A method for unmanned aerial vehicle route planning based on terrain survey

Abstract

The application relates to the field of unmanned aerial vehicle route planning, and discloses an unmanned aerial vehicle route planning method based on terrain surveying, which comprises the following steps: firstly, collecting the radio wave signals of the base stations near the region where the unmanned aerial vehicle is located, the terrain surveying information of the position where the unmanned aerial vehicle is located, and the atmospheric environment; calculating the flight object density in the terrain according to the terrain surveying information; establishing a flight object trajectory route probability image, simulating the unmanned aerial vehicle flight environment according to the flight object trajectory route probability image, and obtaining a collision coefficient; establishing a three-dimensional right-angle coordinate system of the electric signal according to the position where the unmanned aerial vehicle is located; performing data simulation, establishing an unmanned aerial vehicle safe flight route network model; screening the optimal flight route, and generating the unmanned aerial vehicle flight route; the application solves the problems that the unmanned aerial vehicle route is easily disturbed by environmental factors, the unmanned aerial vehicle is easily damaged by colliding with obstacles during flight, and the unmanned aerial vehicle flight time is too long due to deviation from the predetermined route.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight path planning, and more particularly to a UAV flight path planning method based on terrain surveying. Background Technology

[0002] With the advancement of technology, many industries have undergone tremendous changes, especially those that consume a lot of human and material resources. Robots, drones, and other technologies have gradually replaced human labor. Drones, in particular, have been fully utilized in agriculture, military, and entertainment.

[0003] Although drone technology is already very advanced, it still faces some challenges, especially drone flight path planning. This is a major problem in every drone application field. Drones are often interfered with by obstacles, weather, and other flying objects during flight, which can significantly affect their operation and, in severe cases, even damage them, causing them to crash and resulting in casualties. Therefore, a method that can quickly plan flight paths for drones, predict potential emergencies, and thus plan safe and fast flight routes for drones to target objects is urgently needed. Summary of the Invention

[0004] The present invention aims to provide a method for planning unmanned aerial vehicle (UAV) routes based on terrain surveying, in order to solve the problems in the prior art where UAV routes are easily affected by environmental factors, UAVs are prone to collisions with obstacles during flight leading to damage, and UAVs deviate from the planned route, resulting in excessively long flight times.

[0005] To achieve the above objectives, the present invention provides the following method:

[0006] This invention provides a method for UAV flight path planning based on terrain surveying:

[0007] S1: Collect radio signals from base stations, terrain survey information of the drone's location, and drone flight data from the topographic map of the area where the drone is located.

[0008] S2: Calculate the density of flying objects in the terrain based on the terrain survey information of the location of the UAV;

[0009] S3: Establish a probability profile of the trajectory of flying objects based on the density of flying objects in the terrain, simulate the flight environment of the UAV based on the probability profile of the trajectory of flying objects, and obtain the collision coefficient at different locations;

[0010] S4: Based on the radio wave signals of the base station in the topographic map of the area where the UAV is located, establish a simulated three-dimensional rectangular coordinate system of electrical signals with the UAV's location directly below it in the horizontal and vertical directions.

[0011] S5: Simulate the probability profile of the flight path of the flying object with the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the UAV.

[0012] S6: Based on the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficients of different locations passed during flight, and the UAV flight data, the optimal flight route is selected to generate the UAV flight path.

[0013] Preferably, the radio wave signal of the base station in the topographic map of the area where the UAV is located is: the radio wave signal emitted by the base station in the topographic map of the area where the UAV is located and the radio wave bounce signal received when it encounters an obstacle; the topographic survey information of the location of the UAV includes: the number of near-surface obstacles, the number of far-surface obstacles, historical data for the same period, and the density of flying objects before the UAV searches for the target; the UAV flight data includes: the airspeed of the UAV during flight, atmospheric environmental conditions, flight altitude, UAV vibration data, and energy consumption value; wherein, atmospheric environmental conditions include: weather conditions at different times, wind resistance faced by the UAV at different flight altitudes and speeds, and temperature conditions at different altitudes.

[0014] Preferably, the step of calculating the density of flying objects in the terrain based on the terrain survey information of the location of the UAV includes: filtering out the average number of flying objects in the historical period and the average number of flying objects in the early stage of flight based on the flying object density of the same period in history and the flying object density of the UAV before searching for the target; calculating the density P of flying objects in the terrain based on the number of near-surface obstacles and the number of far-surface obstacles, combined with the average number of flying objects in the historical period in history and the average number of flying objects in the early stage of flight.

