influence coefficient is calculated using the corresponding formula, and then expressed using the formula v=v0(1-ε c The ultrasonic transmission rate is corrected, and the electrified distance is calculated according to the formula L=v×Δt / 2. Finally, the electrified distance between the workers and the electrified equipment in each voltage level zone is obtained and uploaded to the control center.

[0073] S4: Power Operation Critical Early Warning Module, which is used to set a safety warning distance for each voltage level according to the critical voltage level of each operator in the control center, and compare the safety warning distance with the live distance between the operator and the live equipment to obtain the operator's critical safety comparison result; the early warning unit 201 performs power operation early warning processing on the operator's critical safety comparison result to execute the corresponding power operation critical edge safety early warning work.

[0074] In this embodiment of the invention, the control center obtains the critical voltage level zone where the operator is located and sets the safety warning distance according to the built-in voltage level-safe distance comparison table: 0.1 meters for safe voltage level zone; 0.3 meters for low voltage level zone; 0.6 meters for medium voltage level zone; 1 meter for high voltage level zone; 2 meters for ultra-high voltage level zone; and 5 meters for extra-high voltage level zone. The operator's live distance is retrieved and compared with the safety warning distance. A program written in C language uses if-else statements to determine if the live distance is greater than the safety warning distance; if it is equal to the safety warning distance, the comparison result is stored; otherwise, the distance is counted. The analysis component generates an audible warning control signal (digitally encoded, including the worker's ID, etc.), which is transmitted via Bluetooth to the audible warning component of the warning unit 201, causing it to emit an 800Hz, 1-second alarm. If the distance is less than 1 meter, both audible and photoelectric warning control signals are generated simultaneously. The audible warning is the same as before, while the photoelectric warning component flashes a red light 3 times per second to achieve a safety warning at the critical edge of power operations. For example, if a worker is in a high-voltage zone and the live distance is 0.8 meters, which is less than the 1-meter safety warning distance, the audible and visual warning is triggered, and the corresponding safety warning work at the critical edge of power operations is finally executed.

[0075] Furthermore, the live-line identification and positioning module includes the following functions:

[0076] The camera component in the image acquisition unit 202 is used to monitor the environment in real time during the real-time operation of the power workers, so as to collect image frames of the environment in which the workers are located in real time.

[0077] In this embodiment of the invention, a smart early warning device 2 for critical edges of power operations is fixed to the upper part of the safety helmet 1 worn by the worker at the power work site. The device has a cylindrical shell structure, with three image acquisition units set at equal angles on the outer circumference. The camera component of each image acquisition unit 202 uses a corresponding image sensor with 12 million pixels and a 120° field of view. It can monitor the real-time working environment of the worker in a 360° range around the worker at a frame rate of 30 frames per second and a resolution of 1920×1080. For example, in the maintenance of a high-voltage line, When workers climb the utility pole to perform their operations, three camera modules work simultaneously to capture real-time footage of workers using tools to operate live equipment such as wires and insulators, as well as the surrounding high-altitude live edges. The camera modules convert the collected analog video signals into digital signals and transmit them to the image transmission module. The image transmission module uses a low-power Wi-Fi module to send image frames in the form of UDP packets using the 802.11n protocol in real time to the control center, ensuring the uniqueness and time sequence of the images. These image frames are temporarily stored in the high-speed cache of the control center.

[0078] Preferably, the image frame of the environment where the worker is located is transmitted to the corresponding image recognition component in the control center through the image transmission component in the image acquisition unit 202. The image recognition component is used to perform live danger segmentation and screening on the received image frame of the environment where the worker is located, so as to identify and screen the image frame corresponding to the live object with metal tool held by the power worker or the live edge near the high place, and generate live danger segmentation frames for each worker.

[0079] In this embodiment of the invention, the image transmission component within the image acquisition unit 202 transmits image frames of the environment where the workers are located to the control center via a Wi-Fi network. The image recognition component built into the control center is constructed based on the processor and the OpenCV library. After receiving the image frame, the image recognition component first uses a Gaussian filtering algorithm to denoise the image, reducing noise interference caused by factors such as changes in ambient light. Then, it uses a cascaded classifier based on Haar features, combined with a pre-trained power operation scenario model, to classify charged objects (such as high-voltage wires and transformers), metal tools (such as pliers and screwdrivers), and charged edges at heights in the image. (e.g., live parts on top of utility poles) are identified. Taking an image from a certain operation as an example, when a worker is detected holding metal pliers close to a 10kV live conductor, the image recognition component determines that the image frame is a live danger frame within 0.1 seconds. It copies the frame from the cache and saves it to the "Live Danger Frame" folder in the device's internal storage module. At the same time, it records the original file name of the image frame, the type of the detected live object, and its location coordinates in the "Live Danger Frame Record Table" of the embedded database (SQLite) in the control center. Image frames that do not detect live danger are directly deleted from the cache, thus completing the live danger frame screening process.

[0080] Preferably, the frames of the live hazard locations of each worker are time-synchronized and sorted to generate a sequence of live hazard image frames of the workers;

[0081] In this embodiment of the invention, the control center reads all records of live-line hazard frames from the "Live-Line Hazard Frame Record Table" in the embedded database, extracts the timestamp information from each record, and uses a sorting program written in C language to sort the live-line hazard frames according to the order of their timestamps using the bubble sort algorithm. For example, a worker generates 15 live-line hazard frames during a half-hour operation, with timestamps such as "2024-11-15 09:30:05" and "2024-11-15 09:30:10". Through the sorting operation, these frames are arranged in chronological order. After sorting, the live-line hazard frames are constructed into a linked list structure in memory, while maintaining the original format of each image frame file name to ensure a one-to-one correspondence with the database records. Finally, a complete and ordered sequence of live-line hazard image frames of the worker is generated, providing an accurate data sequence for subsequent identification and positioning.

[0082] Preferably, the image recognition component is used to identify and locate the worker in a live-lined location by analyzing the sequence of live-lined hazard image frames, so as to obtain the real-time live-lined spatial location of the worker.

