Route planning method and device, equipment, storage medium and product

Abstract

The invention discloses a route planning method and device, equipment, a storage medium and a product, belongs to the field of data processing, and is used for improving the success rate of route searching. The method comprises the steps that first information of a plurality of first areas is acquired, an initial route is generated for each first unmanned aerial vehicle based on the first information, and the initial route is a flight route randomly generated by each first unmanned aerial vehicle in the first area; determining a first route from the plurality of initial routes based on a preset route decision strategy; when the multiple first unmanned aerial vehicles are located in the second area, information to be updated is obtained, the first information of each first area is updated based on the information to be updated, second information of each first area is obtained, and the information to be updated is area information obtained when the multiple first unmanned aerial vehicles fly to the second area; and determining the attraction weight of each first area based on the second information of each first area, so as to adjust the flight routes of the plurality of first unmanned aerial vehicles based on the attraction weights.

Description

Technical Field

[0001] This application belongs to the field of data processing, specifically relating to a route planning method, apparatus, equipment, storage medium, and product. Background Technology

[0002] In ecological environment monitoring and wildlife protection, for the needs of tracking wildlife migration and monitoring anti-poaching across vast areas, path planning is typically performed by integrating UAV collaborative control, artificial intelligence algorithm optimization, and geographic information system (GIS) technology. Existing technologies usually perform path search in a static environment, such as by evaluating path costs using heuristic functions to determine the search path; however, this method has a low success rate in path search.

[0003] Therefore, a route planning method that can improve the success rate of path search is needed. Summary of the Invention

[0004] This application provides a route planning method that can improve the success rate of path search.

[0005] In a first aspect, embodiments of this application provide a route planning method, which includes: acquiring first information of multiple first regions, and generating initial routes for each first drone based on the first information, wherein the first regions are regions for which route planning is to be performed, the first information is relevant information of the first regions, the first drones are drones whose flight routes are to be planned, and the initial routes are flight routes randomly generated by each of the first drones in the first regions; determining a first route from the multiple initial routes based on a preset route determination strategy, wherein the first route is the flight route to be taken by the multiple first drones; when the multiple first drones are located in a second region, acquiring information to be updated and updating the first information of each first region based on the information to be updated to obtain second information of each first region, wherein the second region is a region in the first route, and the information to be updated is region information acquired by the multiple first drones on their way to the second region; determining an attraction weight for each first region based on the second information of each first region, and adjusting the flight routes of the multiple first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the multiple first drones.

[0006] Secondly, embodiments of this application provide a route planning device, comprising: a first acquisition module, configured to acquire first information of multiple first regions and generate initial routes for each first drone based on the first information, wherein the first regions are regions for which route planning is to be performed, the first information is relevant information of the first regions, the first drones are drones whose flight routes are to be planned, and the initial routes are flight routes randomly generated by each of the first drones in the first regions; a first determination module, configured to determine a first route from the multiple initial routes based on a preset route determination strategy, wherein the first route is the flight route to be taken by the multiple first drones; a first update module, configured to acquire information to be updated and update the first information of each first region based on the information to be updated when the multiple first drones are located in a second region, thereby obtaining second information of each first region, wherein the second region is a region in the first route, and the information to be updated is region information acquired by the multiple first drones during their flight to the second region; and a first adjustment module, configured to determine an attraction weight of each first region based on the second information of each first region, and adjust the flight routes of the multiple first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the multiple first drones.

[0007] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0008] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer program product that, when executed by a processor, implements the steps of the method described in the first aspect.

[0010] In a sixth aspect, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0011] In this embodiment, first information of multiple first regions is obtained, and initial routes are generated for each first drone based on the first information. The first region is the region for which route planning is to be performed, the first information is the relevant information of the first region, the first drone is the drone whose flight route is to be planned, and the initial route is the flight route randomly generated by each first drone in the first region. A first route is determined from multiple initial routes based on a preset route determination strategy. The first route is the flight route to be performed by multiple first drones. When multiple first drones are located in a second region, information to be updated is obtained, and the first information of each first region is updated based on the information to be updated to obtain second information of each first region. The second region is the region in the first route, and the information to be updated is the region information obtained by multiple first drones on their way to the second region. The attraction weight of each first region is determined based on the second information of each first region, so as to adjust the flight route of multiple first drones based on the attraction weight. The attraction weight is used to characterize the attraction of the first region to multiple first drones, which can improve the success rate of path search. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a route planning method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a route planning system provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the second route planning method provided in the embodiments of this application; Figure 4 This is a flowchart illustrating the third route planning method provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of a route planning device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a route planning device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] The route planning method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0016] Figure 1 This illustration shows a route planning method provided by an embodiment of the present invention. The method can be executed by an electronic device, which may include a server and / or a terminal device, wherein the terminal device may be, for example, an in-vehicle terminal or a mobile phone terminal. In other words, the method can be executed by software or hardware installed in the route planning device, and the method includes the following steps: Step 102: Obtain first information for multiple first regions, and generate initial routes for each first UAV based on the first information.

[0017] Wherein, the first area is the area to be route planned, the first information is the relevant information of the first area, the first drone is the drone to be planned for flight route, and the initial route is the flight route randomly generated by each of the first drones in the first area.

[0018] The route planning method described in this application can be implemented by a route planning system, route planning software, or other entities. Here, a route planning system is used as an example for explanation.

