Communication line inspection path planning method based on unmanned aerial vehicle

Abstract

The invention belongs to the technical field of path planning, and relates to a communication line inspection path planning method based on an unmanned aerial vehicle, which comprises the following steps of: building a communication line digital elevation model of a target inspection area and drawing an initial inspection path of the unmanned aerial vehicle; obstacle encountering nodes and barrier-free nodes on an initial inspection route are accurately screened by importing environmental data along the line, obstacle encountering road sections on the initial inspection route are obtained and subjected to obstacle avoidance processing, and obstacle avoidance sub-routes of the obstacle encountering road sections are randomly matched to obtain reference inspection routes of the unmanned aerial vehicle. The airworthiness performance of each reference routing inspection route of the unmanned aerial vehicle is explored to screen the optimal routing inspection route, and in order to avoid the influence of signal interference of a target routing inspection area on the hovering error of the unmanned aerial vehicle, the contrast hovering points of each routing inspection fixed point position on the optimal routing inspection route of the unmanned aerial vehicle are finely adjusted, so that the accuracy and efficiency of the routing inspection of the unmanned aerial vehicle are remarkably improved. Therefore, the stable operation capability of the unmanned aerial vehicle in a complex environment is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning and relates to a communication line inspection path planning method based on an unmanned aerial vehicle. Background Art

[0002] With the rapid development of drone technology, drone-based communication line inspection has gradually become an important means of modern communication network maintenance. Traditional manual inspection methods are not only time-consuming and labor-intensive, but also have safety hazards and low efficiency. Especially in some hard-to-reach areas or in severe weather conditions, the difficulties and risks of manual inspection are more prominent. Therefore, the use of drones for communication line inspection can not only improve inspection efficiency and reduce interference from human factors, but also realize real-time monitoring and data collection in complex terrain or dangerous environments. However, how to efficiently plan the inspection path of drones and ensure the safety, reliability and efficiency of the inspection process has become the focus of current research.

[0003] In the prior art, there are also some related solutions involving UAV inspection path planning, for example, the UAV power inspection trajectory planning method and system with Chinese patent publication number CN116203982A, which obtains modeling information and establishes a three-dimensional point cloud space based on the modeling information, and uses a consumption-based global swarm optimization algorithm to plan a global planning path from the starting point S to the target point G in the three-dimensional point cloud space, and judge whether the global planning path passes through obstacles. If not, the global planning path is output as the optimal path. If so, the basic consumption Diikstra algorithm is used for local path planning to obtain the optimal path. By using the consumption-based global swarm optimization algorithm combined with the consumption-based Diikstra algorithm, the optimal trajectory planning result can be quickly solved in the three-dimensional space.

[0004] Another Chinese patent with publication number CN118915781A is a fully automated patrol drone inspection system based on digital twins, which includes a digital twin module, a patrol task generation module, an image pre-acquisition module, a task positioning module, a drone organization module, a path formation module, a patrol module occlusion processing module and an abnormality judgment module. It can limit the patrol time of each drone, avoid some drones’ patrol paths being too long, obtain images of unobstructed targets, and make more accurate image analysis based on this.

[0005] Although some relevant solutions involving UAV inspection path planning are proposed above, the existing UAV inspection path planning technology still has limitations. Specifically: although the existing UAV inspection path planning technology has taken into account many aspects such as UAV inspection obstacle avoidance, inspection low energy consumption and inspection hovering error, the analysis depth is poor.

[0006] In terms of inspection and obstacle avoidance, existing technologies focus on exploring the possibility of collision between each unit point on the inspection path and obstacles along the route, but do not fully consider the potential collision risks of the drone itself relative to obstacles along the route in a complex environment.

[0007] In terms of low-energy inspection, existing technologies only focus on the basic inspection energy consumption of the drone's flight route, and do not take into account real-time dynamic factors such as temperature, air pressure, wind force and wind speed in the inspection environment. However, meteorological environmental factors will significantly change the resistance encountered by the drone during flight, thereby increasing the inspection energy consumption. This makes it impossible to accurately control the actual energy consumption of the drone throughout the inspection mission, making it difficult to achieve refined management and optimization of energy consumption.