[0083] In this embodiment of the invention, the generated sequence of live-line hazard image frames of workers is processed by the image recognition component of the control center. First, a Shi-Tomasi corner detection algorithm is used to extract corner features of targets such as workers, live equipment, and metal tools in the image frames. Then, optical flow (Lucas-Kanade method) is used to track the targets based on the motion trajectories of corner points between adjacent frames, calculating the two-dimensional coordinates of the workers in each image frame. Simultaneously, a three-dimensional reconstruction algorithm (such as a multi-view stereo vision algorithm) is used to convert the two-dimensional image coordinates of the workers into three-dimensional spatial coordinates (x, y, z), ultimately obtaining the real-time live-line spatial positioning of the workers.

[0084] Furthermore, the step of using an image recognition component to identify and locate workers in a live-lined hazardous image frame sequence includes:

[0085] The image recognition component uses Canny edge detection to perform contour topology recognition between the worker and the live equipment in each image frame within the sequence of images of workers in danger of being energized, so as to obtain the contour spatial position and connection relationship between the worker and the live equipment within the image frame sequence.

[0086] In this embodiment of the invention, by wearing a corresponding safety helmet 1 at the power work site, the corresponding image acquisition unit 202 on the helmet acquires a live-line video stream of the worker at a frame rate of 30 frames per second. The video stream is split into image frame sequences and transmitted to the image recognition component in the control center. The Canny edge detection algorithm is used to process the image frames. First, the image frames are converted into grayscale images. A Gaussian filter (kernel size 5×5, standard deviation 1.4) is used to denoise the grayscale images to reduce noise interference in the images. Then, the magnitude and direction of the image gradient are calculated. Non-maximum suppression is used to retain local maxima in the gradient direction to refine the edges. Finally, a dual threshold is set (low threshold of 50, high threshold of 150) to filter gradients with magnitudes less than 150. Pixels with low thresholds are suppressed as non-edge points, while pixels with thresholds higher than high thresholds are identified as edge points. Pixels in between are retained if they are connected to edge points with high thresholds, otherwise suppressed. This method accurately identifies the edge contours of workers and live equipment. The `findContours` function from the OpenCV library is used to extract the contours. By calculating geometric features such as the bounding rectangle and centroid of the contours, the topological relationship between the workers and live equipment is determined. For example, if the worker contour and the live equipment contour have overlapping areas, the overlapping area and the coordinate range of the overlapping part are recorded; if they are adjacent, the coordinate information of the adjacent boundary is recorded. Finally, the spatial position and connection relationship of the contours between workers and live equipment within the image frame sequence are obtained.

[0087] Preferably, based on the contour spatial position and connection relationship between the worker and the live equipment within the image frame sequence, the live relative trajectory analysis is performed on each image frame in the image frame sequence of the worker's live danger, so as to obtain the dynamic trajectory between the relative positions of the worker and the live equipment within the image frame sequence;

[0088] In this embodiment of the invention, by reading the contour spatial position and connection relationship of each image frame in the sequence of images of workers in danger of energization, taking a power line maintenance operation as an example, where workers are replacing insulators on a 10kV live line, and the image frame sequence contains 300 frames, the data is processed using Python's NumPy library. The centroid coordinates of the contours of the workers and the live equipment in adjacent frames are used as key nodes. By calculating the displacement vector of the centroid coordinates in adjacent frames, the changing trend of the relative position between the workers and the live equipment is determined. For example, between frames 10 and 20, the displacement vector of the centroid coordinates in adjacent frames is used to determine the changing trend of the relative position between the workers and the live equipment. The centroid coordinates of the worker's outline move from (100, 200) to (120, 210), and the centroid coordinates of the live equipment's outline move from (300, 300) to (310, 305). This calculates that the worker moves 10 pixels in the x-axis direction and 5 pixels in the y-axis direction relative to the live equipment. By employing the Dynamic Time Warping (DTW) algorithm to minimize the difference in outline shape between adjacent frames, the outline data of different frames are aligned to construct the dynamic trajectory between the worker and the live equipment relative to each other. Finally, the dynamic trajectory between the worker and the live equipment relative to each other within the image frame sequence is obtained.

[0089] Preferably, the relative spatial position between the operator and the live equipment is obtained by the dynamic trajectory between the relative positions of the operator and the live equipment within the image frame sequence, and the relative spatial position between the operator and the live equipment is reconstructed by three-dimensional spatial projection to generate a live projection grid of the operator containing the spatial coordinates.

[0090] In this embodiment of the invention, dynamic trajectory data between the relative positions of the operator and the live equipment is read, and the coordinates of each key node in the dynamic trajectory in three-dimensional space are calculated using triangulation based on the principle of multi-view geometry. Taking a key node as an example, the pixel corresponding to that node is found in the corresponding dynamic trajectory. Based on the camera's intrinsic parameters (such as focal length and principal point coordinates) and extrinsic parameters (rotation matrix and translation vector), the three-dimensional coordinates (x, y, z) of that node in the world coordinate system are calculated using formulas. The three-dimensional coordinates of all key nodes in the dynamic trajectory are calculated to obtain the three-dimensional coordinates of the relative spatial position between the operator and the live equipment. The system collects 3D data and uses the MeshPy library to perform 3D spatial projection reconstruction on the 3D spatial location data. The mesh resolution is set to 0.01m. The 3D models of the workers and live equipment are imported into MeshPy. Based on the relative spatial location data, a live projection mesh of the workers containing spatial coordinates is constructed in 3D space. For example, the 3D model of the workers is represented as a mesh composed of multiple triangular facets. The vertex coordinates of each facet correspond to the actual position in 3D space. The live equipment is also presented in mesh form. The final generated live projection mesh of the workers is stored in the storage system of the control center in OBJ file format.

[0091] Preferably, the relative spatial position between the operator and the live equipment is determined by real-time live projection positioning based on the live projection grid of the operator corresponding to the spatial coordinates, so as to obtain the real-time live spatial positioning of the operator.

