Remote safety early warning method for operation without power cut

Abstract

The invention discloses a remote safety early warning method for operation without power cut, and belongs to the technical field of electric power operation, and the method comprises the following steps: S1, detecting and analyzing an operation state through an analysis module, and obtaining an analysis result; s2, selecting a shooting module according to an analysis result, and collecting image information through the selected shooting module; s3, identifying and judging the image information through an identification module to obtain an identification and judgment result; s4, performing active early warning and reminding through an early warning module according to an identification and judgment result, and uploading early warning information to an operation command model by the early warning module; in the non-power-cut operation, the safety distance active early warning system can provide extra guarantee for the non-power-cut operation of the robot, and it is ensured that the non-power-cut operation task is completed; for workers in the public and electric power workplaces, the safety distance active early warning system can effectively improve the safety management and control level of a non-power-cut working site and reduce the probability of power outage accidents and human injuries caused by subjective experience judgment.

Description

Technical Field

[0001] The present application belongs to the technical field of electric power operation, and specifically relates to a remote safety warning method for non-power-off operation. Background Art

[0002] With the continuous improvement of my country's power coverage rate, the scale of the power system has gradually expanded. As an important means of maintenance and transformation, the non-stop operation of distribution lines has played a very important role in the safe and reliable operation of the power system and the improvement of economic benefits. Non-stop operation technology plays an increasingly important role in the safe and reliable operation of the power system and the improvement of economic benefits.

[0003] Due to the unconscious movement of the operators and the error in distance judgment, there is a risk of electric shock when the human body exceeds the safe working area. Therefore, non-stop workers at home and abroad must strictly follow the safety operating procedures and adopt the correct working methods and necessary safety protection measures when performing live work. For operators in non-stop work on distribution lines, when the human body touches a potential body, it is the key to ensure the safety of personnel and equipment in non-stop work. For the non-stop operation of the operating robot, the safe distance active warning system can provide additional guarantees for the robot's live operation and ensure the smooth completion of the live operation task. For the public and general power operation site workers, it is very important to develop a cheap, durable, movable and portable active safety warning system to improve their work safety protection.

[0004] A 0.4kV distribution network non-stop power operation risk warning method and device was disclosed on 2024-01-26, with the publication number CN117458332A, including a power detector and a target detection and extraction device and a warning device electrically connected to the power detector respectively, the power detector includes an electric power output device, a power protection device and a power control device, the electric power output device is electrically connected to the power protection device, the power protection device is electrically connected to the power control device, and the target detection and extraction device is electrically connected to the warning device. In the present invention, the number of households at the time of power outage can be evaluated by combining various types of power outage events in the base year, types of operation projects, causes of power outages, power supply reliability targets and other parameters, and estimated data can be provided for the demand for non-stop power operations during planned power outages, and the demand or demand for non-stop power operations can be accurately budgeted in advance, so as to maximize the power supply reliability rate by optimizing various index parameters of the distribution network to achieve the target power supply reliability rate.

[0005] However, the above-mentioned warning method only provides safety warnings for operating personnel, and lacks warning judgments for on-site operating robots and operations in special long-distance scenarios. During operation, it may not be possible to make timely judgments on hidden dangers and risks in the power distribution operation process. The safety of the distribution network's non-stop operation process needs to be further improved. Summary of the invention

[0006] In order to solve the above problems and technical defects, the embodiment of the present application adopts the following technical solution, a remote safety warning method for non-power-off operation, the steps are as follows:

[0007] S1. Detect and analyze the operation status through the analysis module to obtain the analysis results;

[0008] S2. Select a shooting module according to the analysis result, and collect image information through the selected shooting module;

[0009] S3, identifying and judging the image information through the recognition module to obtain the recognition and judgment result;

[0010] S4. Active warning and reminder are carried out through the early warning module according to the recognition and judgment results. At the same time, the early warning module uploads the early warning information to the operation command model.

[0011] Preferably, the shooting module includes a shooting module, a camera module and a laser radar module;

[0012] The operating status includes operating personnel working without power outages, operating robots working without power outages, and working in special long-distance scenarios.

