Unmanned aerial vehicle flight path optimization method and system based on artificial intelligence

Abstract

The application discloses an unmanned aerial vehicle flight path optimization method and system based on artificial intelligence, relates to the unmanned aerial vehicle flight control field, and comprises a planning module, a construction module and the like.The planning module is used for uploading a topographic model of an unmanned aerial vehicle flight area, selecting a passing point of an unmanned aerial vehicle flight task in the topographic model, and determining a flight task reference path.The construction module is used for receiving the flight task reference path determined in the planning module, shifting based on the flight task reference path, and constructing a flight task application path sketch.The application determines a flight reference path according to a flight area topographic model, optimizes the flight reference path to form an application path, makes the unmanned aerial vehicle flight route more suitable for actual environment requirements, can sense obstacles in real time during the flight process, can accurately control the unmanned aerial vehicle to avoid obstacles in time once the obstacles are found, and can quickly return to the original flight path after avoiding the obstacles, so that the collision risk is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, specifically to a method and system for optimizing UAV flight paths based on artificial intelligence. Background Technology

[0002] Drone flight path optimization is a technology that uses algorithms and techniques to plan efficient and safe flight routes for drones in complex environments. It comprehensively considers factors such as terrain, obstacles, and mission objectives, and uses algorithms such as AI and reinforcement learning to calculate the optimal path and dynamically adjust it according to the real-time environment. It is widely used in logistics delivery, surveying and mapping, and other fields to improve the efficiency and safety of drone operations.

[0003] Patent application No. 202410716222.7 discloses a method for optimizing UAV flight paths based on an improved gray wolf optimization algorithm. The method includes: initializing the population size, population dimension, and maximum number of iterations, and randomly generating an initial population based on the flight planning space and different central mountain locations; initializing gray wolves using a gray wolf initialization model; calculating the fitness value of each gray wolf according to the UAV path cost model function; determining α wolf, B wolf, and δ wolf based on the fitness values ​​of each gray wolf; updating the gray wolf positions based on the positions of α wolf, B wolf, and δ wolf; calculating the fitness values ​​of the gray wolves after the position update; and re-determining the optimal flight path based on the updated fitness values ​​of the gray wolves. Alpha Wolf, B Wolf, and Wolf: If the current round reaches the maximum number of iterations, the iteration ends, the optimal individual is output, and the final minimum fitness value of the UAV flight trajectory is obtained; if the current round does not reach the maximum number of iterations, the process returns to the step of updating the position of the gray wolf based on the positions of alpha wolf, β wolf, and δ wolf. This application aims to address the problem that "the optimization of UAV path planning has been proven to be uncertain polynomial-time complete. Optimization algorithms are widely used methods for solving NP problems and can handle complex real-world scenarios that traditional modeling often cannot solve. However, optimization algorithms in related technologies cannot cope with complex terrain and suffer from problems such as poor convergence accuracy and slow convergence speed."

[0004] In existing technologies, UAV flight path optimization technology often focuses on path optimization or in-flight optimization, and there are few applications of UAV flight path optimization technology that combines the two.

[0005] To address this, we propose an artificial intelligence-based method and system for optimizing drone flight paths. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an artificial intelligence-based method and system for optimizing the flight path of unmanned aerial vehicles, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses an artificial intelligence-based drone flight path optimization system, comprising: The system comprises four modules: a planning module (uploading a terrain model of the UAV's flight area and selecting waypoints for the UAV's flight mission to determine the reference path), a construction module (receiving the reference path from the planning module and translating it to construct a preliminary application path), a coordination module (receiving the preliminary application path from the construction module and coordinating it to obtain the final application path), a drive module (receiving the application path from the coordination module, feeding it back to the UAV, and driving it to execute the flight mission based on the application path), a perception module (sensing obstacles encountered by the UAV during its flight mission), and an obstacle avoidance module (observing obstacles from the perception module in real time, controlling the UAV to detach from the application path to avoid obstacles and return to the application path when detected by the perception module).

[0008] Furthermore, in the planning module, during the stage of selecting UAV flight mission waypoints, the number of waypoints selected is no less than two, and each waypoint falls on the surface of the terrain model. After the waypoints are selected, the waypoints are connected sequentially along the surface of the terrain model based on the waypoints and the order of their selection to obtain the UAV flight mission reference path. This ensures that, from the top-down view of the terrain model, the road segment between any two adjacent points on the UAV flight mission reference path is a straight line. The reference path for the drone flight mission is synchronized with the drone's internal storage after it is acquired.

