Unmanned aerial vehicle route planning method based on topographic survey

Abstract

The invention relates to the field of unmanned aerial vehicle route planning, and discloses a terrain survey-based unmanned aerial vehicle route planning method, which comprises the following steps of: firstly, acquiring an electric wave signal of a base station near a region where an unmanned aerial vehicle is located, terrain survey information of the position where the unmanned aerial vehicle is located and an atmospheric environment condition; calculating the density of flyers in the terrain according to the terrain survey information; establishing a flyer trajectory path probability portrait, and simulating an unmanned aerial vehicle flight environment according to the flyer trajectory path probability portrait to obtain a collision coefficient; establishing an electric signal simulation three-dimensional rectangular coordinate system according to the position of the unmanned aerial vehicle according to the electric wave signal; performing data simulation, and establishing a safe flight route network model of the unmanned aerial vehicle; screening an optimal flight route, and generating a flight route of the unmanned aerial vehicle; the problems that in the prior art, the route of the unmanned aerial vehicle is prone to being interfered and influenced by environmental factors, the unmanned aerial vehicle is damaged due to the fact that the unmanned aerial vehicle collides with obstacles in the flight process, and the flight time of the unmanned aerial vehicle is too long due to deviation from the preset route are solved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) route planning, and in particular to a UAV route planning method based on terrain survey. Background Art

[0002] With the advancement of science and technology, many industries have undergone earth-shaking changes, especially some industries that consume manpower and material resources. Human labor replacements such as robots and drones have gradually emerged; especially drones have been fully utilized in agriculture, military, and performance.

[0003] Although drone technology is very advanced at present, it still faces some problems, especially drone route planning technology, which is a major problem that needs to be faced in every drone application field. Drones are often interfered by obstacles, weather, and flying objects during flight, which can easily have a great impact on the operation of drones. In serious cases, the drone may even be damaged, causing the drone to fall and cause casualties. Therefore, a method that can realize rapid route planning of drones, predict emergencies that drones may encounter, and thus plan safe and fast flight routes for drones and realize rapid route planning of drones to target objects is urgently needed. Summary of the invention

[0004] The present invention aims to provide a method for UAV route planning based on terrain survey, so as to solve the problems in the prior art that the UAV route is easily affected by environmental factors, the UAV is prone to hit obstacles during flight, causing damage to the UAV, and the UAV deviates from the planned route, causing the UAV flight time to be too long.

[0005] In order to achieve the above object, the present invention provides the following method:

[0006] The present invention provides a method for UAV route planning based on terrain survey:

[0007] S1: Collect the radio wave signal of the base station in the topographic map of the area where the drone is located, the topographic survey information of the drone's location, and the drone's flight data;

[0008] S2: Calculating the density of flying objects in the terrain according to the terrain survey information of the location of the UAV;

[0009] S3: establishing a probability profile of a flying object trajectory according to the density of flying objects in the terrain, simulating a UAV flight environment according to the probability profile of the flying object trajectory, and obtaining collision coefficients at different positions;

[0010] S4: according to the radio wave signal of the base station in the topographic map of the area where the drone is located, establish an electric signal simulation three-dimensional rectangular coordinate system directly below the horizontal and vertical position of the drone;

[0011] S5: Performing data simulation on the probability portrait of the flying object trajectory and the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the UAV;

[0012] S6: Filter the optimal flight route according to the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficient of different positions during the flight, and the UAV flight data to generate the UAV flight route.

[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 rebound signal recovered by the radio wave signal emitted by the base station in the topographic map of the area where the UAV is located when encountering 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, the density of flying objects in the same period of history and in the early stage of the UAV's search for the target object; the UAV flight data includes: the airspeed of the UAV during flight, the atmospheric environment conditions, the flight altitude, the vibration data of the UAV and the energy consumption value; among which, the atmospheric environment conditions include: weather conditions at different time periods, the wind resistance faced by the UAV at different flight altitudes and flight speeds, and the temperature conditions at different altitudes.

