GIS-based unmanned aerial vehicle inspection network planning method and system

Abstract

The invention relates to the field of unmanned aerial vehicles, in particular to a GIS-based unmanned aerial vehicle inspection network planning method and system, and the system comprises a GIS data processing module, a signal prediction module, a relay network generation module, a path planning module and a monitoring module. The GIS data processing module is used for loading and analyzing various data of an inspection area, the signal prediction module is used for predicting communication signal intensity distribution of unmanned aerial vehicles in the inspection area, the relay network generation module is used for obtaining a deployment scheme of relay unmanned aerial vehicles, and the path planning module is used for generating flight paths of the unmanned aerial vehicles. The monitoring module is used for monitoring the state of each unmanned aerial vehicle in real time. According to the scheme, by setting the signal strength prediction index, prediction of the signal strength of each part of the inspection area is facilitated, and setting of the network relay point location according to the signal strength index is facilitated, so that the stability of the signal sent by the unmanned aerial vehicle in the inspection process is ensured, and the reliability degree of unmanned aerial vehicle inspection is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles (UAVs), and particularly to a method and system for UAV inspection network planning based on GIS. Background Art

[0002] UAV inspection technology has been widely applied in many fields in recent years. Especially in industries such as power, energy, and transportation, UAVs, with their characteristics of high efficiency, flexibility, and high safety, have gradually replaced traditional manual inspection methods.

[0003] For example, the prior art of CN113313852A discloses a UAV inspection system. The system includes: a central station, a management background, a third-party system, and UAVs; the central station is electrically connected to the management background, the third-party system, and the UAVs respectively; the central station is used to obtain the basic data of the objects to be inspected from the third-party system, generate inspection tasks based on the basic data, send the inspection tasks to the UAVs, and control the UAVs to execute the inspection tasks; the management background is used to create permission accounts for the central station, authenticate the target accounts accessing the central station, establish the association relationship between the UAVs and the inspection devices, and manage the inventory of the inspection devices; the third-party system is used to store the basic data of the objects to be inspected in the power system; the UAVs are used to receive the inspection tasks and execute the inspection tasks based on the flight instructions issued by the central station to complete the inspection of the objects to be inspected.

[0004] Another typical prior art of CN111653000A discloses a UAV inspection system. The system includes: a UAV body, a photographing device, a radio frequency identification device, a wireless communication module, and a processor; the photographing device, the radio frequency identification device, and the wireless communication module are all connected to the processor, and the photographing device, the radio frequency identification device, the wireless communication module, and the processor are all mounted on the UAV body; the photographing device is used to photograph the part to be inspected under the control of the processor to obtain an inspection image; an electronic tag is set at the part to be inspected, and the information of the part to be inspected is stored in the electronic tag; the radio frequency identification device is used to identify the electronic tag under the control of the processor to obtain the information of the part to be inspected as inspection information; the wireless communication module is used to send the inspection image and the inspection information to the ground terminal.

[0005] Let's look at the existing drone inspection system disclosed in the prior art such as CN107656542A. The drone inspection system includes multiple position transmitters, drones, and charging stations. Each position transmitter is adapted to be installed on a meter or power equipment for transmitting a position signal and an equipment identifier, and one meter or power equipment corresponds to one equipment identifier; the drone is used to receive the position signal and fly to the meter or power equipment corresponding to the equipment identifier, and collect the dial image of the meter or detect the temperature of the power equipment; the charging station includes a charging platform for carrying the drone, a charging sensor for detecting the landing signal of the drone, and a charging module for charging the drone.

[0006] In traditional drone inspections in remote mountainous areas, data disconnection often occurs due to insufficient signal coverage. The single - machine operation efficiency is low and it is difficult to cover a large - range area. To solve the problems commonly existing in this field, the present invention is made. Summary of the Invention

[0007] The object of the present invention is to propose a GIS - based drone inspection network planning method and system for the current deficiencies.