[0019] The route planning system first obtains first information about multiple first regions. The first region is the area where route planning is to be performed, and the first information is the relevant information of the first region. For example, the first information can be the coverage area of ​​the first region, the location of the center point of the first region, and the information of the flora and fauna included in the first region. Each first region has corresponding first information, and the area of ​​each first region can be the same or different.

[0020] Specifically, when acquiring the first information of multiple first regions, the route planning system first obtains the task region information of the task area and then performs rasterization processing on the task area to obtain multiple first regions and their corresponding first information. The task region is the overall area for which route planning needs to be performed, and the task region information is the relevant information of the task region. In other words, the route planning system first obtains the relevant information of the task region, then divides the task region into multiple grids according to preset standards, thereby obtaining multiple first regions, and finally determines the relevant information of each first region based on the relevant information of the task region. For example, when the task region is a 50 km * 50 km area, the route planning system can perform rasterization processing on this area, dividing it into multiple grids of length M and width N.

[0021] After determining the initial information for multiple first regions, the route planning system first identifies multiple first UAVs, and then generates initial routes for each first UAV based on the initial information for the multiple first regions. Here, a first UAV is the UAV that requires flight route planning, and the initial route is a randomly generated initial flight route within the multiple first regions by the route planning system based on the initial information. In other words, the route planning system generates a unique initial route for each first UAV based on the initial information for each first region; each first UAV has a corresponding unique route (initial route).

[0022] Specifically, the first information includes the location information of the first region and the flora and fauna information included in the first region. Therefore, when the route planning system generates the initial routes of each first UAV based on the first information of each first region, it can randomly generate the initial routes of each first UAV based on the location information of the first region, for example, determining the initial routes of the first UAVs based on multiple geographically connected first regions. It can also randomly generate the initial routes of each first UAV based on the flora and fauna information included in the first region, for example, determining the initial routes of each first UAV based on first regions with relatively rich flora and fauna information. More specifically, the initial routes generated by the route planning system can be represented as: A d =G1 d G2 d G3 d G4 d , ...G K d , where A d G represents the order in which the first UAV d calculates the flyby area. K d This represents the Kth region that the first drone d intends to fly over.

[0023] Furthermore, such as Figure 2The diagram shown is a structural schematic of a route planning system provided in one embodiment of this specification. Figure 2 As shown, the route planning system may include multiple route planning subsystems, the number of which is the same as the number of first UAVs, and each route planning subsystem is installed in each of the first UAVs. That is, after obtaining the first information of each first region, the route planning subsystem installed in each first UAV randomly generates an initial route for its respective first UAV based on the first information of each first region.

[0024] Step 104: Determine the first route from the multiple initial routes based on the preset route determination strategy.

[0025] The first route is the flight path of multiple first UAVs.

[0026] After determining the initial routes of each first UAV, the route planning system selects an initial route from multiple initial routes based on a preset route determination strategy and designates it as the first route, where the first route is the flight route to be taken by multiple first UAVs.

[0027] In other words, the route planning system selects a route as the first route from the initial routes generated by each of the first UAVs based on a preset route determination strategy. The first route is a common flight path for multiple first UAVs, meaning that multiple first UAVs fly along the same flight path (the first route). Furthermore, each route planning subsystem installed in each first UAV makes a decision based on the preset route determination strategy, determining an initial route as the first route from the initial routes generated by each first UAV, and then each first UAV flies along the route of the first route.

[0028] Specifically, when determining the first route from multiple initial routes based on the route determination strategy, the route planning system can identify the third area involved in each initial route, and then determine the flora and fauna information included in the initial route (e.g., animal type, plant type, number of animals, plant area) based on the flora and fauna information included in each third area. Finally, the first route is determined based on the flora and fauna information included in each initial route. For example, the initial route with the most species of flora and fauna is determined as the first route, or the initial route with the most animals is determined as the first route.

[0029] Step 106: When multiple first drones are located in the second area, obtain the information to be updated and update the first information of each first area based on the information to be updated to obtain the second information of each first area.

[0030] The second region is the region in the first route, and the information to be updated is the region information obtained by multiple first UAVs during their flight to the second region.

[0031] When multiple first UAVs fly along a first route and are located in a second area, the route planning system acquires information to be updated and updates the first information corresponding to each first area based on the acquired information to be updated, thereby obtaining the updated information (second information) for each first area. The second area is an area in the first route, which can be any area in the first route. The information to be updated is the information about each area acquired by the multiple first UAVs during their flight from the starting position to the second area, such as information about animals present in each area.

[0032] In other words, each of the first UAVs is equipped with a camera (or video recording device), which can acquire relevant information about the area where the first UAV is located and the adjacent areas during the flight of the first UAV, such as information about plants and animals, and determine the relevant information of each area as information to be updated.

[0033] Specifically, after determining the first route, multiple first UAVs fly along the first route. When they reach any area (i.e., the second area), the multiple first UAVs acquire area information obtained by the camera (or video recording) device during their flight, and determine the acquired area information as the information to be updated. After determining the information to be updated, the route planning system determines the information that needs to be updated for each first area, and updates the first information, thereby obtaining the updated second information for each first area.

[0034] Step 108: Determine the attraction weight of each first region based on the second information of each first region, so as to adjust the flight path of the multiple first UAVs based on the attraction weight.

[0035] The attraction weight is used to characterize the degree to which the first region attracts multiple of the first drones.

