Real-time unmanned aerial vehicle scheduling strategy control method

Abstract

The invention provides a real-time unmanned aerial vehicle scheduling strategy control method based on task information and constraints, and the method comprises the steps: obtaining an initial path planning result of a multi-target distribution point of an unmanned aerial vehicle, and building an initial mapping relation between a pheromone matrix and a position matrix; a weight coefficient is injected into the pheromone matrix according to the priority information of the multi-target distribution points, and if a high-priority distribution point is detected, the pheromone concentration enhancement amplitude of a peripheral path is increased; if it is monitored that the wind speed or the rainfall intensity in the weather change data exceeds a preset range, updating the concentration distribution of the pheromone matrix by adopting the dynamically adjusted volatilization rate; if the concentration value of the pheromone matrix is lower than a preset threshold value and the position updating direction deviates from a target path, re-initializing the pheromone concentration distribution of the region; and generating a final path planning scheme according to the updated pheromone matrix and the position matrix.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a real-time unmanned aerial vehicle scheduling strategy control method based on mission information and constraints. Background Art

[0002] In the scheduling problem of multi-target delivery points of drones in complex environments, there are multiple delivery points and dynamic environmental constraints, and it is necessary to optimize the delivery path and time efficiency at the same time. When dealing with such problems, traditional methods often find it difficult to balance global search capabilities and local fine-tuning. Combining the pheromone update mechanism of the ant colony algorithm with the position update of the particle swarm optimization algorithm attempts to resolve this contradiction, but it still faces technical difficulties in practical applications.

[0003] The pheromone update mechanism of the ant colony algorithm relies on the accumulation of pheromones on the path, and guides the search direction through the volatilization and enhancement of pheromones. However, in a dynamic environment, drones need to adjust their paths in real time to avoid obstacles or respond to emergencies. The frequency and intensity of pheromone updates are difficult to adapt to such rapid changes, which may cause the search to fall into a local optimum or fail to respond to environmental changes in a timely manner. Summary of the invention

[0004] The present invention provides a real-time UAV scheduling strategy control method based on task information and constraints, which mainly includes: The initial path planning results of the multi-target delivery points of the UAV are obtained, and the initial mapping relationship between the pheromone matrix and the position matrix is ​​established; if the location information of the dynamic obstacle is detected in the initial path planning results, the concentration value of the pheromone matrix corresponding to the path is reduced, and the concentration value of the pheromone matrix is ​​increased in the adjacent area; according to the current concentration value of the pheromone matrix, the particle swarm optimization algorithm is used to adjust the position update direction of the UAV, and if the concentration value is higher than the preset threshold, the direction is preferentially selected; according to the priority information of the multi-target delivery points, the weight coefficient is injected into the pheromone matrix, and if a high-priority delivery point is detected, the pheromone concentration enhancement amplitude of the surrounding paths is increased; if the wind speed or rainfall intensity in the monitored weather change data exceeds the preset range, the concentration distribution of the pheromone matrix is ​​updated using the dynamically adjusted volatilization rate; if the concentration value of the pheromone matrix is ​​lower than the preset threshold and the position update direction deviates from the target path, the pheromone concentration distribution of the area is reinitialized; the final path planning scheme is generated according to the updated pheromone matrix and the position matrix.