[0092] In this embodiment of the invention, the energized projection grid of the operator corresponding to the spatial coordinates is read from the storage system of the control center and rendered into a three-dimensional projection grid in real time. The rendering frame rate is set to 30 frames / second to ensure real-time display. By obtaining the three-dimensional coordinate data of the relative position of the operator and the energized equipment at the current moment, the corresponding grid cell is found in the three-dimensional projection grid and marked as the current spatial position of the operator. For example, if the current three-dimensional coordinates of the operator are (2, 3, 1.5), the grid cell with the corresponding coordinates is found in the projection grid and its color is set to red to highlight it. The real-time energized spatial positioning information of the operator (including three-dimensional coordinates, grid cell number, etc.) is transmitted to the server of the control center through the Modbus TCP protocol. The relative positional relationship between the operator and the energized equipment is displayed intuitively in the form of a three-dimensional model, and the real-time energized spatial positioning of the operator is finally obtained.

[0093] Furthermore, the electric field critical voltage partitioning module includes the following functions:

[0094] Based on the real-time energized spatial positioning of the operator and combined with the field strength sensing unit, the electric field strength of the operator is measured in real time during the real-time operation process. The electric field strength of the operator in the corresponding real-time energized positioning environment is measured in real time by the field strength sensing unit to obtain the electric field strength corresponding to the operator's positioning.

[0095] In this embodiment of the invention, during power operations, after the control center completes the real-time live spatial positioning of the workers using image recognition components, the field strength sensing unit begins to operate. The field strength sensing unit employs an electric field sensor based on the capacitive coupling principle. This sensor has high sensitivity and can detect electric field strength in the range of 0.1V / m-100kV / m. Taking a substation equipment maintenance operation as an example, when workers are repairing a 110kV transformer, the field strength sensing unit measures the electric field strength in the environment where the workers are currently positioned. The sensor converts the sensed electric field signal into a voltage signal, which is then transmitted via a 16-bit ADC (analog-to-digital converter) at a voltage of 1... The 00Hz sampling frequency converts the analog voltage signal into a digital signal and transmits it to the control center. After receiving the data, the control center processes the digital signal using the built-in data analysis component. The data analysis component operates based on a microcontroller and removes noise interference from the signal through digital filtering algorithms (such as IIR filters). Then, according to the sensor calibration parameters, the processed digital signal is converted into the actual electric field strength value. For example, the electric field strength corresponding to the current location of the worker is calculated to be 25kV / m, and information including the work number, personnel ID, location, and electric field strength value is recorded to finally obtain the electric field strength corresponding to the worker's location.

[0096] Preferably, the electric field intensity distribution gradient is calculated based on the electric field intensity corresponding to the location of the operator, so as to obtain the electric field intensity distribution gradient between the locations of the operator;

[0097] In this embodiment of the invention, after the control center has acquired real-time charged spatial positioning data of the workers at different locations, it begins to determine the electric field distance between each pair of locations. The control center performs the calculation based on the three-dimensional coordinate data (x, y, z) stored in an embedded database (SQLite). This table records the positioning information of each worker at different times, so as to utilize the distance formula between two points in space. The system performs calculations to determine the electric field distance between each pair of positions. The control center extracts electric field strength data for each worker at different positions from an embedded database. This table records the electric field strength for each worker's position. A Python script is used to calculate the electric field strength between each pair of worker positions, subtracting the values ​​to determine the difference in electric field strength. The formula is ΔE = E2 - E1, where E1 and E2 are the electric field strength values ​​at the two positions. Simultaneously, the electric field distance and difference in electric field strength between each pair of worker positions are retrieved from previous steps. Using a microcontroller-based data analysis component, the electric field strength distribution gradient is calculated using the formula G = ΔE / d, where G is the electric field strength distribution gradient, ΔE is the electric field strength difference, and d is the electric field distance. Finally, the electric field strength distribution gradient between the worker's positions is obtained.

[0098] Preferably, the critical voltage level is determined based on the gradient of electric field intensity distribution between the positions of the workers, so as to obtain the critical voltage level zone of each worker and upload it to the control center.

[0099] In this embodiment of the invention, the data analysis component of the control center reads the electric field intensity distribution gradient data between the positions of the workers from the previous steps, and evaluates the critical voltage level according to a pre-set rule corresponding to the electric field intensity distribution gradient and voltage level. The rule is set as follows: the electric field intensity distribution gradient of the safe voltage level is 0-0.05V / m. 2 The corresponding critical voltage levels are 6V-42V; the electric field intensity distribution gradient at low voltage levels is 0.05-0.2V / m. 2 The corresponding critical voltage level is 220V-380V; the electric field intensity distribution gradient for the medium voltage level is 0.2-2V / m. 2 The corresponding critical voltage levels are 3.6kV-10kV; the electric field intensity distribution gradient of the high-voltage level is 2-10V / m. 2 The corresponding critical voltage level is 110kV-220kV; the electric field intensity distribution gradient of the ultra-high voltage level is 10-30V / m. 2 The corresponding critical voltage level is 330kV-750kV; the electric field intensity distribution gradient of the ultra-high voltage level is greater than 30V / m. 2 Furthermore, the corresponding critical voltage levels are 1000kV and above AC and ±800kV and above DC. Taking the electric field intensity distribution gradient data of worker D as an example, if the calculated gradient value over a certain period of time is 1.2V / m... 2According to the rules, the area where the operator was located during this period was determined to be a medium-voltage zone. The data analysis component compared and evaluated all the gradient data of the operator throughout the entire operation process, determined the critical voltage level zone where each operator was located, and uploaded the corresponding critical voltage level zone of the operator to the control center.

[0100] Furthermore, the calculation of the electric field intensity distribution gradient based on the electric field intensity corresponding to the operator's location includes:

[0101] The electric field distance between any two positions is determined by the positions of the operators.

[0102] In this embodiment of the invention, after the control center has acquired real-time charged spatial positioning data of the workers at different locations, it begins to determine the electric field distance between each pair of locations. The control center performs calculations based on the three-dimensional coordinate data (x, y, z) stored in an embedded database (SQLite). This table records the positioning information of each worker at different times. For example, worker A's coordinates at time t1 are (10, 5, 3), and at time t2 are (12, 6, 3). A calculation program written in C language is used to calculate the distance between two points in space according to the formula... Taking the positioning of worker A at times t1 and t2 as an example, the coordinate values ​​are substituted into the formula, and we can get d≈2.24 meters, that is, the electric field distance between the two positions is about 2.24 meters. The program traverses all the positioning data of the worker in the database, calculates the electric field distance between each pair of positions, and finally obtains the electric field distance between each pair of positions.