[0008] To overcome the deficiencies of the prior art, the present invention adopts the following technical solutions: A GIS - based drone inspection network planning system includes a GIS data processing module, a signal prediction module, a relay network generation module, a path planning module, and a monitoring module; the GIS data processing module is used to load and analyze various data of the inspection area, the signal prediction module is used to predict the communication signal strength distribution of the drone in the inspection area according to the data obtained by the GIS data processing module, the relay network generation module is used to obtain the deployment plan of the relay drones according to the prediction result of the signal prediction module, the path planning module is used to generate the flight paths of each drone according to the data obtained by the GIS data processing module and the deployment plan generated by the relay network generation module, and the monitoring module is used to monitor the status of each drone in real time.

[0009] Furthermore, the GIS data processing module includes a multi - source data acquisition unit, a data cleaning and fusion unit, and a three - dimensional geographical modeling unit. The multi - source data acquisition unit is used to communicate with external devices and obtain various data of the inspection area. The data cleaning and fusion unit is used to pre - process the acquisition data of the multi - source data acquisition unit. The three - dimensional geographical modeling unit is used to generate a model of the inspection area according to the pre - processed data.

[0010] Further, the signal prediction module includes a signal strength prediction unit and a heat map generation unit. The signal strength prediction unit is used to calculate the signal strength prediction indexes of each position in the inspection area according to the data obtained by the GIS data processing module, and the heat map generation unit is used to generate a signal strength heat map of the inspection area according to the obtained signal strength prediction indexes.

[0011] Further, the relay network generation module includes a relay node acquisition unit and a topology generation unit. The relay node acquisition unit is used to obtain the positions of relay points according to the generated heat map, and the topology generation unit is used to construct multi-hop communication links according to the nodes obtained by the relay node acquisition unit, so as to form a mesh network topology.

[0012] Further, the path planning module includes an initial path generation unit, a path adjustment unit and an obstacle avoidance unit. The initial path generation unit is used to generate an initial inspection path of the UAV according to the inspection task of the UAV. The path adjustment unit is used to adjust the initial inspection path according to the deployment scheme of the relay UAVs, so as to generate the actual inspection path of the UAV. The obstacle avoidance unit is used to control the UAV to perform emergency obstacle avoidance during the inspection process of the UAV according to the actual inspection path.

[0013] A GIS-based UAV inspection network planning method, which includes the following steps: S1, the GIS data processing module loads the data of the inspection area; S2, the signal prediction module obtains the signal strength prediction indexes of each coordinate in the inspection area and generates a corresponding heat map; S3, the relay network generation module generates the deployment schemes of each relay UAV according to the prediction results of the signal prediction module; S4, the path planning module generates the flight paths of each UAV according to the data obtained by the GIS data processing module and the deployment schemes generated by the relay network generation module; S5, the monitoring module monitors each UAV in real time during the flight of the UAV.

[0014] Further, the path planning module generates the flight paths of each UAV, including the following steps: S41, the initial path generation unit generates an initial inspection path of the inspection UAV according to the inspection task of the UAV, and at the same time generates a moving path of the relay UAV according to the relay points; S42, the path adjustment unit adjusts the initial inspection path according to the deployment scheme of the relay UAVs to obtain the actual inspection path of the UAV; S43. The relay UAV moves according to its corresponding movement path. After the relay UAV arrives at the position, the inspection UAV conducts inspections according to the actual inspection path. S44. The obstacle avoidance unit conducts emergency obstacle avoidance for obstacles during the inspection process of the inspection UAV.

[0015] The beneficial effects achieved by the present invention are as follows: 1. By setting the signal strength prediction index, it is beneficial to predict the signal strength of each part of the inspection area, and it is beneficial to set the network relay points according to the signal strength index, thereby ensuring the stability of the signals sent by the UAV during the inspection process and improving the reliability of UAV inspections.

