Power inspection device and method based on humanoid robot

Abstract

The invention discloses an electric power inspection device and method based on a humanoid robot. The device comprises the humanoid robot and a terminal upper computer, wherein the humanoid robot comprises an upper limb module, a lower limb module, a head module and a control module; the upper limb module is provided with a partial discharge sensor, a vibration sensor, a distance measuring sensor and a pressure sensor; the head module at least comprises a binocular camera, a laser radar and a near field communication module; and the control module is configured to obtain data of each sensor and transmit the data to the terminal upper computer in a communication manner, and is configured to receive a control signal from the terminal upper computer and control each module to execute inspection in an optimal path according to the control signal. According to the invention, the humanoid robot is utilized to realize the mobile self-adaptive inspection and prediction inspection capability of the power equipment, and a high-cost fixed monitoring system is replaced.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for power equipment, specifically relating to a power inspection device and method based on a humanoid robot with multimodal sensing, adaptive motion control and wireless data transmission, for the mobile, full-scene inspection and condition diagnosis of power equipment. Background Technology

[0002] With the continuous expansion of smart grids and power facilities, real-time monitoring and accurate diagnosis of the operating status of substations, transmission and distribution lines, and supporting equipment have become crucial for ensuring the safety and stability of the power grid. Currently, common monitoring methods mainly include fixed monitoring devices and manual inspections.

[0003] Fixed monitoring systems typically install various sensors at key nodes of power equipment to collect parameters such as temperature, humidity, partial discharge, vibration, current, and voltage in real time. However, these systems suffer from high deployment and maintenance costs, and their coverage is limited due to the fixed sensor locations, often making it difficult to achieve full coverage monitoring in complex spatial structures (such as confined substation areas or three-dimensional layouts). Furthermore, the system's scalability and adaptability are poor once equipment layout or environmental conditions change.

[0004] While manual inspection offers some flexibility, it poses significant safety risks in high-voltage electrical environments. Inspection efficiency is greatly affected by factors such as time, manpower, and weather, and the continuity and objectivity of inspection data are insufficient, making it difficult to meet the needs for long-term traceability analysis of equipment health status.

[0005] To overcome the limitations of manual inspection, a series of mobile robot inspection systems have emerged in recent years. These systems are mostly based on wheeled or tracked mobile mechanisms and can complete simple path planning and image acquisition tasks. However, due to insufficient structural flexibility, these robots usually struggle to achieve omnidirectional movement in complex terrain and lack the ability to perform precise robotic arm operations. Consequently, they are significantly inadequate when performing tasks requiring contact sensing (such as partial discharge measurement point calibration, vibration contact detection, and precise temperature measurement of connection terminals). Furthermore, most existing inspection robots rely on a single sensing mode (such as vision or infrared) and lack multimodal sensing fusion mechanisms, making it difficult to achieve collaborative detection and comprehensive diagnosis of multiple physical quantities.

[0006] In summary, existing power equipment monitoring and inspection technologies still have significant shortcomings in terms of mobility, sensing accuracy, and adaptability. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a power inspection device and method based on a humanoid robot. This invention is an intelligent inspection system that integrates multi-physical quantity collaborative perception, omnidirectional movement, precision robotic arm operation, and wireless data communication. It can move flexibly in complex spaces and perform various types of precision detection tasks, thereby realizing full-scene, dynamic, and intelligent inspection and condition diagnosis of power facilities.

[0008] The technical solution adopted to achieve the above-mentioned objectives of this invention is as follows: Firstly, a power inspection device based on a humanoid robot is provided, comprising: The invention includes a humanoid robot and a terminal host computer, characterized in that the humanoid robot includes an upper limb module, a lower limb module, a head module, and a control module; The upper limb module is equipped with a partial discharge sensor, a vibration sensor, a distance sensor, and a pressure sensor; The partial discharge sensor collects partial discharge data from a preset partial discharge monitoring point in a preset area; the vibration sensor collects vibration data from the preset partial discharge monitoring point; the distance sensor measures the distance between the reference position of the upper limb module and the preset partial discharge monitoring point; and the pressure sensor measures the force exerted when the upper limb module contacts the preset partial discharge monitoring point. The head module includes at least a binocular camera, a lidar, and a near-field communication module. The lidar is configured to acquire an electronic map of the preset area. The binocular camera is configured to acquire image data and perform target recognition and ranging. The near-field communication module is configured to sense near-field signals at monitoring points and read and write near-field communication tags. The control module is configured to acquire data from each sensor and transmit it to the terminal host computer, and is also configured to receive control signals from the terminal host computer and control each module to perform inspections along the optimal path according to the control signals.