[0009] Furthermore, during the construction module's operation phase, the terrain model and flight mission reference path uploaded in the planning module are both placed in an adapted three-dimensional space, so that the terrain model is located at the center of the three-dimensional space. Based on the terrain model and flight mission reference path represented in the three-dimensional space, the coordinates of any position on the terrain model and flight mission reference path are known. The construction module obtains the coordinates of the highest point and the lowest point on the flight mission reference path, determines the height difference between the two coordinates, and uses the determined height difference as the translation distance of the flight mission reference path placed on the surface of the terrain model in three-dimensional space. The translation operation is performed, and the translation direction is always set to vertically upward, so as to complete the construction of the prototype of the flight mission application path. Before the translation operation is performed, the flight mission reference path is accumulated into the translation distance by user-defined values ​​on the system side. During the translation operation, the translation operation is performed based on the path points of each UAV flight mission in the flight mission reference path as translation operation points.

[0010] Furthermore, the coordination module is equipped with a capture unit and a reduction unit. The capture unit is used to capture a road segment that is straight from a top-down perspective and consists of no less than three waypoints in the preliminary flight mission application path. The reduction unit is used to obtain the road segment captured by the capture unit, identify whether there are any coordinateable waypoints on the road segment, and reduce the coordinateable waypoints on the road segment when the identification result is yes. When the capture unit captures an empty set of road segments, the coordination module ends its operation, reduces the module's operation phase, and identifies whether the two ends of a sub-road segment composed of three or more adjacent waypoints on the acquired road segment are higher than other waypoints between the two ends. If all other waypoints are lower than the two ends, the two ends of the sub-road segment are connected by a straight line to obtain a new road segment. The corresponding sub-road segment is iterated with the new road segment, and a perpendicular line is drawn upward through the waypoints on the atomic road segment. The intersection of the perpendicular line and the new road segment is recorded as the mapping point of the waypoint where the perpendicular line is located.

[0011] Furthermore, during the operation phase of the drive module, after receiving the flight mission application path, it synchronously obtains the scale of the terrain model and the actual terrain, as well as the actual position coordinates of any point on the flight mission application path, in the terrain model. Based on the actual position coordinates and the scale of the terrain model and the actual terrain, it obtains the actual position coordinates of any point on the flight mission application path. The drive module drives the UAV to fly and execute the flight mission based on the actual coordinates corresponding to each point along the flight mission application path.

[0012] Furthermore, the sensing module is integrated with an infrared ranging module. When the UAV is flying between any two waypoints during the analysis task based on the flight mission application path, the ranging end of the infrared ranging module of the sensing module always points to the nearest waypoint in the flight direction and performs ranging operations in real time. The sensing distance of the infrared ranging module meets the following conditions: ; In the formula: V represents the sensing distance of the infrared ranging module; V is the preset flight speed of the UAV when performing its flight mission; t is the value of V after converting the UAV's flight distance in units of time to seconds.

[0013] Furthermore, the logic for the infrared ranging module to perceive obstacles is as follows: ; In the formula: The infrared ranging module provides real-time ranging results. Among them, the operating frequency of the infrared ranging module is set to be no less than 1ms; The condition is met when the sensing module detects an obstacle.

[0014] Furthermore, during the operation phase of the obstacle avoidance module, based on the ranging results of the infrared ranging module and with the flight mission application path as a reference, the obstacle position coordinates are determined. A circle is drawn with the obstacle position coordinates as the center and the road segment corresponding to the current ranging result of the infrared ranging sensor as the radius. The drawn circle is symmetrically divided into equal parts by the flight mission application path. The semicircle on either side is selected as the obstacle avoidance path and applied to the drone's obstacle avoidance flight. The two ends of the semicircle are connected to the flight mission application path, and the sections defined by the two ends of the semicircle on the flight mission application path are replaced by the semicircle.

[0015] Furthermore, the planning module is interconnected with a construction module and a coordination module via a wireless network. The coordination module is interconnected with a capture unit and a reduction unit via a wireless network at its lower level. The coordination module is interconnected with a driving module, a sensing module, and an avoidance module via a wireless network.