[0016] 2. By considering the terrain, obstacles, and the distance from the signal station to calculate the signal strength prediction index, it is beneficial to predict the signal strength by considering multiple factors, improving the accuracy of prediction, and effectively utilizing the corresponding tools of the GIS data processing module to reduce the calculation burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention can be further understood from the following description in conjunction with the drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0018] Figure 1 It is a schematic structural diagram of the present invention.

[0019] Figure 2 It is a working flow chart of the present invention.

[0020] Figure 3 It is a flow chart for the path planning module of the present invention to generate the flight paths of each UAV.

[0021] Figure 4 It is a relationship diagram between the terrain roughness, terrain slope, and path loss parameters of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to the actual dimensions, and this is stated in advance. The following embodiments will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0023] Embodiment 1: According to Figure 1 、Figure 2 , Figure 3 and Figure 4 , this embodiment provides a UAV inspection network planning system based on GIS, including a GIS data processing module, a signal prediction module, a relay network generation module, a path planning module, and a monitoring module; the GIS data processing module is used to load and analyze various data of the inspection area, the signal prediction module is used to predict the communication signal strength distribution of the UAV in the inspection area according to the data obtained by the GIS data processing module, the relay network generation module is used to obtain the deployment plan of the relay UAV according to the prediction result of the signal prediction module, the path planning module is used to generate the flight paths of each UAV according to the data obtained by the GIS data processing module and the deployment plan generated by the relay network generation module, and the monitoring module is used to monitor the status of each UAV in real time.

[0024] Furthermore, the GIS data processing module includes a multi-source data acquisition unit, a data cleaning and fusion unit, and a 3D geographical modeling unit. The multi-source data acquisition unit is used to communicate with external devices and obtain various data of the inspection area. The data cleaning and fusion unit is used to preprocess the acquisition data of the multi-source data acquisition unit. The 3D geographical modeling unit is used to generate a model of the inspection area according to the preprocessed data.

[0025] Specifically, the preprocessing includes but is not limited to noise reduction and format conversion. The 3D geographical modeling unit realizes modeling through existing technologies such as TIN model, regular grid model, or BIM model.

[0026] Furthermore, the signal prediction module includes a signal strength prediction unit and a heat map generation unit. The signal strength prediction unit is used to calculate the signal strength prediction index of each position in the inspection area according to the data obtained by the GIS data processing module. The heat map generation unit is used to generate a signal strength heat map of the inspection area according to the obtained signal strength prediction index.

[0027] Specifically, the heat map generation unit obtains the heat map by normalizing the signal strength prediction index and mapping it to the grid unit of the GIS 3D model.

[0028] Furthermore, the relay network generation module includes a relay node acquisition unit and a topology generation unit. The relay node acquisition unit is used to obtain the positions of relay points according to the generated heat map. The topology generation unit is used to construct multi-hop communication links according to the nodes obtained by the relay node acquisition unit, so as to form a mesh network topology.

[0029] Specifically, the relay network generation module uses a reinforcement learning algorithm to maximize the network coverage rate and minimize the number of relay UAVs, thereby calculating the optimal relay node positions as relay points; the topology generation unit constructs multi-hop communication links through the CDS algorithm.

[0030] Furthermore, the path planning module includes an initial path generation unit, a path adjustment unit, and an obstacle avoidance unit. The initial path generation unit is used to generate the initial inspection path of the UAV according to the inspection task of the UAV. The path adjustment unit is used to adjust the initial inspection path according to the deployment plan of the relay UAVs, thereby generating the actual inspection path of the UAV. The obstacle avoidance unit is used to control the UAV to perform emergency obstacle avoidance during the inspection process of the UAV according to the actual inspection path.

[0031] Specifically, the path adjustment unit uses the constraint rule CP algorithm to constrain the initial inspection path with the mesh network topology corresponding to the deployment plan of the relay UAVs as the spatial constraint condition, and solves the combinatorial explosion problem under multi-constraint coupling through MPC rolling optimization, thereby obtaining the actual inspection path; the obstacle avoidance unit controls the UAV to perform emergency obstacle avoidance through the obstacle avoidance algorithm.