[0009] As a preferred embodiment, the lower limb module is equipped with a water immersion sensor at a preset location, configured to detect whether there is water immersion on the ground surface along the optimal path.

[0010] As a preferred embodiment, a wireless communication module is also included, configured to perform communication transmission between the control module and the terminal host computer.

[0011] As a preferred embodiment, the robot also includes an environmental sensor array, which includes at least a temperature sensor, a humidity sensor, and a gas sensor.

[0012] Secondly, a power inspection method based on a humanoid robot is provided for a terminal host computer. S1, receiving 3D point cloud data and environmental image data from the humanoid robot, preprocessing the 3D point cloud data to generate an electronic map of the target area, wherein the electronic map includes the coordinates of electrical equipment, the distribution of obstacles, and the safety area. S2. Based on the current location of the humanoid robot on the electronic map, determine multiple candidate inspection paths for each partial discharge monitoring point, and select the optimal path based on path length, obstacle density, and safe area coverage, and feed it back to the humanoid robot. S3. Generate a first control signal to the humanoid robot to control it to move to a preset distance threshold of the partial discharge monitoring point, and receive partial discharge data, vibration data and environmental data after receiving the upper limb module contact signal; S4. Repeat S3 until partial discharge feedback data from all partial discharge monitoring points are received; S5. Based on the partial discharge feedback data and environmental data, perform a correlation analysis of the power equipment fault status; When it is determined that a partial discharge monitoring point is in a fault state, an update instruction is generated and sent to the humanoid robot. The update instruction contains the updated monitoring location data. S6. When the surface water immersion data is received from the water immersion sensor, the alternative inspection path planning process is initiated, and an evacuation control signal is generated to the humanoid robot.

[0013] As a preferred embodiment, the step of determining the optimal path in S2 includes: S21. Based on the coordinates of each partial discharge monitoring point in the electronic map, a weighted path map is constructed, wherein the weights are determined by the obstacle density, the coverage of the safe area, and the historical inspection failure rate. S22. Use a path optimization algorithm to traverse the path map and calculate the cumulative weight value from the current position to each partial discharge monitoring point; S23. Select the path with the smallest cumulative weight value as the optimal path, wherein the cumulative weight value satisfies: ; in, For path segment obstacle density, For path segment The coverage area of ​​the safe zone For path segment Historical inspection failure rate, For preset weighting coefficients and .

[0014] As a preferred approach, the step of initiating alternative inspection path planning in S6 includes: S61. When surface waterlogging data is received from the water immersion sensor, retrieve the humidity data sequence matching the current inspection time from the historical database and calculate the probability of water immersion. : ; in, This is the current humidity sensor reading. This is a seasonal time coefficient. These are parameters fitted based on historical flooding events; S62, when When the probability exceeds a preset threshold, a preset obstacle avoidance path point is dynamically inserted into the optimal path. The obstacle avoidance path point is generated based on the following preset conditions: Extract safe path segments from the historical database that are within the same season and humidity range; According to time decay factor Weighted historical path validity, among which Record the difference between the historical path recording time and the current inspection time. The attenuation coefficient; S63. Based on the updated path points, recalculate the cumulative weight value, generate alternative inspection paths, and feed them back to the humanoid robot.