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

[0015]

[0016] Among them, P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same period of history, 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 portrait of the trajectory of an object according to the density of flying objects in the terrain comprises: transmitting the terrain survey information of the location of the UAV to the ground station server through the ground base station, performing 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 occurrence of flying objects at different spatial positions according to the density of flying objects in the terrain and the radio wave rebound signal recovered by the radio wave signal emitted by the base station when encountering an obstacle in the terrain map of the area where the UAV is located; calculating the mean probability of occurrence of flying objects for the first simulated trajectory route according to the first simulated trajectory route and the probability of occurrence of flying objects at the different spatial positions: making a one-to-one correspondence between the first simulated trajectory route and the corresponding mean probability of occurrence of flying objects for the first simulated trajectory route, and adding a flying object occurrence probability label to obtain a probability portrait of the trajectory of an object.

[0018] Preferably, the formula for calculating the mean probability of occurrence of the flying object in the first simulated trajectory route is:

[0019]

[0020] Among them, FZ is the mean probability of the flying object appearing in the first simulated trajectory route, g1~gn are the probability of the flying object appearing in multiple spatial positions passed by the flight trajectory route in the first simulated trajectory route, and n is the number of spatial position points passed by the flying object in each first simulated trajectory route.

[0021] Preferably, the step of simulating the UAV flight environment according to the probability portrait of the flying object's trajectory route to obtain collision coefficients at different positions includes: simulating the UAV flight environment according to the probability portrait of the flight trajectory route; in the UAV flight environment, using the control variable method to control different flight speeds, altitudes and flight routes for the UAV to construct a simulated UAV flight process; in the UAV simulated flight process, recording the simultaneous intersection spatial point of the UAV and the flying object, and marking the simultaneous intersection spatial point of the UAV and the flying object as a flight collision point; marking the closest spatial distance and time from the flight collision point during each UAV simulated flight process, and comparing the closest spatial distance and time each time to obtain collision coefficients at different positions.

[0022] Preferably, the step of establishing a three-dimensional rectangular coordinate system for simulating electric signals directly below the horizontal and vertical position of the drone based on the radio wave signals of the base station in the topographic map of the area where the drone is located includes: obtaining an electric signal simulation environment based on radio wave rebound signals recovered when the radio wave signals emitted by the base station in the topographic map of the area where the drone is located encounter obstacles; establishing a three-dimensional rectangular coordinate system for simulating electric signals with the horizontal surface point directly below the horizontal and vertical position of the drone as the coordinate origin; marking the coordinates of the position of the target object in the three-dimensional rectangular coordinate system for simulating electric signals to obtain the electric signal simulation coordinates of the target object.

[0023] Preferably, the step of performing data simulation on the probability portrait of the flying object's trajectory route and the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the unmanned aerial vehicle includes: performing data simulation on the three-dimensional rectangular coordinate system simulated by the electrical signal and the number of near-surface obstacles and the number of far-surface obstacles, digitizing the substantial shapes of near-surface obstacles and far-surface obstacles to obtain obstacle data shapes; performing equal contour expansion and extension on the obstacle data shapes to obtain the obstacle safety range of the obstacles; importing and marking the obstacle safety range of the obstacles in the probability portrait of the flying object's trajectory route, and combining the obstacle data shape and the obstacle safety range to obtain a second simulated trajectory route; and performing multi-dimensional target flight simulation on the electrical signal simulation coordinates of the target object on the basis of the second simulated trajectory route to establish a safe flight route network model for the unmanned aerial vehicle.

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

[0025] JL=2×log10(G+D+A)

[0026] Among them, JL is the extension distance of the same profile, G is gravity, D is the weight of the drone, and A is the flight speed of the drone.

[0027] Preferably, the optimal flight route is selected according to the energy consumption information of different routes in the drone safe flight route network model, the collision coefficients of different positions during the flight, and the drone flight data, and the step of generating the drone flight route includes: sorting the drone flight energy consumption of different routes in the drone safe flight route network model to obtain the route energy consumption sorting, and obtaining the energy consumption coefficient of the flight route according to the route energy consumption sorting; integrating the weather conditions at different time periods, the wind resistance faced by the drone at different flight altitudes and different flight speeds, and the temperature conditions at different altitudes to calculate the drone atmospheric environment coefficient; integrating the energy consumption coefficient of the flight route, the drone atmospheric environment coefficient, and the collision coefficients of the different positions during the flight to obtain the overall coefficient of the drone flight trajectory, the formula is:

[0028] X 1 =0.25X 2 +0.15X 3 +0.6X 4

[0029] Among them, X 1 is the overall coefficient of the UAV flight trajectory, X 2 is the energy consumption coefficient of the flight route, X 3 is the atmospheric environment coefficient of the UAV, X 4 is the collision coefficient of passing through the different positions during the flight; the overall coefficient of the UAV flight trajectory is added to the UAV safe flight route network model to obtain the optimal flight route and generate the UAV flight route.