[0008] In terms of hovering errors during inspections, although existing technologies use a grid positioning mode in the initial stage of path planning to reduce hovering errors, the environment in which the inspection path is located is complex and changeable. Once there is signal interference in the inspection area, such as in urban canyon areas with high-rise buildings or industrial areas with complex electromagnetic environments, the drone's hovering positioning point is prone to deviations, which in turn affects the accuracy of data collection and the smooth progress of inspection tasks. Summary of the invention

[0009] In view of this, in order to solve the problems raised in the above background technology, a communication line inspection path planning method based on drone is proposed.

[0010] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a communication line inspection path planning method based on a drone, including: S1. Obtaining the communication line direction and line suspension height data of the target inspection area, building a digital elevation model of the communication line in the target inspection area, and sequentially entering the inspection point positions of the communication line in the target inspection area and its inspection task requirements, including the shooting object angle requirements and the shooting object coverage area requirements, so as to outline the initial inspection route of the drone.

[0011] S2. Import the environmental data along the communication line into the digital elevation model of the target inspection area, obtain the obstacle sections on the initial inspection route of the UAV, perform obstacle avoidance processing on each obstacle section to obtain its corresponding obstacle avoidance sub-routes, retain the obstacle avoidance sub-routes of each obstacle section and randomly match them, so as to obtain the reference inspection routes of the UAV.

[0012] S3. Import the inspection meteorological data of the day into the digital elevation model of the communication route of the target inspection area again, explore the airworthiness of each reference inspection route of the UAV, and select the optimal inspection route.

[0013] S4. Pre-test the impact of signal interference in the target inspection area on the drone hovering error, and fine-tune the control hovering points of each inspection fixed point on the drone's optimal inspection route.

[0014] S5. Feedback the optimal inspection route of the UAV relative to the communication line of the target inspection area and the control hovering point after fine-tuning the position of each inspection point.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention constructs a digital elevation model of the communication line in the target inspection area to outline the initial inspection route of the UAV, providing a clear direction for the in-depth planning of the subsequent UAV inspection route, thereby improving the efficiency of UAV inspection path planning.

[0016] (2) The present invention accurately screens the obstacle nodes and obstacle-free nodes on the initial inspection route, obtains the obstacle sections on the initial inspection route and performs obstacle avoidance processing to obtain the corresponding obstacle avoidance sub-routes, and then randomly matches them to obtain the reference inspection routes of the drone. This not only improves the flexibility and adaptability of the inspection route, but also enhances the reliability of the drone inspection, thereby promoting the intelligent and automated development of the drone inspection.

[0017] (3) The present invention explores the airworthiness of each reference inspection route of the UAV by comprehensively considering the inspection energy consumption ratio and the inspection difficulty ratio, and selects the optimal inspection route based on this, ensuring that the inspection tasks are completed in a balanced and efficient manner, and effectively improving the overall inspection resource utilization, inspection efficiency and economy.

[0018] (4) The present invention pre-tests the effect of signal interference in the target inspection area on the hovering error of the UAV, and fine-tunes the control hovering points of each inspection fixed point on the optimal inspection route of the UAV, thereby significantly reducing the hovering error caused by signal interference, thereby improving the precision and accuracy of the inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0021] Figure 2 A logical schematic diagram of each obstacle section on the initial inspection route of the drone is obtained for the present invention.

[0022] Figure 3 The figure is a logical diagram of the obstacle avoidance process for the road section encountering obstacles according to the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in 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.

[0024] See also Figure 1 As shown, the present invention provides a communication line inspection path planning method based on unmanned aerial vehicle, including: S1. Acquire the communication line direction and line suspension height data of the target inspection area, build a digital elevation model of the communication line of the target inspection area, and enter the inspection point positions of the communication line in the target inspection area and its inspection task requirements in sequence, including the shooting object angle requirements and the shooting object coverage area requirements, so as to outline the initial inspection route of the unmanned aerial vehicle.

[0025] Specifically, the construction of the digital elevation model of the communication line of the target inspection area includes: determining the spatial scope of the target inspection area and planning its spatial model with three-dimensional software, gridding its spatial model according to a preset spatial resolution, and constructing a three-dimensional rectangular coordinate system with the lower left corner point of the spatial model as the origin.