[0015] Thirdly, a power line inspection method based on a humanoid robot is provided, characterized by the following steps: S10. The head module uses a lidar and binocular camera to scan the target area, generate an electronic map containing the coordinates of partial discharge monitoring points, and transmit it to the terminal host computer. S11. Receive the optimal path and the first control signal from the terminal host computer; S12. Drive the lower limb module to move to the preset coarse distance threshold of the partial discharge monitoring point according to the first control signal; when the near field communication module senses the near field signal of the monitoring point, control the binocular camera to scan the QR code mark, and read the precise coordinate data stored in the near field communication tag through the near field communication module; S13. Control the upper limb module to move toward the partial discharge monitoring point, and monitor the distance between the upper limb end and the monitoring point in real time through the distance sensor. When the distance is not greater than the first threshold, reduce the moving speed. S14. When the pressure sensor detects that the contact force reaches the second threshold, the movement of the upper limb module is stopped, and the partial discharge sensor and vibration sensor are triggered to collect data synchronously. S15. During the data acquisition process, the temperature, humidity and harmful gas data around the monitoring point are continuously collected through the environmental sensor group. S16. When the water immersion sensor detects that the surface resistance value is not greater than the third threshold, it feeds back the water immersion data to the terminal host computer and drives the lower limb module to move to the dry area according to the evacuation control signal. S17. When an update command is received from the terminal host computer, the updated monitoring location data is written to the near-field communication tag of the monitoring point through the near-field communication module.

[0016] As a preferred embodiment, the step of reducing the moving speed in S13 includes: S131, When the distance value fed back by the ranging sensor At that time, the movement speed of the upper limb module Adjusted to: ; in, Initial movement speed, The preset distance threshold; S132, when And the pressure sensor detected the applied force. When this occurs, stop the upper limb module movement.

[0017] As a preferred method, the updated data packet is represented as follows: ; The three-dimensional coordinates of the new monitoring point determined after fault analysis. To update the timestamp.

[0018] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: 1. This invention uses a technologically mature humanoid robot as a carrier to carry multiple physical quantity sensors. The humanoid robot has omnidirectional mobility, thereby realizing mobile adaptive inspection of power equipment and replacing the high-cost fixed monitoring system.

[0019] 2. This invention solves the problem of precise positioning and contact force control of robotic arms in complex scenarios by controlling the upper limb end of a humanoid robot to contact a set partial discharge monitoring point and collecting partial discharge data and vibration data during the contact process.

[0020] 3. This invention constructs a multi-source data fusion analysis system by coordinating the sensing and measurement of multiple physical quantities (partial discharge + vibration + environment + water immersion), thereby improving the comprehensiveness and accuracy of fault early warning.

[0021] 4. This invention eliminates the need for periodic manual replacement / calibration of sensors, reducing the number of monitoring devices and the frequency of manual maintenance. This reduces long-term operation and maintenance costs.

[0022] 5. This invention uses a technologically mature humanoid robot to replace manual labor, avoiding high-risk human operation and protecting personal safety. Attached Figure Description

[0023] Figure 1 This is a structural block diagram of a power inspection device based on a humanoid robot provided in an embodiment of the present invention. Detailed Implementation

[0024] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0025] This embodiment first provides a power inspection device based on a humanoid robot, such as... Figure 1 As shown, it includes a humanoid robot and a terminal host computer, characterized in that the humanoid robot includes an upper limb module, a lower limb module, a head module and a control module; The upper limb module is equipped with a partial discharge sensor, a vibration sensor, a distance sensor, and a pressure sensor; The partial discharge sensor collects partial discharge data from a preset partial discharge monitoring point in a preset area; the vibration sensor collects vibration data from the preset partial discharge monitoring point; the distance sensor measures the distance between the reference position of the upper limb module and the preset partial discharge monitoring point; and the pressure sensor measures the force exerted when the upper limb module contacts the preset partial discharge monitoring point. The head module includes at least a binocular camera, a lidar, and a near-field communication module. The lidar is configured to acquire an electronic map of the preset area. The binocular camera is configured to acquire image data and perform target recognition and ranging. The near-field communication module is configured to sense near-field signals at monitoring points and read and write near-field communication tags. The control module is configured to acquire data from each sensor and transmit it to the terminal host computer, and is also configured to receive control signals from the terminal host computer and control each module to perform inspections along the optimal path according to the control signals.

[0026] As a preferred embodiment, the lower limb module is equipped with a water immersion sensor at a preset location, configured to detect whether there is water immersion on the ground surface along the optimal path.

[0027] As a preferred embodiment, a wireless communication module is also included, configured to perform communication transmission between the control module and the terminal host computer.

[0028] As a preferred embodiment, the robot also includes an environmental sensor array, which includes at least a temperature sensor, a humidity sensor, and a gas sensor.