[0103] Preferably, the electric field strength difference between each pair of positions of the operator is calculated to obtain the electric field strength difference between each pair of positions.

[0104] In this embodiment of the invention, the control center extracts the electric field strength data corresponding to the operator at different positioning times from the embedded database. The table records the electric field strength of the operator at each positioning time in detail. For example, the electric field strength of operator B at time t3 is 22kV / m and the electric field strength at time t4 is 27kV / m. Using a script program written in Python, the electric field strength between each pair of positions of the operator is calculated. The difference in electric field strength is calculated by direct subtraction. The formula is ΔE=E2-E1, where E1 and E2 are the electric field strength values ​​at the two positioning times, respectively. Taking operator B at times t3 and t4 as an example, substituting the data into the formula, we get ΔE=27-22=5kV / m, that is, the difference in electric field strength between these two positions is 5kV / m. The script program processes the electric field strength data of all pairwise positioning combinations of the operator in sequence, and finally obtains the difference in electric field strength between each pair of positions.

[0105] Preferably, the electric field intensity distribution gradient between the two locations is calculated based on the electric field distance between them, so as to obtain the electric field intensity distribution gradient between the locations of the workers.

[0106] In this embodiment of the invention, the control center reads the electric field distance and electric field strength difference data between each pair of workers' positions from previous steps. For example, if the electric field distance between worker C's positions 1 and 2 is found to be 3 meters and the electric field strength difference is 6 kV / m, the electric field strength distribution gradient is calculated using a data analysis component based on a microcontroller, according to the formula G = ΔE / d, where G is the electric field strength distribution gradient, ΔE is the electric field strength difference, and d is the electric field distance. Substituting the relevant data of worker C into the formula, we get G = 6 / 3 = 2 kV / m. 2 That is, the electric field strength gradient between the two positions of worker C is 2kV / m. 2 The data analysis component iterates through and calculates the pairwise positioning data of all workers. This gradient data will then be used to assess the voltage hazard level of the area where the workers are located, and finally obtain the electric field intensity distribution gradient between the positions of the workers.

[0107] Furthermore, the critical voltage level zones for each operator are specifically determined based on the distribution range corresponding to the electric field intensity distribution gradient, classifying them into safe voltage level, low voltage level, medium voltage level, high voltage level, ultra-high voltage level, and extra-high voltage level. The electric field intensity distribution gradient for the safe voltage level is 0-0.05V / m. 2 The corresponding critical voltage levels are 6V-42V; the electric field intensity distribution gradient at low voltage levels is 0.05-0.2V / m. 2 The corresponding critical voltage levels are 220V-380V; the electric field intensity distribution gradient for medium voltage levels is 0.2-2V / m. 2 The corresponding critical voltage levels are 3.6kV-10kV; the electric field intensity distribution gradient of the high-voltage level is 2-10V / m. 2 The corresponding critical voltage level is 110kV-220kV; the electric field intensity distribution gradient of the ultra-high voltage level is 10-30V / m. 2 The corresponding critical voltage level is 330kV-750kV; the electric field intensity distribution gradient of the ultra-high voltage level is greater than 30V / m. 2 The corresponding critical voltage levels are 1000kV and above AC and ±800kV and above DC.

[0108] Furthermore, the live-line distance measurement module includes the following functions:

[0109] The radar ranging unit uses 203 radar transmitters to transmit ultrasonic beams between the metal tools and live equipment of the power workers in the critical voltage level zone where each worker is located. The ultrasonic beams are received in the radar receiving unit, and the data analysis unit in the control center monitors and determines the duration between transmission and reception.

[0110] In this embodiment of the invention, at the power work site, after the control center determines the critical voltage level zone for the workers, it activates the radar ranging unit. The radar transmitting component 203 of the radar ranging unit 203 uses a piezoelectric ultrasonic transducer with a working frequency of 40kHz, capable of emitting an ultrasonic beam with a wavelength of approximately 8.5mm. Taking a worker in a high-voltage zone (110kV-220kV) as an example, who is using a metal wrench to inspect live equipment, the radar transmitting component on the safety helmet emits an ultrasonic beam into the space between the worker's metal wrench and the live equipment at an angle of 1°. 5°, ensuring beam coverage of the target area. The radar receiving component also uses a piezoelectric ultrasonic transducer (matching the model of the transmitting component) to monitor and receive the reflected ultrasonic beam in real time. The data analysis component in the control center runs on a processor and records the ultrasonic beam transmission time t1 and reception time t2 using a high-precision timer (resolution up to 1μs). For example, in a certain measurement, the transmission time t1 is 10:00:00.000001 and the reception time t2 is 10:00:00.000501. Thus, the duration between transmission and reception is determined to be 500μs, and the corresponding duration between transmission and reception is finally determined.

[0111] Preferably, the difference in electric field strength distribution between the operator and the live equipment is determined by the metal tools held by the operator within the critical voltage level zone where each operator is located and the live equipment.

[0112] In this embodiment of the invention, the control center retrieves data from the embedded database to determine the electric field strength distribution difference between the operator and the live equipment within the critical voltage level zone where each operator is located. Taking the operator in the high voltage level zone as an example, the electric field strength E1 = 18kV / m is obtained when the operator is close to the live equipment (position x1), and the electric field strength E2 = 12kV / m is obtained when the operator is farther away from the live equipment (position x2). The electric field strength distribution difference is calculated using the formula ΔE = E1 - E2. Substituting the data, we get ΔE = 18 - 12 = 6kV / m. For multiple measurement positions between the operator and the live equipment, the electric field strength distribution difference between adjacent positions is calculated sequentially to finally determine the electric field strength distribution difference between the operator and the live equipment.

[0113] Preferably, the transmission influence of the emitted ultrasonic beam is evaluated using the electric field influence assessment calculation formula based on the difference in electric field intensity distribution between the operator and the live equipment, and the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam is obtained.