[0026] The communication line direction and line suspension height data of the target inspection area are converted into three-dimensional coordinates of each unit node on the communication line, and imported into the three-dimensional rectangular coordinate system of the target inspection area spatial model to determine the specific position of each unit node on the communication line. The digital elevation model of the communication line of the target inspection area is constructed by connecting and combining the grids at the specific position of each unit node.

[0027] It should be noted that the above-mentioned communication line direction and line suspension height data of the target inspection area include the latitude and longitude coordinates and suspension height of each unit node on the communication line direction, and the specific method of converting them into the three-dimensional coordinates of each unit node on the communication line is: extract the latitude and longitude coordinates and suspension height of a unit node on the communication line direction, and record them as , , according to the formula , , Calculate the transformed horizontal coordinate, vertical coordinate and elevation coordinate of the unit node on the communication line direction respectively, where is the preset radius of curvature of the Maoyou circle, The eccentricity is preset to obtain the three-dimensional coordinates of the unit node on the communication line. Similarly, the three-dimensional coordinates of each unit node on the communication line are obtained.

[0028] Specifically, outlining the initial inspection route of the UAV includes: determining the hovering posture and hovering distance of the UAV relative to each inspection fixed point position of the communication line in the target inspection area according to the shooting object angle requirements and the shooting object coverage area requirements of each inspection fixed point position of the communication line in the target inspection area, so as to obtain the comparison hovering points of the UAV relative to each inspection fixed point position of the communication line in the target inspection area, and outlining the initial inspection route of the UAV by sequentially connecting the comparison hovering points of the UAV relative to each inspection fixed point position of the communication line in the target inspection area.

[0029] It should be noted that the specific process of determining the hovering posture and hovering distance of the above-mentioned UAV relative to each inspection fixed point of the communication line in the target inspection area is as follows: based on the preset hovering posture of the UAV for each angle interval of the inspection shooting object stored in the WEB cloud, the preset hovering posture of the UAV for the shooting object angle requirement interval of each inspection fixed point of the communication line in the target inspection area is obtained, and this is used as the hovering posture of the UAV relative to each inspection fixed point of the communication line in the target inspection area.

[0030] According to the horizontal field of view of the drone lens provided by the drone manufacturer and stored in the WEB cloud and vertical field of view , combined with the coverage area requirements of the photographed objects at each inspection point of the communication line in the target inspection area , It is the number of each inspection point of the communication line in the target inspection area. , through the formula Obtain the hovering distance of the drone relative to each inspection point of the communication line in the target inspection area .

[0031] The embodiment of the present invention builds a digital elevation model of the communication line of the target inspection area, outlines the initial inspection route of the drone, and provides a clear direction for the in-depth planning of the subsequent drone inspection route, thereby improving the efficiency of drone inspection path planning.

[0032] S2. Import the environmental data along the communication line into the digital elevation model of the target inspection area, obtain the obstacle sections on the initial inspection route of the UAV, perform obstacle avoidance processing on each obstacle section to obtain its corresponding obstacle avoidance sub-routes, retain the obstacle avoidance sub-routes of each obstacle section and randomly match them, so as to obtain the reference inspection routes of the UAV.

[0033] See also Figure 2As shown, specifically, the obtaining of each obstacle section on the initial inspection route of the UAV includes: projecting and transforming the environmental data along the route according to the three-dimensional coordinate system of the digital elevation model of the communication line of the target inspection area and the preset spatial resolution, thereby adding each obstacle space area of ​​the environment along the route to the digital elevation model of the communication line of the target inspection area; if a unit node on the initial inspection route of the UAV exists in a certain obstacle space area of ​​the environment along the route, the unit node on the initial inspection route of the UAV is marked as an obstacle node; if a unit node on the initial inspection route of the UAV does not exist in any obstacle space area of ​​the environment along the route, the unit node on the initial inspection route of the UAV is marked as a potential obstacle-free node, thereby screening each obstacle node and each potential obstacle-free node on the initial inspection route of the UAV.

[0034] A three-dimensional sub-model of the drone is further constructed within the digital elevation model of the communication line in the target inspection area. The minimum distance between the three-dimensional sub-model of the drone and the spatial area of ​​environmental obstacles along the route when the sub-model is located at each potential obstacle-free node on the initial inspection route of the drone is retrieved, and compared with the preset flight safety distance threshold of the drone relative to obstacles stored in the WEB cloud. If the comparison relationship is less than, the label of the potential obstacle-free node is upgraded to an obstacle node. If the comparison relationship is greater than or equal to, the label of the potential obstacle-free node is upgraded to an obstacle-free node, thereby obtaining each obstacle node and each obstacle-free node on the initial inspection route of the drone.