[0005] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: the real-time UAV scheduling strategy control method based on task information and constraints provided by the present invention can construct the initial mapping relationship between the pheromone matrix and the position matrix by obtaining the initial path planning results of the multi-target delivery point of the UAV, and provide a basic data structure for path optimization. Further, the present invention reduces the pheromone concentration of the corresponding path by detecting the dynamic obstacles in the initial path, and increases the pheromone concentration in the adjacent area to guide the UAV to avoid obstacles. Further, the present invention also uses a particle swarm optimization algorithm to adjust the UAV position update direction according to the pheromone concentration, gives priority to the direction of high concentration value, and balances global search and local optimization. Finally, the present invention also injects a weight coefficient according to the priority of the delivery point, and the pheromone concentration enhancement amplitude of the path around the high-priority delivery point is increased to ensure that important tasks are completed first. In addition, when the wind speed or rainfall intensity is monitored to exceed the preset range, the present invention dynamically adjusts the pheromone volatilization rate, updates the concentration distribution, and adapts to the impact of weather changes on flight. When the pheromone concentration is lower than the threshold and the position update direction deviates from the target path, the regional pheromone concentration distribution is reinitialized to avoid local optimality and path deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 The present invention is a flowchart of a real-time UAV scheduling strategy control method. DETAILED DESCRIPTION

[0007] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0008] like Figure 1 In this embodiment, a real-time UAV scheduling strategy control method based on task information and constraints may specifically include: S101, obtaining the initial path planning results of the UAV multi-target delivery points, and establishing the initial mapping relationship between the pheromone matrix and the position matrix.

[0009] Obtain the initial path planning results of the multi-target delivery points of the drone and extract the coordinate information of the path nodes. Based on the coordinate information of the path nodes, construct a position matrix to store the longitude and latitude data of each node. According to the number of nodes in the position matrix, initialize the pheromone matrix and set the matrix structure of the same dimension. Traverse each node in the position matrix, calculate the Euclidean distance between the nodes, and obtain the distance matrix. Normalize each element value in the distance matrix and convert it into the initial concentration value of the pheromone. According to the initial concentration value of the pheromone, fill the corresponding position of the pheromone matrix to complete the matrix initialization. Establish the mapping relationship between the pheromone matrix and the position matrix, and realize the data association between the matrices through the node index.

[0010] Specifically, the path planning of multi-target delivery points of drones is a complex task that requires consideration of multiple factors and optimization. The initial path planning results are usually based on simple heuristic algorithms, such as the nearest neighbor method or the greedy algorithm. These algorithms can quickly generate initial solutions, but they are usually not globally optimal. Extracting the path node coordinate information is a preparation for subsequent optimization. Each node represents a delivery point or transfer station, and its coordinates are usually expressed in longitude and latitude. Constructing a location matrix can easily store and access these coordinate data, providing a basis for subsequent distance calculation and path optimization. The pheromone matrix is ​​a key concept in the ant colony algorithm, which is used to simulate the chemical information traces left by ants in nature. When initializing the pheromone matrix, all elements are usually set to the same small positive number to ensure that the probability of each path being selected is equal at the beginning of the algorithm. This can prevent the algorithm from converging to a local optimal solution too early. The Euclidean distance between nodes is calculated in order to construct a distance matrix. In one embodiment, the following formula can be used to calculate the spherical distance between two points, specifically: , where d represents the spherical distance between nodes, R represents the radius of the earth, φ1 and φ2 represent the latitudes of the two points, and λ1 and λ2 represent the longitudes of the two points.

[0011] The distance matrix provides an important reference for subsequent path selection. It is a common technique to normalize the distance value and convert it into the initial pheromone concentration value. This makes the initial pheromone distribution inversely proportional to the actual distance, that is, the closer the distance, the higher the initial pheromone concentration. This setting can help the algorithm converge to a better solution faster. The mapping relationship between the pheromone matrix and the position matrix is ​​established to facilitate coordinate search and path construction during the optimization process. Through the node index, you can quickly switch between the two matrices to improve the efficiency of the algorithm. For example, suppose there is a scenario with 5 delivery points. The initial path may be directly connected in the order of input: A→B→C→D→E. Based on the coordinates of A→B→C→D→E, the distance matrix can be calculated. After normalizing these distances, the initial pheromone concentration can be obtained. For example, if the shortest distance is set to 1 and other distances are scaled proportionally, the pheromone concentration may show a distribution with high near distances and low far distances. This initialization method provides a good starting point for subsequent path optimization. On this basis, the ant colony algorithm can iteratively search, gradually adjust the pheromone distribution, and finally find a better delivery path. The optimized path may appear in a sequence such as A→E→C→B→D, which may significantly reduce the total distance of the initial path. In this way, not only the geographical location information is taken into account, but also the necessary data structure is prepared for the intelligent optimization algorithm. The advantage of this method is that it combines deterministic distance information and random search strategies to find a near-optimal solution in a reasonable time.