[0032] A method for UAV inspection network planning based on GIS, the method includes the following steps: S1, the GIS data processing module loads various data of the inspection area; S2, the signal prediction module obtains the signal strength prediction indexes of each coordinate in the inspection area and generates the corresponding heat map; Specifically, the signal strength prediction index of a certain coordinate (x, y, z) can be calculated according to the following formula: ;

[0033] ;

[0034] ;

[0035] Wherein, is the coordinate The signal strength prediction index of the point is an index used to characterize the predicted signal strength received by the drone at the current position. The larger the index is, the greater the predicted signal strength received by the drone at this position is. S is the maximum signal strength that the drone can transmit. The maximum signal strength can be obtained by referring to the antenna manual or technical specification, preferably the antenna gain. DIS is the distance between the point and the coordinates of the nearest signal station to the point in the three-dimensional coordinates (WGS-84 coordinate system) obtained by the GIS data processing module. dis is the communication distance corresponding to the maximum signal strength of the drone. k is the path loss parameter. z is the coordinate height of the point. is the average ground height of the inspection area, is the maximum ground height of the inspection area, The ground height of the inspection area is the minimum; is 0.001; is the signal loss parameter; is the result of normalizing the terrain roughness of the point to 0 to 1. is the result of normalizing the terrain slope of the point to 0 to 1, slope is the normalized result of the terrain slope mean value of the area corresponding to the point, preferably the normalized result of the slope mean value within the maximum signal intensity radiation range of the drone at the point, the above normalization is performed by using the minimum-maximum normalization method for the corresponding data in the GIS data processing module, a is the roughness weight, b is the slope weight, and the weight is set by technicians in this field according to the ground type of the inspection area. When the terrain is plain (simple terrain, small slope effect), a=1 and b=0.7, when the terrain is hilly (medium roughness, slope effect is significant), a=2 and b=1.5, when the terrain is mountainous (high roughness, steep slope needs to be corrected), a=3 and b=2.5; like Figure 4 As shown, Figure 4 This is the relationship between terrain roughness, terrain slope and path loss parameters when the terrain is assumed to be plain and the slope is 0.5; X is the number of obstacles encountered on the line connecting the coordinates of the point and the signal station. When the number of obstacles on the line is 0, ZAW is set to 0. is the estimated signal loss value corresponding to the obstacle type of the x-th obstacle on the line connecting the point and the signal station. The estimated signal loss value can be obtained by querying existing data. For example, the penetration loss of a 2.4GHz frequency band signal through a concrete wall is about 15~20dB, and the drone signal frequency band is a 2.4GHz frequency band signal. If the x-th obstacle is concrete, then The average value within the corresponding range can be taken, which is 17.5 dB. When there is an obstacle without a corresponding signal loss value, the signal loss value corresponding to the obstacle closest to this obstacle is used as the signal loss value of this obstacle. When the closest obstacle also has no corresponding signal loss value, the average value of the signal loss values of the obstacles obtained in the past is taken as the signal loss value of this obstacle.

[0036] Specifically, the terrain roughness can be obtained by using the "Surface Roughness" tool of the GIS data processing module. The corresponding program is as follows: # Check whether the extension module is activated arcpy.CheckExtension("Spatial") # Set the 3x3 neighborhood range to calculate the standard deviation of elevation roughness_std = arcpy.sa.FocalStatistics( in_raster="DEM", neighborhood="Rectangle 3 3 CELL", statistics_type="STD", ignore_nodata="DATA" ) roughness_std.save("Roughness_STD.tif") Specifically, the terrain slope can be obtained by using the "Slope" tool of the GIS data processing module. The corresponding program is as follows: import numpy as np from scipy.ndimage import generic_gradient_magnitude def calculate_slope(dem, cell_size): dzdx, dzdy = np.gradient(dem) slope_rad = np.arctan(np.sqrt(dzdx**2 + dzdy**2)) return np.degrees(slope_rad) * (cell_size / 10) # Unit conversion (assuming the unit of DEM is meters) S3. The relay network generation module generates the deployment plans of each relay UAV according to the prediction results of the signal prediction module; S4. The path planning module generates the flight paths of each UAV according to the data obtained by the GIS data processing module and the deployment plan generated by the relay network generation module; Specifically, the paths generated by the path planning module include the inspection paths of the inspection UAVs and the movement paths of the relay UAVs to reach the specified relay points.