[0036] After determining the second information of each first region, the route planning system determines the attraction weight of each first region based on the second information of each first region through a preset attraction weight determination formula. Then, it adjusts the flight routes of multiple first drones based on the attraction weight of each first region. For example, the first region with an attraction weight greater than a preset threshold is determined as the target region, and the adjusted route is determined based on the second information of the target region (that is, the adjusted route is the route for multiple first drones to fly to the target region). The attraction weight is used to characterize the degree of attraction of each first region to multiple first drones. The larger the attraction weight of the first region, the more attractive the first region is to multiple first drones. The smaller the attraction weight of the first region, the less attractive the first region is to multiple first drones.

[0037] In other words, the route planning system determines the attractiveness of each first region to multiple first UAVs based on the second information of each first region, then identifies the first region with the greater attractiveness as the target region, and adjusts the flight route to the route to the target region.

[0038] Specifically, when determining the attraction weight of each first region based on the second information of each first region, the route planning system can determine the flora and fauna information of the first region based on the second information of each first region, and determine the attraction weight based on the flora and fauna information. For example, the first region with a higher variety of flora and fauna is determined as the region with a higher attraction weight, and the first region with a higher number of flora and fauna is determined as the region with a higher attraction weight, etc.

[0039] Furthermore, such as Figure 2 As shown, the route planning subsystem installed in each first UAV can not only generate an initial route for its first UAV based on the first information of each first region, but also make decisions based on each initial route to determine the first route from multiple initial routes, and finally adjust the flight route based on the updated second information.

[0040] The route planning method provided in this invention obtains first information of multiple first regions and generates initial routes for each first drone based on the first information. The first region is the region to be planned, the first information is related information of the first region, the first drone is the drone whose flight route is to be planned, and the initial route is a flight route randomly generated by each first drone within the first region. A first route is determined from the multiple initial routes based on a preset route determination strategy; the first route is the flight route to be taken by the multiple first drones. When the multiple first drones are located in a second region, information to be updated is obtained, and the first information of each first region is updated based on this information to obtain second information for each first region. The second region is the region within the first route, and the information to be updated is the region information obtained by the multiple first drones during their flight to the second region. An attraction weight for each first region is determined based on the second information of each first region, and the flight routes of the multiple first drones are adjusted based on this attraction weight. The attraction weight characterizes the degree of attraction of the first region to the multiple first drones, which can improve the success rate of path search.

[0041] In one implementation, determining the first route from the plurality of initial routes based on a preset route determination strategy (step 104) can be performed via steps A1-A4: Step A1: Determine multiple third regions in the initial route and the first area corresponding to each of the third regions.

[0042] The third region is the first region involved in the initial route, and the first area is the coverage area when multiple first drones pass through the third region.

[0043] When determining the first route from multiple initial routes based on a preset route determination strategy, the route planning system first determines multiple third regions corresponding to each initial route and a first area corresponding to each third region. The third region is the first region that multiple first UAVs need to pass through when flying along the initial route, and the first area is the coverage area of ​​the third region when multiple first UAVs pass through it.

[0044] Specifically, when the first UAV flies over a certain third area, the route planning system can determine the overlapping area between the area flown by the first UAV and the third area as the first area, or it can determine the overlapping area between the area covered by the camera (or video recording) device during the flight of the first UAV and the third area as the first area.

[0045] Step A2: Based on the first information of each of the third regions, determine the first target included in each of the third regions and the first probability of each first target.

[0046] Wherein, the first target is a preset area of ​​interest, and the first probability is the probability of the first target appearing in the third area.

[0047] When determining the first route from multiple initial routes based on the preset route determination strategy, the route planning system will also determine the first target and first probability in each third region involved by each initial route based on the first information of each third region involved by each initial route. The first target is the preset regional interest object included in the third region. The regional interest object can be animals and plants (such as buffalo, Tibetan antelope, snow leopard, domestic horse, etc.) and other interest objects (such as vehicles, etc.). The first probability is the probability of various regional interest objects (first targets) appearing in the third region.

[0048] Specifically, the first target is a pre-defined target that needs to be tracked, such as buffalo, Tibetan antelope, snow leopard, domestic horse, vehicle, etc. The probability (first probability) of the first target appearing in each third area is determined by the number of times the first target passes through the third area.

[0049] Step A3: Based on the first area, the first target, and the first probability of each of the third regions, determine the first score corresponding to each of the initial routes through a preset route scoring strategy.

[0050] After determining the first area, first objective, and first probability of each third region involved in each initial route, the route planning system scores each initial route based on the first area, first objective, and first probability of each third region included in the initial route, and finally obtains the first score of each initial route.

[0051] Specifically, when determining the first score of the initial route based on the first area, first target, and first probability of each third region included in the initial route, the route planning system first determines the target information entropy based on the first target and the first probability, then determines the first sub-score of each third region based on the target information entropy and the first area, and then determines the sum of the first sub-scores of the multiple third regions included in the initial route as the first score. Here, the target information entropy is used to characterize the information entropy of the multiple first targets included in the third region.

[0052] When determining the target information entropy, the route planning system can represent the target information entropy as H. g The quantitative index of the target information entropy in the third region can be expressed as entropy(G). k d The formula for calculating entropy is: entropy(G k d )=H g =-∑p k *log2pk +λ△t g Where 1≤k≤C, C is the total number of types of objects of interest in the region, and p k λ represents the probability of the existence of the k-th type of target. Here, the relevant information of the target of interest in the region identified in the first information of the third region each time passes through the region is used as the basis for determining the information entropy. λ is the attenuation coefficient, which can be taken as a small value, such as 0.05-0.1. Δt g This indicates the length of time since the third region was last observed in the current update window.