[0114] In this embodiment of the invention, a suitable electric field impact assessment calculation formula is constructed by combining the electric field range between the operator and the live equipment (i.e., the area between the operator and the live equipment), the electric field spatial location parameters, the difference in electric field strength distribution between the operator and the live equipment at position r, the ultrasonic propagation frequency corresponding to the ultrasonic beam (fixed at 40kHz), the ultrasonic propagation energy attenuation coefficient of the ultrasonic beam in the corresponding electric field range (obtained by linear interpolation based on the current electric field strength using an attenuation coefficient table obtained in advance under different electric field strength environments; for example, under an electric field strength of 15kV / m, the corresponding energy attenuation coefficient is 0.02 obtained by looking up the table and interpolation), and the propagation distance of the ultrasonic beam from the corresponding position to the ultrasonic propagation target. This formula quantifies the transmission impact coefficient of the corresponding ultrasonic beam within the electric field range, ultimately obtaining the electric field difference transmission impact coefficient of the ultrasonic beam. Furthermore, this electric field impact assessment calculation formula can also use any ultrasonic transmission attenuation analysis method in the art to replace the transmission impact assessment process, and is not limited to this electric field impact assessment calculation formula.

[0115] The specific formula for calculating the impact of the electric field is as follows:

[0116]

[0117] In the formula, ε c denoted as the electric field difference transmission influence coefficient, V as the electric field range between the operator and the live equipment, r as the electric field spatial position parameter, E(r) as the difference in electric field intensity distribution between the operator and the live equipment at position r, ρ(r) as the ultrasonic propagation frequency of the ultrasonic beam at position r, α as the ultrasonic propagation energy attenuation coefficient of the ultrasonic beam within the corresponding electric field range, and d(r) as the propagation distance of the ultrasonic beam from position r to the ultrasonic propagation target.

[0118] This invention, through the use of a specific mathematical model and verification, derives a calculation formula for assessing the impact of electric field differences on the transmission of emitted ultrasonic beams. This formula fully considers the transmission impact coefficient ε of the electric field difference. cThe electric field range V between the operator and the live equipment, the electric field spatial location parameter r, the difference in electric field intensity distribution E(r) between the operator and the live equipment at location r, the ultrasonic propagation frequency ρ(r) of the ultrasonic beam at location r, the ultrasonic propagation energy attenuation coefficient α of the ultrasonic beam within the corresponding electric field range, the propagation distance d(r) of the ultrasonic beam from location r to the ultrasonic propagation target, and the electric field difference transmission influence coefficient ε. c The interrelationships between the above parameters constitute a functional relationship. This formula enables the assessment of the transmission impact of the emitted ultrasonic beam. By comprehensively considering factors such as the difference in electric field strength distribution between the worker and the energized equipment, the ultrasonic propagation frequency, and the energy attenuation coefficient, it can accurately evaluate the transmission impact of the ultrasonic beam in actual power operation environments. This allows for more accurate prediction of the ultrasonic beam's propagation in complex electric field environments during power operations, avoiding errors that may occur with traditional methods. In power operations, the distance between the worker and the energized equipment directly affects safety risks. Using this formula to assess the propagation impact of ultrasonic waves ensures the accuracy of ultrasonic ranging, thereby guaranteeing the precise calculation of the actual energized distance between the worker and the equipment. This formula considers not only the difference in electric field strength distribution but also factors such as the ultrasonic propagation frequency and attenuation coefficient, making the assessment more comprehensive and detailed. This approach allows for the optimization of ultrasonic ranging technology in complex electric field environments, thereby improving the system's reliability and applicability. Furthermore, based on this formula, the transmission rate of the ultrasonic beam can be corrected to better adapt to the complex electric field environment in power operations. This adjustment can significantly improve the accuracy of ultrasonic ranging, reduce errors caused by environmental changes, and thus improve the accuracy and efficiency of the entire operation process. Using this formula, the energized distance between the operator and the live equipment can be accurately measured, and relevant data can be further corrected, thereby ensuring the safety of every step in power operations and avoiding the error accumulation caused by traditional electric field analysis methods. This provides the control center with more accurate real-time data support.

[0119] Preferably, the transmission rate of the ultrasonic beam is corrected based on the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam to obtain the ultrasonic transmission correction rate.

[0120] In this embodiment of the invention, the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam is read by the control center. Combined with the standard propagation speed of ultrasound in air, v0 = 340 m / s, the transmission speed of the ultrasonic beam is corrected. The correction formula is v = v0(1 - ε c For example, if the electric field difference transmission influence coefficient ε is calculated... c=0.35, substituting the data into the formula, we get v = 340 × (1 - 0.35) = 221 m / s, which gives the ultrasonic transmission correction rate as 221 m / s. Finally, we obtain the corresponding ultrasonic transmission correction rate.

[0121] Preferably, based on the ultrasonic transmission correction rate and combined with the corresponding duration between transmission and reception, the live distance between the metal tools held by the power workers in each critical voltage level zone and the live equipment is measured to obtain the live distance between the workers and the live equipment in each voltage level zone, and then uploaded to the control center.

[0122] In this embodiment of the invention, the ultrasonic transmission correction rate v and the corresponding transmission and reception duration Δt within the critical voltage level zone where each worker is located are obtained through the control center. The charged distance is measured according to the formula L = v × Δt / 2 (considering the round-trip propagation of the ultrasonic wave). Taking a worker as an example, within the corresponding high-voltage level zone, the ultrasonic transmission correction rate v = 221 m / s, and the transmission and reception duration Δt = 500 μs = 5 × 10⁻⁶ m / s. -4 Substituting the above parameters into the formula, we get L = 221 × 5 × 10 -4 / 2=0.05525m=5.525cm, which means the live distance between the worker and the live equipment is 5.525cm. The same calculation is performed in each zone to obtain the live distance between the worker and the live equipment in each voltage level zone, and then the data is uploaded to the control center.