[0035] Connect the consecutive obstacle nodes to obtain the obstacle sections on the initial inspection route of the drone.

[0036] See also Figure 3 As shown, specifically, the obstacle avoidance processing for each obstacle section includes: using the obstacle-free nodes adjacent to the obstacle section in front and behind as the starting node and the ending node of the path search respectively, constructing a spherical space area with a preset radius with the path search starting node as the sphere center, randomly sampling a node in the spherical space area, expanding the randomly sampled node according to a preset step length to obtain a new node, judging whether there is a possibility of collision between the connection line between the random sampling point and the corresponding expanded new node and the obstacle space area along the line, if the judgment is yes, reselecting the random sampling node in the spherical space area and repeating the operation, if the judgment is no, adding the extended new node corresponding to the random sampling point as the obstacle avoidance sub-route array, and continuing to construct the spherical space area of ​​the extended new node corresponding to the random sampling point, and continuing the random sampling and expansion operations until the connection branch of a certain extended new node in the obstacle avoidance sub-route array reaches the path search ending node, forming one of the obstacle avoidance sub-routes of the obstacle section, thereby obtaining each obstacle avoidance sub-route corresponding to each obstacle section, and realizing obstacle avoidance processing for each obstacle section.

[0037] It should be noted that there are restrictions on the preset radius of the above-mentioned spherical space area, which restricts the elevation coordinate value of each node in the spherical space area to be less than the preset flight limit height of the drone in the target inspection area.

[0038] The above-mentioned preset step length is set by comprehensively considering the physical size, motion characteristics and environmental characteristics of the drone. Specifically, the preset step length is less than or equal to the minimum turning radius of the drone, adapting to the dynamic characteristics of the drone, ensuring that the selected step length does not cause the path exploration to be too fast or too slow, and is consistent with the density of environmental obstacles along the route, maintaining a certain inspection speed while reducing the risk of collision.

[0039] The embodiment of the present invention accurately screens the obstacle nodes and obstacle-free nodes on the initial inspection route, obtains the obstacle sections on the initial inspection route and performs obstacle avoidance processing to obtain the corresponding obstacle avoidance sub-routes, and then randomly matches them to obtain various reference inspection routes for drones, which not only improves the flexibility and adaptability of the inspection routes, but also enhances the reliability of drone inspections, thereby promoting the intelligent and automated development of drone inspections.

[0040] S3. Import the inspection meteorological data of the day into the digital elevation model of the communication route of the target inspection area again, explore the airworthiness of each reference inspection route of the UAV, and select the optimal inspection route.

[0041] Specifically, the exploration of the airworthiness performance of each reference inspection route of the drone includes: requiring the drone to maintain a preset flight speed to carry out inspections, obtaining the route length of each reference inspection route of the drone and performing a ratio analysis with the preset flight speed to obtain the inspection time of each reference inspection route of the drone, and further multiplying it with the reference power of the drone at the preset flight speed provided by the drone manufacturer and stored in the WEB cloud to obtain the basic inspection energy consumption of each reference inspection route of the drone. , is the number of each reference inspection route of the drone, .

[0042] According to the preset route length, each reference inspection route of the drone is cut into sections to obtain each segmented section of each reference inspection route of the drone. Based on the inspection meteorological data of the day imported from the digital elevation model of the communication route of the target inspection area, the meteorological environmental parameters of each segmented section of each reference inspection route of the drone during its corresponding inspection period are obtained, including temperature, pressure, wind speed and wind direction, so as to calculate the inspection resistance and corresponding additional inspection energy consumption of each segmented section of each reference inspection route of the drone , For reference, the numbers of the sections of the inspection route are .

[0043] It should be noted that the specific calculation process of the inspection resistance of each segment of each reference inspection route of the above-mentioned drone is: according to the temperature value of each segment of each reference inspection route of the drone during its corresponding inspection period With air pressure , according to the formula Calculate the air density of each segment of each reference inspection route of the drone during its corresponding inspection period, where Preset the average molar mass for air, is the preset ideal gas constant.