[0012] S102: If it is detected that there is dynamic obstacle position information in the initial path planning result, the pheromone matrix concentration value corresponding to the path is reduced, and the pheromone matrix concentration value is increased in an adjacent area.

[0013] Get the initial path planning result and extract the dynamic obstacle location information. According to the dynamic obstacle location information, determine the index value of the path where it is located. For the path corresponding to the index value, reduce the pheromone concentration value of the path in the pheromone matrix. According to the dynamic obstacle location information, determine the range of its adjacent area. For the path in the adjacent area, increase the pheromone concentration value of the corresponding path in the pheromone matrix. Update the pheromone matrix and save the adjusted pheromone concentration value. Based on the updated pheromone matrix, recalculate the priority of path planning.

[0014] Specifically, during the path planning process, when the system detects the presence of dynamic obstacle location information in the initial path planning results, the system will first calculate the range of influence on the path based on the position and motion trajectory of the obstacle. Assuming that the obstacle is located at the coordinates (x=10, y=20) and its influence radius is 5 meters, the system will reduce the pheromone matrix concentration value corresponding to the path from the initial value of 10 to 6 to reduce the attractiveness of the path. At the same time, the system will increase the pheromone concentration value in the adjacent area, for example, at the coordinates (x=12, y=22), the pheromone concentration value will be increased from 0 to 4 to guide the path planning algorithm to select a safer path. In other embodiments, this process is implemented by an ant colony algorithm, and the pheromone update formula in the algorithm is:

[0015] τ ij represents the path pheromone concentration, ρ represents the pheromone volatility coefficient, Δτ ij The pheromone increment is dynamically adjusted according to the impact range of obstacles and the safety of the path, and t represents the current time. The system will analyze the movement trajectory of obstacles in real time, predict their future positions, and update the pheromone matrix to ensure the real-time and safety of path planning. In this way, the system can achieve efficient path planning in a dynamic environment and avoid collisions with obstacles.

[0016] S103: According to the current concentration value of the pheromone matrix, a particle swarm optimization algorithm is used to adjust the position update direction of the UAV, and if the concentration value is higher than a preset threshold, this direction is preferentially selected.

[0017] Get the concentration values ​​of all paths in the pheromone matrix and determine whether they are higher than the preset threshold. If the concentration value is higher than the preset threshold, use the particle swarm optimization algorithm to calculate the direction of the drone position update. According to the results of the particle swarm optimization algorithm, determine the priority of the drone position update direction. Adjust the drone position update path for the direction with the highest priority. Update the concentration value of the corresponding path in the pheromone matrix. Recalculate the drone path planning result based on the updated pheromone matrix. Save the adjusted drone path planning result.

[0018] Specifically, in the current pheromone matrix, assume that the pheromone concentrations at the location of the drone are [8, 6, 9, 7], and the preset threshold is 7. When the particle swarm optimization algorithm is used to adjust the update direction of the drone's position, the difference between the pheromone concentration and the threshold in each direction is first calculated to obtain [1, -1, 2, 0]. According to the update formula of the particle swarm optimization algorithm, the speed of the drone is updated as: v=w*v+c1*rand()*(p best - x) + c2*rand()*(g best- x), where w is the inertia weight set to 5, c1 and c2 are learning factors set to 5 and 5 respectively, rand() is the random number generation function, p best , g best , are the individual optimal position and the global optimal position respectively.