[0029] It should be noted that in the actual use environment of this embodiment, if the communication capability of the near-field communication module is reduced or the bit error rate is high due to the electrical environment, the near-field communication module can achieve communication through signal separation, signal shielding or anti-interference means commonly used in the art, such as anti-interference shielding layer or shielding structure of specific band and gain antenna. These are common technical means in the art and will not be described in detail here.

[0030] In this embodiment, the terminal host computer is used to process real-time data from the humanoid robot and generate control commands. As the core of this embodiment, the terminal host computer performs the following steps: S1. Receive 3D point cloud data and environmental image data from a humanoid robot, preprocess the 3D point cloud data to generate an electronic map of the target area, the electronic map including the coordinates of electrical equipment, the distribution of obstacles and the safety zone; In this embodiment, S1 acquires 3D point cloud data of the power distribution room using the LiDAR on the humanoid robot's head module. This point cloud data has centimeter-level accuracy and a sampling frequency of 10Hz. The preprocessing process includes three stages: point cloud filtering, feature extraction, and map construction. First, outliers are removed using a radius filter with a filtering radius set to 0.05 meters. Second, planar features are extracted using the RANSAC algorithm to identify the power distribution cabinet, the ground, and the walls. Finally, the ICP algorithm is used to register multiple frames of point cloud data to generate a complete electronic map.

[0031] As a preferred method for implementing S1 in this embodiment, the electronic map is stored using an octree data structure, with each node measuring 0.1m × 0.1m × 0.1m, which ensures map accuracy while controlling storage space. It should be noted that the electronic map in this embodiment not only contains geometric information but also integrates temperature, humidity, and hazardous gas data collected by environmental sensors, forming a multi-dimensional environmental perception model.

[0032] S2. Based on the current location of the humanoid robot on the electronic map, determine multiple candidate inspection paths for each partial discharge monitoring point, and select the optimal path based on path length, obstacle density, and safe area coverage, and feed it back to the humanoid robot. As a preferred method for implementing S2 in this embodiment, this embodiment first constructs a weighted path graph based on the coordinates of each partial discharge monitoring point in the electronic map. Specifically, the electronic map is divided into 1m×1m grid cells, each grid cell serving as a node in the path graph. Connecting edges are established between adjacent nodes, and the weight values ​​satisfy the following: ; in, For path segment obstacle density, For path segment The coverage area of ​​the safe zone For path segment The historical inspection failure rate, obtained from historical database statistics, represents the frequency of equipment failures near this path segment. For preset weighting coefficients and .

[0033] In this embodiment, the value of is dynamically adjusted according to the power distribution room environment, such as in a normal environment, a humid environment, or a densely populated equipment area. It should be noted that this embodiment uses an algorithm to traverse the path graph, calculate the cumulative weight value from the current position to each partial discharge monitoring point, and selects the path with the smallest cumulative weight value as the optimal path. As a further preferred method for implementing S2 in this embodiment, when the difference in cumulative weight values ​​of multiple paths is less than a preset value, this embodiment will additionally consider energy consumption factors and prioritize the path with fewer turns to reduce the energy consumption of the humanoid robot.

[0034] S3. Generate a first control signal to the humanoid robot to control it to move to a preset distance threshold of the partial discharge monitoring point, and receive partial discharge data, vibration data and environmental data after receiving the upper limb module contact signal; As a preferred method for implementing S3 in this embodiment, the preset distance threshold in this embodiment is dynamically set according to the type of monitoring point: for high-voltage switchgear, the threshold is set to 0.5 meters; for transformers, the threshold is set to 0.8 meters; and for cable joints, the threshold is set to 0.3 meters. This differentiated setting takes into account the safety distance requirements and detection accuracy requirements of different devices. After receiving the upper limb module contact signal, the terminal host computer will initiate a data verification mechanism to check the consistency of partial discharge data, vibration data, and environmental data. When the data fluctuation exceeds the preset threshold, a repeated detection process will be triggered.