[0013] Furthermore, when the operation status is that the operator is working without power outage, the specific process is as follows:

[0014] The shooting module records the operator to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the distance between the operator, the charged object, and the grounded object, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and the early warning module transmits the early warning information to the operation command model. At the same time, the early warning module broadcasts the early warning information to the operator;

[0015] The identification system comprises a human body key point identification module and a charged body and grounded body identification module.

[0016] Furthermore, the human body key point recognition module includes a human limb key point position recognition module and a key point safety protection judgment module. The human limb key point position recognition module receives the image captured by the shooting module and quickly identifies the position of the human limb key points based on the improved convolutional pose machine network; the key point safety protection judgment module receives the image captured by the shooting module and the output data of the human limb key point position recognition module.

[0017] Furthermore, when the operation status is that the robot operates without power outage, the specific process is as follows:

[0018] The shooting module records the operation robot to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the spatial position between the charged body and the grounded body at the key operation point of the robot, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and then transmits the early warning information to the operation command model through the early warning module. At the same time, the early warning module will control the parking system to actively park;

[0019] The identification system includes a robot operation key point identification module and a charged body and grounded body identification module.

[0020] Furthermore, the robot operation key point recognition module includes a target detection module, which includes a feature fusion module FFM, a refinement U-shaped module TUM and a scale feature aggregation module SFAM. The target detection module fuses the features extracted by the backbone network; then extracts more representative multi-scale features through the refinement U-shaped module TUM and the feature fusion module FFM; finally, multi-level features are fused through the scale feature aggregation module SFAM to obtain a multi-level feature pyramid for the final stage of prediction, and then the SSD method is used to predict dense bounding boxes and category scores, and the final detection results are obtained through NMS.

[0021] Preferably, when the operation status is a long-distance special scene operation, the specific process is as follows:

[0022] The camera module and the lidar module simultaneously shoot and sample the working scene, and after sampling, the shooting data is sent to the data fusion module for fusion, and the fused data is sent to the spatial position judgment module for spatial position judgment. The spatial position judgment module sends the judgment result to the early warning module, and then the early warning information is transmitted to the operation command model through the early warning module. At the same time, the early warning module will broadcast the early warning information on site to remind the operating personnel.

[0023] Furthermore, the shooting module is a binocular stereo camera.

[0024] Furthermore, the scale feature aggregation module SFAM is used to aggregate the multi-level and multi-scale features generated by the refinement U-type module TUM to construct a multi-level feature pyramid; the target detection model includes 8 refinement U-type modules TUM, each refinement U-type module TUM includes 5 strided convolution kernels and 5 upsampling operations, and the output of each TUM includes 6 features of different scales.

[0025] Furthermore, the encoder of the refined U-shaped module TUM uses a convolutional layer of size 3×3 and a stride of 2 for feature extraction.

[0026] Compared with the prior art, the beneficial effects of the embodiments of the present application are:

[0027] (1) In the present application, the active safety distance warning system can provide additional guarantees for the non-stop operation of the operating robot, ensuring the smooth completion of the non-stop operation task; at the same time, for the public and general power operation site workers, the active safety distance warning system can effectively improve the safety management level of the non-stop operation site, reduce the probability of power outage accidents and personal accidents caused by subjective experience judgment, improve the safety of the distribution network non-stop operation process, and ensure the smooth completion of the non-stop operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In the attached picture:

[0029] Figure 1 This is a flowchart of an embodiment of the present application;

[0030] Figure 2 This is a flowchart of active safety warning during the operation process of operators without power outages in the embodiment of the present application;

[0031] Figure 3 This is a flowchart of active safety warning during the operation of the operating robot without power outage in the embodiment of the present application;

[0032] Figure 4 This is a flowchart of active safety warning for long-distance mobile operation process in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0034] Example

[0035] like Figure 1-Figure 4 As shown, a remote safety warning method for non-power-off operation, the steps are as follows:

[0036] S1. Detect and analyze the operation status through the analysis module to obtain the analysis results;

[0037] S2. Select a shooting module according to the analysis result, and collect image information through the selected shooting module;

[0038] S3, identifying and judging the image information through the recognition module to obtain the recognition and judgment result;

[0039] S4. Active warning and reminder are carried out through the early warning module according to the recognition and judgment results. At the same time, the early warning module uploads the early warning information to the operation command model.