[0015]

[0016] Where P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same historical period, A2 is the average number of flying objects in the early stage of flight, B1 is the number of near-surface obstacles, and B2 is the number of far-surface obstacles.

[0017] Preferably, the step of establishing a probability profile of aircraft trajectory routes based on the density of aircraft in the terrain includes: transmitting terrain survey information of the location of the UAV to a ground station server via a ground base station for data simulation analysis, simulating all flight trajectory routes from the hovering position of the UAV to the target object, and obtaining a first simulated trajectory route; obtaining the probability of aircraft occurrence at different spatial locations based on the density of aircraft in the terrain and the radio wave reflection signals received by the radio wave signals emitted by the base station when encountering obstacles in the terrain map of the area where the UAV is located; calculating the average probability of aircraft occurrence of the first simulated trajectory route based on the first simulated trajectory route and the probability of aircraft occurrence of the first simulated trajectory route; matching the first simulated trajectory route with the corresponding average probability of aircraft occurrence of the first simulated trajectory route, and adding an aircraft occurrence probability label to obtain a probability profile of aircraft trajectory routes.

[0018] Preferably, the formula for calculating the average probability of an object appearing along the first simulated trajectory is:

[0019]

[0020] Where FZ is the average probability of an object appearing on the first simulated trajectory route, g1~gn are the probabilities of objects appearing on multiple spatial locations along the first simulated trajectory route, and n is the number of spatial location points passed by objects on each first simulated trajectory route.

[0021] Preferably, the step of simulating the UAV flight environment based on the probability profile of the flight trajectory to obtain the collision coefficient at different positions includes: simulating the UAV flight environment based on the probability profile of the flight trajectory; in the UAV flight environment, using the controlled variable method to control different flight speeds, altitudes, and flight routes to construct a simulated UAV flight process; during the simulated UAV flight process, recording the simultaneous intersection points of the UAV and the flight object, and marking the simultaneous intersection points of the UAV and the flight object as a flight collision point; marking the nearest spatial distance and time to the flight collision point during each simulated UAV flight process, and comparing the nearest spatial distance and time each time to obtain the collision coefficient at different positions.

[0022] Preferably, the step of establishing a simulated three-dimensional rectangular coordinate system of electrical signals based on the radio wave signals of the base station in the topographic map of the area where the UAV is located, with the UAV's location directly below horizontally and vertically, includes: obtaining a simulated electrical signal environment based on the radio wave signals emitted by the base station in the topographic map of the area where the UAV is located and the radio wave bounce signals received by the obstacles; establishing a simulated three-dimensional rectangular coordinate system of electrical signals with the horizontal plane point directly below the UAV's location as the origin; marking the position of the target object in the simulated three-dimensional rectangular coordinate system of electrical signals to obtain the simulated electrical signal coordinates of the target object.

[0023] Preferably, the step of establishing a UAV safe flight route network model by simulating the probabilistic profile of the flight trajectory with the simulated three-dimensional Cartesian coordinate system of the electrical signal includes: simulating the actual shapes of the near-surface and far-surface obstacles using the simulated three-dimensional Cartesian coordinate system of the electrical signal and the number of obstacles; digitizing the obstacle data shapes by extending the obstacle data shapes with equal contours to obtain the obstacle safety range; importing and marking the obstacle safety ranges into the probabilistic profile of the flight trajectory, and combining the obstacle data shapes and the obstacle safety ranges to obtain a second simulated trajectory; and performing multi-dimensional target flight simulation on the simulated coordinates of the target's electrical signal based on the second simulated trajectory to establish a UAV safe flight route network model.

[0024] Preferably, the formula for calculating the equal contour extension distance of the obstacle data shape is as follows:

[0025] JL = 2 × log10(G + D + A)

[0026] Where JL is the extended distance of the same contour, G is gravity, D is the weight of the UAV, and A is the flight speed of the UAV.

