Power grid intelligent inspection management system based on physical ID

Abstract

The invention relates to the technical field of power grid intelligent inspection, and discloses a power grid intelligent inspection management system based on a real object ID, which comprises an inspection path planning module, an unmanned aerial vehicle, an obstacle avoidance module and a settlement detection module. The inspection route planning module generates an inspection route based on the map model of the mountain area of the power distribution network, then the inspection route information and the unmanned aerial vehicle are used for starting inspection according to the inspection route information after the inspection route information is received by the unmanned aerial vehicle, and the obstacle avoidance module re-plans the inspection route when a sudden obstacle exists in front of the obstacle avoidance module. The settlement detection module is used for detecting the settlement condition. An inspection route is generated based on a map model of a power distribution network mountainous area through the inspection route planning module, and reasonable track points are determined by using a specific algorithm of the route planning unit, so that an inspection route suitable for complex terrains of the mountainous area can be effectively planned, the inspection efficiency is improved, and unnecessary flight distance and time consumption are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power grid inspection, and in particular to an intelligent power grid inspection management system based on physical ID. Background Art

[0002] Inspection work is crucial in the operation and maintenance of distribution network in mountainous areas. Traditional inspection methods mainly rely on manual walking or with the help of simple tools, which has many limitations. On the one hand, the terrain of distribution network in mountainous areas is complex and the environment is harsh, making manual inspection difficult, inefficient and risky. On the other hand, traditional inspection methods make it difficult to achieve real-time and comprehensive monitoring of the operating status of equipment, and it is difficult to detect and deal with some potential safety hazards in a timely manner.

[0003] With the rapid development of drone technology, drone inspection has gradually become a new inspection method. However, most existing drone inspection systems can only perform simple aerial photography and image acquisition, and cannot achieve intelligent inspection path planning and settlement detection. At the same time, for obstacles that may be encountered during the inspection process, the existing systems often lack effective obstacle avoidance measures, resulting in interruption or failure of the inspection task.

[0004] In order to solve the above problems, the present invention proposes a power grid intelligent inspection and management system based on physical ID. Summary of the invention

[0005] The present invention proposes a power grid intelligent inspection and management system based on physical ID, which solves the problems of great difficulty, low efficiency and high risk in distribution network inspection in mountainous areas, as well as the difficulty of traditional inspection methods to achieve intelligent path planning and settlement detection, and the lack of effective obstacle avoidance measures.

[0006] The technical solution of the present invention is as follows:

[0007] A power grid intelligent inspection and management system based on physical ID, comprising: an inspection path planning module, a drone, an obstacle avoidance module, and a settlement detection module.

[0008] The inspection route planning module is set on the UAV, which generates the inspection route based on the map model of the mountainous area of ​​the distribution network, and then sends the inspection route information to the UAV and the obstacle avoidance module.

[0009] All monitoring points in the power grid are marked with the State Grid physical ID.

[0010] The UAV is communicatively connected with the inspection path planning module, and is used for performing inspections according to the inspection route information after receiving the inspection route information. When receiving the re-planned inspection route information, the UAV performs inspections according to the re-planned inspection route information. After the UAV arrives at the monitoring point position, it generates an arrival monitoring point position signal, and then sends the arrival monitoring point position signal to the settlement detection module.

[0011] The obstacle avoidance module is set on the drone and is connected to the inspection path planning module and the drone communication. It is used to receive the inspection route information and detect in real time whether there are obstacles ahead. When there are sudden obstacles ahead, the inspection route is re-planned and the re-planned inspection route information is sent to the drone.

[0012] The settlement detection module is set on the drone and communicated with the drone and the manager's mobile terminal. It is used to start detecting the settlement situation after receiving the signal of arriving at the monitoring point location, and then send the settlement situation to the manager's mobile terminal.

[0013] Preferably, the inspection route planning module includes a map model database and a route planning unit.

[0014] The map model database is used to store the map model of the mountainous area of ​​the distribution network.