[0123] Furthermore, the critical early warning module for power operation includes the following functions:

[0124] By setting safe warning distances for each voltage level in the control center according to the critical voltage level where each operator is located;

[0125] In this embodiment of the invention, at the power work site, the control center sets a safety warning distance based on the critical voltage level zone where each worker is located. The data analysis component built into the control center runs on a processor and operates by accessing a voltage level-safety distance lookup table pre-stored in an embedded database (SQLite). This lookup table clearly specifies: the safety warning distance for a safe voltage level zone is 0.1 meters; for a low-voltage zone, it is 0.3 meters; for a medium-voltage zone, it is 0.6 meters; for a high-voltage zone, it is 1 meter; and for an ultra-high-voltage zone, it is 1 meter. The safety warning distance for each zone is 2 meters; the safety warning distance for each UHV level zone is 5 meters. Taking a worker in a high-voltage zone (110kV-220kV) as an example, the control center obtains the worker's voltage level zone information from the previous data, automatically sets 1 meter as the corresponding safety warning distance according to the reference table, and stores this setting information, along with the work number, personnel ID, voltage level zone, and other data, in the "Safety Warning Distance Table". For all workers, the control center completes the setting of the safety warning distance for each voltage level in this manner to ensure that each worker has the corresponding safety distance standard.

[0126] Preferably, the critical safety comparison result of the operator is obtained by comparing the safety warning distance with the corresponding energized distance between the operator and the energized equipment, including the comparison result corresponding to the energized distance being greater than, equal to or less than the safety warning distance;

[0127] In this embodiment of the invention, the control center retrieves the safety warning distance and live distance data for each operator from previous steps. Taking operator A in a high-voltage zone as an example, the safety warning distance is obtained as 1 meter from the "Safety Warning Distance Table," and the live distance between operator A and the live equipment is obtained as 0.8 meters. A comparison program written in C language compares the live distance and the safety warning distance. The program uses if-else statements to judge whether the live distance is greater than the safety warning distance. If the live distance is equal to the safety warning distance, the program outputs a comparison result of "greater than"; if the live distance is equal to the safety warning distance, the program outputs a comparison result of "equal to"; if the live distance is less than the safety warning distance, the program outputs a comparison result of "less than." In the example of operator A, since 0.8 meters is less than 1 meter, the program outputs a comparison result of "less than." The control center stores the comparison results for each operator, along with the operation number, personnel ID, voltage level zone, safety warning distance, and live distance, in the "Critical Safety Comparison Result Table," providing accurate data support for subsequent response analysis.

[0128] Preferably, by performing response analysis on the critical safety comparison results of the operators on the corresponding data analysis component in the control center, if the energized distance is greater than the safety warning distance, the corresponding comparison result is stored in the control center; if the energized distance is equal to the safety warning distance, the corresponding sound warning control signal is generated and uploaded to the sound warning component through the signal transmission component corresponding to the warning unit 201 to start the corresponding power operation critical safety sound alarm work; if the energized distance is less than the safety warning distance, the corresponding sound and photoelectric warning control signals are generated and uploaded to the sound warning component and the photoelectric warning component through the signal transmission component corresponding to the warning unit 201 to simultaneously execute the corresponding power operation critical sound and light warning work.

[0129] In this embodiment of the invention, the data analysis component of the control center reads the critical safety comparison result data of the operator from the "Critical Safety Comparison Result Table" and performs response analysis. Taking operator B as an example, if the comparison result is "greater than" (assuming the safety warning distance is 0.6 meters and the live distance is 0.8 meters), the data analysis component directly stores the comparison result and related information such as the operation number, personnel ID, and voltage level partition to the embedded database of the control center through a C language program, without performing any additional warning operation. If the comparison result is "equal to" (assuming the safety warning distance is 1 meter and the live distance is 1 meter), the data analysis component runs a Python script to generate a sound warning control signal. This signal adopts a digital encoding format and contains information such as the warning start command and the operator ID. The signal is wirelessly uploaded to the sound warning component on the warning unit 201 through the signal transmission component (Bluetooth module HC-05) of the warning unit 201. After receiving the signal, the sound warning component immediately emits an alarm sound with a frequency of 800Hz and a duration of 1 second to remind the operator that the critical safety distance has been reached. If the comparison result is "less than" (assuming the safety warning distance is 2 meters and the live distance is 1.5 meters), the data analysis component simultaneously generates sound and photoelectric warning control signals. The sound warning control signal is similar to the "equal to" case mentioned above. The photoelectric warning control signal controls the photoelectric warning component on the warning unit 201 to flash red lights at a frequency of 3 times per second. The two signals are synchronously uploaded to the sound warning component and the photoelectric warning component through the signal transmission component, so that both can simultaneously perform the critical sound and light warning work for power operation, thereby effectively reminding the workers to take safety measures in time and avoid danger.

[0130] Furthermore, the present invention also provides an intelligent early warning device 2 for critical edges of power operation (such as...). Figure 3As shown), the device is used to execute the intelligent early warning system for critical edges of power operations as described above. The intelligent early warning device 2 for critical edges of power operations includes a housing. The housing has a cylindrical shell structure. Several image acquisition units 202 are arranged on the outer circumference of the cylindrical shell structure. The image acquisition units 202 on the housing are arranged at equal angles. A radar ranging unit 203 is arranged on the upper side of each image acquisition unit 202. Inside the housing, a field strength sensing unit, an early warning unit 201, and a control center are arranged. The control center has built-in image recognition components and data analysis components. The image acquisition units 202, the radar ranging unit 203, the field strength sensing unit, and the early warning unit 201 are all electrically connected to the control center (e.g., ...). Figure 4 As shown, the image acquisition unit 202 includes a camera component and an image transmission component. The camera component is electrically connected to the image transmission component, and the image transmission component is electrically connected to the image recognition component. The radar ranging unit 203 includes a radar transmitting component and a radar receiving component. The radar transmitting component is electrically connected to the radar receiving component, and the radar receiving component is electrically connected to the data analysis component. The early warning unit 201 includes a sound early warning component, a photoelectric early warning component, and a signal transmission component. The sound early warning component and the photoelectric early warning component are both electrically connected to the signal transmission component, and the signal transmission component is electrically connected to the control center.

[0131] Furthermore, the present invention also provides an intelligent early warning method for critical edges of power operations. This method is implemented based on the intelligent early warning system for critical edges of power operations described above. The intelligent early warning method for critical edges of power operations includes:

[0132] The image acquisition unit 202 acquires real-time image frames of the environment where the workers are located and transmits them to the corresponding image recognition component in the control center; the image recognition component performs live identification and positioning of the workers in the environmental image frames to obtain the real-time live spatial positioning of the workers.