[0027] Preferably, the step of selecting the optimal flight route and generating the UAV flight path based on the energy consumption information of different routes in the UAV safe flight path network model, the collision coefficients at different locations during flight, and the UAV flight data includes: sorting the UAV flight energy consumption of different routes in the UAV safe flight path network model to obtain a route energy consumption ranking; obtaining the energy consumption coefficient of the flight path based on the route energy consumption ranking; integrating the weather conditions at different times, the wind resistance faced by the UAV at different flight altitudes and speeds, and the temperature conditions at different altitudes to calculate the UAV atmospheric environment coefficient; and integrating the energy consumption coefficient of the flight path, the UAV atmospheric environment coefficient, and the collision coefficients at different locations during flight to obtain the overall coefficient of the UAV flight trajectory, with the formula:

[0028] X1 = 0.25X2 + 0.15X3 + 0.6X4

[0029] Wherein, X1 is the overall coefficient of the UAV flight trajectory, X2 is the energy consumption coefficient of the flight route, X3 is the atmospheric environment coefficient of the UAV, and X4 is the collision coefficient at the different locations passed during flight; by adding the overall coefficient of the UAV flight trajectory to the UAV safe flight route network model, the optimal flight route is obtained, and the UAV flight path is generated.

[0030] The beneficial effects of this invention are as follows: This invention filters the average number of flying objects in the same historical period and the average number of flying objects in the early stage of the drone's target search to calculate the density of flying objects in the terrain. By considering the density of flying objects in two periods, the drone's daily flying object density calculation is more accurate. Then, by using the radio wave signals emitted by the base station near the terrain where the drone is located, the actual shape of the nearby obstacles is obtained, and the probability of the flying objects appearing is calculated, thereby obtaining a probability profile of the flying object trajectory. The collision coefficient is obtained through the probability profile of the flight trajectory. By considering the collision probability of each spatial point during the drone's flight, the collision between the drone and flying objects or obstacles can be further avoided. Finally, a safe flight route network model of the drone is simulated, which considers the energy consumption coefficient of the flight route, the atmospheric environment coefficient of the drone, and the collision coefficient of passing through different positions during the flight. This allows the optimal drone flight route to be obtained from countless drone flight routes, realizing the optimal flight path planning of the drone. Attached Figure Description

[0031] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0032] Figure 1 This is a flowchart illustrating a method for UAV route planning based on terrain surveying provided in an embodiment of the present invention.

[0033] Figure 2 This is a process flow diagram of UAV route planning based on terrain surveying provided in an embodiment of the present invention.

[0034] Figure 3 This is a diagram showing the overall coefficient composition of the drone flight trajectory provided in an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] Although drone technology is already very advanced, it still faces some challenges, especially drone flight path planning. This is a major problem in every drone application field. Drones are often interfered with by obstacles, weather, and other flying objects during flight, which can significantly affect their operation and, in severe cases, even damage them, causing them to crash and resulting in casualties. Therefore, a method that can quickly plan flight paths for drones, predict potential emergencies, and thus plan safe and fast flight routes for drones to target objects is urgently needed.

[0039] The present invention aims to provide a method for planning unmanned aerial vehicle (UAV) routes based on terrain surveying, in order to solve the problems in the prior art where UAV routes are easily affected by environmental factors, UAVs are prone to collisions with obstacles during flight leading to damage, and UAVs deviate from the planned route, resulting in excessively long flight times.

[0040] This invention provides a method for UAV flight path planning based on terrain surveying, as follows: Figure 1 , Figure 2 , Figure 3 As shown, it includes the following steps:

[0041] S1: Collect radio signals from base stations, topographic survey information of the drone's location, and drone flight data from the topographic map of the area where the drone is located.