[0015] The route planning unit is connected to the map model database, the UAV, and the obstacle avoidance module for communication, and is used to call the map model information of the mountainous area of ​​the distribution network, determine the starting point, several monitoring points, the final target point, and the track planning space of the track planning, determine a random track point in the track planning space, search through the set track point set, find the track point in the track point set that is closest to the random track point, calculate the direction vector between the closest track point and the new track point, the direction vector between the closest track point and the final target point, and the angle between the two direction vectors, and adjust the probability of the new track point according to the direction vector between the closest track point and the final target point, and the angle between the two direction vectors. According to the probability of the adjusted new track point, a new track point is selected from the preset new track points, the new track point is added to the track point set, and an inspection route is generated until the generated inspection route reaches the final target point, and then the inspection route information is sent to the UAV and the obstacle avoidance module.

[0016] Preferably, the route planning unit is used to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors, including:

[0017] Use the following calculation formula to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors:

[0018] E new =(x′ new -x′ now , y′ new -y′ now , z′ new -z′ now ).

[0019] E tar =(x′ tar -x′ now , y′ tar -y′ now , z′ tar -z′ now ).

[0020]

[0021] Among them, E new is the direction vector between the nearest track point and the new track point, x now =(x′ now , y′ now , z′ now ) is the coordinate of the nearest track point, unit: cm, x new =(x′ new , y′ new , z′ new ) is the coordinate of the new track point, unit: cm. tar is the direction vector between the nearest track point and the final target point, x tar =(x′ tar , y′ tar , z′ tar ) is the coordinate of the final target point, θ is the angle between the two direction vectors, unit: degree.

[0022] Preferably, the obstacle avoidance module includes a laser radar, a camera and a microprocessor.

[0023] The route planning unit is used to generate the total distance of the inspection route after generating the inspection route, and then send the total distance of the inspection route to the microprocessor.

[0024] The drone is used to send the distance of the inspection route that has been inspected to the microprocessor.

[0025] LiDAR is used to measure the distance between the drone and its surroundings, and then sends the distance information between the drone and its surroundings to a microprocessor.

[0026] The camera is used to capture the image information in front and then send the image information in front to the microprocessor.

[0027] The microprocessor is connected to the route planning unit, the UAV, the laser radar, the camera, and the mobile terminal of the manager to construct a three-dimensional map of the UAV's surrounding environment after receiving the inspection route information, the distance information between the UAV and the surrounding environment, and the image information in front, and to detect in real time whether there are obstacles on the inspection route. When there is a sudden obstacle in front, the aircraft constraint parameters of the UAV are determined according to the operating parameters of the UAV, the inspection requirements are obtained, and the operating parameters and flight constraint parameters of the UAV are determined. The inspection requirements at least include the monitoring points and the target positions of the monitoring points. According to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the UAV, the flight constraint parameters, and the inspection influencing factors, the urgency of the UAV's monitoring points and the inspection route information are determined, the inspection route is re-planned, and the distance of the re-planned inspection route is generated. Then, the delay time for the UAV to reach the final target point is calculated according to the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the UAV, and the distance of the re-planned inspection route, and then the re-planned inspection route information is sent to the UAV, and the delay time for the UAV to reach the final target point is sent to the mobile terminal of the manager.

[0028] Preferably, the microprocessor is used to calculate the delay time of the drone arriving at the final target point according to the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the drone, and the distance of the re-planned inspection route. The expression of the delay time of the drone arriving at the final target point is:

[0029]

[0030] Among them, t represents the delay time of the UAV to reach the final target point, unit: s, V represents the speed of the UAV, unit: m / s, l s Indicates the distance of the re-planned inspection route, unit: m, l y Indicates the total distance of the original inspection route, unit: m, l n Indicates the inspection route distance that has been inspected, unit: m.

[0031] Preferably, the microprocessor is used to determine the monitoring point and the target position of the monitoring point according to the monitoring point coordinate expression to obtain the inspection demand, and the monitoring point coordinate expression is:

[0032]

[0033] Among them, F n is the nth monitoring point, F n ∈[(x min , x max ), (y min ,y max )(z min , zmax )], N is the total number of monitoring points, is the coordinate of the nth monitoring point on the x-axis, is the coordinate of the nth monitoring point on the y-axis, is the coordinate of the nth monitoring point on the z axis, x min ,y min , z min , x max ,y max , z max They are respectively the minimum and maximum area values ​​in the three-dimensional coordinates within the inspection area.