[0030] The beneficial effects of the present invention are as follows: the present invention selects the average number of flying objects in the same period of history and the average number of flying objects in the early stage of flight through the flying object density in the same period of history and the early stage of the drone searching for the target object, calculates the flying object density in the terrain, takes into account the flying object density in the two periods, and makes the flying object density calculation of the drone on the day more accurate, and then obtains the specific actual shape of the nearby obstacles through the radio wave signal emitted by the base station near the terrain where the drone is located, and calculates the probability of the flying object appearing, thereby obtaining the flying object trajectory route probability portrait, and obtaining the collision coefficient through the flight trajectory route probability portrait, considering the collision probability of each spatial point during the flight of the drone, which can further avoid the collision between the drone and the flying object or obstacle, and finally simulates the drone safe flight route network model, taking into account the energy consumption coefficient of the flight route, the drone atmospheric environment coefficient and the collision coefficient of different positions during the flight, so that the optimal drone flight route is obtained among countless drone flight routes, and the optimal route planning of the drone is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. 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 according to the actual scale.

[0032] Figure 1 It is a flowchart of a method for UAV route planning based on terrain survey provided by an embodiment of the present invention.

[0033] Figure 2 It is a process flow chart of UAV route planning based on terrain survey provided by an embodiment of the present invention.

[0034] Figure 3 It is a composition diagram of the overall coefficients of the UAV flight trajectory provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the technical personnel in the technical field better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment 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.

[0036] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

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

[0038] Although drone technology is very advanced at present, it still faces some problems, especially drone route planning technology, which is a major problem that needs to be faced in every drone application field. Drones are often interfered by obstacles, weather, and flying objects during flight, which can easily have a great impact on the operation of drones. In serious cases, the drone may even be damaged, causing the drone to fall and cause casualties. Therefore, a method that can realize rapid route planning of drones, predict emergencies that drones may encounter, and thus plan safe and fast flight routes for drones and realize rapid route planning of drones to target objects is urgently needed.

[0039] The present invention aims to provide a method for UAV route planning based on terrain survey, so as to solve the problems in the prior art that the UAV route is easily affected by environmental factors, the UAV is prone to hit obstacles during flight, causing damage to the UAV, and the UAV deviates from the planned route, causing the UAV flight time to be too long.

[0040] The specific embodiment of the present invention provides a method for UAV route planning based on terrain survey, the method is as follows Figure 1 , Figure 2 , Figure 3 As shown, the following steps are included:

[0041] S1: Collect the radio wave signal of the base station in the topographic map of the area where the drone is located, the topographic survey information of the drone's location, and the drone's flight data.

[0042] In an embodiment of the present invention, the radio wave signal of the base station in the topographic map of the area where the drone is located is: the radio wave rebound signal recovered by the radio wave signal emitted by the base station in the topographic map of the area where the drone is located when encountering an obstacle; the terrain survey information of the location of the drone includes: the number of near-surface obstacles, the number of far-surface obstacles, the density of flying objects in the same period of history and in the early stage of the drone's search for the target object; the drone flight data includes: the airspeed of the drone during flight, the atmospheric environment conditions, the flight altitude, the vibration data of the drone and the energy consumption value; among which, the atmospheric environment conditions include: the weather conditions at different times, the wind resistance faced by the drone at different flight altitudes and different flight speeds, and the temperature conditions at different altitudes; the atmospheric environment conditions refer to the physical, chemical and biological properties of the air on which organisms depend for survival, including the temperature, humidity, wind speed, air pressure and drop of the air. water; Currently, the commonly used methods for detecting low-flying, slow and small flying objects include radar detection, radio detection, acoustic detection and photoelectric detection; the flying objects used for detection here are mainly low-flying, slow and small flying objects. The radar reflection area of ​​low-flying, slow and small flying objects is small. Some drones are made of non-metallic materials, and the surface current generated is very weak, which is not easy to be detected by radar; active radar will emit high-power electromagnetic wave signals, and the airport has strict requirements on the electromagnetic access system, which restricts the use of radar. Radio cannot detect drones that fly autonomously and remain silent. Acoustic detection is greatly disturbed by environmental noise and has a limited range. Photoelectric detection monitors the sky area through a camera and can detect various types of targets at a long distance. It is a passive detection and will not affect aviation safety. It is also 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 location of the UAV.