[0016] On the other hand, the AI-based drone flight path optimization method includes the following steps: Upload a terrain model of the UAV flight area, select the waypoints for the UAV flight mission in the terrain model, and create a reference path for the flight mission based on the selected waypoints; obtain the reference path for the flight mission, translate the reference path for the flight mission to construct a preliminary application path for the flight mission; obtain the preliminary application path for the flight mission, coordinate the preliminary application path for the flight mission to obtain the application path for the flight mission; upload the application path for the flight mission to the UAV, control the UAV to fly according to the application path for the flight mission, and perceive obstacles in real time during the flight; when no obstacles are perceived, fly according to the application path for the flight mission; when obstacles are perceived, generate an avoidance path; after using the avoidance path to leave the application path for the flight mission to avoid obstacles, return to the application path for the flight mission.

[0017] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides an artificial intelligence-based method and system for optimizing drone flight paths. During operation, the method and system determine a reference flight path based on a terrain model of the flight area, and then optimize it to form an application path, making the drone's flight route more closely match the actual environmental requirements. During flight, the system can perceive obstacles in real time; once detected, it can precisely control the drone to avoid them in a timely manner, and after avoidance, it can quickly return to the original flight path, greatly reducing the risk of collision. Simultaneously, the system can accurately obtain the real coordinates of each point on the flight path based on the scale of the model and the actual terrain, driving the drone to accurately execute tasks, greatly improving flight accuracy, reducing flight errors, and ensuring that the drone can stably and efficiently complete various flight tasks. This has significant practical value in surveying, inspection, and other fields. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 This is a schematic diagram of an AI-based drone flight path optimization system. Figure 2 This is a flowchart illustrating an AI-based method for optimizing drone flight paths. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] The present invention will be further described below with reference to embodiments.

[0022] Example 1:

[0023] The AI-based drone flight path optimization system in this embodiment, such as... Figure 1 As shown, it includes: The planning module is used to upload a terrain model of the drone flight area, select waypoints for the drone flight mission in the terrain model, and determine the reference path for the flight mission. In the planning module, during the selection of UAV flight mission waypoints, at least two waypoints are selected, and each waypoint falls on the surface of the terrain model. After the waypoints are selected, the waypoints are connected sequentially along the surface of the terrain model based on the waypoints and the selection order to obtain the UAV flight mission reference path. This ensures that, from the top-down view of the terrain model, the road segment between any two adjacent points on the UAV flight mission reference path is a straight line. Among them, the reference path for the drone flight mission is synchronized with the internal storage of the drone after it is acquired; The construction module is used to receive the flight mission reference path determined in the planning module, and translate it based on the flight mission reference path to construct a prototype of the flight mission application path; During the construction module operation phase, the terrain model and flight mission reference path uploaded in the planning module are placed in the adapted three-dimensional space, so that the terrain model is located at the center of the three-dimensional space. Based on the terrain model and flight mission reference path represented in the three-dimensional space, the coordinates of any position on the terrain model and flight mission reference path are known. The construction module obtains the coordinates of the highest and lowest points on the flight mission reference path, determines the height difference between the two coordinates, and uses the determined height difference as the translation distance of the flight mission reference path placed on the surface of the terrain model in three-dimensional space. The translation operation is performed, and the translation direction is always set to vertically upward to complete the construction of the prototype of the flight mission application path. Before the flight mission reference path is translated based on the translation distance, the user-defined value is added to the translation distance on the system side, and then the translation operation is performed. During the translation operation, the path points of each UAV flight mission in the flight mission reference path are used as translation operation points to perform the translation operation. The coordination module is used to obtain the prototype of the flight mission application path built in the construction module, and coordinate the prototype of the flight mission application path to obtain the flight mission application path. The coordination module is equipped with a capture unit and a reduction unit. The capture unit is used to capture a road segment that is straight from the top view and consists of no less than three waypoints in the preliminary flight mission application path. The reduction unit is used to obtain the road segment captured by the capture unit, identify whether there are any coordinateable waypoints on the road segment, and reduce the coordinateable waypoints on the road segment when the identification result is yes. When the capture unit captures an empty set of road segments, the coordination module ends its operation, reduces the module's operation phase, and identifies whether the two ends of a sub-road segment composed of three or more adjacent waypoints on the acquired road segment are higher than other waypoints between the two ends. If all other waypoints are lower than the two ends, the two ends of the sub-road segment are connected by a straight line to obtain a new road segment. The corresponding sub-road segment is iterated with the new road segment, and a perpendicular line is drawn upward through the waypoints on the atomic road segment. The intersection of the perpendicular line and the new road segment is recorded as the mapping point of the waypoint where the perpendicular line is located. The drive module is used to receive the flight mission application path obtained by the coordination module, feed back the flight mission application path to the UAV, and drive the UAV to execute the flight mission based on the flight mission application path. During the operation phase of the drive module, after receiving the flight mission application path, it synchronously obtains the scale of the terrain model and the real terrain and the real position coordinates of any point in the flight mission application path in the terrain model. Based on the real position coordinates and the scale of the terrain model and the real terrain, it obtains the real position coordinates of any point on the flight mission application path. The drive module drives the UAV to fly and execute the flight mission based on the real coordinates of each passing point on the flight mission application path. The perception module is used to detect obstacles during the flight mission executed by the UAV based on the flight mission application path; The perception module is integrated with an infrared ranging module. When the UAV is flying between any two waypoints during the analysis task based on the flight mission application path, the ranging end of the infrared ranging module of the perception module always points to the nearest waypoint in the flight direction and performs ranging operations in real time. The sensing distance of the infrared ranging module meets the following conditions: ; In the formula: V represents the sensing distance of the infrared ranging module; V is the preset flight speed of the UAV when performing its flight mission; t is the value of V, which is the UAV flight distance converted to seconds. The logic for the infrared ranging module to detect obstacles is as follows: ; In the formula: The infrared ranging module provides real-time ranging results. Among them, the operating frequency of the infrared ranging module is set to be no less than 1ms; The condition is met, indicating that the sensing module has detected an obstacle. The obstacle avoidance module is used to obtain the obstacle perception results from the perception module in real time. When the perception module detects an obstacle, it controls the UAV to leave the flight mission application path to avoid the obstacle and return to the flight mission application path. During the obstacle avoidance module operation phase, the obstacle position coordinates are determined based on the ranging results of the infrared ranging module and the flight mission application path. A circle is drawn with the obstacle position coordinates as the center and the road segment corresponding to the current ranging result of the infrared ranging sensor as the radius. The drawn circle is symmetrically divided into equal parts by the flight mission application path. The semicircle on either side is selected as the obstacle avoidance path and applied to the drone's obstacle avoidance flight. The two ends of the semicircle are connected to the flight mission application path, and the sections defined by the two ends of the semicircle on the flight mission application path are replaced by the semicircle. The planning module is interconnected with the construction module and the coordination module via a wireless network. The coordination module is interconnected with the capture unit and the reduction unit via a wireless network. The coordination module is interconnected with the drive module, the perception module and the avoidance module via a wireless network.

[0024] In this embodiment, the planning module uploads a terrain model of the UAV flight area, selects waypoints for the UAV flight mission within the model to determine the reference path for the flight mission, and the construction module subsequently receives the reference path determined by the planning module. Based on the reference path, it translates to construct a preliminary application path for the flight mission. The coordination module further acquires the preliminary application path for the flight mission constructed in the construction module and coordinates it to obtain the final application path. The capture unit simultaneously captures road segments in the preliminary application path for the flight mission that are straight from a top-down perspective and consist of at least three waypoints. The reduction unit acquires the captured data in real time. The capture unit operates to capture road segments and identify whether there are any coordinateable waypoints on the road segments. If the identification result is yes, the coordinateable waypoints on the road segments are reduced. The drive module then receives the flight mission application path obtained by the coordination module and feeds the flight mission application path back to the UAV. The UAV is driven to perform the flight mission based on the flight mission application path. The perception module senses obstacles during the UAV's flight mission based on the flight mission application path. The obstacle avoidance module obtains the obstacle perception results from the perception module in real time. When the perception module detects an obstacle, it controls the UAV to leave the flight mission application path to avoid the obstacle and return to the flight mission application path.

[0025] Through the system operation in the above embodiments, for UAV flight mission scenarios, it provides flight path optimization and obstacle avoidance optimization to ensure that the UAV can stably complete the flight mission, improves the obstacle avoidance capability, and makes the flight mission path simpler and more reliable.