[0034] The aircraft constraint parameters of the UAV are determined according to the operating parameters of the UAV, including: constraining the minimum safe distance between the UAV and obstacles, and obtaining the constraint condition formula:

[0035]

[0036] Among them, d safe is the minimum safety distance, unit: m, d UAV-obs is the distance between the UAV and the obstacle, in meters, UAV means UAV, obs means obstacle, x UAV is the coordinate of the drone in the x-axis direction, unit: m, x obs is the coordinate of the obstacle in the x direction, unit: m, y UAV is the coordinate of the drone in the y-axis direction, unit: m, y obs is the coordinate of the obstacle in the y direction, unit: z UAV is the coordinate of the drone in the z-axis direction, unit: m, z obs The coordinate of the obstacle in the z direction, unit: m.

[0037] Preferably, the microprocessor is used to determine the urgency of the monitoring point of the drone according to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the drone, the flight constraint parameters and the inspection influencing factors. The formula is:

[0038]

[0039] in, is the monitoring point F n The urgency of n is the monitoring point F n The weighting coefficient of .

[0040] Preferably, the settlement detection module is connected to the route planning unit and the mobile terminal of the management personnel for communication, and is used to collect the calibrated height data of the monitoring point after receiving the signal of arriving at the monitoring point position, compare the calibrated height of the monitoring point with the benchmark calibrated height, obtain the height difference by subtracting the calibrated height of the monitoring point from the benchmark calibrated height, take the absolute value of the height difference as the height deviation value, and then after obtaining the height deviation value, establish a data set, calculate the mean height deviation in the data set, and evaluate the overall settlement condition of the foundation in combination with the mean height deviation and the standard deviation of the height deviation.

[0041] If the mean height deviation is less than or equal to the deviation threshold, and the standard deviation of the height deviation is less than or equal to the standard deviation threshold, the overall condition of the foundation is assessed to be excellent, with no settlement or warping problems, and there is no need to manage the foundation.

[0042] If the height deviation mean is greater than the deviation threshold, the overall condition of the foundation is assessed to be poor, and the settlement information of this monitoring point will be sent to the mobile terminal of the manager.

[0043] Preferably, the drone is communicatively connected with the settlement detection module to record the physical ID of the State Grid at each monitoring point it stops at, and to send the physical ID information of the State Grid at each monitoring point it stops at to the settlement detection module after reaching the final target point.

[0044] The route planning unit sends the total number of monitoring points to the settlement detection module.

[0045] The settlement detection module is used to receive the total number of monitoring points and the physical ID information of the State Grid of each monitoring point stopped at each time. When the number of detected monitoring points is less than the total number of monitoring points, the State Grid physical ID information of each monitoring point stopped at each time is sent to the mobile terminal of the manager, and the mobile terminal of the manager is controlled to issue an alarm.

[0046] Preferably, the formula for calculating the mean value of the height deviation in the data set by the settlement detection module is:

[0047]

[0048] In the formula, μ is the mean value of height deviation, σ is the standard deviation of height deviation, N is the number of monitoring points, g is n is the height deviation value of the nth monitoring point.

[0049] The beneficial effects of the present invention are:

[0050] 1. The inspection route planning module generates inspection routes based on the map model of the mountainous area of ​​the distribution network. The specific algorithm of the route planning unit is used to determine reasonable track points. It can effectively plan inspection routes that adapt to the complex terrain of the mountainous area, improve inspection efficiency, and reduce unnecessary flight distances and time consumption.

[0051] 2. Equipment such as lidar and cameras can monitor the drone's surrounding environment in real time, and timely re-plan the inspection route when encountering sudden obstacles to avoid drone collisions, thereby ensuring the safety of the drone during the inspection process. At the same time, combined with the microprocessor's consideration of the drone's operating parameters and constraints, the rationality and effectiveness of the re-planned route are ensured.

[0052] 3. The settlement detection module can collect the calibration height data of the monitoring point and compare it with the benchmark calibration height to accurately evaluate the settlement of the foundation. At the same time, through parameters such as the height deviation mean and height deviation standard deviation in the data set, the present invention can comprehensively evaluate the overall settlement of the foundation and provide decision support for managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments;

[0054] Figure 1 This is a schematic diagram of a block diagram of a power grid intelligent inspection management system based on physical ID according to the present invention; DETAILED DESCRIPTION

[0055] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 The present invention provides a power grid intelligent inspection management system based on physical ID, including: an inspection path planning module, a drone, an obstacle avoidance module, a settlement detection module, and a state grid physical ID.