[0044] In the embodiment of the present invention, the average number of flying objects in the same period of history and the average number of flying objects in the early stage of the flight are screened out according to the flying object density in the same period of history and the early stage of the drone searching for the target object; the flying object density P in the terrain is calculated according to the number of near-surface obstacles and the number of far-surface obstacles combined with the average number of flying objects in the same period of history and the average number of flying objects in the early stage of the flight:

[0045]

[0046] Among them, P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same period in history, 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; the early stage of flight is from half a month ago to the day before the operation of the drone. Topographic survey refers to a series of work on measuring, analyzing, evaluating and designing land, groundwater, geological structure, buildings, etc.; its basic content includes the following aspects: 1. Land measurement: including measuring the area, shape, topography, landform, groundwater level, soil type and other information of the land; 2. Groundwater exploration: through the measurement of groundwater level, water quality, hydrogeology and other aspects; 3. Geological survey: through investigation and analysis of geological structure, stratigraphic structure, geological disasters and other aspects; 4. Building measurement: including the measurement of the floor plan, elevation, section, structural diagram and other aspects of the building; 5. Building detection.

[0047] S3: A probability profile of the flying object's trajectory is established according to the flying object density in the terrain, and the UAV flight environment is simulated according to the probability profile of the flying object's trajectory to obtain the collision coefficient at different positions.

[0048] In an embodiment of the present invention, according to the terrain survey information of the location of the UAV, the information is transmitted to the ground station server through the ground base station, and data simulation analysis is performed to simulate all flight trajectory routes from the hovering position of the UAV to the target object to obtain a first simulated trajectory route; according to the density of flying objects in the terrain and the radio wave rebound signal recovered by the radio wave signal emitted by the base station in the terrain map of the area where the UAV is located when encountering an obstacle, the probability of flying objects appearing at different spatial positions is obtained; the first simulated trajectory route and the probability of flying objects appearing at different spatial positions are data integrated to calculate the mean probability of flying objects appearing for each first simulated trajectory route: each first simulated trajectory route is matched one-to-one with the mean probability of flying objects appearing for the corresponding first simulated trajectory route, and a flying object appearance probability label is added to obtain a flying object trajectory route probability portrait; the formula for calculating the mean probability of flying objects appearing for each first simulated trajectory route is:

[0049]

[0050] Among them, FZ is the mean probability of the flying object appearing in the first simulated trajectory route, g1~gn is the probability of the flying object appearing in the flight trajectory route passing through multiple spatial positions in the first simulated trajectory route, and n is the number of spatial position points passed by the flying object in each first simulated trajectory route; the UAV flight environment is simulated according to the flight trajectory route probability portrait; in the UAV flight environment, the control variable method is used for the UAV to control different flight speeds, altitudes and flight routes to construct a UAV simulation flight process; in the UAV simulation flight process, the simultaneous intersection spatial points of the UAV and the flying object are recorded, and the simultaneous intersection spatial points of the UAV and the flying object are marked as a flight collision point; the closest spatial distance and time from the flight collision point in each UAV simulation flight process are marked, and the closest spatial distance and time each time are compared to obtain the collision coefficients at different positions, for example: the UAV simulation flight The process route is divided into 6 segments, and the flight collision points marked in each segment are divided by the sum of the flight collision points during the simulated flight of the drone to obtain the collision coefficients at different positions; the low-altitude environment in the city is changeable, with dense buildings, messy wires, and many birds, which has a higher collision risk than the high-altitude environment, greatly increasing the insecurity of drone operation; because drone operations follow fixed routes and operating rules, and the locations of buildings are fixed, they rarely collide with buildings, but bird flight is random and its exact position cannot be predicted. It can only be summarized based on past experience data, which brings great uncertainty to the estimation of collision probability. The impact of flying birds on the collision probability of drones is based on the position error probability model, and the collision probabilities in the longitudinal, lateral, and vertical directions are calculated respectively, thereby predicting the collision risk between drones and flying birds; when the flight trajectories of drones and flying birds are in an intersecting state, it is defined as a cross-collision situation. However, due to the particularity of the intersection of flight trajectories, the risk of collision is mainly caused by the UAV deviating from the preset route trajectory due to positioning errors, human manipulation and other factors, or due to the large uncertainty caused by the randomness of the position and speed of the bird's flight, which may eventually cause the distance between the UAV and the bird to be less than the interval standard, resulting in a collision.