[0035] S4. Repeat S3 until partial discharge feedback data from all partial discharge monitoring points are received; S5. Based on the partial discharge feedback data and environmental data, perform a correlation analysis of the power equipment fault status; when it is determined that there is a fault status at the partial discharge monitoring point, generate an update instruction to the humanoid robot, the update instruction containing the updated monitoring location data; As a further preferred method for implementing S5 in this embodiment, a Bayesian network model is used to perform fault state correlation analysis. Specifically, partial discharge data, vibration data, and environmental data are used as input nodes, and the fault probability is used as the output node. The network parameters are trained using historical fault data. When the calculated fault probability exceeds 85%, a fault state is determined to exist. At this time, the terminal host computer generates an update command, which contains the updated monitoring location data. It should be noted that the updated monitoring location is usually located within 5-10 cm of the original monitoring point to avoid the damaged contact area and ensure the effectiveness of subsequent detection.

[0036] S6. When the surface water immersion data is received from the water immersion sensor, the alternative inspection path planning process is initiated, and an evacuation control signal is generated to the humanoid robot.

[0037] As a preferred method for implementing S6 in this embodiment, this embodiment first performs a water immersion probability test. calculate: ; in, This is the current humidity sensor reading, in %RH. The seasonal time coefficient is 0.2 for spring, 0.8 for summer, 0.3 for autumn, and 0.1 for winter in this embodiment; The parameters, fitted based on historical flooding events, are determined using the least squares method.

[0038] In this embodiment, a water immersion risk is determined when the value is greater than 0.7. At this point, step S62 is executed to dynamically insert preset obstacle avoidance path points into the optimal path. The generation of obstacle avoidance path points follows these rules: First, safe path segments within the same season and humidity range are extracted from the historical database; second, they are generated according to a time decay factor. Weighted historical path validity, among which Record the difference between the historical path recording time and the current inspection time. This is the attenuation coefficient.

[0039] As a further preferred method for implementing S62 in this embodiment, the validity score of the historical path is calculated as follows: in, This indicates the percentage of historical routes that successfully completed inspections. This indicates the timeliness of the path data, with more recently recorded paths receiving higher scores. This embodiment selects the highest-scoring historical path segments for fusion to generate new obstacle avoidance path points. It should be noted that this embodiment sets a maximum valid time for historical path segments; historical paths exceeding this time will not be considered to ensure the timeliness of the path data.

[0040] The humanoid robot in this embodiment adopts a humanoid design and can autonomously navigate and perform detection tasks in the power distribution room. The robot's head is equipped with a binocular camera, LiDAR, and a near-field communication module, the upper limbs integrate multiple detection sensors, and the lower limbs are equipped with water immersion sensors.

[0041] S10. The head module uses a lidar and binocular camera to scan the target area, generate an electronic map containing the coordinates of partial discharge monitoring points, and transmit it to the terminal host computer. As a preferred implementation method of S10 in this embodiment, a coarse scan is first performed: a humanoid robot walks around the power distribution room at a speed of 0.3 m / s, a lidar collects point cloud data at a frequency of 10 Hz, and a binocular camera captures environmental images at a frame rate of 5 fps. Then, the lidar point cloud and the binocular image are fused using a feature matching algorithm to identify key equipment such as power distribution cabinets, switches, and transformers. Finally, based on equipment characteristics and historical data, the coordinates of partial discharge monitoring points are automatically marked. It should be noted that the partial discharge monitoring point coordinate marking in this embodiment adopts a semi-automatic method: the system first recommends possible monitoring points based on equipment type and historical experience, and the operator confirms or adjusts them through a remote interface to ensure the accuracy of the point setting.

[0042] S11. Receive the optimal path and the first control signal from the terminal host computer; S12. Drive the lower limb module to move to the preset coarse distance threshold of the partial discharge monitoring point according to the first control signal; when the near field communication module senses the near field signal of the monitoring point, control the binocular camera to scan the QR code mark, and read the precise coordinate data stored in the near field communication tag through the near field communication module; As a further preferred method for implementing S12 in this embodiment, this embodiment addresses the limitations of visual detection mentioned in the technical disclosure. Specifically, each partial discharge monitoring point is equipped with a physical identifier: a metal plate with a QR code printed on its surface and an NFC tag embedded inside. A preset coarse distance threshold is set to 1.0 meter. When the humanoid robot moves into this range, the near-field communication module begins to sense the NFC signal. Once the signal strength exceeds the threshold, the binocular camera is triggered to scan the QR code, and at the same time, precise coordinate data is read via NFC.