[0053] When determining the first sub-score of the third region based on the target information entropy and the first area, the route planning system can determine to weight the first area and the target information entropy separately based on preset weight coefficients, and determine the weighted sum as the first score. The formula for calculating the first score of the initial route can be expressed as: S(A d =∑(α*coverarea(G)) k d )+β* entropy(G k d ), (1≤k≤K), where S(A d ) represents the first score of the d-th initial route, and α is the coverarea(G k d The weighting coefficients of coverarea(G) k d ) represents the coverage area of ​​the k-th third region in the d-th initial route, and β represents the entropy(G) k d The weighting coefficients of ) and entropy(G) k d The target information entropy of the kth third region in the dth initial route, where K is the number of third regions included in the dth initial route.

[0054] Step A4: Determine the first route based on the initial route where the first score is greater than the first threshold.

[0055] After determining the first score corresponding to each initial route, the route planning system selects one route from multiple initial routes as the first route based on the first score of each initial route. Furthermore, the route planning system can directly determine the initial route with a first score greater than a first threshold as the first route.

[0056] Specifically, when there is only one initial route with a first score greater than the first threshold, the route planning system determines that initial route as the first route; when there are multiple initial routes with a first score greater than the first threshold, the route planning system determines the initial route with the highest first score as the first route.

[0057] In one implementation, determining the first route based on the initial route where the first score is greater than a first threshold (step A4) can be performed via steps B1-B4: Step B1: The initial route with the first score greater than the first threshold is determined as the second route.

[0058] There are multiple routes.

[0059] When determining the first route based on the first score of each initial route, the route planning system can directly identify the initial route with the first score greater than the first threshold as the first route, or it can perform further processing on the initial route with the first score greater than the first threshold to determine the first route based on the result of the further processing.

[0060] During further processing, the route planning system first identifies the initial routes with a first score greater than a first threshold as second routes. There are multiple second routes, and these multiple second routes are the initial routes that need further processing to determine the first route.

[0061] Step B2: Determine the number of conflict zones between the second route and each of the initial routes.

[0062] The number of conflict zones is the number of identical first zones that exist between the second route and each of the initial routes.

[0063] After determining multiple second routes, for each second route, the route planning system determines the number of conflict areas between the second route and each initial route. The number of conflict areas is the number of first areas that are the same between the second route and the initial route. For example, when the first areas involved in the second route are A, B, C, and D, and the first areas involved in a certain initial route are C, D, E, and F, then the number of conflict areas can be determined to be 2.

[0064] Specifically, the route planning subsystem installed in each first UAV can determine the number of conflict zones between the initial route of its first UAV and each second route, so as to carry out subsequent processing based on the number of conflict zones.

[0065] Step B3: Vote for each of the second routes based on the number of conflict areas to obtain the first vote for each of the second routes.

[0066] After determining the number of conflict zones between each second route and each initial route, the route planning system votes on each second route based on the number of conflict zones and obtains the voting results (the first vote for each second route).

[0067] Specifically, during the voting process, each initial route has one vote. For each initial route, the route planning system determines the second route with a number of conflict areas less than a preset threshold to vote on (or selects the second route with the smallest number of conflict areas to vote on) based on the number of conflict areas between the initial route and each second route. At this time, the vote count of the second route being voted on is increased by one, and then the next initial route is determined and the next round of voting begins.

[0068] More specifically, the route planning subsystem installed in each first UAV can vote on multiple second routes with a first score greater than a first threshold (for example, voting on the three second routes with the highest first scores). The voting rules are as follows: each route planning subsystem determines the number of conflict areas between each second route and the first route of its respective first UAV, and votes on the second route with the fewest conflict areas; when there are multiple second routes with zero conflict areas, the route planning subsystem can vote on the second route with the highest first score among the multiple second routes with zero conflict areas.

[0069] Step B4: The second route with the first number of votes greater than the second threshold is determined as the first route.

[0070] After voting is completed for each initial route, the route planning system selects one second route from multiple second routes based on the first number of votes and designates it as the first route. Furthermore, the route planning system can determine the first route based on second routes whose first number of votes is greater than a second threshold.

[0071] Specifically, when there is one second route with a first vote count greater than the second threshold, the route planning system directly identifies that second route as the first route; when there are multiple second routes with a first vote count greater than the second threshold, the route planning system can identify the second route with the largest first vote count as the first route.

[0072] Figure 3 This is a flowchart illustrating a second route planning method provided in one embodiment of this specification, as shown below. Figure 3 As shown, the schematic diagram includes: Step 302: Obtain first information for multiple first regions, and generate initial routes for each first UAV based on the first information.

[0073] Wherein, the first area is the area to be route planned, the first information is the relevant information of the first area, the first drone is the drone to be planned for flight route, and the initial route is the flight route randomly generated by each of the first drones in the first area.

[0074] Step 304: Determine the multiple third regions in the initial route and the first area corresponding to each of the third regions.

[0075] The third region is the first region involved in the initial route, and the first area is the coverage area when multiple first drones pass through the third region.

[0076] Step 306: Based on the first information of each of the third regions, determine the first target included in each of the third regions and the first probability of each first target.

[0077] Wherein, the first target is a preset area of ​​interest, and the first probability is the probability of the first target appearing in the third area.

[0078] Step 308: Based on the first area, the first target, and the first probability of each of the third regions, determine the first score corresponding to each of the initial routes through a preset route scoring strategy.