[0051] S4: Based on the radio wave signal of the base station in the topographic map of the area where the drone is located, an electrical signal simulation three-dimensional rectangular coordinate system is established directly below the horizontal and vertical position of the drone.

[0052] In an embodiment of the present invention, an electric signal simulation environment is obtained based on the electric wave rebound signal recovered by the electric wave signal emitted by the base station when encountering an obstacle in the topographic map of the area where the drone is located; a three-dimensional rectangular coordinate system for electric signal simulation is established with the horizontal surface point directly below the horizontal vertical position of the drone as the coordinate origin; the position of the target object in the three-dimensional rectangular coordinate system for electric signal simulation is marked with coordinates to obtain the electric signal simulation coordinates of the target object; the coordinates of near-ground obstacles are different from the coordinates of far-ground obstacles.

[0053] S5: Perform data simulation on the probability portrait of the flying object's trajectory and the three-dimensional rectangular coordinate system of the electrical signal simulation to establish a safe flight route network model for UAVs.

[0054] In an embodiment of the present invention, data simulation is performed based on the electrical signal simulation three-dimensional rectangular coordinate system and the number of near-surface obstacles and the number of far-surface obstacles, and the substantial shapes of near-surface obstacles and far-surface obstacles are digitized to obtain obstacle data shapes; the obstacle data shapes are extended and extended with equal contours to obtain the obstacle safety range of the obstacles; the flight route is simulated based on the obstacle safety range of the obstacles according to the probability portrait of the flying object trajectory route to obtain a second simulated trajectory route; based on the second simulated trajectory route, multi-dimensional target flight simulation is performed on the electrical signal simulation coordinates of the target object to establish a safe flight route network model for a drone; the equal contour extension distance calculation formula for the equal contour extension of the obstacle data shape is:

[0055] JL=2×log10(G+D+A)

[0056] Among them, JL is the extension 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, and far-surface obstacles are birds and insects.

[0057] S6: Filter the optimal flight route according to the energy consumption information of different routes in the drone safe flight route network model, the collision coefficient of different positions during the flight, and the drone flight data to generate the drone flight route.

[0058] In an embodiment of the present invention, the flight energy consumption of drones on different routes in the drone safe flight route network model is ranked to obtain the route energy consumption ranking, and the energy consumption coefficient of the flight route is obtained according to the route energy consumption ranking; the weather conditions at different time periods, the wind resistance faced by the drone at different flight altitudes and different flight speeds, and the temperature conditions at different altitudes are integrated to calculate the drone atmospheric environment coefficient; the energy consumption coefficient of the flight route, the drone atmospheric environment coefficient, and the collision coefficients at different locations during the flight are integrated to obtain the overall coefficient of the drone flight trajectory, and the formula is:

[0059] X 1 =0.25X 2 +0.15X 3 +0.6X 4

[0060] Among them, X 1 is the overall coefficient of the UAV flight trajectory, X 2 is the energy consumption coefficient of the flight route, X 3 is the atmospheric environment coefficient of the UAV, X 4It is the collision coefficient of different positions during the flight; the overall coefficient of the drone flight trajectory is added to the drone safe flight route network model to obtain the optimal flight route and generate the drone flight route.