[0037] S5. The monitoring module monitors each UAV in real time during the flight of the UAV.

[0038] Furthermore, the generation of the flight paths of each UAV by the path planning module includes the following steps: S41. The initial path generation unit generates the initial inspection path of the inspection UAV according to the inspection tasks of the UAVs, and at the same time generates the movement paths of the relay UAVs according to the relay points; S42. The path adjustment unit adjusts the initial inspection path according to the deployment plan of the relay UAVs to obtain the actual inspection path of the UAVs; S43. The relay UAVs move according to their corresponding movement paths. After the relay UAVs arrive at their positions, the inspection UAVs conduct inspections according to the actual inspection paths; S44. The obstacle avoidance unit conducts emergency obstacle avoidance for obstacles during the inspection process of the inspection UAVs.

[0039] The beneficial effects of this solution: 1. Signal strength prediction indicators are set based on the transmission signal strength of the UAV, the distance between the prediction point and the corresponding signal station, the signal coverage range of the UAV, the terrain complexity of the prediction point, and the signal obstacle situation between the prediction point and the corresponding signal station. This is beneficial for scientifically predicting the signal strength of each part of the inspection area, and for setting network relay points according to the signal strength indicators, thereby ensuring the stability of the signals sent by the UAVs during the inspection process and improving the reliability of UAV inspections.

[0040] 2. By considering the terrain, obstacles, and the distance to the signal station to calculate the signal strength prediction indicators, it is beneficial to predict the signal strength by considering multiple factors, improving the prediction accuracy, and effectively using the corresponding tools of the GIS data processing module to reduce the calculation burden.

[0041] Embodiment 2: This embodiment should be understood as including all the features of any one of the foregoing embodiments and further improved thereon. It also includes a signal compensation method, which is used to compensate the UAV signals received at the relay points after the relay points are set. When the UAV makes a sharp turn, the UAV signals received at the relay points will generate frequency shifts, resulting in an increase in the signal error rate. Especially in areas such as mountains where UAVs need to frequently avoid obstacles, this embodiment compensates the frequency shift amount of the UAV signals received at the relay points through the following formula: ;

[0042] Wherein, BC is the frequency shift amount to be compensated, c is the speed of light, and v is the initial speed of the UAV during a sharp turn. This speed can be obtained by the GIS data processing module through GPS detection of the movement of the UAV in a three-dimensional map. is the instantaneous angle between the movement direction and the signal wave (which can be obtained by a gyroscope). is the signal frequency emitted by the UAV. is the average duration of each signal reception cycle at the relay point. is the average value of the duration of noise in each signal reception cycle at the relay point. e is the natural constant, Y is the number of obstacles encountered on the coordinate connection line between the UAV and the relay point. When the number of obstacles on the connection line is 0, is set to 0. is the estimated signal loss value corresponding to the obstacle type of the y-th obstacle on the connection line between the UAV and the relay point. is the instantaneous angle between the movement direction and the signal wave at the start of the sharp turn.

[0043] Advantages of this embodiment: By obtaining the compensation amount of the frequency shift, it is beneficial to compensate the received signal at the relay point, thereby improving the reliability of the signal and reducing the bit error rate of the signal.

[0044] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated. The above units are only examples, and those skilled in the art can make different designs according to actual needs and adopt corresponding units when implementing this solution.