[0057] The inspection route planning module is set on the UAV, which generates the inspection route based on the map model of the mountainous area of ​​the distribution network, and then sends the inspection route information to the UAV and the obstacle avoidance module.

[0058] All monitoring points in the power grid are marked with the State Grid physical ID.

[0059] The UAV is communicatively connected with the inspection path planning module, and is used for performing inspections according to the inspection route information after receiving the inspection route information. When receiving the re-planned inspection route information, the UAV performs inspections according to the re-planned inspection route information. After the UAV arrives at the monitoring point position, it generates an arrival monitoring point position signal, and then sends the arrival monitoring point position signal to the settlement detection module.

[0060] The obstacle avoidance module is set on the UAV and is connected to the inspection path planning module and the UAV communication. It is used to receive the inspection route information and detect in real time whether there are obstacles ahead. When there are sudden obstacles ahead, the inspection route is re-planned and the re-planned inspection route information is sent to the UAV.

[0061] The settlement detection module is set on the drone and communicated with the drone and the manager's mobile terminal. It is used to start detecting the settlement situation after receiving the signal of arriving at the monitoring point location, and then send the settlement situation to the manager's mobile terminal.

[0062] In this embodiment, the inspection route planning module includes a map model database and a route planning unit.

[0063] The map model database is used to store the map model of the mountainous area of ​​the distribution network.

[0064] The route planning unit is communicated with the map model database, the UAV, and the obstacle avoidance module. The route planning unit uses a small processor to plan the inspection route through the map model information of the mountainous area of ​​the distribution network, the starting point of the track planning, several monitoring points, and the final target point, so that the UAV can inspect the area according to the planned inspection route. It is used to call the map model information of the mountainous area of ​​the distribution network, determine the starting point, several monitoring points, the final target point and the track planning space of the track planning, determine a random track point in the track planning space, search through the set track point set, find the track point in the track point set that is closest to the random track point, calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors, adjust the probability of the new track point according to the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors. By reducing unnecessary flights and turns, the UAV can reach the target area faster and complete the inspection task. According to the probability of the adjusted new track point, a new track point is selected from the preset new track points, the new track point is added to the track point set, and an inspection route is generated until the generated inspection route reaches the final target point, and then the inspection route information is sent to the UAV and obstacle avoidance module.

[0065] In this embodiment, the route planning unit uses a small processor to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors, and adjusts the probability of the new track point according to the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors. The method for calculating the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors includes:

[0066] Use the following calculation formula to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors:

[0067] E new =(x′ new -x′ now , y′ new -y′ now , z′ new -z′ now ).

[0068] E tar =(x′ tar -x′ now , y′ tar -y′ now , z′ tar -z′ now ).

[0069]

[0070] Among them, E new is the direction vector between the nearest track point and the new track point, x now =(x′ now , y′ now , z′ now ) is the coordinate of the nearest track point, unit: cm, x new =(x′ new , y′ new , z′ new ) is the coordinate of the new track point, unit: cm. tar is the direction vector between the nearest track point and the final target point, x tar =(x′ tar , y′ tar , z′ tar ) is the coordinate of the final target point, θ is the angle between the two direction vectors, unit: degree.

[0071] For example: the coordinates of the nearest track point are: (0.2, 0.3, 0.15), the coordinates of the new track point are: (0.34, 0.3, 0.22), the coordinates of the final target point are: (16, -22, 8), and the angle θ between the two direction vectors is calculated to be: 51.75 degrees.

[0072] In this embodiment, the obstacle avoidance module includes a laser radar, a camera and a microprocessor.

[0073] The route planning unit is used to generate the total distance of the inspection route after generating the inspection route, and then send the total distance of the inspection route to the microprocessor.

[0074] The drone is used to send the distance of the inspection route that has been inspected to the microprocessor.

[0075] LiDAR is used to measure the distance between the drone and its surroundings, and then sends the distance information between the drone and its surroundings to a microprocessor.

[0076] The camera is used to capture the image information in front and then send the image information in front to the microprocessor.