[0061] The beneficial effects of the present invention are as follows: the present invention selects the average number of flying objects in the same period of history and the average number of flying objects in the early stage of flight through the flying object density in the same period of history and the early stage of the drone searching for the target object, calculates the flying object density in the terrain, takes into account the flying object density in the two periods, and makes the flying object density calculation of the drone on the day more accurate, and then obtains the specific actual shape of the nearby obstacles through the radio wave signal emitted by the base station near the terrain where the drone is located, and calculates the probability of the flying object appearing, thereby obtaining the flying object trajectory route probability portrait, and obtaining the collision coefficient through the flight trajectory route probability portrait, considering the collision probability of each spatial point during the flight of the drone, which can further avoid the collision between the drone and the flying object or obstacle, and finally simulates the drone safe flight route network model, taking into account the energy consumption coefficient of the flight route, the drone atmospheric environment coefficient and the collision coefficient of different positions during the flight, so that the optimal drone flight route is obtained among countless drone flight routes, and the optimal route planning of the drone is realized.

[0062] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical schemes or characteristics in the scheme is not described in detail here; it should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the scheme of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for UAV route planning based on terrain survey, characterized in that: The method comprises: S1: Collect the radio wave signal of the base station in the topographic map of the area where the drone is located, the topographic survey information of the drone's location, and the drone's flight data; S2: Calculating the density of flying objects in the terrain according to the terrain survey information of the location of the UAV; S3: establishing a probability profile of a flying object trajectory according to the density of flying objects in the terrain, simulating a UAV flight environment according to the probability profile of the flying object trajectory, and obtaining collision coefficients at different positions; S4: according to the radio wave signal of the base station in the topographic map of the area where the drone is located, establish an electric signal simulation three-dimensional rectangular coordinate system directly below the horizontal and vertical position of the drone; S5: Performing data simulation on the probability portrait of the flying object trajectory and the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the UAV; S6: Filter the optimal flight route according to the energy consumption information of different routes in the UAV safe flight route network model, the collision coefficient of different positions during the flight, and the UAV flight data to generate the UAV flight route.

2. The method for UAV route planning based on terrain survey according to claim 1, characterized in that: The radio wave signal of the base station in the topographic map of the area where the drone is located is: the radio wave rebound signal recovered when the radio wave signal sent by the base station in the topographic map of the area where the drone is located 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, and the density of flying objects in the same period of history and before the UAV searches for the target object; The UAV flight data includes: the airspeed, atmospheric environment, flight altitude, UAV vibration data and energy consumption value of the UAV during flight; Among them, the atmospheric environment conditions include: weather conditions at different times, wind resistance faced by drones at different flight altitudes and speeds, and temperature conditions at different altitudes.

3. The method for UAV route planning based on terrain survey according to claim 2, characterized in that: The step of calculating the density of flying objects in the terrain according to the terrain survey information of the location of the UAV comprises: According to the density of flying objects in the same period of history and the previous period of the drone searching for the target object, the average number of flying objects in the same period of history and the average number of flying objects in the previous period of flight are selected; The flying object density P in the terrain is calculated according to the number of near-surface obstacles and the number of far-surface obstacles combined with the average number of flying objects in the same period of history and the average number of flying objects in the early stage of flight: Among them, P is the density of flying objects in the terrain, A1 is the average number of flying objects in the same period of history, 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.

4. The method for UAV route planning based on terrain survey according to claim 3 is characterized in that: The step of establishing a probability profile of a flying object's trajectory route according to the flying object density in the terrain comprises: According to the terrain survey information of the location of the UAV, the information is transmitted to the ground station server through the ground base station, and data simulation analysis is performed to simulate all flight trajectory routes from the hovering position of the UAV to the target object to obtain a first simulated trajectory route; The probability of the flying objects appearing at different spatial positions is obtained according to the density of the flying objects in the terrain and the rebound signal of the radio wave signal sent by the base station and encountered by obstacles in the terrain map of the area where the UAV is located; According to the first simulated trajectory route and the flying object appearance probabilities at the different spatial positions, the average flying object appearance probability of the first simulated trajectory route is calculated: The first simulated trajectory route and the corresponding mean value of the flying object appearance probability of the first simulated trajectory route are matched one by one, and a flying object appearance probability label is added to obtain a flying object trajectory route probability portrait.