[0079] Step 310: The initial route with the first score greater than the first threshold is determined as the second route.

[0080] There are multiple routes.

[0081] Step 312: Determine the number of conflict zones between the second route and each of the initial routes.

[0082] The number of conflict zones is the number of identical first zones that exist between the second route and each of the initial routes.

[0083] Step 314: Vote for each of the second routes based on the number of conflict areas to obtain the first number of votes for each of the second routes.

[0084] Step 316: The second route with the first number of votes greater than the second threshold is determined as the first route.

[0085] The first route is the flight path of multiple first UAVs.

[0086] Step 318: When multiple first drones are located in the second area, obtain information to be updated and update the first information of each first area based on the information to be updated to obtain the second information of each first area.

[0087] The second region is the region in the first route, and the information to be updated is the region information obtained by multiple first UAVs during their flight to the second region.

[0088] Step 320: Determine the attraction weight of each first region based on the second information of each first region, so as to adjust the flight path of the multiple first UAVs based on the attraction weight.

[0089] The attraction weight is used to characterize the degree to which the first region attracts multiple of the first drones.

[0090] In the embodiments described in the specification, by scoring the initial routes of each first UAV based on the first information of each first region, determining multiple second routes based on the scoring results, and then voting on each second route to determine the first route, the optimal path can be selected from multiple initial routes, thereby improving the success rate of path search.

[0091] In one implementation, the step of determining the attraction weight of each first region based on the second information of each first region (step 108) may further include steps C1-C3: Step C1: Based on the second information of each first region, determine the plurality of first targets included in each first region and the first type of each first target.

[0092] Wherein, the first target is a preset area of ​​interest, and the first type is the type of the first target.

[0093] After multiple first UAVs fly to the second area, the route planning system determines the first target and the first type of the first target included in each first area based on the second information of each first area. The first target is a preset area of ​​interest, and the first type is the type to which the first target belongs.

[0094] In other words, after multiple first UAVs fly to the second area, the route planning system will update the first information of each first area based on the information obtained by the multiple first UAVs during their flight and obtain the updated second information. Then, based on the updated second information, it will determine the multiple first targets included in each first area and the first type corresponding to each first target.

[0095] Step C2: Based on the first target and the first type in each of the first regions, determine the attraction weight of each of the first regions using a preset attraction weight determination formula.

[0096] After determining the multiple first objectives included in each first region and the first type corresponding to each first objective, the route planning system determines the attractiveness weight of the first region based on the multiple first objectives included in the first region and the first type of the first objective.

[0097] Specifically, when determining the attraction weight of each first region using a preset attraction weight determination formula, the route planning system calculates independent attraction weights 'a' for different first objectives and the first type of the first objective. ij k Here, k represents information related to the k-th type of target within the first region, and a ij k The calculation takes into account the distribution of similar targets in the adjacent regions (neighborhoods) of the first region. The neighborhood Л(i,j) is defined as an n*n window centered on the first region (i,j), such as 3*3 or 5*5. The radius of the neighborhood determines the range of the correlation space.

[0098] More specifically, the preset formula for determining the attraction weight can be:

[0099] Where pk is the position coordinate of the first target point, pij is the position coordinate of the center point of the first region, and I (pk∈Л(i,j)) is a binary function that takes only 0 or 1 to constrain the target point to be within the neighborhood Л(i,j). The Gaussian kernel function in the above formula uses Euclidean distance to constrain the correlation to the center. d is a density smoothing parameter. Decreasing the value can reduce the influence of the target point, while increasing the value can spread the influence to a more distant area.

[0100] Step C3: Determine the first region where the attraction weight is greater than the third threshold as the target region, and update the flight paths of multiple first UAVs based on the target region.

[0101] The target area refers to the area to which multiple first UAVs are to fly.

[0102] After determining the attraction weight of each first region, the route planning system identifies the first region with an attraction weight greater than the third threshold as the target region, and updates the flight routes of multiple first UAVs based on the target region. The updated flight routes are the routes to the target region.

[0103] Furthermore, when there are multiple first regions with attraction weights greater than the third threshold, the route planning system determines the first region with the largest attraction weight as the target region, and then updates the flight route based on the determined target region.

[0104] In one implementation, after determining the first route from the plurality of initial routes based on a preset route determination strategy (step 106), steps D1-D2 may also be performed: Step D1: Determine the third route.

[0105] The third route is a route taken by multiple first drones from the fourth region to the fifth region. The fourth region is any of the first regions involved in the first route, and the fifth region is the first region in the first route that is adjacent to the fourth region and located after the fourth region.

[0106] During the flight of multiple first UAVs, the route planning system can also determine a third route. This third route is the path taken by the multiple first UAVs from a fourth region to a fifth region. The third route includes any one of the first regions within the first route, and the fifth region is the first region adjacent to and following the fourth region within the first route. Furthermore, the fourth region here refers to the current location of the multiple first UAVs, and the fifth region is the next first region the multiple first UAVs need to fly to.

[0107] In other words, when multiple first UAVs fly from a first area (fourth area) to the next area (fifth area) according to a route, the route planning system determines the flight route to the next area (fifth area).

[0108] Step D2: Establish a comprehensive potential field based on the first information in the fourth region and the first information in the fifth region to adjust the third route of the multiple first UAVs.