[0026] Example 2:

[0027] At the implementation level, based on Example 1, this example refers to... Figure 2 The artificial intelligence-based drone flight path optimization system in Example 1 will be further described in detail below: The AI-based method for optimizing drone flight paths includes the following steps: Step 1: Upload the terrain model of the drone flight area, select the waypoints for the drone flight mission in the terrain model, and create a reference path for the flight mission based on the selected waypoints; Step 2: Obtain the flight mission reference path and translate the flight mission reference path to construct a preliminary flight mission application path; Step 3: Obtain a preliminary flight mission application path and coordinate the preliminary flight mission application path to obtain the final flight mission application path. Step 4: Upload the flight mission application path to the drone, control the drone to fly based on the flight mission application path, and perceive obstacles in real time during the flight; Step 5: When no obstacle is detected, fly according to the flight mission application path; when an obstacle is detected, generate an avoidance path. Step 6: After using the obstacle avoidance path to leave the flight mission application path and avoid the obstacle, return to the flight mission application path.

[0028] In summary, the methods and systems described in the above embodiments determine the flight reference path based on the terrain model of the flight area during operation, and then optimize it to form the application path. This makes the UAV flight route more closely match the actual environmental requirements. During flight, the system can perceive obstacles in real time. Once detected, it can accurately control the UAV to avoid obstacles in a timely manner, and after avoiding obstacles, it can quickly return to the original flight path, greatly reducing the risk of collision. At the same time, the system can accurately obtain the real coordinates of each point on the flight path according to the scale of the model and the real terrain, driving the UAV to accurately execute tasks, greatly improving flight accuracy, reducing flight errors, and ensuring that the UAV can stably and efficiently complete various flight tasks. This has important practical value in fields such as surveying and inspection.

[0029] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence-based drone flight path optimization system, characterized in that, include: The planning module is used to upload a terrain model of the drone flight area, select waypoints for the drone flight mission in the terrain model, and determine the reference path for the flight mission. The construction module is used to receive the flight mission reference path determined in the planning module, and translate it based on the flight mission reference path to construct a prototype of the flight mission application path; During the construction module's operation phase, the terrain model and flight mission reference path uploaded in the planning module are both placed in an adapted three-dimensional space, so that the terrain model is located at the center of the three-dimensional space. Based on the terrain model and flight mission reference path represented in the three-dimensional space, the coordinates of any position on the terrain model and flight mission reference path are known. The construction module obtains the coordinates of the highest point and the lowest point on the flight mission reference path, determines the height difference between the two coordinates, and uses the determined height difference as the translation distance of the flight mission reference path placed on the surface of the terrain model in three-dimensional space. The translation operation is performed, and the translation direction is always set to vertically upward, so as to complete the construction of the prototype of the flight mission application path. Before the flight mission reference path is translated based on the translation distance, the user-defined value is added to the translation distance on the system side, and then the translation operation is performed. During the translation operation, the path points of each UAV flight mission in the flight mission reference path are used as translation operation points to perform the translation operation. The coordination module is used to obtain the prototype of the flight mission application path built in the construction module, and coordinate the prototype of the flight mission application path to obtain the flight mission application path. The coordination module is equipped with a capture unit and a reduction unit. The capture unit is used to capture a road segment that is straight from a top-down perspective and consists of no less than three waypoints in the preliminary flight mission application path. The reduction unit is used to obtain the road segment captured by the capture unit and identify whether there are any coordinateable waypoints on the road segment. The coordinateable waypoints are the waypoints between the two ends of the sub-road segment and whose elevation is lower than the two ends of the waypoints. When the identification result is yes, the coordinateable waypoints on the road segment are reduced. When the capture unit captures an empty set of road segments, the coordination module ends its operation, reduces the unit's operation phase, and identifies whether the two ends of the sub-road segment composed of three or more adjacent waypoints on each acquired road segment are higher than other waypoints between the two ends. If all other waypoints are lower than the two ends, the two ends of the sub-road segment are connected by a straight line to obtain a new road segment. The corresponding sub-road segment is iterated with the new road segment, and a perpendicular line is drawn upward through the waypoints on the atomic road segment. The intersection of the perpendicular line and the new road segment is recorded as the mapping point of the waypoint where the perpendicular line is located. The drive module is used to receive the flight mission application path obtained by the coordination module, feed back the flight mission application path to the UAV, and drive the UAV to execute the flight mission based on the flight mission application path. The perception module is used to detect obstacles during the flight mission executed by the UAV based on the flight mission application path; The obstacle avoidance module is used to obtain the obstacle perception results from the perception module in real time. When the perception module detects an obstacle, it controls the UAV to leave the flight mission application path to avoid the obstacle and return to the flight mission application path. During the operation phase of the obstacle avoidance module, the obstacle's position coordinates are determined based on the ranging results of the infrared ranging module and the flight mission application path. A circle is drawn with the obstacle's position coordinates as the center and the current ranging result of the infrared ranging sensor as the radius. The flight mission application path passes through the center of the circle to achieve symmetrical division of the circle. Any half circle on one side is selected as the obstacle avoidance path and applied to the UAV's obstacle avoidance flight. The two ends of the semicircle are connected to the flight mission application path, and the sections defined by the two ends of the semicircle on the flight mission application path are replaced by the semicircle.