[0077] The microprocessor is connected to the route planning unit, drone, lidar, camera, and mobile terminal of the manager to construct a three-dimensional map of the drone's surrounding environment after receiving the inspection route information, the distance information between the drone and the surrounding environment, and the image information in front. It can detect in real time whether there are obstacles on the inspection route. Through real-time environmental perception, intelligent route planning and dynamic adjustment, it can significantly improve the accuracy and safety of drone inspections, reduce flight accidents caused by obstacles, and ensure that key monitoring points are inspected in a timely and effective manner. When there is a sudden obstacle in front, the aircraft constraint parameters of the drone are determined according to the operating parameters of the drone, the inspection requirements are obtained, and the operating parameters and flight constraint parameters of the drone are determined. The inspection requirements include at least the monitoring points and the target positions of the monitoring points. According to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the UAV, the flight constraint parameters and the inspection influencing factors, the urgency of the UAV's monitoring points and the inspection route information are determined, and the inspection route is re-planned to ensure that the UAV safely avoids obstacles and continues to perform the mission. The distance of the re-planned inspection route is generated, and then the delay time for the UAV to reach the final target point is calculated based on the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the UAV, and the distance of the re-planned inspection route. The re-planned inspection route information is then sent to the UAV, and the delay time for the UAV to reach the final target point is sent to the manager's mobile terminal.

[0078] In this embodiment, the microprocessor is used to calculate the delay time of the drone arriving at the final target point based on the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the drone, and the distance of the re-planned inspection route, which helps managers to understand the progress of the task in a timely manner and make corresponding adjustments to ensure the smooth progress of the inspection task. The expression for the delay time of the drone arriving at the final target point is:

[0079]

[0080] Among them, t represents the delay time of the UAV to reach the final target point, unit: s, V represents the speed of the UAV, unit: m / s, l s Indicates the distance of the re-planned inspection route, unit: m, l yIndicates the total distance of the original inspection route, unit: m, l n Indicates the inspection route distance that has been inspected, unit: m.

[0081] For example, if the speed V of the drone is 3 m / s, the distance of the re-planned inspection route is l s =5456m, the total distance of the original inspection route is l y =12860m, ​​the inspection route distance that has been inspected is l n The distance is 6639m, and the time t that the UAV needs to reach the final target point is 256s.

[0082] For example, if the speed V of the drone is 3 m / s, the distance of the re-planned inspection route is l s =12686m, the total distance of the original inspection route is l y =12860m, ​​the inspection route distance that has been inspected is l n The calculated delay time t for the drone to reach the final target point is: 2155s.

[0083] In this embodiment, the microprocessor is used to determine the monitoring point and the target position of the monitoring point according to the monitoring point coordinate expression to obtain the inspection demand. The inspection demand at least includes the monitoring point and the target position of the monitoring point. The monitoring point coordinate expression is:

[0084]

[0085] Among them, F n is the nth monitoring point, F n ∈[(x min , x max ), (y min ,y max )(z min , z max )], N is the total number of monitoring points, is the coordinate of the nth monitoring point on the x-axis, is the coordinate of the nth monitoring point on the y-axis, is the coordinate of the nth monitoring point on the z axis, x min ,y min , z min , x max ,y max , z max They are respectively the minimum and maximum area values ​​in the three-dimensional coordinates within the inspection area.

[0086] The aircraft constraint parameters of the UAV are determined according to the operating parameters of the UAV. The operating parameters of the UAV at least include the energy consumption and endurance of the UAV. The aircraft constraint parameters at least include the flight altitude, the distance from other objects, etc., including: constraining the minimum safe distance between the UAV and obstacles, and obtaining the constraint condition formula:

[0087]

[0088] Among them, d safe is the minimum safety distance, unit: m, d UAV-obs is the distance between the UAV and the obstacle, in meters, UAV means UAV, obs means obstacle, x UAV is the coordinate of the drone in the x-axis direction, unit: m, x obs is the coordinate of the obstacle in the x direction, unit: m, y UAV is the coordinate of the drone in the y-axis direction, unit: m, y obs is the coordinate of the obstacle in the y direction, unit: z UAV is the coordinate of the drone in the z-axis direction, unit: m, z obs The coordinate of the obstacle in the z direction, unit: m.

[0089] For example: When the preset minimum safety distance d safe =0.3m, the coordinate x of the drone in the x-axis direction UAV is: 0m, the coordinate x of the obstacle in the x direction obs : 0.5m, the coordinate y of the drone in the y-axis direction UAW is: 0m, the coordinate y of the obstacle in the y direction obs =0.6m, which is the coordinate z of the drone in the z-axis direction UAV : 0m, the coordinate z of the obstacle in the z direction obs =0.4m, and the distance d between the drone and the obstacle is calculated UAV-obs It is 0.87m, which is greater than the preset minimum safety distance and can pass normally.