[0040] The shooting module includes a shooting module, a camera module and a laser radar module;

[0041] The working status in step S1 includes working by operators without power outages, working by working robots without power outages, and working in special long-distance scenarios.

[0042] When the operation status is that the operator is working without power outage, the specific process is as follows:

[0043] The shooting module records the operator to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the distance between the operator, the charged object, and the grounded object, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and the early warning module transmits the early warning information to the operation command model. At the same time, the early warning module broadcasts the early warning information to the operator;

[0044] The recognition system includes a human body key point recognition module and a charged body and grounded body recognition module.

[0045] The human body key point recognition module includes a human limb key point position recognition module and a key point safety protection judgment module. The human limb key point position recognition module receives the image captured by the shooting module and quickly recognizes the position of the human limb key points based on the improved convolutional pose machine network; the key point safety protection judgment module receives the image captured by the shooting module and the output data of the human limb key point position recognition module.

[0046] When the operation status is that the robot is operating without power outage, the specific process is as follows:

[0047] The shooting module records the operation robot to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the spatial position between the charged body and the grounded body at the key operation point of the robot, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and then transmits the early warning information to the operation command model through the early warning module. At the same time, the early warning module will control the parking system to actively park;

[0048] The identification system includes a robot operation key point identification module and a charged body and grounded body identification module.

[0049] The robot operation key point recognition module includes a target detection module, which includes a feature fusion module FFM, a refinement U-shaped module TUM and a scale feature aggregation module SFAM. The target detection module fuses the features extracted by the backbone network; then extracts more representative multi-scale features through the refinement U-shaped module TUM and the feature fusion module FFM; finally, multi-level features are fused through the scale feature aggregation module SFAM to obtain a multi-level feature pyramid for the final stage of prediction, and then the SSD method is used to predict dense bounding boxes and category scores, and the final detection results are obtained through NMS.

[0050] When the operation status is long-distance special scene operation, the safety warning process is as follows:

[0051] The camera module and the lidar module simultaneously shoot and sample the working scene, and after sampling, the shooting data is sent to the data fusion module for fusion, and the fused data is sent to the spatial position judgment module for spatial position judgment. The spatial position judgment module sends the judgment result to the early warning module, and then the early warning information is transmitted to the operation command model through the early warning module. At the same time, the early warning module will broadcast the early warning information on site to remind the operating personnel.

[0052] The shooting module is a binocular stereo camera.

[0053] The scale feature aggregation module SFAM is used to aggregate the multi-level and multi-scale features generated by the refinement U-type module TUM to construct a multi-level feature pyramid; the target detection model contains 8 refinement U-type modules TUM, each of which contains 5 strided convolution kernels and 5 upsampling operations, and the output of each TUM contains 6 features of different scales.

[0054] The encoder of the Thinned U-Module TUM uses a 3×3 convolutional layer with a stride of 2 for feature extraction.

[0055] During the non-stop operation of the operating robot, the safe distance active warning system can provide additional guarantees for the robot's non-stop operation, ensuring the smooth completion of the non-stop operation task; at the same time, for the public and staff in general power operation sites, the safe distance active warning system can effectively improve the safety management level of the non-stop operation site, reduce the probability of power outages and personal accidents caused by subjective experience and judgment, improve the safety of the distribution network non-stop operation process, and ensure the smooth completion of the non-stop operation.