[0042] In this embodiment of the invention, the radio wave signal of the base station in the topographic map of the area where the UAV is located is: the radio wave signal emitted by the base station in the topographic map of the area where the UAV is located and the radio wave bounce signal received when it encounters an obstacle; the topographic survey information of the UAV's location includes: the number of near-surface obstacles, the number of far-surface obstacles, historical data for the same period, and the density of flying objects before the UAV searches for the target; the UAV flight data includes: the airspeed of the UAV during flight, atmospheric environmental conditions, flight altitude, UAV vibration data, and energy consumption; wherein, atmospheric environmental conditions include: weather conditions at different times, wind resistance faced by the UAV at different flight altitudes and speeds, and temperature conditions at different altitudes; atmospheric environmental conditions refer to the physical, chemical, and biological characteristics of the air on which living organisms depend for survival, including air temperature, humidity, wind speed, air pressure, and precipitation. Water; Currently, commonly used methods for detecting low, slow, and small flying objects include radar detection, radio detection, acoustic detection, and photoelectric detection. The flying objects to be detected here are mainly low, slow, and small flying objects. Low, slow, and small flying objects have small radar cross-sections, and some drones are made of non-metallic materials, generating very weak surface currents, making them difficult to detect by radar. Active radar emits high-power electromagnetic wave signals, but airports have strict requirements for electromagnetic access control, which restricts the use of radar. Radio cannot detect drones that fly autonomously or remain silent. Acoustic detection is greatly affected by environmental noise and has a limited range. Photoelectric detection monitors the sky area through cameras, enabling the detection of various types of targets at a relatively long distance. Furthermore, it is a passive detection method and will not affect aviation safety, making it the flying object detection method used here.

[0043] S2: Calculate the density of flying objects in the terrain based on the terrain survey information of the drone's location.

[0044] In this embodiment of the invention, the average number of flying objects in the historical period and the average number of flying objects in the early stage of the UAV's target search are selected based on the flying object density of the same period in history and the flying object density of the early stage of the UAV. The flying object density P in the terrain is calculated based on the number of near-surface obstacles and far-surface obstacles, combined with the average number of flying objects in the historical period and the average number of flying objects in the early stage of the UAV.

[0045]

[0046] Where P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same historical period, A2 is the average number of flying objects in the pre-flight period, B1 is the number of near-surface obstacles, and B2 is the number of far-surface obstacles; the pre-flight period is from half a month before the drone operation to the day before the drone operation. Terrain surveying refers to a series of tasks involving the measurement, analysis, evaluation, and design of land, groundwater, geological structures, buildings, etc. Its basic contents include the following aspects: 1. Land surveying: including measuring the area, shape, topography, landform, groundwater level, soil type, etc. of the land; 2. Groundwater exploration: through the measurement of groundwater level, water quality, hydrogeology, etc.; 3. Geological survey: through the investigation and analysis of geological structures, stratigraphic structures, geological hazards, etc.; 4. Building surveying: including the measurement of building plans, elevations, sections, structural diagrams, etc.; 5. Building inspection.

[0047] S3: Establish a probability profile of the trajectory of flying objects based on the density of flying objects in the terrain, simulate the flight environment of the drone based on the probability profile of the trajectory of flying objects, and obtain the collision coefficient at different locations.

[0048] In this embodiment of the invention, based on the terrain survey information of the UAV's location, data is transmitted to a ground station server via a ground base station for data simulation analysis. This simulates all flight paths from the UAV's hovering position to the target object, resulting in a first simulated trajectory path. Based on the density of flying objects in the terrain and the radio wave reflection signals from obstacles encountered by the radio wave signals emitted by the base station in the terrain map of the UAV's location, the probability of flying objects appearing at different spatial locations is obtained. The first simulated trajectory path and the probability of flying objects appearing at different spatial locations are integrated to calculate the average probability of flying objects appearing on each first simulated trajectory path. Each first simulated trajectory path is then mapped one-to-one with its corresponding average probability of flying objects appearing on the first simulated trajectory path, and a probability tag for flying object appearance is added to obtain a probability profile of the flying object trajectory path. The formula for calculating the average probability of flying objects appearing on each first simulated trajectory path is as follows:

[0049]