[0109] The comprehensive potential field includes one or more of the following: obstacle potential field, terrain potential field, moving obstacle potential field, meteorological disturbance potential field, target point potential field, energy efficiency optimization potential field, and trajectory smoothing potential field. The obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the obstacle dimension. The terrain potential field is used to adjust the flight paths of multiple first UAVs based on the terrain dimension. The moving obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the moving obstacle dimension. The meteorological disturbance potential field is used to adjust the flight paths of multiple first UAVs based on the meteorological dimension. The target point potential field is used to adjust the flight paths of multiple first UAVs based on the position dimension of the fifth region. The energy efficiency optimization potential field is used to adjust the flight paths of multiple first UAVs based on the energy utilization dimension. The trajectory smoothing potential field is used to adjust the flight paths of multiple first UAVs based on the trajectory smoothing dimension.

[0110] When determining the third route and multiple first UAVs flying along the third route, the route planning system establishes a comprehensive potential field based on the first information of the fourth region and the first information of the fifth region, and adjusts the flight route according to the established comprehensive potential field.

[0111] The comprehensive potential field established by the route planning system is composed of multiple sub-potential fields. These sub-potential fields can be one or more of the following: obstacle potential field, terrain potential field, moving obstacle potential field, weather disturbance potential field, target point potential field, energy efficiency optimization potential field, and trajectory smoothing potential field. The route planning system uses these multiple sub-potential fields to adjust the flight trajectory of the first UAV from multiple different dimensions, and then uses the comprehensive potential field formed by combining these sub-potential fields to adjust the flight trajectories of multiple first UAVs.

[0112] Specifically, the obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the obstacle dimension. When an obstacle is detected in the flight path of multiple first UAVs, the route planning system adjusts the flight path based on the location of the obstacle to avoid collisions between the multiple first UAVs and the obstacle during flight. The formula for determining the obstacle potential field can be:

[0113] Where Qobs,i represents the magnitude of the obstacle repulsion force, p is the attenuation exponent, robs is the obstacle position, and r is the current position of multiple first UAVs. Here, the route planning system will also configure a safe distance Rsafe, and the potential field will be activated when |△r| < Rsafe.

[0114] The terrain field is used to adjust the flight paths of multiple first UAVs based on the terrain dimension. When multiple first UAVs detect an increase in altitude above the ground (descending terrain) during flight, the route planning system adjusts the flight altitude of the multiple first UAVs to decrease; when multiple first UAVs detect a decrease in altitude above the ground (increasing terrain) during flight, the route planning system adjusts the flight altitude of the multiple first UAVs to increase. The time required to determine the terrain field can be:

[0115] Among them, the kt coefficient controls the influence weight of the terrain potential field, z ref It is the reference altitude, that is, the cruising altitude configured for multiple first drones.

[0116] The moving obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the dimensions of the moving obstacle. When a moving obstacle (such as a bird) is detected in the flight path of the multiple first UAVs, the route planning system adjusts the flight path based on the position of the moving obstacle to avoid collisions between the multiple first UAVs and the moving obstacle during flight. The formula for determining the moving obstacle potential field can be:

[0117] Among them, Q dynThe dynamic repulsive force intensity is represented, which determines the avoidance distance. λ is the time decay factor used to control for historical influences. dyn It refers to the dynamic obstacle position, r dyn The transient position or short-time dynamics based on the measured velocity can be used to represent r. dyn =r dyn，0 +v rel *t pred r dyn，0 It is the current position of the moving target, v rel It is relative velocity, t pred It is the time it takes for the obstacle to move.

[0118] The meteorological disturbance potential field is used to adjust the flight paths of multiple first UAVs based on meteorological dimensions. When multiple first UAVs detect a headwind during flight, the route planning system adjusts the flight path based on the wind direction, for example, adjusting the route to a tailwind or crosswind route when there is a headwind, in order to reduce energy consumption and jitter. The formula for determining the meteorological disturbance potential field can be:

[0119] Among them, v wind Let v be the current wind speed vector. drone Let k be the current airspeed vector of the UAV. w |v is the weather penalty coefficient. wind -v drone |2 represents the square modulus of the difference between airspeed and wind speed, i.e., relative airflow intensity.

[0120] The target point potential field is used to adjust the flight paths of multiple first UAVs based on the position dimension of the fifth region. The target point potential field is used to encourage the multiple first UAVs to approach the target point more quickly. The target point can be the center point of the next region. The formula for determining the target point potential field is:

[0121] Where, k g Let r be the target attraction coefficient, and r be the current position vector of the first UAV. goal This is the target position vector.

[0122] The energy efficiency optimization potential field is used to adjust the flight paths of multiple first UAVs based on the energy utilization dimension. When multiple first UAVs are flying, the route planning system approaches the target point with a lower airspeed or a shorter path, thus enabling multiple first UAVs to reach the target point with lower energy consumption. Here, the formula for determining the potential field of the target point can be:

[0123] Where, k eHere, v is the energy consumption weighting coefficient, v is the current airspeed of the first UAV, and t is the energy consumption weighting coefficient. remain The estimated remaining flight time is given by airspeed, which is the velocity vector relative to the air.

[0124] The trajectory smoothing potential field is used to adjust the flight paths of multiple first UAVs based on the trajectory smoothing dimension. When multiple first UAVs fly along the path, the route planning system optimizes the flight path of the first UAVs to make the flight path trajectory of the first UAVs smoother. The formula for determining the trajectory smoothing potential field can be:

[0125] Where ks represents the smoothing weight coefficient, kmax is the maximum allowable acceleration constant, and r is the second derivative of the current position vector of the first UAV with respect to time, i.e., the acceleration vector.