[0133] Based on the real-time energized spatial positioning of the workers and combined with the field strength sensing unit, the electric field strength of the workers is measured in real time to obtain the electric field strength corresponding to the positioning of the workers; the critical voltage level is evaluated based on the electric field strength corresponding to the positioning of the workers to obtain the critical voltage level zone of each worker, and the data is uploaded to the control center.

[0134] The radar ranging unit 203 is used to measure the energized distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, so as to obtain the energized distance between the workers and the live equipment in each voltage level zone and upload it to the control center.

[0135] By setting safety warning distances for each voltage level in the control center according to the critical voltage level of each operator, and comparing the safety warning distance with the energized distance between the operator and the live equipment, the critical safety comparison result of the operator is obtained; the warning unit 201 performs power operation warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety warning work.

[0136] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0137] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A smart early warning system for critical edges of power operations, characterized in that, An intelligent early warning device for critical edges of power operations is applied, the device being fixed to the upper part of a safety helmet. The device includes a housing with a cylindrical shell structure. The intelligent early warning system for critical edges of power operations includes the following modules: The live-line identification and positioning module is used to acquire real-time image frames of the environment where the operator is located using the image acquisition unit and transmit them to the corresponding image recognition component in the control center; the image recognition component performs live-line identification and positioning of the operator in the image frames of the environment where the operator is located to obtain the real-time live-line spatial positioning of the operator; the image acquisition unit is set on the outer circumference of the cylindrical shell structure and is set at equal angles; The electric field critical voltage zoning module is used to measure the electric field strength corresponding to the real-time operation process of the power workers based on the real-time energized spatial positioning of the workers and in conjunction with the field strength sensing unit, so as to obtain the electric field strength corresponding to the positioning of the workers; and to evaluate the critical voltage level based on the electric field strength corresponding to the positioning of the workers, so as to obtain the critical voltage level zoning of each worker, and upload it to the control center. The live-line distance measurement module is used to measure the live-line distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, using a radar ranging unit, so as to obtain the live-line distance between the workers and the live equipment in each voltage level zone and upload it to the control center. The critical early warning module for power operations is used to set safe early warning distances for each voltage level in the control center according to the critical voltage level of each operator, and to compare the safe early warning distance with the energized distance between the operator and the live equipment to obtain the critical safety comparison result of the operator; the early warning unit performs power operation early warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety early warning work.

2. The intelligent early warning system for critical edges of power operations according to claim 1, characterized in that, The live-line identification and positioning module includes the following functions: The camera component in the image acquisition unit is used to monitor the environment in real time during the actual operation of the power workers, so as to collect image frames of the environment in which the workers are located in real time. The image transmission component in the image acquisition unit transmits the image frames of the environment where the workers are located to the corresponding image recognition component in the control center. The image recognition component then uses the received image frames of the environment where the workers are located to perform live hazard segmentation and screening to identify and screen the image frames corresponding to live equipment with metal tools held by the power workers or those near live edges at high altitudes, thus generating live hazard segments for each worker. The frames representing the live electrical hazards of each worker are time-synchronized and sorted to generate a sequence of live electrical hazard images of the workers. The image recognition component is used to identify and locate the worker in real-time in the live electrical space by analyzing the sequence of live electrical hazard images.

3. The intelligent early warning system for critical edges of power operations according to claim 2, characterized in that, The step of identifying and locating workers in a live-lined danger image frame sequence using an image recognition component includes: The image recognition component uses Canny edge detection to perform contour topology recognition between the worker and the live equipment in each image frame within the sequence of images showing the danger of live equipment, so as to obtain the contour spatial position and connection relationship between the worker and the live equipment within the image frame sequence. Based on the contour spatial position and connection relationship between the worker and the live equipment in the image frame sequence, the live relative trajectory analysis is performed on each image frame in the image frame sequence of the worker's live hazard image frame sequence to obtain the dynamic trajectory between the relative positions of the worker and the live equipment in the image frame sequence. The relative spatial position between the operator and the live equipment is obtained by the dynamic trajectory between the relative positions of the operator and the live equipment within the image frame sequence, and the relative spatial position between the operator and the live equipment is reconstructed by three-dimensional spatial projection to generate a live projection mesh of the operator containing the spatial coordinates. Based on the live-line projection grid containing the corresponding spatial coordinates of the operator, the relative spatial position between the operator and the live equipment is located in real time by live-line projection positioning, so as to obtain the real-time live-line spatial positioning of the operator.

4. The intelligent early warning system for critical edges of power operations according to claim 1, characterized in that, The electric field critical voltage partitioning module includes the following functions: Based on the real-time energized spatial positioning of the operator and combined with the field strength sensing unit, the electric field strength of the operator is measured in real time during the real-time operation process. The electric field strength of the operator in the corresponding real-time energized positioning environment is measured in real time by the field strength sensing unit to obtain the electric field strength corresponding to the operator's positioning. The electric field intensity distribution gradient is calculated based on the electric field intensity corresponding to the location of the operator, and the electric field intensity distribution gradient between the locations of the operator is obtained. The critical voltage level is determined based on the gradient of electric field intensity distribution between the positions of the workers, so as to obtain the critical voltage level zone of each worker and upload it to the control center.

5. The intelligent early warning system for critical edges of power operations according to claim 4, characterized in that, The calculation of the electric field intensity distribution gradient based on the electric field intensity corresponding to the location of the operator includes: The electric field distance between any two positions is determined by the positions of the operators. The electric field strength difference between each pair of positions of the workers is calculated to obtain the electric field strength difference between each pair of positions. The electric field intensity distribution gradient between the two locations is calculated based on the electric field distance between them, thus obtaining the electric field intensity distribution gradient between the locations of the workers.