[0050] Where FZ is the average probability of an object appearing along the first simulated trajectory, g1 to gn are the probabilities of objects appearing at multiple spatial locations along the first simulated trajectory, and n is the number of spatial locations traversed by objects along each first simulated trajectory. The UAV flight environment is simulated based on the probability profile of the flight trajectory. In this UAV flight environment, a controlled variable method is used to control different flight speeds, altitudes, and flight paths to construct the UAV simulated flight process. During the UAV simulated flight, the simultaneous intersection points between the UAV and objects are recorded, and these points are marked as collision points. The nearest spatial distance and time to the collision point are marked during each UAV simulated flight, and the collision coefficient at different locations is obtained by comparing the nearest spatial distance and time each time. For example, the UAV simulated flight... The flight path is divided into 6 segments. The collision coefficient at different locations is obtained by dividing the marked collision point in each segment by the sum of the collision points during the simulated flight of the UAV. The urban low-altitude environment is variable, with dense buildings, tangled power lines, and numerous birds, which poses a higher collision risk than the high-altitude environment, greatly increasing the safety of UAV operation. Because UAVs follow fixed routes and operating rules, and the locations of buildings are fixed, collisions with buildings are rare. However, bird flight is random, and its exact location cannot be predicted. It can only be based on the aggregation of past experience data, which brings great uncertainty to the collision probability estimation. The impact of birds on the collision probability of UAVs is calculated based on the position error probability model, and the collision probability in the longitudinal, lateral, and vertical directions is calculated to predict the collision risk between UAVs and birds. When the flight paths of UAVs and birds intersect, it is defined as an intersecting collision. However, due to the unique nature of intersecting flight paths, the risk of collision mainly stems from the fact that the drone deviates from the preset flight path due to factors such as positioning errors or human manipulation, or from the uncertainty caused by the randomness of the position and speed of birds flying. Ultimately, this may result in the distance between the drone and the bird being less than the standard distance, thus causing a collision.

[0051] S4: Based on the radio wave signals of the base station in the topographic map of the area where the UAV is located, establish a simulated three-dimensional rectangular coordinate system of electrical signals directly below the horizontal and vertical position of the UAV.

[0052] In this embodiment of the invention, an electrical signal simulation environment is obtained based on the radio wave signals emitted by the base station and the radio wave bounce signals received by obstacles in the topographic map of the area where the UAV is located; a three-dimensional rectangular coordinate system for electrical signal simulation is established with the horizontal point directly below the UAV's location as the origin; the position of the target object in the three-dimensional rectangular coordinate system for electrical signal simulation is marked with coordinates to obtain the electrical signal simulation coordinates of the target object; the coordinates of near-ground obstacles are different from the coordinates of far-ground obstacles.

[0053] S5: Simulate the probabilistic profile of the flight path and the three-dimensional rectangular coordinate system of the electrical signal to establish a safe flight path network model for UAVs.

[0054] In this embodiment of the invention, data simulation is performed based on the number of near-surface obstacles and the number of far-surface obstacles in a simulated three-dimensional rectangular coordinate system using electrical signals. The substantial shapes of near-surface and far-surface obstacles are digitized to obtain obstacle data shapes. The obstacle data shapes are then extended by equal contours to obtain the obstacle safety range. Based on the probability profile of the flight path, a simulated flight path is performed on the obstacle safety range to obtain a second simulated trajectory path. Based on the second simulated trajectory path, a multi-dimensional target flight simulation is performed on the simulated coordinates of the target's electrical signals to establish a UAV safe flight path network model. The formula for calculating the equal contour extension distance of the obstacle data shapes is as follows:

[0055] JL = 2 × log10(G + D + A)

[0056] Where JL is the extended distance of the same contour, G is gravity, D is the weight of the UAV, and A is the flight speed of the UAV; near-surface obstacles are mainly ground vegetation and rocks, while far-surface obstacles are birds and insects.

[0057] S6: Based on the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficients of different locations passed during flight, and UAV flight data, the optimal flight route is selected and the UAV flight path is generated.

[0058] In this embodiment of the invention, the energy consumption of UAVs on different routes in the UAV safe flight route network model is ranked to obtain the route energy consumption ranking. The energy consumption coefficient of the flight route is then obtained based on this ranking. Data on weather conditions at different times, wind resistance at different flight altitudes and speeds, and temperature conditions at different altitudes are integrated to calculate the UAV atmospheric environment coefficient. Finally, the energy consumption coefficient of the flight route, the UAV atmospheric environment coefficient, and the collision coefficients at different locations during flight are combined to obtain the overall coefficient of the UAV flight trajectory, as shown in the formula:

[0059] X1 = 0.25X2 + 0.15X3 + 0.6X4

[0060] Where X1 is the overall coefficient of the UAV flight trajectory, X2 is the energy consumption coefficient of the flight route, X3 is the atmospheric environment coefficient of the UAV, and X4 is the collision coefficient at different locations during flight. By adding the overall coefficient of the UAV flight trajectory to the UAV safe flight route network model, the optimal flight route is obtained and the UAV flight path is generated.