[0019] In one embodiment, assume that the current position of the drone is [0, 0], the speed is [5, 5], the individual optimal position is [8, 9], and the global optimal position is [7, 8]. By calculation, the new speed v x = 5 * 5 + 5 * 3 * (8 - 0) + 5 * 4 * (7 - 0) = 25 - 09 - 18 = -02, v y = 5 * 5 + 5 * 2 * (9 - 0) + 5 * 3 * (8 -0) = 25 - 03 - 09 = 13. The updated position is x = x + v, that is, [0 + (-02), 0 + 13] = [98,13]. Since the direction with the highest pheromone concentration is 9, this direction is preferred for position update. Finally, the position of the drone is adjusted to [98, 13], ensuring that it can explore and perform tasks in areas with high pheromone concentrations.

[0020] S104, injecting weight coefficients into the pheromone matrix according to the priority information of the multiple target delivery points, and increasing the pheromone concentration enhancement amplitude of the surrounding paths if a high priority delivery point is detected.

[0021] Obtain the priority information of the distribution points and assign a weight coefficient to each distribution point according to the priority. Inject the weight coefficient into the pheromone matrix and adjust the pheromone concentration value of the corresponding path in the matrix. Use the detection algorithm to identify high-priority distribution points and determine the range of their surrounding paths. Calculate the enhancement amplitude of the pheromone concentration for the surrounding paths of high-priority distribution points. Adjust the concentration value of the corresponding path in the pheromone matrix according to the enhancement amplitude and update the matrix data. Use the path planning algorithm to calculate the UAV flight path based on the updated pheromone matrix. Determine the final UAV flight path and save the path planning results.

[0022] The weight coefficient of each distribution point can be obtained by the following formula:

[0023] Among them, W i Represents the weight coefficient of the distribution point, P i represents the priority value of the distribution point, α i represents the impact factor, β i represents the adjustment coefficient, and n represents the total number of distribution points.

[0024] The weight coefficient is injected into the pheromone matrix to adjust the pheromone concentration value of the corresponding path in the matrix, which is specifically obtained by the following formula:

[0025] τ ij represents the path pheromone concentration, ρ represents the pheromone volatility coefficient, Δτ ij represents the pheromone increment, W p represents the weight adjustment value, and t represents the current time.

[0026] Furthermore, the influence range of high priority distribution points can be calculated by the following formula:

[0027] Among them, R p represents the influence range of high priority delivery points, γ d represents the distance attenuation coefficient, d represents the distance value, σ represents the standard deviation, and D represents the maximum impact distance.

[0028] The pheromone enhancement amount is affected by the influence range of the high priority delivery point and can be calculated by the following formula:

[0029] Among them, Δτ represents the pheromone enhancement amount, λ represents the enhancement coefficient, Q represents the pheromone constant, L represents the path length, and ω represents the weight coefficient.

[0030] Finally, the path selection probability is calculated according to the formula: ; P ij represents the path selection probability, τ ij represents the pheromone concentration, η ij represents heuristic information, α represents the pheromone importance coefficient, β represents the heuristic factor importance coefficient, N i Represents a set of selectable paths. A high-probability path or a path greater than a certain threshold can be selected to determine the final flight path of the drone.

[0031] S105: If the wind speed or rainfall intensity in the monitored weather change data exceeds a preset range, the concentration distribution of the pheromone matrix is ​​updated using the dynamically adjusted volatilization rate.

[0032] Obtain the real-time monitored wind speed and rainfall values, and compare them with the preset wind speed threshold and rainfall threshold. If the wind speed or rainfall value exceeds the preset threshold, start the dynamic adjustment mechanism to calculate the volatilization rate value. According to the dynamically adjusted volatilization rate value, determine the update amplitude of the concentration distribution in the pheromone matrix. Use the update amplitude to redistribute the concentration values ​​in the pheromone matrix to obtain the adjusted pheromone matrix. Use the adjusted pheromone matrix as input and combine it with the ant colony algorithm to perform path optimization calculation. Based on the optimization calculation results, generate a new path planning scheme. Compare the new path planning scheme with the original scheme and output the final path selection result.