[0126] In addition, the comprehensive potential field can also include a regional coverage potential field. This regional coverage potential field is used to adjust the flight paths of multiple first UAVs based on the dimension of the coverage area during their flight. When multiple first UAVs fly along a route, the route planning system optimizes the flight paths of the first UAVs to maximize the coverage area of ​​their flight paths. The formula for determining the regional coverage potential field can be:

[0127] Where kc represents the area coverage coefficient, area( (uncovered) represents the coverage area corresponding to the first drone.

[0128] The formula for the comprehensive potential field determined by the route planning system can be expressed as:

[0129] Where wk(t) is the weighting parameter, corresponding to the weighting ratio of different potential fields at different times and states, and Uk(r,t) is the different sub-potential fields in each first region.

[0130] Figure 4 This is a flowchart illustrating the third route planning method provided in one embodiment of this specification, as shown below. Figure 4 As shown, the schematic diagram includes: Step 402: Obtain first information for multiple first regions, and generate initial routes for each first UAV based on the first information.

[0131] Wherein, the first area is the area to be route planned, the first information is the relevant information of the first area, the first drone is the drone to be planned for flight route, and the initial route is the flight route randomly generated by each of the first drones in the first area.

[0132] Step 404: Determine the first route from the multiple initial routes based on the preset route determination strategy.

[0133] The first route is the flight path of multiple first UAVs.

[0134] Step 406: When multiple first UAVs are located in the second area, obtain the information to be updated and update the first information of each first area based on the information to be updated to obtain the second information of each first area.

[0135] The second region is the region in the first route, and the information to be updated is the region information obtained by multiple first UAVs during their flight to the second region.

[0136] Step 408: Based on the second information of each first region, determine the plurality of first targets included in each first region and the first type of each first target.

[0137] Wherein, the first target is a preset area of ​​interest, and the first type is the type of the first target.

[0138] Step 410: Based on the first target and the first type in each of the first regions, determine the attraction weight of each of the first regions using a preset attraction weight determination formula.

[0139] Step 412: Determine the first region where the attraction weight is greater than the third threshold as the target region, and update the flight paths of multiple first UAVs based on the target region.

[0140] The target area refers to the area to which multiple first UAVs are to fly.

[0141] Step 414: Determine the third route.

[0142] The third route is a route taken by multiple first drones from the fourth region to the fifth region. The fourth region is any of the first regions involved in the first route, and the fifth region is the first region in the first route that is adjacent to the fourth region and located after the fourth region.

[0143] Step 416: Establish a comprehensive potential field based on the first information in the fourth region and the first information in the fifth region to adjust the third route of the multiple first UAVs.

[0144] The comprehensive potential field includes one or more of the following: obstacle potential field, terrain potential field, moving obstacle potential field, meteorological disturbance potential field, target point potential field, energy efficiency optimization potential field, and trajectory smoothing potential field. The obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the obstacle dimension. The terrain potential field is used to adjust the flight paths of multiple first UAVs based on the terrain dimension. The moving obstacle potential field is used to adjust the flight paths of multiple first UAVs based on the moving obstacle dimension. The meteorological disturbance potential field is used to adjust the flight paths of multiple first UAVs based on the meteorological dimension. The target point potential field is used to adjust the flight paths of multiple first UAVs based on the position dimension of the fifth region. The energy efficiency optimization potential field is used to adjust the flight paths of multiple first UAVs based on the energy utilization dimension. The trajectory smoothing potential field is used to adjust the flight paths of multiple first UAVs based on the trajectory smoothing dimension.

[0145] In the embodiments described in the specification, by acquiring the information to be updated and updating the first information, and then adjusting the route based on the updated second information, the route can be adjusted according to the latest updated information during the flight of each first UAV, thereby improving the success rate of route search. By adjusting the flight routes of multiple first UAVs through the comprehensive potential field, it is possible to ensure that each first UAV maintains the optimal path during the path search process and reduce environmental interference during the search process.

[0146] In one implementation, generating initial routes for each first UAV based on the first information (step 102) can be performed via steps E1-E3: Step E1: Obtain the first battery level of each of the first drones.

[0147] The first power level refers to the remaining power of the first drone.

[0148] When generating initial routes for each first UAV based on first information from multiple first regions, the route planning system first obtains the first battery level of the first UAV, where the first battery level is the current remaining battery level of the first UAV. In other words, when generating the initial route for the first UAV, the route planning system first obtains the remaining battery level of the first UAV.

[0149] Specifically, when determining the initial route of each first UAV, the route planning subsystem installed in each first UAV first obtains the remaining power of the first UAV and uses it as the first power.

[0150] Step E2: Determine the first value of each first region based on the first information of each first region.

[0151] The first value is used to characterize the value generated when multiple first drones pass through the first area.

[0152] After acquiring the first battery level of the first drone, the route planning system determines the first value of each first area based on the first information of each first area. The first value is used to characterize the value that multiple first drones can generate when passing through the first area. The first value can be determined based on the type or number of plants and animals (area of ​​interest) in the first area.

[0153] Step E3: Generate the initial route for each of the first drones based on the first battery level of each of the first drones and the first value of the plurality of first areas.

[0154] After determining the first battery level and first value of each first region, the route planning system generates an initial route for each first drone based on its first battery level and the first value of each first region.

[0155] Specifically, when generating an initial route based on the first battery level and the first value, the route planning system first determines the number of flyable areas based on the first battery level, and then generates an initial route based on the number of flyable areas and the first value. The number of flyable areas is the number of first areas that the first UAV can fly over when it is at its current battery level.