[0061] The beneficial effects of this invention are as follows: This invention filters the average number of flying objects in the same historical period and the average number of flying objects in the early stage of the drone's target search to calculate the density of flying objects in the terrain. By considering the density of flying objects in two periods, the drone's daily flying object density calculation is more accurate. Then, by using the radio wave signals emitted by the base station near the terrain where the drone is located, the actual shape of the nearby obstacles is obtained, and the probability of the flying objects appearing is calculated, thereby obtaining a probability profile of the flying object trajectory. The collision coefficient is obtained through the probability profile of the flight trajectory. By considering the collision probability of each spatial point during the drone's flight, the collision between the drone and flying objects or obstacles can be further avoided. Finally, a safe flight route network model of the drone is simulated, which considers the energy consumption coefficient of the flight route, the atmospheric environment coefficient of the drone, and the collision coefficient of passing through different positions during the flight. This allows the optimal drone flight route to be obtained from countless drone flight routes, realizing the optimal flight path planning of the drone.

[0062] The above descriptions are merely embodiments of the present invention. Commonly known technical solutions or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for unmanned aerial vehicle (UAV) flight path planning based on terrain surveying, characterized in that, The method includes: S1: Collect radio signals from base stations, terrain survey information of the drone's location, and drone flight data from the topographic map of the area where the drone is located. S2: Calculate the density of flying objects in the terrain based on the terrain survey information of the location of the UAV; S3: Establish a probability profile of the trajectory of flying objects based on the density of flying objects in the terrain, simulate the flight environment of the UAV based on the probability profile of the trajectory of flying objects, and obtain the collision coefficient at different locations; S4: Based on the radio wave signals of the base station in the topographic map of the area where the UAV is located, establish a simulated three-dimensional rectangular coordinate system of electrical signals with the UAV's location directly below it in the horizontal and vertical directions. S5: Simulate the probability profile of the flight path of the flying object with the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the UAV. S6: Based on the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficients of different locations passed during flight, and the UAV flight data, the optimal flight route is selected to generate the UAV flight path. The radio wave signal of the base station in the topographic map of the area where the UAV is located is: the radio wave signal emitted by the base station in the topographic map of the area where the UAV is located and the radio wave bounce signal received when it encounters an obstacle. The terrain survey information of the location of the UAV includes: the number of near-surface obstacles, the number of far-surface obstacles, historical data for the same period, and the density of flying objects in the early stage of the UAV's search for the target. The UAV flight data includes: airspeed, atmospheric conditions, flight altitude, UAV vibration data, and energy consumption. The atmospheric environment includes: weather conditions at different times, wind resistance faced by the drone at different flight altitudes and speeds, and temperature conditions at different altitudes; The step of calculating the density of flying objects in the terrain based on the terrain survey information of the location of the UAV includes: Based on the historical data and the density of flying objects in the early stages of drone target search, the average number of flying objects in the historical data and the average number of flying objects in the early stages of drone search were selected. The airborne object density P in the terrain is calculated based on the number of near-surface obstacles, the number of far-surface obstacles, the historical average number of airborne objects for the same period, and the average number of airborne objects in the early stages of flight. Where P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same historical period, A2 is the average number of flying objects in the early stage of flight, B1 is the number of near-surface obstacles, and B2 is the number of far-surface obstacles.

2. The method for UAV flight path planning based on terrain surveying according to claim 1, characterized in that, The step of establishing a probability profile of the trajectory of an object based on the density of objects in the terrain includes: Based on the terrain survey information of the location of the UAV, the data is transmitted to the ground station server through the ground base station for data simulation analysis. The simulation results in all flight trajectories from the hovering position of the UAV to the target object, and the first simulated trajectory route is obtained. Based on the density of flying objects in the terrain and the radio wave reflection signals received by the base station when it encounters obstacles in the terrain map of the area where the UAV is located, the probability of flying objects appearing at different spatial locations is obtained. Based on the first simulated trajectory and the probability of objects appearing at different spatial locations, calculate the average probability of objects appearing along the first simulated trajectory: The first simulated trajectory route and the corresponding average probability of the appearance of the flying object along the first simulated trajectory route are matched one-to-one, and a probability label of the appearance of the flying object is added to obtain a probability profile of the flying object trajectory route.