[0033] Specifically, when the wind speed in the monitored weather change data exceeds the preset 10 m / s or the rainfall intensity exceeds the preset 50 mm / h, the system will start the dynamic adjustment mechanism. First, real-time data is collected through the wind speed sensor and the rain gauge. The wind speed sensor collects data once a second and the rain gauge collects data once a minute, and the data is input into the data processing module. The data processing module uses the Kalman filter algorithm to smooth the collected data to reduce noise interference. For example, when the wind speed is 12 m / s, the Kalman filter algorithm will smooth this abnormal value to 15 m / s to ensure the stability of the data. Then, the system will calculate the volatilization rate based on the smoothed data using the dynamic adjustment algorithm. Specifically, when the wind speed is 15 m / s, the volatilization rate is adjusted to 2 times the original; when the rainfall intensity is 60 mm / h, the volatilization rate is adjusted to 5 times the original. The adjusted volatilization rate will immediately update the concentration distribution of the pheromone matrix. The pheromone matrix adopts the Gaussian distribution model to dynamically adjust the diffusion range of pheromones by calculating the concentration changes of each node. For example, when the volatilization rate is adjusted to 2 times, the concentration of each node in the pheromone matrix will be updated according to the Gaussian distribution formula, and the new concentration distribution will reflect the impact of wind speed and rainfall intensity on pheromone diffusion. Finally, the system will store the updated pheromone matrix in the database and push it to the relevant application modules in real time for subsequent path planning or decision support system use. The entire process is implemented through an automated process without manual intervention, ensuring the real-time and accuracy of data processing.

[0034] S106: If the concentration value of the pheromone matrix is ​​lower than a preset threshold and the position update direction deviates from the target path, reinitialize the pheromone concentration distribution in the area.

[0035] The concentration value of the pheromone matrix is ​​obtained according to the preset threshold, and it is determined whether the concentration value is lower than the threshold. If the concentration value is lower than the threshold, the direction value of the position update is obtained to determine whether the direction value deviates from the target path. If the direction value deviates from the target path, the pheromone distribution in the area is determined, and the pheromone distribution is regenerated using the initialization algorithm. The reinitialized pheromone matrix is ​​obtained, and the path planning is updated using the ant colony algorithm. Based on the updated path planning, the genetic algorithm is used to optimize the target path. The final pheromone distribution matrix is ​​obtained through the optimized target path.

[0036] For example, in the ant colony optimization algorithm, if the concentration value of the pheromone matrix is ​​lower than the preset threshold value 1, and the deviation angle between the position update direction and the target path exceeds 15 degrees, the system will automatically trigger the reinitialization of the pheromone concentration in the area. Specifically, the system first evaluates the degree of deviation by calculating the cosine similarity between the current position and the target path. If the similarity is less than 9, it is determined to be a deviation. Subsequently, the system uses a Gaussian distribution function to regenerate the pheromone concentration in the area, with the mean set to 5 and the standard deviation set to 1 to ensure a more uniform distribution of pheromones. At the same time, the system will use the optimal path information in the historical data to adjust the newly generated pheromone concentration through the weighted average method, so that the new distribution is more inclined to guide ants to the target path. This process is implemented through matrix operations to ensure computational efficiency and accuracy. Through this dynamic adjustment mechanism, the system can effectively deal with path planning problems in complex environments and improve the stability and convergence speed of the algorithm.

[0037] S107: Generate a final path planning solution according to the updated pheromone matrix and the position matrix.