[0044] According to the wind speed and wind direction of each segment of each reference inspection route of the drone during its corresponding inspection period, the relative flight speed of each segment of each reference inspection route during its corresponding inspection period is synthesized by the parallelogram rule. , and then by the formula Analyze the inspection resistance of each segment of each reference inspection route of the UAV, including is the preset resistance coefficient, The preset windward area of ​​the drone provided by the drone manufacturer and stored in the WEB cloud.

[0045] It should be noted that the above-mentioned air temperature values ​​specifically refer to thermodynamic temperature values.

[0046] The calculation formula for the additional inspection energy consumption corresponding to each segment of each reference inspection route of the above-mentioned UAV is: ,in To preset the flight speed, For drone Reference inspection route The inspection time corresponds to each segmented road section.

[0047] By formula Analyze the inspection energy consumption ratio of each reference inspection route of the drone, among which The rated energy consumption capacity of the drone provided by the drone manufacturer and stored in the WEB cloud.

[0048] Specifically, the exploration of the airworthiness of each reference inspection route of the drone also includes: extracting the corresponding curves of each segmented section of each reference inspection route of the drone, obtaining the curvature, steering degree and drop degree of each segmented section of each reference inspection route of the drone, and calculating the flight difficulty of each segmented section of each reference inspection route of the drone. , according to the formula Analyze the inspection difficulty ratio of each reference inspection route of the drone, among which Preset reference flight difficulty for split sections, The number of sections into which the reference inspection route is divided.

[0049] It should be noted that the specific process of obtaining the curvature, turning degree and drop degree of each segmented section of each reference inspection route of the above-mentioned drone is as follows: the corresponding curves of each segmented section of each reference inspection route of the drone are filtered out of the elevation coordinates and imported into the Matlab software, and the best fitting function of the corresponding curves of each segmented section of each reference inspection route of the drone is obtained by the software fitting tool. The first-order and second-order derivatives of the best fitting function are respectively performed, and the horizontal and vertical coordinates of each unit node on the curve are substituted, and the first-order derivative reference value of the corresponding curve of each segmented section of each reference inspection route of the drone is obtained by mean calculation. And the second-order derivative reference value , according to the formula Calculate the curvature of each segment of each reference inspection route of the UAV.

[0050] Calculate the turning angle between each adjacent unit node in each segmented section of each reference inspection route of the drone. If the turning angle is greater than the preset turning angle, it means that there is a turning operation of the drone between the adjacent unit nodes. Count the cumulative number of turning operations of the drone in each segmented section of each reference inspection route, and calculate the ratio of it to the preset route length to obtain the turning frequency of the drone in each segmented section of each reference inspection route. Use the value of the turning frequency as the turning degree, thereby obtaining the turning degree of each segmented section of each reference inspection route of the drone.

[0051] The elevation coordinates of each unit node of each segmented section of each reference inspection route of the UAV are extracted, and the maximum elevation coordinate and the minimum elevation coordinate of the unit node are screened for difference. The difference is further analyzed with the preset reference elevation coordinate difference to obtain the drop degree of each segmented section of each reference inspection route of the UAV.

[0052] It is particularly noted that the steering angle between the adjacent unit nodes can be calculated by referring to the vector dot product formula.

[0053] By formula Calculate the airworthiness performance evaluation index of each reference inspection route of the UAV, where The preset weights correspond to the inspection energy consumption ratio and the inspection difficulty ratio, respectively, so as to explore the airworthiness performance of each reference inspection route of the UAV.

[0054] Specifically, the optimal inspection route is a reference inspection route corresponding to the maximum airworthiness performance evaluation index.

[0055] The embodiment of the present invention explores the airworthiness performance of various reference inspection routes of the UAV by comprehensively considering the inspection energy consumption ratio and the inspection difficulty ratio, and selects the optimal inspection route based on this to ensure that the inspection tasks are completed in a balanced and efficient manner, and effectively improve the overall inspection resource utilization, inspection efficiency and economy.

[0056] S4. Pre-test the impact of signal interference in the target inspection area on the drone hovering error, and fine-tune the control hovering points of each inspection fixed point on the drone's optimal inspection route.