[0090] In this embodiment, the microprocessor is used to determine the urgency of the monitoring point of the drone according to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the drone, the flight constraint parameters and the inspection influencing factors, which at least include the terrain and weather of the inspection area. The formula is:

[0091]

[0092] in, is the monitoring point F n The urgency of n is the monitoring point Fn The weighting coefficient of .

[0093] For example: after the last inspection, it was found that the monitoring point had a slight warping or collapse, but it did not cause any problems, so the weighting coefficient of the monitoring point was increased during this monitoring, so that the monitoring point was tested first. If the weighting coefficients of all monitoring points are 0 at the beginning, when a slight warping or collapse is found in the last inspection, the weighting coefficient of the monitoring point is increased by 1, so that the monitoring point is tested first during this inspection.

[0094] In this embodiment, the settlement detection module is connected to the route planning unit and the mobile terminal of the management personnel. The settlement detection module uses a small processor to compare the height deviation mean with the height deviation threshold to determine whether the monitoring point needs to be managed. After receiving the arrival signal of the monitoring point position, the module collects the calibrated height data of the monitoring point, compares the calibrated height of the monitoring point with the benchmark calibrated height, and obtains the height difference by subtracting the calibrated height of the monitoring point from the benchmark calibrated height. The absolute value of the height difference is taken as the height deviation value. After obtaining the height deviation value, a data set is established, and the height deviation mean in the data set is calculated. The overall settlement condition of the basis is evaluated in combination with the height deviation mean and the height deviation standard deviation.

[0095] If the mean height deviation is less than or equal to the deviation threshold, and the standard deviation of the height deviation is less than or equal to the standard deviation threshold, the overall condition of the foundation is assessed to be excellent, with no settlement or warping problems, and there is no need to manage the foundation.

[0096] If the height deviation mean is greater than the deviation threshold, the overall condition of the foundation is assessed to be poor, and the settlement information of this monitoring point will be sent to the mobile terminal of the manager.

[0097] In this embodiment, the drone is communicatively connected with the settlement detection module to record the physical ID of the State Grid at each monitoring point it stops at, and to send the physical ID information of the State Grid at each monitoring point it stops at to the settlement detection module after reaching the final target point.

[0098] The route planning unit sends the total number of monitoring points to the settlement detection module.

[0099] The settlement detection module is used to receive the total number of monitoring points and the physical ID information of the national grid of each monitoring point stopped at each time, and send the physical ID information of the national grid of each monitoring point stopped at each time to the mobile terminal of the manager when the number of detected monitoring points is less than the total number of monitoring points, and control the mobile terminal of the manager to issue an alarm. After receiving the alarm, the manager re-detects the monitoring points that have not been checked.

[0100] In this embodiment, the formula for calculating the mean value of the height deviation in the data set by the settlement detection module is:

[0101]

[0102] In the formula, μ is the mean value of height deviation, σ is the standard deviation of height deviation, N is the number of monitoring points, g is n is the height deviation value of the nth monitoring point.

[0103] For example: the first height deviation value is 0.54, the second height deviation value is 0.74, the third height deviation value is 0.44, the fourth height deviation value is 0.46, and the fifth height deviation value is 0.53. The calculated height deviation standard deviation σ is: 0.736.

[0104] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A power grid intelligent inspection and management system based on physical ID, characterized in that: include: Inspection path planning module, drone, obstacle avoidance module, and settlement detection module; The inspection route planning module is arranged on the UAV, generates an inspection route based on a map model of the mountainous area of ​​the distribution network, and then sends the inspection route information to the UAV and the obstacle avoidance module; Monitoring points in the power grid are marked with the State Grid physical ID; The UAV is in communication connection with the inspection route planning module, and is used for the UAV to perform inspection according to the inspection route information after receiving the inspection route information, and is used for performing inspection according to the re-planned inspection route information when receiving the re-planned inspection route information. After the UAV reaches the monitoring point position, it generates an arrival monitoring point position signal, and then sends the arrival monitoring point position signal to the settlement detection module; The obstacle avoidance module is arranged on the drone, and is in communication connection with the inspection path planning module and the drone, and is used to receive the inspection route information, and detect in real time whether there are obstacles ahead, and when there are sudden obstacles ahead, re-plan the inspection route, and then send the re-planned inspection route information to the drone; The settlement detection module is arranged on the drone and is connected to the drone and the mobile terminal of the manager for communication. After receiving a signal indicating that the location of the monitoring point has been reached, the module starts to detect the settlement and then sends the settlement to the mobile terminal of the manager.