[0038] Get the updated pheromone matrix and position matrix. If the pheromone value of a node in the pheromone matrix is ​​higher than the preset threshold, the node is determined to be a candidate node. Extract the candidate node coordinates from the position matrix, use the clustering algorithm to group the candidate nodes, and obtain the node clustering results. According to the node clustering results, calculate the center point coordinates of each group of nodes and generate a center point set. According to the center point set, use the shortest path algorithm to calculate the optimal path between the center points to obtain the preliminary path planning results. If there is path overlap in the preliminary path planning results, adjust the node order of the overlapping paths and optimize the path planning results. Generate the final path planning plan through the optimized path planning results to determine the node access order and path direction. Match the final path planning plan with the position matrix, and output the detailed node coordinates and path information of the path planning plan.

[0039] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A real-time UAV scheduling strategy control method based on task information and constraints, characterized in that: The method comprises: The initial path planning results of the multi-target delivery points of the UAV are obtained, and the initial mapping relationship between the pheromone matrix and the position matrix is ​​established; if the location information of the dynamic obstacle is detected in the initial path planning results, the concentration value of the pheromone matrix corresponding to the path is reduced, and the concentration value of the pheromone matrix is ​​increased in the adjacent area; according to the current concentration value of the pheromone matrix, the particle swarm optimization algorithm is used to adjust the position update direction of the UAV, and if the concentration value is higher than the preset threshold, the direction is preferentially selected; according to the priority information of the multi-target delivery points, the weight coefficient is injected into the pheromone matrix, and if a high-priority delivery point is detected, the pheromone concentration enhancement amplitude of the surrounding paths is increased; if the wind speed or rainfall intensity in the monitored weather change data exceeds the preset range, the concentration distribution of the pheromone matrix is ​​updated using the dynamically adjusted volatilization rate; if the concentration value of the pheromone matrix is ​​lower than the preset threshold and the position update direction deviates from the target path, the pheromone concentration distribution of the area is reinitialized; the final path planning scheme is generated according to the updated pheromone matrix and the position matrix.

2. The method according to claim 1, characterized in that The method of obtaining the initial path planning results of the multi-target delivery points of the drone and establishing the initial mapping relationship between the pheromone matrix and the position matrix includes: Obtain the initial path planning results of the UAV's multi-target delivery points and extract the path node coordinate information; For the path node coordinate information, a position matrix is ​​constructed to store the longitude and latitude data of each node; Initialize the pheromone matrix according to the number of nodes in the position matrix and set the matrix structure of the same dimension; Traverse each node in the position matrix, calculate the Euclidean distance between nodes, and obtain the distance matrix; Normalize each element value in the distance matrix and convert it into the initial pheromone concentration value; According to the initial pheromone concentration value, fill the corresponding position of the pheromone matrix to complete the matrix initialization; A mapping relationship between the pheromone matrix and the position matrix is ​​established, and data association between matrices is achieved through node indexing.

3. The method according to claim 1, characterized in that: If it is detected that there is dynamic obstacle position information in the initial path planning result, the pheromone matrix concentration value corresponding to the path is reduced, and the pheromone matrix concentration value is increased in the adjacent area, including: Obtain the initial path planning results and extract the dynamic obstacle location information; According to the location information of dynamic obstacles, determine the index value of the path where the obstacle is located; For the path corresponding to the index value, reduce the pheromone concentration value of the path in the pheromone matrix; According to the dynamic obstacle location information, determine the scope of its adjacent area; For the paths in the adjacent area, increase the pheromone concentration value of the corresponding path in the pheromone matrix; Update the pheromone matrix and save the adjusted pheromone concentration value; Based on the updated pheromone matrix, the priority of path planning is recalculated.

4. The method according to claim 1, characterized in that The method of adjusting the position update direction of the UAV by using a particle swarm optimization algorithm according to the current concentration value of the pheromone matrix, and giving priority to the direction if the concentration value is higher than a preset threshold, includes: Obtain the concentration values ​​of all paths in the pheromone matrix and determine whether they are higher than the preset threshold; If the concentration value is higher than the preset threshold, the particle swarm optimization algorithm is used to calculate the update direction of the drone position; According to the results of the particle swarm optimization algorithm, the priority of the drone position update direction is determined; Adjust the drone position update path for the direction with the highest priority; Update the concentration value of the corresponding path in the pheromone matrix; Recalculate the UAV path planning results based on the updated pheromone matrix; Save the adjusted drone path planning results.