Claims

1. An unmanned aerial vehicle inspection network planning system based on GIS, characterized in that, It includes a GIS data processing module, a signal prediction module, a relay network generation module, a path planning module, and a monitoring module; the GIS data processing module is used to load and parse various data of the inspection area, the signal prediction module is used to predict the communication signal strength distribution of the UAV in the inspection area according to the data obtained by the GIS data processing module, the relay network generation module is used to obtain the deployment plan of the relay UAVs according to the prediction results of the signal prediction module, the path planning module is used to generate the flight paths of each UAV according to the data obtained by the GIS data processing module and the deployment plan generated by the relay network generation module, and the monitoring module is used to monitor the status of each UAV in real time.

2. The UAV inspection network planning system based on GIS according to claim 1, characterized in that, The GIS data processing module includes a multi-source data acquisition unit, a data cleaning and fusion unit, and a 3D geographical modeling unit. The multi-source data acquisition unit is used to communicate with external devices and obtain various data of the inspection area. The data cleaning and fusion unit is used to preprocess the data collected by the multi-source data acquisition unit. The 3D geographical modeling unit is used to generate a model of the inspection area according to the preprocessed data.

3. The UAV inspection network planning system based on GIS according to claim 1, wherein The signal prediction module includes a signal strength prediction unit and a heat map generation unit. The signal strength prediction unit is used to calculate the signal strength prediction index of each position in the inspection area according to the data obtained by the GIS data processing module. The heat map generation unit is used to generate a signal strength heat map of the inspection area according to the obtained signal strength prediction index.

4. A UAV inspection network planning system based on GIS according to claim 1, characterized in that The relay network generation module includes a relay node acquisition unit and a topology generation unit. The relay node acquisition unit is used to obtain the positions of the relay points according to the generated heat map. The topology generation unit is used to construct a multi-hop communication link according to the nodes obtained by the relay node acquisition unit, so as to form a mesh network topology.

5. A UAV inspection network planning system based on GIS according to claim 1, characterized in that, The path planning module includes an initial path generation unit, a path adjustment unit, and an obstacle avoidance unit. The initial path generation unit is used to generate the initial inspection path of the UAV according to the inspection task of the UAV. The path adjustment unit is used to adjust the initial inspection path according to the deployment plan of the relay UAVs, so as to generate the actual inspection path of the UAV. The obstacle avoidance unit is used to control the UAV to perform emergency obstacle avoidance during the inspection process of the UAV according to the actual inspection path.

6. A method for planning an unmanned aerial vehicle (UAV) inspection network based on GIS, which is applied to a system for planning an unmanned aerial vehicle (UAV) inspection network based on GIS as described in claim 5, characterized in that, The method includes the following steps: S1. The GIS data processing module loads various data of the inspection area; S2. The signal prediction module obtains the signal strength prediction index of each coordinate in the inspection area and generates a corresponding heat map; S3. The relay network generation module generates the deployment plan of each relay UAV according to the prediction results of the signal prediction module; S4. The path planning module generates the flight paths of each UAV according to the data obtained by the GIS data processing module and the deployment plan generated by the relay network generation module; S5. The monitoring module monitors each UAV in real time during the flight of the UAV.

7. A method for planning a drone inspection network based on GIS according to claim 6, characterized in that, The path planning module generates the flight paths of each UAV, including the following steps: S41. The initial path generation unit generates the initial inspection path of the inspection UAV according to the inspection task of the UAV, and simultaneously generates the movement path of the relay UAV according to the relay points; S42. The path adjustment unit adjusts the initial inspection path according to the deployment plan of the relay UAV to obtain the actual inspection path of the UAV; S43. The relay UAV moves according to its corresponding movement path. After the relay UAV arrives at the position, the inspection UAV conducts inspections according to the actual inspection path; S44. The obstacle avoidance unit conducts emergency obstacle avoidance for obstacles during the inspection process of the inspection UAV.

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