[0057] Specifically, the pre-test of the effect of signal interference in the target inspection area on the hovering error of the drone includes: obtaining the signal interference intensity of the current environment of each segmented section of the optimal inspection route based on the signal monitoring instrument deployed in the communication line of the target inspection area.

[0058] A simulated signal interference source is used to perform a hovering test on the UAV in a preset experimental area, and the relative deviation distance and relative deviation angle vector of the UAV's hovering position under the signal interference intensity of the current environment of each segmented section of the optimal inspection route are recorded. On this basis, the UAV is simulated to perform inspection work at each inspection fixed point on each segmented section of the optimal inspection route. The shooting object angle and shooting object coverage area of ​​the UAV at each inspection fixed point on each segmented section of the optimal inspection route under the condition of signal interference hovering deviation are compared with their corresponding shooting object angle requirements and shooting object coverage area requirements, respectively, to obtain the shooting object angle and shooting object coverage area requirements of the UAV at each inspection fixed point on each segmented section of the optimal inspection route under the condition of signal interference hovering deviation. The deviation angle of the shooting object and the lack of coverage area of ​​the shooting object, the ratio analysis of the shooting object deviation angle of each inspection fixed point and its corresponding shooting object angle requirement value is performed to obtain the shooting angle deviation ratio, the ratio analysis of the lack of coverage area of ​​the shooting object at each inspection fixed point and its corresponding shooting object coverage requirement value is performed to obtain the shooting area lack ratio, and the cumulative value of the shooting area lack ratio and the shooting angle deviation ratio is used as the influence degree of the hovering error, and then the influence degree of the hovering error of each inspection fixed point on each segmented section of the optimal inspection route is obtained, and the influence degree of the signal interference of each segmented section of the optimal inspection route on the hovering error of the drone is obtained by averaging the influence degree of the hovering error of each inspection fixed point.

[0059] Specifically, the control hovering point of each inspection fixed point on the optimal inspection route of the drone is fine-tuned, including: if the signal interference of a certain segment of the optimal inspection route affects the hovering error of the drone Less than or equal to the threshold of the degree of influence of signal interference on the preset permitted hovering error of the drone by the WEB cloud storage , it means that there is no need to fine-tune the reference hovering points at the inspection fixed points on the divided road section, and the original reference hovering point positions are maintained.

[0060] On the contrary, the relative deviation distance of the drone's hovering position under the signal interference intensity of the current environment of the segmented section is extracted and the relative deviation angle vector , respectively, by the formula , The correction distance and correction angle of the control hovering point of the inspection fixed point on the divided road section are analyzed, and the control hovering point of each inspection fixed point on the divided road section is fine-tuned in combination with the relative deviation angle direction presented by the relative deviation angle vector.

[0061] In this way, the reference hovering points of each inspection point on the optimal inspection route of the UAV can be fine-tuned.

[0062] The embodiment of the present invention pre-tests the effect of signal interference in the target inspection area on the hovering error of the UAV, and fine-tunes the control hovering points of each inspection fixed point on the optimal inspection route of the UAV, thereby significantly reducing the hovering error caused by signal interference, thereby improving the precision and accuracy of the inspection.

[0063] S5. Feedback the optimal inspection route of the UAV relative to the communication line of the target inspection area and the control hovering point after fine-tuning the position of each inspection point.

[0064] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A communication line inspection path planning method based on drone, characterized in that: include: S1. Obtain the communication line direction and line suspension height data of the target inspection area, build a digital elevation model of the communication line of the target inspection area, and sequentially enter the inspection point positions of the communication line of the target inspection area and its inspection task requirements, including the shooting object angle requirements and the shooting object coverage area requirements, so as to outline the initial inspection route of the drone; S2. Import the environmental data along the communication line into the digital elevation model of the target inspection area, obtain the obstacle sections on the initial inspection route of the UAV, perform obstacle avoidance processing on each obstacle section to obtain its corresponding obstacle avoidance sub-routes, retain the obstacle avoidance sub-routes of each obstacle section and randomly match them, so as to obtain the reference inspection routes of the UAV; S3. Import the inspection weather data of the day into the digital elevation model of the communication route of the target inspection area again, explore the airworthiness of each reference inspection route of the UAV, and select the optimal inspection route; S4. Pre-test the effect of signal interference in the target inspection area on the hovering error of the drone, and fine-tune the control hovering points of each inspection point on the optimal inspection route of the drone; S5. Feedback the optimal inspection route of the UAV relative to the communication line of the target inspection area and the control hovering point after fine-tuning the position of each inspection point.