5. The method according to claim 1, characterized in that The step of injecting weight coefficients into the pheromone matrix according to the priority information of the multi-target delivery points and increasing the pheromone concentration enhancement amplitude of the surrounding paths if a high-priority delivery point is detected includes: Obtain the priority information of the distribution points and assign a weight coefficient to each distribution point according to the priority level; Inject the weight coefficient into the pheromone matrix and adjust the pheromone concentration value of the corresponding path in the matrix; Use detection algorithms to identify high-priority delivery points and determine the range of their surrounding paths; Calculate the enhancement of pheromone concentration for the surrounding paths of high-priority delivery points; The concentration value of the corresponding path in the pheromone matrix is ​​adjusted according to the enhancement amplitude, and the matrix data is updated; A path planning algorithm is used to calculate the flight path of the drone based on the updated pheromone matrix; Determine the final UAV flight path and save the path planning results.

6. The method according to claim 1, characterized in that If the wind speed or rainfall intensity in the monitored weather change data exceeds a preset range, the concentration distribution of the pheromone matrix is ​​updated using the dynamically adjusted volatilization rate, including: Obtain the real-time monitored wind speed and rainfall values, and compare them with the preset wind speed threshold and rainfall threshold; If the wind speed value or rainfall value exceeds the preset threshold, the dynamic adjustment mechanism is activated to calculate the volatilization rate value; According to the volatilization rate value after dynamic adjustment, the update amplitude of the concentration distribution in the pheromone matrix is ​​determined; The concentration values ​​in the pheromone matrix are redistributed using the update amplitude to obtain an adjusted pheromone matrix; The adjusted pheromone matrix is ​​used as input and combined with the ant colony algorithm to perform path optimization calculation; Generate a new path planning solution based on the optimization calculation results; Compare the new path planning scheme with the original scheme and output the final path selection result.

7. The method according to claim 1, characterized in that If the concentration value of the pheromone matrix is ​​lower than a preset threshold and the position update direction deviates from the target path, the pheromone concentration distribution of the area is reinitialized, including: Obtaining the concentration value of the pheromone matrix according to a preset threshold value, and determining whether the concentration value is lower than the threshold value; If the concentration value is lower than the threshold, obtain the direction value of the position update and determine whether the direction value deviates from the target path; If the direction value deviates from the target path, the pheromone distribution in the area is determined and the pheromone distribution is regenerated using an initialization algorithm; Get the reinitialized pheromone matrix and use the ant colony algorithm to update the path planning; According to the updated path planning, the target path is optimized using genetic algorithm; The final pheromone distribution matrix is ​​obtained through the optimized target path.

8. The method according to claim 1, characterized in that: Generating a final path planning scheme according to the updated pheromone matrix and the position matrix includes: Obtain the updated pheromone matrix and position matrix. If the pheromone value of a node in the pheromone matrix is ​​higher than a preset threshold, the node is determined to be a candidate node. Extract candidate node coordinates from the position matrix, use clustering algorithm to group candidate nodes, and obtain node clustering results; Based on the node clustering results, calculate the center point coordinates of each group of nodes and generate a center point set; According to the set of center points, the shortest path algorithm is used to calculate the optimal path between the center points to obtain the preliminary path planning result; If there are overlapping paths in the preliminary path planning results, the node order of the overlapping paths is adjusted to optimize the path planning results; Generate the final path planning solution through the optimized path planning results to determine the node access sequence and path direction; Match the final path planning solution with the position matrix and output the detailed node coordinates and path information of the path planning solution.

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