2. The AI-based UAV flight path optimization system according to claim 1, characterized in that, In the planning module, during the selection of UAV flight mission waypoints, at least two waypoints are selected, and each waypoint falls on the surface of the terrain model. After the waypoints are selected, the waypoints are connected sequentially along the surface of the terrain model based on the waypoints and the selection order to obtain the UAV flight mission reference path. This ensures that, from the top-down view of the terrain model, the road segment between any two adjacent points on the UAV flight mission reference path is a straight line. The reference path for the drone flight mission is synchronized with the drone's internal storage after it is acquired.

3. The artificial intelligence-based UAV flight path optimization system according to claim 1, characterized in that, During the operation phase of the driving module, after receiving the flight mission application path, it synchronously obtains the scale of the terrain model and the actual terrain, as well as the actual position coordinates of any point on the flight mission application path, in the terrain model. Based on the actual position coordinates and the scale of the terrain model and the actual terrain, it obtains the actual position coordinates of any point on the flight mission application path. The driving module drives the UAV to fly and execute the flight mission based on the actual coordinates of each point along the flight mission application path.

4. The artificial intelligence-based UAV flight path optimization system according to claim 1, characterized in that, The sensing module is integrated with an infrared ranging module. When the UAV is flying between any two waypoints during the analysis task based on the flight mission application path, the ranging end of the infrared ranging module of the sensing module always points to the nearest waypoint in the flight direction and performs ranging operation in real time. The sensing distance of the infrared ranging module meets the following conditions: ； In the formula: The sensing distance of the infrared ranging module; The preset flight speed for the drone when performing flight missions; for The value of the flight distance of the UAV in China is converted to seconds.

5. The artificial intelligence-based UAV flight path optimization system according to claim 4, characterized in that, The logic for the infrared ranging module to detect obstacles is as follows: ； In the formula: The infrared ranging module provides real-time ranging results. Among them, the operating frequency of the infrared ranging module is set to be no less than 1ms; The condition is met when the sensing module detects an obstacle.

6. The artificial intelligence-based UAV flight path optimization system according to claim 1, characterized in that, The planning module is interconnected with a construction module and a coordination module via a wireless network. The coordination module is interconnected with a capture unit and a reduction unit via a wireless network. The coordination module is interconnected with a driving module, a sensing module and an avoidance module via a wireless network.

7. A method for optimizing the flight path of a drone based on artificial intelligence, wherein the method is an implementation method of the drone flight path optimization system based on artificial intelligence as described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: Upload the terrain model of the drone flight area, select the waypoints for the drone flight mission in the terrain model, and create a reference path for the flight mission based on the selected waypoints; Step 2: Obtain the flight mission reference path and translate the flight mission reference path to construct a preliminary flight mission application path; Step 3: Obtain a preliminary flight mission application path and coordinate the preliminary flight mission application path to obtain the final flight mission application path. Step 4: Upload the flight mission application path to the drone, control the drone to fly based on the flight mission application path, and perceive obstacles in real time during the flight; Step 5: When no obstacle is detected, fly according to the flight mission application path; when an obstacle is detected, generate an avoidance path. Step 6: After using the obstacle avoidance path to leave the flight mission application path and avoid the obstacle, return to the flight mission application path.

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