[0043] It should be noted that the data format stored in the NFC tag in this embodiment is as follows: { "coord":{"x":0.12,"y":0.85,"z":1.32}, "type":"switchgear", "last_update":"2023-10-15T08:30:00Z" } The `coord` field represents the precise coordinates relative to the device's reference point, the `type` field represents the device type, and the `last_update` field is the last update timestamp. This design overcomes the limitations of a pure QR code solution: NFC provides near-field triggering, QR codes are only used for visual-assisted positioning and error checking, and the actual monitored location is stored by an updatable NFC tag.

[0044] S13. Control the upper limb module to move toward the partial discharge monitoring point, and monitor the distance between the upper limb end and the monitoring point in real time through the distance sensor. When the distance is not greater than the first threshold, reduce the moving speed. As a preferred method for implementing S13 in this embodiment, this embodiment adopts a speed adjustment strategy: in, The initial moving speed is set to 0.05 m / s in this embodiment; The preset distance threshold is set according to the type of monitoring point. For example, 0.15 meters is set for switch cabinets, 0.20 meters for transformers, and 0.10 meters for cable joints. When the distance value fed back by the ranging sensor is less than the preset threshold, the moving speed of the upper limb module decreases linearly according to the above formula.

[0045] In this embodiment, a laser rangefinder is used as the ranging sensor, with an accuracy of ±1mm and a sampling frequency of 100Hz. As a further preferred method for implementing S13 in this embodiment, when the distance reaches a certain value, the system switches to force control mode, monitoring the contact force through a pressure sensor to ensure the smoothness of the contact process. It should be noted that the speed adjustment strategy in this embodiment effectively avoids the collision risk caused by traditional fixed-speed movement, especially when detecting devices with uneven surfaces, and can significantly improve contact accuracy.

[0046] S14. When the pressure sensor detects that the contact force reaches the second threshold, the movement of the upper limb module is stopped, and the partial discharge sensor and vibration sensor are triggered to collect data synchronously. As a preferred embodiment for implementing S14, the second threshold in this embodiment is dynamically set according to the device type. For example, it is 2.0N for metal surfaces, 1.0N for insulating surfaces, and 0.5N for fragile surfaces. The pressure sensor uses its Z-axis pressure data to perform sampling at a preset sampling frequency. When a force is detected and lasts for 0.5 seconds, it is determined to be stable contact, triggering data acquisition. It should be noted that this embodiment continuously monitors pressure changes during contact. If the pressure fluctuation exceeds a preset proportion of the threshold, the upper limb posture is automatically adjusted to maintain stable contact.

[0047] S15. During the data acquisition process, the temperature, humidity and harmful gas data around the monitoring point are continuously collected through the environmental sensor group. S16. When the water immersion sensor detects that the surface resistance value is not greater than the third threshold, it feeds back the water immersion data to the terminal host computer and drives the lower limb module to move to the dry area according to the evacuation control signal. As a further preferred embodiment of S16, the water immersion sensor in this embodiment adopts an electrode design, with the third threshold set to 100kΩ. When a resistance value ≤100kΩ is detected, it is determined that there may be water immersion on the ground surface. At this time, the humanoid robot immediately stops its current detection task, transmits the water immersion data, including location coordinates, resistance value, and timestamp, to the terminal host computer via the wireless communication module, and waits for evacuation instructions. It should be noted that in this embodiment, the water immersion situation is continuously monitored during the movement of the lower limb module. If the water immersion area is found to be expanding, the evacuation path will be updated in real time to ensure the robot's safety.

[0048] S17. When an update command is received from the terminal host computer, the updated monitoring location data is written to the near-field communication tag of the monitoring point through the near-field communication module.

[0049] As a preferred method for implementing S17 in this embodiment, this embodiment adopts a data packet format: in, The three-dimensional coordinates of the new monitoring point determined after fault analysis. To update the timestamp, this embodiment uses the AES-128 encryption algorithm to encrypt UpdateData, ensuring data security. The writing process includes three steps: first, reading the current tag content via NFC; second, verifying the tag integrity; and finally, writing the encrypted UpdateData into the designated field.