2. According to claim 1, a physical ID-based intelligent inspection and management system for power grids is characterized by: The inspection route planning module includes a map model database and a route planning unit; The map model database is used to store the map model of the mountainous area of ​​the distribution network; The route planning unit is communicatively connected with the map model database, the UAV, and the obstacle avoidance module, and is used to call the map model information of the mountainous area of ​​the distribution network, determine the starting point, several monitoring points, the final target point, and the track planning space of the track planning, determine a random track point in the track planning space, search through the set track point set, find the track point in the track point set that is closest to the random track point, calculate the direction vector between the closest track point and the new track point, the direction vector between the closest track point and the final target point, and the angle between the two direction vectors, adjust the probability of the new track point according to the direction vector between the closest track point and the final target point, and the angle between the two direction vectors; select a new track point from the preset new track points according to the adjusted probability of the new track point, add the new track point to the track point set, and generate an inspection route until the generated inspection route reaches the final target point, and then send the inspection route information to the UAV and the obstacle avoidance module.

3. According to claim 2, a physical ID-based intelligent power grid inspection and management system is characterized by: The route planning unit is used to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors, including: Use the following calculation formula to calculate the direction vector between the nearest track point and the new track point, the direction vector between the nearest track point and the final target point, and the angle between the two direction vectors: E new =(x′ new -x′ now ,y′ new -y′ now ,z′ new -z′ now ); E tar =(x′ tar -x′ now ,y′ tar -y′ now ,z′ tar -z′ now ); Among them, E new is the direction vector between the nearest track point and the new track point, x now =(x′ now , y′ now , z′ now ) is the coordinate of the nearest track point, unit: cm, x new =(x′ new , y′ new , z′ new ) is the coordinate of the new track point, unit: cm; E tar is the direction vector between the nearest track point and the final target point, x tar =(x′ tar , y′ tar , z′ tar ) is the coordinate of the final target point, θ is the angle between the two direction vectors, unit: degree.

4. According to claim 3, a physical ID-based intelligent inspection and management system for power grids is characterized by: The obstacle avoidance module includes a laser radar, a camera and a microprocessor; The route planning unit is used to generate a total distance of the inspection route after generating the inspection route, and then send the total distance of the inspection route to the microprocessor; The drone is used to send the distance of the inspection route that has been inspected to the microprocessor; The laser radar is used to measure the distance between the UAV and the surrounding environment, and then send the distance information between the UAV and the surrounding environment to the microprocessor; The camera is used to capture the image information in front, and then send the image information in front to the microprocessor; The microprocessor is communicatively connected with the route planning unit, the drone, the laser radar, the camera, and the mobile terminal of the manager, and is used to construct a three-dimensional map of the drone's surrounding environment after receiving the inspection route information, the distance information between the drone and the surrounding environment, and the image information in front, and to detect in real time whether there are obstacles on the inspection route. When there are sudden obstacles in front, the aircraft constraint parameters of the drone are determined according to the operating parameters of the drone, the inspection requirements are obtained, and the operating parameters and flight constraint parameters of the drone are determined. The inspection requirements at least include monitoring points and target positions of monitoring points. According to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the drone, the flight constraint parameters, and the inspection influencing factors, the urgency of the monitoring points and the inspection route information of the drone are determined, the inspection route is re-planned, and the distance of the re-planned inspection route is generated. Then, the delay time for the drone to reach the final target point is calculated according to the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the drone, and the distance of the re-planned inspection route, and then the re-planned inspection route information is sent to the drone, and the delay time for the drone to reach the final target point is sent to the mobile terminal of the manager.