5. The method for UAV route planning based on terrain survey according to claim 4, characterized in that: The formula for calculating the mean probability of the flying object appearing on the first simulated trajectory is: Among them, FZ is the mean probability of the flying object appearing in the first simulated trajectory route, g1~gn are the probability of the flying object appearing in multiple spatial positions passed by the flight trajectory route in the first simulated trajectory route, and n is the number of spatial position points passed by the flying object in each first simulated trajectory route.

6. The method for UAV route planning based on terrain survey according to claim 4, characterized in that: The step of simulating the UAV flight environment according to the flying object trajectory probability portrait to obtain collision coefficients at different positions includes: Simulating the UAV flight environment according to the flight trajectory route probability portrait; In the UAV flight environment, the UAV is controlled by a control variable method to control different flight speeds, altitudes and flight routes, and a simulated UAV flight process is constructed; During the simulated flight of the drone, a spatial point where the drone and the flying object intersect at the same time is recorded, and the spatial point where the drone and the flying object intersect at the same time is marked as a flight collision point; The closest spatial distance and time from the flight collision point during each simulated flight of the UAV are marked, and the closest spatial distance and time each time are compared to obtain collision coefficients at different positions.

7. The method for UAV route planning based on terrain survey according to claim 2, characterized in that: The step of establishing an electric signal simulation three-dimensional rectangular coordinate system directly below the horizontal and vertical position of the drone according to the radio wave signal of the base station in the topographic map of the area where the drone is located includes: The electric signal simulation environment is obtained according to the electric wave rebound signal received when the electric wave signal sent by the base station encounters an obstacle in the topographic map of the area where the UAV is located; Taking the horizontal point directly below the drone's position as the coordinate origin, establish a three-dimensional rectangular coordinate system for electrical signal simulation; The position of the target object in the electrical signal simulation three-dimensional rectangular coordinate system is marked with coordinates to obtain the electrical signal simulation coordinates of the target object.

8. The method for UAV route planning based on terrain survey according to claim 7, characterized in that: The step of performing data simulation on the probability portrait of the flying object trajectory and the three-dimensional rectangular coordinate system simulated by the electrical signal to establish a safe flight route network model for the unmanned aerial vehicle includes: Performing data simulation based on the electrical signal simulated three-dimensional rectangular coordinate system and the number of near-surface obstacles and the number of far-surface obstacles, digitizing the substantial shapes of the near-surface obstacles and the far-surface obstacles, and obtaining obstacle data shapes; Performing equal contour expansion and extension on the obstacle data shape to obtain an obstacle safety range of the obstacle; Importing and marking the obstacle safety range of the obstacle in the probability image of the flying object trajectory, and combining the obstacle data shape and the obstacle safety range to obtain a second simulated trajectory route; Based on the second simulated trajectory route, a multi-dimensional target flight simulation is performed on the target object's electrical signal simulation coordinates to establish a UAV safe flight route network model.

9. The method for UAV route planning based on terrain survey according to claim 8, characterized in that: The calculation formula for the equal contour extension distance of the obstacle data shape by equal contour extension is: JL=2×log10(G+D+A) Among them, JL is the extension distance of the same profile, G is gravity, D is the weight of the drone, and A is the flight speed of the drone.

10. The method for UAV route planning based on terrain survey according to claim 8, characterized in that: The step of selecting the optimal flight route according to the energy consumption information of different routes in the drone safe flight route network model, the collision coefficients of different positions during the flight, and the drone flight data to generate the drone flight route includes: Sorting the flight energy consumption of drones on different routes in the drone safe flight route network model to obtain a route energy consumption ranking, and obtaining an energy consumption coefficient of the flight route according to the route energy consumption ranking; The weather conditions at different time periods, the wind resistance faced by the drone at different flight altitudes and different flight speeds, and the temperature conditions at different altitudes are integrated to calculate the atmospheric environment coefficient of the drone; The energy consumption coefficient of the flight route, the atmospheric environment coefficient of the UAV and the collision coefficient of the different locations during the flight are integrated to obtain the overall coefficient of the UAV flight trajectory, and the formula is: X1=0.25X2+0.15X3+0.6X4 Among them, 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 the different positions during the flight; The overall coefficient of the UAV flight trajectory is added to the UAV safe flight route network model to obtain the optimal flight route and generate the UAV flight route.

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