[0050] In this embodiment, determining the coordinates of the new monitoring point includes the following steps: Using the original monitoring point as the center, offset the points in the X, Y, and Z directions to a preset range of grid points, and select the point with the lowest historical partial discharge value as the new monitoring point. It should be noted that this embodiment performs a read verification after writing new data to ensure correct data writing. If writing fails, it will attempt to rewrite a preset number of times; if it still fails, an error log will be recorded and reported to the terminal host computer.

[0051] As a further preferred embodiment, this embodiment achieves deep collaboration between the terminal host computer and the humanoid robot. Specifically, when the humanoid robot executes step S12, the terminal host computer synchronously loads historical data of the monitoring point, including historical partial discharge values, vibration characteristics, and environmental parameters, for real-time comparative analysis. If the deviation between the current data and the historical data is found to exceed a threshold, the terminal host computer dynamically adjusts the first threshold in S13 and the second threshold in S14 to improve detection accuracy.

[0052] It should be noted that the near-field communication technology in this embodiment is not only used for monitoring point location but also enables the system's self-learning capability. After each detection, the humanoid robot compares the current detection data with historical data in the NFC tag and calculates the rate of change. If the rate of change exceeds a preset threshold, an early warning message is automatically generated and uploaded to the terminal host computer, triggering a more detailed fault analysis process.

[0053] As a preferred embodiment, the data sampled by the environmental sensor group in this embodiment is also used to correct the environmental impact factor. Specifically, the environmental impact factor is obtained by weighted average of humidity, temperature and harmful gas concentration relative to the normal range value. When the environmental impact factor exceeds the preset threshold, the detection threshold of partial discharge data and vibration data is adjusted in this embodiment, and the environmental impact factor and the detection threshold show a linear regression relationship.

[0054]

Claims

1. A power inspection device based on a humanoid robot, comprising a humanoid robot and a terminal host computer, characterized in that, The humanoid robot includes an upper limb module, a lower limb module, a head module, and a control module; The upper limb module is equipped with a partial discharge sensor, a vibration sensor, a distance sensor, and a pressure sensor; The partial discharge sensor collects partial discharge data from a preset partial discharge monitoring point in a preset area; the vibration sensor collects vibration data from the preset partial discharge monitoring point; the distance sensor measures the distance between the reference position of the upper limb module and the preset partial discharge monitoring point; and the pressure sensor measures the force exerted when the upper limb module contacts the preset partial discharge monitoring point. The head module includes at least a binocular camera, a lidar, and a near-field communication module. The lidar is configured to acquire an electronic map of the preset area. The binocular camera is configured to acquire image data and perform target recognition and ranging. The near-field communication module is configured to sense near-field signals at monitoring points and read and write near-field communication tags. The control module is configured to acquire data from each sensor and transmit it to the terminal host computer, and is also configured to receive control signals from the terminal host computer and control each module to perform inspections along the optimal path according to the control signals.

2. The power inspection device based on a humanoid robot according to claim 1, characterized in that, The lower limb module is equipped with a water immersion sensor at a preset location and is configured to collect data on whether there is water immersion on the ground surface along the optimal path.

3. The power inspection device based on a humanoid robot according to claim 1, characterized in that, It also includes a wireless communication module configured to perform communication transmission between the control module and the terminal host computer.

4. The power inspection device based on a humanoid robot according to claim 1, characterized in that, It also includes an environmental sensor array installed on the humanoid robot, which includes at least a temperature sensor, a humidity sensor, and a gas sensor.

5. A power line inspection method based on a humanoid robot, characterized in that, For use with a terminal host computer, the following steps are included: S1. Receive 3D point cloud data and environmental image data from a humanoid robot, preprocess the 3D point cloud data to generate an electronic map of the target area, the electronic map including the coordinates of electrical equipment, the distribution of obstacles and the safety zone; S2. Based on the current location of the humanoid robot on the electronic map, determine multiple candidate inspection paths for each partial discharge monitoring point, and select the optimal path based on path length, obstacle density, and safe area coverage, and feed it back to the humanoid robot. S3. Generate a first control signal to the humanoid robot to control it to move to a preset distance threshold of the partial discharge monitoring point, and receive partial discharge data, vibration data and environmental data after receiving the upper limb module contact signal; S4. Repeat S3 until partial discharge feedback data from all partial discharge monitoring points are received; S5. Based on the partial discharge feedback data and environmental data, perform a correlation analysis of the power equipment fault status; When it is determined that a partial discharge monitoring point is in a fault state, an update instruction is generated and sent to the humanoid robot. The update instruction contains the updated monitoring location data. S6. When the surface water immersion data is received from the water immersion sensor, the alternative inspection path planning process is initiated, and an evacuation control signal is generated to the humanoid robot.