3. The method for UAV flight path planning based on terrain surveying according to claim 2, characterized in that, The formula for calculating the average probability of an object appearing along the first simulated trajectory is as follows: Where FZ is the average probability of an object appearing on the first simulated trajectory route, g1~gn are the probabilities of objects appearing on multiple spatial locations along the first simulated trajectory route, and n is the number of spatial location points passed by objects on each first simulated trajectory route.

4. The method for UAV flight path planning based on terrain surveying according to claim 2, characterized in that, The step of simulating the UAV flight environment based on the probability profile of the flight path to obtain the collision coefficient at different locations includes: The drone flight environment is simulated based on the probability profile of the flight trajectory. In the aforementioned UAV flight environment, the controlled variable method is used to control different flight speeds, altitudes, and flight paths to construct a simulated UAV flight process; During the simulated flight of the UAV, the simultaneous intersection points between the UAV and the flying object are recorded, and these points are marked as a flight collision point. The nearest spatial distance and time to the flight collision point are marked during each simulated flight of the UAV, and the collision coefficient at different locations is obtained by comparing the nearest spatial distance and time each time.

5. The method for UAV flight path planning based on terrain surveying according to claim 1, characterized in that, The step of establishing a simulated three-dimensional rectangular coordinate system based on the radio wave signals of the base station in the topographic map of the area where the UAV is located, with the UAV's position horizontally and vertically directly below it, includes: The electromagnetic environment is simulated by the electromagnetic wave signal obtained from the radio wave signal emitted by the base station encountering obstacles and being reflected back by the radio wave signal in the topographic map of the area where the UAV is located. A three-dimensional rectangular coordinate system for simulating electrical signals is established with the point directly below the drone's location as the origin. The position of the target object in the simulated three-dimensional rectangular coordinate system of the electrical signal is marked with coordinates to obtain the simulated electrical signal coordinates of the target object.

6. The method for UAV flight path planning based on terrain surveying according to claim 5, characterized in that, The step of simulating the probabilistic profile of the flight path and the three-dimensional Cartesian coordinate system simulated by the electrical signal to establish a safe flight path network model for the UAV includes: Based on the electrical signal simulation of a three-dimensional rectangular coordinate system and the number of near-surface obstacles and the number of far-surface obstacles, data simulation is performed to digitize the substantial shapes of near-surface obstacles and far-surface obstacles, thereby obtaining obstacle data shapes; The obstacle data shape is extended by the same contour to obtain the obstacle safety range; The obstacle safety range of the obstacle is imported and marked in the probability profile of the flight trajectory, and the obstacle data shape and the obstacle safety range are combined to obtain the second simulated trajectory route; Based on the second simulated trajectory route, a multi-dimensional target flight simulation is performed on the simulated coordinates of the target object's electrical signal to establish a safe flight route network model for the UAV.

7. The method for UAV flight path planning based on terrain surveying according to claim 6, characterized in that: The formula for calculating the equal contour extension distance of the obstacle data shape is as follows: JL = 2 × log10(G + D + A) Where JL is the extended distance of the same contour, G is gravity, D is the weight of the UAV, and A is the flight speed of the UAV.

8. The method for UAV flight path planning based on terrain surveying according to claim 6, characterized in that, The step of selecting the optimal flight route and generating the UAV flight path based on the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficients at different locations during flight, and the UAV flight data includes: The energy consumption of UAVs on different routes in the UAV safe flight route network model is sorted to obtain the route energy consumption ranking, and the energy consumption coefficient of the flight route is obtained based on the route energy consumption ranking. The atmospheric environment coefficient of the UAV is calculated by integrating the data on weather conditions at different times, wind resistance at different flight altitudes and speeds, and temperature conditions at different altitudes. By integrating the energy consumption coefficient of the flight path, the atmospheric environment coefficient of the UAV, and the collision coefficient at different locations during flight, the overall coefficient of the UAV flight trajectory is obtained, as shown in the formula: X1 = 0.25X2 + 0.15X3 + 0.6X4 Where X1 is the overall coefficient of the UAV flight trajectory, X2 is the energy consumption coefficient of the flight route, X3 is the atmospheric environment coefficient of the UAV, and X4 is the collision coefficient of passing through the different locations during the flight. By adding the overall coefficient of the UAV flight trajectory to the UAV safe flight route network model, the optimal flight route is obtained, and the UAV flight path is generated.

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