[0056] The above-mentioned embodiments only express the preferred implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several modifications, improvements and substitutions can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A remote safety warning method for non-power-off operation, characterized in that: Here are the steps: S1. Detect and analyze the operation status through the analysis module to obtain the analysis results; S2. Select a shooting module according to the analysis result, and collect image information through the selected shooting module; S3, identifying and judging the image information through the recognition module to obtain the recognition and judgment result; S4. Active warning and reminder are carried out through the early warning module according to the recognition and judgment results. At the same time, the early warning module uploads the early warning information to the operation command model.

2. A remote safety warning method for non-power-off operation according to claim 1, characterized in that: The shooting module includes a shooting module, a camera module and a laser radar module; The operating status includes operating personnel working without power outages, operating robots working without power outages, and working in special long-distance scenarios.

3. A remote safety warning method for non-power-off operation according to claim 2, characterized in that: When the operation status is that the operator is working without power outage, the specific process is as follows: The shooting module records the operator to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the distance between the operator, the charged object, and the grounded object, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and the early warning module transmits the early warning information to the operation command model. At the same time, the early warning module broadcasts the early warning information to the operator; The identification system comprises a human body key point identification module and a charged body and grounded body identification module.

4. A remote safety warning method for non-power-off operation according to claim 3, characterized in that: The human body key point recognition module includes a human body limb key point position recognition module and a key point safety protection judgment module. The human body limb key point position recognition module receives the image captured by the shooting module and quickly recognizes the position of the human body limb key points based on the improved convolutional pose machine network; the key point safety protection judgment module receives the image captured by the shooting module and the output data of the human body limb key point position recognition module.

5. A remote safety warning method for non-power-off operation according to claim 2, characterized in that: When the operation status is that the robot is operating without power outage, the specific process is as follows: The shooting module records the operation robot to obtain shooting information, and transmits the shooting information to the recognition system. The recognition system is used to identify the spatial position between the charged body and the grounded body at the key operation point of the robot, and sends the recognition result to the spatial position judgment module for distance judgment. After the distance judgment is completed, the spatial position judgment module sends the judgment result to the early warning module, and then transmits the early warning information to the operation command model through the early warning module. At the same time, the early warning module will control the parking system to actively park; The identification system includes a robot operation key point identification module and a charged body and grounded body identification module.

6. A remote safety warning method for non-power-off operation according to claim 5, characterized in that: The robot operation key point recognition module includes a target detection module, which includes a feature fusion module FFM, a refinement U-shaped module TUM and a scale feature aggregation module SFAM. The target detection module fuses the features extracted by the backbone network; then extracts more representative multi-scale features through the refinement U-shaped module TUM and the feature fusion module FFM; finally, multi-level features are fused through the scale feature aggregation module SFAM to obtain a multi-level feature pyramid for the final stage of prediction, and then the SSD method is used to predict dense bounding boxes and category scores, and the final detection result is obtained through NMS.

7. A remote safety warning method for non-power-off operation according to claim 2, characterized in that: When the operation status is long-distance special scene operation, the specific process is as follows: The camera module and the lidar module simultaneously shoot and sample the working scene, and after sampling, the shooting data is sent to the data fusion module for fusion, and the fused data is sent to the spatial position judgment module for spatial position judgment. The spatial position judgment module sends the judgment result to the early warning module, and then the early warning information is transmitted to the operation command model through the early warning module. At the same time, the early warning module will broadcast the early warning information on site to remind the operating personnel.

8. The remote safety warning method for non-power-off operation according to claim 2, characterized in that: The shooting module is a binocular stereo camera.

9. A remote safety warning method for non-power-off operation according to claim 6, characterized in that: The scale feature aggregation module SFAM is used to aggregate the multi-level and multi-scale features generated by the refinement U-type module TUM to construct a multi-level feature pyramid; the target detection model includes 8 refinement U-type modules TUM, each refinement U-type module TUM includes 5 strided convolution kernels and 5 upsampling operations, and the output of each TUM includes 6 features of different scales.

10. A remote safety warning method for non-power-off operation according to claim 9, characterized in that: The encoder of the thinned U-shaped module TUM uses a convolutional layer of size 3×3 and stride 2 for feature extraction.

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