6. The power line inspection method based on a humanoid robot according to claim 5, characterized in that, The steps for determining the optimal path in S2 include: S21. Based on the coordinates of each partial discharge monitoring point in the electronic map, a weighted path map is constructed, wherein the weights are determined by the obstacle density, the coverage of the safe area, and the historical inspection failure rate. S22. Use a path optimization algorithm to traverse the path map and calculate the cumulative weight value from the current position to each partial discharge monitoring point; S23. Select the path with the smallest cumulative weight value as the optimal path, wherein the cumulative weight value satisfies: ； in, For path segment obstacle density, For path segment The coverage area of ​​the safe zone For path segment Historical inspection failure rate, For preset weighting coefficients and .

7. The power line inspection method based on a humanoid robot according to claim 6, characterized in that, The steps for initiating alternative inspection path planning in S6 include: S61. When surface waterlogging data is received from the water immersion sensor, retrieve the humidity data sequence matching the current inspection time from the historical database and calculate the probability of water immersion. : ； in, This is the current humidity sensor reading. This is a seasonal time coefficient. These are parameters fitted based on historical flooding events; S62, when When the probability exceeds a preset threshold, a preset obstacle avoidance path point is dynamically inserted into the optimal path. The obstacle avoidance path point is generated based on the following preset conditions: Extract safe path segments from the historical database that are within the same season and humidity range; According to time decay factor Weighted historical path validity, among which Record the difference between the historical path recording time and the current inspection time. The attenuation coefficient; S63. Based on the updated path points, recalculate the cumulative weight value, generate alternative inspection paths, and feed them back to the humanoid robot.

8. A power line inspection method based on a humanoid robot, characterized in that, For use in humanoid robots, the following steps are included: S10. The head module uses a lidar and binocular camera to scan the target area, generate an electronic map containing the coordinates of partial discharge monitoring points, and transmit it to the terminal host computer. S11. Receive the optimal path and the first control signal from the terminal host computer; S12. Drive the lower limb module to move to the preset coarse distance threshold of the partial discharge monitoring point according to the first control signal; when the near field communication module senses the near field signal of the monitoring point, control the binocular camera to scan the QR code mark, and read the precise coordinate data stored in the near field communication tag through the near field communication module; S13. Control the upper limb module to move toward the partial discharge monitoring point, and monitor the distance between the upper limb end and the monitoring point in real time through the distance sensor. When the distance is not greater than the first threshold, reduce the moving speed. S14. When the pressure sensor detects that the contact force reaches the second threshold, the movement of the upper limb module is stopped, and the partial discharge sensor and vibration sensor are triggered to collect data synchronously. S15. During the data acquisition process, the temperature, humidity and harmful gas data around the monitoring point are continuously collected through the environmental sensor group. S16. When the water immersion sensor detects that the surface resistance value is not greater than the third threshold, it feeds back the water immersion data to the terminal host computer and drives the lower limb module to move to the dry area according to the evacuation control signal. S17. When an update command is received from the terminal host computer, the updated monitoring location data is written to the near-field communication tag of the monitoring point through the near-field communication module.

9. The power line inspection method based on a humanoid robot according to claim 8, characterized in that, The step of reducing the moving speed in S13 includes: S131, When the distance value fed back by the ranging sensor At that time, the movement speed of the upper limb module Adjusted to: ； in, Initial movement speed, The preset distance threshold; S132, when And the pressure sensor detected the applied force. When this occurs, stop the upper limb module movement.

10. The power line inspection method based on a humanoid robot according to claim 6 or 8, characterized in that, The update data packet is represented as: ； The three-dimensional coordinates of the new monitoring point determined after fault analysis. To update the timestamp.

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