6. The intelligent early warning system for critical edges of power operations according to claim 4, characterized in that, The critical voltage level zones for each operator are specifically determined based on the distribution range corresponding to the electric field intensity distribution gradient. These zones correspond to safe voltage levels, low voltage levels, medium voltage levels, high voltage levels, ultra-high voltage levels, and extra-high voltage levels. The electric field intensity distribution gradient for safe voltage levels is 0-0.05V / m. 2 The corresponding critical voltage levels are 6V-42V; the electric field intensity distribution gradient at low voltage levels is 0.05-0.2V / m. 2 The corresponding critical voltage levels are 220V-380V; the electric field intensity distribution gradient for medium voltage levels is 0.2-2V / m. 2 The corresponding critical voltage levels are 3.6kV-10kV; the electric field intensity distribution gradient of the high-voltage level is 2-10V / m. 2 The corresponding critical voltage level is 110kV-220kV; the electric field intensity distribution gradient of the ultra-high voltage level is 10-30V / m. 2 The corresponding critical voltage level is 330kV-750kV; the electric field intensity distribution gradient of the ultra-high voltage level is greater than 30V / m. 2 The corresponding critical voltage levels are AC above 1000kV and DC above ±800kV.

7. The intelligent early warning system for critical edges of power operations according to claim 1, characterized in that, The live-line distance measurement module includes the following functions: The radar ranging unit uses the radar transmitting component to transmit corresponding ultrasonic beams between the metal tools and live equipment of the power workers in the critical voltage level zone where each worker is located. The ultrasonic beams are received in the radar receiving component, and the data analysis component in the control center monitors and determines the corresponding duration between transmission and reception. The difference in electric field strength distribution between the workers and the live equipment is determined by the metal tools and live equipment held by the workers in the critical voltage level zone where each worker is located. Based on the difference in electric field intensity distribution between the operator and the live equipment, the transmission influence of the emitted ultrasonic beam is evaluated using the electric field influence assessment calculation formula, and the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam is obtained. The specific formula for calculating the impact of the electric field is as follows: ； In the formula, The electric field difference transmission influence coefficient is... The electric field range between the operator and the live equipment. For the spatial position parameters of the electric field, For the position of the operator and the live equipment The electric field intensity distribution at that location is poor. For the position of the ultrasonic beam The ultrasonic propagation frequency at that location, Let be the attenuation coefficient of ultrasonic propagation energy within the corresponding electric field range. For the ultrasonic beam from position The propagation distance between the ultrasonic wave and the target; Based on the transmission influence coefficient of the electric field difference corresponding to the ultrasonic beam, the transmission rate corresponding to the ultrasonic beam is corrected for the transmission influence, and the ultrasonic transmission correction rate is obtained. Based on the ultrasonic transmission correction rate and combined with the corresponding time between transmission and reception, the live distance between the metal tools held by the power workers and the live equipment in each critical voltage level zone is measured to obtain the live distance between the workers and the live equipment in each voltage level zone, and then uploaded to the control center.

8. The intelligent early warning system for critical edges of power operations according to claim 1, characterized in that, The critical early warning module for power operations includes the following functions: By setting safe warning distances for each voltage level in the control center according to the critical voltage level where each operator is located; The critical safety comparison results for the operator are obtained by comparing the safety warning distance with the corresponding energized distance between the operator and the energized equipment. This includes the comparison results corresponding to whether the energized distance is greater than, equal to or less than the safety warning distance. By analyzing the critical safety comparison results of the workers on the corresponding data analysis component in the control center, if the energized distance is greater than the safety warning distance, the corresponding comparison result is stored in the control center; if the energized distance is equal to the safety warning distance, the corresponding sound warning control signal is generated and uploaded to the sound warning component through the signal transmission component of the warning unit to start the corresponding power operation critical safety sound alarm. If the energized distance is less than the safe warning distance, the corresponding sound and photoelectric warning control signals will be generated and uploaded to the sound warning component and photoelectric warning component through the signal transmission component corresponding to the warning unit. At the same time, the corresponding power operation critical sound and light warning work will be executed.

9. A smart early warning device for critical edges of power operations, characterized in that, For executing the intelligent early warning system for critical edges of power operations as described in any one of claims 1-8, the image acquisition unit is provided with a radar ranging unit on its upper side, and the housing is provided with a field strength sensing unit, an early warning unit, and a control center. The control center has built-in image recognition components and data analysis components. The image acquisition unit, the radar ranging unit, the field strength sensing unit, and the early warning unit are all electrically connected to the control center. The image acquisition unit includes a camera component and an image transmission component. The camera component is electrically connected to the image transmission component, and the image transmission component is electrically connected to the image recognition component. The radar ranging unit includes a radar transmitting component and a radar receiving component. The radar transmitting component is electrically connected to the radar receiving component, and the radar receiving component is electrically connected to the data analysis component. The early warning unit includes a sound early warning component, a photoelectric early warning component, and a signal transmission component. The sound early warning component and the photoelectric early warning component are both electrically connected to the signal transmission component, and the signal transmission component is electrically connected to the control center.

10. A method for intelligent early warning of critical edges in power operations, characterized in that, The method is implemented based on the intelligent early warning system for critical edges of power operations as described in claim 1, and the intelligent early warning method for critical edges of power operations includes: The image acquisition unit acquires real-time image frames of the environment where the workers are located and transmits them to the corresponding image recognition component in the control center; the image recognition component then uses the image frames of the environment where the workers are located to identify and locate the workers while they are in power, so as to obtain the real-time live spatial location of the workers. Based on the real-time energized spatial positioning of the workers and combined with the field strength sensing unit, the electric field strength of the workers is measured in real time to obtain the electric field strength corresponding to the positioning of the workers; the critical voltage level is evaluated based on the electric field strength corresponding to the positioning of the workers to obtain the critical voltage level zone of each worker, and the data is uploaded to the control center. The radar ranging unit is used to measure the energized distance between the metal tools held by the power workers and the live equipment in the critical voltage level zone where each worker is located, so as to obtain the energized distance between the workers and the live equipment in each voltage level zone, and then upload it to the control center. By setting safety warning distances for each voltage level in the control center according to the critical voltage level of each operator, and comparing the safety warning distance with the energized distance between the operator and the live equipment, the critical safety comparison result of the operator is obtained. The warning unit performs power operation warning processing on the critical safety comparison result of the operator to execute the corresponding power operation critical edge safety warning work.

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