5. According to claim 4, a power grid intelligent inspection and management system based on physical ID is characterized by: The microprocessor is used to calculate the delay time of the drone arriving at the final target point according to the total distance of the original inspection route, the distance of the inspection route that has been inspected, the speed of the drone, and the distance of the re-planned inspection route. The expression of the delay time of the drone arriving at the final target point is: Among them, t represents the delay time of the drone reaching the final target point, unit: s, V represents the speed of the drone, unit: m / s, l s Indicates the distance of the re-planned inspection route, unit: m, l y Indicates the total distance of the original inspection route, unit: m, l n Indicates the inspection route distance that has been inspected, unit: m.

6. According to claim 5, a physical ID-based intelligent inspection and management system for power grids, characterized in that: The microprocessor is used to determine the monitoring point and the target position of the monitoring point according to the monitoring point coordinate expression to obtain the inspection demand. The monitoring point coordinate expression is: Among them, F n is the nth monitoring point, F n ∈[(x min , x max ), (y min ,y max )(z min , z max )], N is the total number of monitoring points, is the coordinate of the nth monitoring point on the x-axis, is the coordinate of the nth monitoring point on the y-axis, is the coordinate of the nth monitoring point on the z axis, x min ,y min , z min , x max ,y max , z max They are the minimum and maximum area values ​​in the three-dimensional coordinates within the inspection area respectively; Determining the aircraft constraint parameters of the UAV according to the operating parameters of the UAV includes: constraining the minimum safe distance between the UAV and the obstacle to obtain the constraint condition formula: Among them, d safe is the minimum safety distance, unit: m, d UAV-obs is the distance between the UAV and the obstacle, in meters, UAV represents the UAV, obs represents the obstacle, x UAV is the coordinate of the drone in the x-axis direction, unit: m, x obs is the coordinate of the obstacle in the x direction, unit: m, y UAV is the coordinate of the drone in the y-axis direction, unit: m, y obs is the coordinate of the obstacle in the y direction, unit: z UAV is the coordinate of the drone in the z-axis direction, unit: m, z obs The coordinate of the obstacle in the z direction, unit: m.

7. According to claim 5, a physical ID-based intelligent inspection and management system for power grids is characterized by: The microprocessor is used to determine the urgency of the monitoring point of the drone according to the three-dimensional map of the inspection area, the inspection requirements, the operating parameters of the drone, the flight constraint parameters and the inspection influencing factors. The formula is: in, is the monitoring point F n The urgency of n is the monitoring point F n The weighting coefficient of .

8. According to claim 4, a physical ID-based intelligent inspection and management system for power grids is characterized by: The settlement detection module is connected to the route planning unit and the mobile terminal of the management personnel for communication, and is used to collect the calibration height data of the monitoring point after receiving the arrival signal of the monitoring point position, compare the calibration height of the monitoring point with the reference calibration height, obtain the height difference by subtracting the calibration height of the monitoring point from the reference calibration height, take the absolute value of the height difference as the height deviation value, and then after obtaining the height deviation value, establish a data set, calculate the height deviation mean in the data set, and evaluate the overall settlement condition of the foundation in combination with the height deviation mean and the height deviation standard deviation; If the height deviation mean is less than or equal to the deviation threshold, and the height deviation standard deviation is less than or equal to the standard deviation threshold, the overall condition of the foundation is evaluated to be excellent, with no settlement or warping problems, and no foundation management is required; If the height deviation mean is greater than the deviation threshold, the overall condition of the foundation is assessed to be poor, and the settlement information of this monitoring point will be sent to the mobile terminal of the manager.

9. The intelligent inspection and management system for power grid based on physical ID according to claim 8, characterized in that: The drone is in communication connection with the settlement detection module, and is used to record the physical ID of the state grid of the monitoring point each time it stops, and is used to send the physical ID information of the state grid of the monitoring point each time it stops to the settlement detection module after reaching the final target point; The route planning unit sends the total number of monitoring points to the settlement detection module; The settlement detection module is used to send the physical ID information of the State Grid at each monitoring point stopped at each time to the mobile terminal of the manager after receiving the total number of monitoring points and the physical ID information of the State Grid at each monitoring point stopped at each time, and control the mobile terminal of the manager to issue an alarm when the number of detected monitoring points is less than the total number of monitoring points.

10. The power grid intelligent inspection and management system based on physical ID according to claim 8, characterized in that: The formula for calculating the mean value of the height deviation in the data set by the settlement detection module is: In the formula, μ is the mean value of height deviation, σ is the standard deviation of height deviation, N is the number of monitoring points, g is n is the height deviation value of the nth monitoring point.

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