2. According to claim 1, a communication line inspection path planning method based on an unmanned aerial vehicle is characterized in that: The construction of the digital elevation model of the communication line of the target inspection area includes: determining the spatial scope of the target inspection area and planning its spatial model using three-dimensional software, meshing its spatial model according to a preset spatial resolution, and constructing a three-dimensional rectangular coordinate system using the lower left corner point of the spatial model as the origin; The communication line direction and line suspension height data of the target inspection area are converted into three-dimensional coordinates of each unit node on the communication line, and imported into the three-dimensional rectangular coordinate system of the target inspection area spatial model to determine the specific position of each unit node on the communication line. The digital elevation model of the communication line of the target inspection area is constructed by connecting and combining the grids at the specific position of each unit node.

3. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 1, characterized in that: The method of outlining the initial inspection route of the UAV includes: determining the hovering posture and hovering distance of the UAV relative to each inspection fixed point position of the communication line in the target inspection area according to the shooting object angle requirements and the shooting object coverage area requirements of each inspection fixed point position of the communication line in the target inspection area, thereby obtaining the comparison hovering points of the UAV relative to each inspection fixed point position of the communication line in the target inspection area, and outlining the initial inspection route of the UAV by sequentially connecting the comparison hovering points of the UAV relative to each inspection fixed point position of the communication line in the target inspection area.

4. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 2, characterized in that: The obtaining of each obstacle section on the initial inspection route of the drone includes: projecting and transforming the environmental data along the route according to the three-dimensional coordinate system and preset spatial resolution of the digital elevation model of the communication line of the target inspection area, and resampling, thereby adding each obstacle space area of ​​the environment along the route to the digital elevation model of the communication line of the target inspection area; if a unit node on the initial inspection route of the drone exists in a certain obstacle space area of ​​the environment along the route, then the unit node on the initial inspection route of the drone is marked as an obstacle node; if a unit node on the initial inspection route of the drone does not exist in any obstacle space area of ​​the environment along the route, then the unit node on the initial inspection route of the drone is marked as a potential obstacle-free node, so as to screen each obstacle node and each potential obstacle-free node on the initial inspection route of the drone; A three-dimensional sub-model of the drone is further constructed in the digital elevation model of the communication line in the target inspection area. The minimum distance between the three-dimensional sub-model of the drone and the spatial area of ​​environmental obstacles along the route when the sub-model is located at each potential obstacle-free node on the initial inspection route of the drone is retrieved, and compared with the preset flight safety distance threshold of the drone relative to the obstacle stored in the WEB cloud. If the comparison relationship is less than, the label of the potential obstacle-free node is upgraded to an obstacle node. If the comparison relationship is greater than or equal to, the label of the potential obstacle-free node is upgraded to an obstacle-free node, thereby obtaining each obstacle node and each obstacle-free node on the initial inspection route of the drone; Connect the consecutive obstacle nodes to obtain the obstacle sections on the initial inspection route of the drone.

5. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 4, characterized in that: The obstacle avoidance process for each obstacle section includes: using adjacent obstacle-free nodes before and after the obstacle section as the starting node and the ending node of the path search respectively, constructing a spherical space area with a preset radius with the path search starting node as the sphere center, randomly sampling a node in the spherical space area, expanding the randomly sampled node according to a preset step length to obtain a new node, judging whether there is a possibility of collision between the line between the random sampling point and the corresponding expanded new node and the obstacle space area along the line, if the judgment is yes, reselecting the random sampling node in the spherical space area and repeating the operation, if the judgment is no, adding the extended new node corresponding to the random sampling point as an obstacle avoidance sub-route array, and continuing to construct the spherical space area of ​​the extended new node corresponding to the random sampling point, and continuing the random sampling and expansion operations until the line branch of a certain extended new node in the obstacle avoidance sub-route array reaches the path search ending node, forming one of the obstacle avoidance sub-routes of the obstacle section, thereby obtaining each obstacle avoidance sub-route corresponding to each obstacle section, and realizing obstacle avoidance process for each obstacle section.

6. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 1, characterized in that: The exploration of the airworthiness of each reference inspection route of the drone includes: requiring the drone to maintain a preset flight speed to carry out inspection work, obtaining the route length of each reference inspection route of the drone and performing a ratio analysis with the preset flight speed to obtain the inspection time of each reference inspection route of the drone, and further multiplying it with the reference power of the drone at the preset flight speed provided by the drone manufacturer and stored in the WEB cloud to obtain the basic inspection energy consumption of each reference inspection route of the drone , is the number of each reference inspection route of the drone, ; According to the preset route length, each reference inspection route of the drone is cut into sections to obtain each segmented section of each reference inspection route of the drone. Based on the inspection meteorological data of the day imported from the digital elevation model of the communication route of the target inspection area, the meteorological environmental parameters of each segmented section of each reference inspection route of the drone during its corresponding inspection period are obtained, including temperature, pressure, wind speed and wind direction, so as to calculate the inspection resistance and corresponding additional inspection energy consumption of each segmented section of each reference inspection route of the drone , For reference, the numbers of the sections of the inspection route are ; By formula Analyze the inspection energy consumption ratio of each reference inspection route of the drone, among which The rated energy consumption capacity of the drone provided by the drone manufacturer and stored in the WEB cloud.

7. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 6, characterized in that: The exploration of the airworthiness of each reference inspection route of the drone also includes: extracting the corresponding curves of each segmented section of each reference inspection route of the drone, obtaining the curvature, steering degree and drop degree of each segmented section of each reference inspection route of the drone, and calculating the flight difficulty of each segmented section of each reference inspection route of the drone. , according to the formula Analyze the inspection difficulty ratio of each reference inspection route of the drone, among which Preset reference flight difficulty for split sections, The number of sections divided for reference inspection route; By formula Calculate the airworthiness performance evaluation index of each reference inspection route of the UAV, where The preset weights correspond to the inspection energy consumption ratio and the inspection difficulty ratio, respectively, so as to explore the airworthiness performance of each reference inspection route of the UAV.

8. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 7, characterized in that: The optimal inspection route is a reference inspection route corresponding to the maximum airworthiness performance evaluation index.

9. The method for planning a communication line inspection path based on an unmanned aerial vehicle according to claim 6, characterized in that: The pre-test of the effect of signal interference in the target inspection area on the hovering error of the drone includes: obtaining the signal interference intensity of the current environment of each segmented section of the optimal inspection route based on the signal monitoring instrument arranged in the communication line of the target inspection area; A simulated signal interference source is used to carry out a hovering test on the UAV in a preset experimental area, and the relative deviation distance and relative deviation angle vector of the UAV's hovering position under the signal interference intensity of the current environment of each segment of the optimal inspection route are recorded. On this basis, the UAV is simulated to perform inspection work on each inspection fixed point on each segment of the optimal inspection route. The shooting object angle and shooting object coverage area of ​​each inspection fixed point on each segment of the optimal inspection route under the condition of signal interference hovering deviation are compared with the corresponding shooting object angle requirements and shooting object coverage area requirements, so as to obtain the influence of signal interference in each segment of the optimal inspection route on the hovering error of the UAV.

10. A communication line inspection path planning method based on an unmanned aerial vehicle according to claim 9, characterized in that: The control hovering point of each inspection fixed point on the optimal inspection route of the drone is fine-tuned, including: if the signal interference of a certain segment of the optimal inspection route affects the hovering error of the drone Less than or equal to the threshold of the degree of influence of signal interference on the preset permitted hovering error of the drone by the WEB cloud storage , it means that there is no need to fine-tune the reference hovering points of each inspection fixed point on the segmented road section, and the original reference hovering point position is maintained; On the contrary, the relative deviation distance of the drone's hovering position under the signal interference intensity of the current environment of the segmented section is extracted and the relative deviation angle vector , respectively, by the formula , Analyze the correction distance and correction angle of the reference hovering point of the inspection fixed point on the segmented road section, and fine-tune the reference hovering point of each inspection fixed point on the segmented road section in combination with the relative deviation angle direction presented by the relative deviation angle vector; In this way, the reference hovering points of each inspection point on the optimal inspection route of the UAV can be fine-tuned.

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