[0156] When determining the number of flyable areas, the route planning system calculates the loiter capability of the first UAV based on its current energy reserves, such as the remaining power of its battery, and thus determines how many first areas the first UAV can fly at most based on its current energy. The number of flyable areas of the first UAV can be represented by K.

[0157] After determining the number of flyable areas, the route planning system determines an initial route based on the number of flyable areas and a first value. Specifically, the route planning system generates an initial route with a higher overall value within the number of flyable areas.

[0158] In the embodiments described in the specification, an initial route is generated by taking into account the current battery level of each first UAV and the regional information of each first region. The battery level of the first UAV and the regional value of each first region are taken into account to ensure that the generated initial solutions are all relatively good routes. This ensures that the first route selected further is a route with a high success rate in path search.

[0159] It should be noted that the route planning method provided in this application embodiment can be executed by a route planning device or a control module within that route planning device for executing the route planning method. This application embodiment uses the execution of the route planning method by a route planning device as an example to illustrate the route planning device provided in this application embodiment.

[0160] Figure 5 This is a schematic diagram of the structure of a route planning device according to an embodiment of the present invention. Figure 5 As shown, the route planning device includes: a first acquisition module 502, a first determination module 504, a first update module 506, and a first adjustment module 508.

[0161] The first acquisition module 502 is used to acquire first information of multiple first areas and generate an initial route for each first drone based on the first information. The first area is an area to be planned, the first information is relevant information of the first area, the first drone is a drone to be planned a flight route, and the initial route is a flight route randomly generated by each first drone in the first area. The first determining module 504 is used to determine a first route from a plurality of initial routes based on a preset route determining strategy, wherein the first route is a flight route to be taken by a plurality of the first UAVs. The first update module 506 is used to acquire information to be updated and update the first information of each first area based on the information to be updated when multiple first drones are located in the second area, so as to obtain the second information of each first area. The second area is an area in the first route, and the information to be updated is the area information acquired by multiple first drones on their way to the second area. The first adjustment module 508 is used to determine the attraction weight of each first region based on the second information of each first region, so as to adjust the flight path of multiple first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the multiple first drones.

[0162] The route planning device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0163] The route planning device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0164] The route planning device provided in this application embodiment can achieve... Figures 1 to 4 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0165] Based on the same technical concept, embodiments of this application also provide an electronic device for performing the route planning method described above. Figure 6 This is a schematic diagram of the structure of an electronic device to implement various embodiments of this application. The electronic device can vary significantly due to differences in configuration or performance, and may include a processor 602, a communications interface 604, a memory 606, and a communication bus 608. The processor 602, communications interface 604, and memory 606 communicate with each other via the communication bus 608. The processor 602 can call a computer program stored in the memory 606 and executable on the processor 602 to perform the following steps: First information of multiple first regions is obtained, and an initial route is generated for each first UAV based on the first information. The first region is the region where route planning is to be performed. The first information is the relevant information of the first region. The first UAV is the UAV whose flight route is to be planned. The initial route is the flight route randomly generated by each first UAV in the first region. Based on a preset route determination strategy, a first route is determined from multiple initial routes, wherein the first route is the flight path to be taken by multiple first UAVs; When multiple first UAVs are located in the second area, information to be updated is obtained and the first information of each first area is updated based on the information to be updated to obtain the second information of each first area. The second area is the area in the first route, and the information to be updated is the area information obtained by multiple first UAVs on their way to the second area. Based on the second information of each first region, an attraction weight is determined for each first region, so as to adjust the flight path of a plurality of first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the plurality of first drones.

[0166] In one implementation, determining the first route from a plurality of initial routes based on a preset route determination strategy includes: Determine multiple third regions in the initial route and a first area corresponding to each of the third regions, wherein the third region is the first region involved in the initial route, and the first area is the coverage area when multiple first UAVs pass through the third region; Based on the first information of each of the third regions, a first target included in each of the third regions and a first probability of each first target are determined. The first target is a preset regional interest object, and the first probability is the probability of the first target appearing in the third region. Based on the first area, the first target, and the first probability of each of the third regions, a first score corresponding to each of the initial routes is determined by a preset route scoring strategy. The first route is determined based on the initial route where the first score is greater than the first threshold.

[0167] In one implementation, determining the first route based on the initial route where the first score is greater than a first threshold includes: The initial route whose first score is greater than the first threshold is determined as the second route, and there are multiple second routes; Determine the number of conflict zones between the second route and each of the initial routes, wherein the number of conflict zones is the number of identical first zones that exist between the second route and each of the initial routes; Based on the number of conflict zones, each of the second routes is voted on to obtain the first number of votes for each of the second routes. The second route whose first vote count is greater than the second threshold is identified as the first route.

[0168] In one implementation, determining the attraction weight of each of the first regions based on the second information of each of the first regions includes: Based on the second information of each first region, a plurality of first targets included in each first region and a first type of each first target are determined, wherein the first target is a preset regional interest object and the first type is the type of the first target; Based on the first target and the first type in each of the first regions, the attraction weight of each of the first regions is determined by a preset attraction weight determination formula; The first region where the attraction weight is greater than a third threshold is determined as the target region, and the flight routes of multiple first UAVs are updated based on the target region, wherein the target region is the area to be flown to by the multiple first UAVs.

[0169] In one implementation, after determining the first route from the plurality of initial routes based on a preset route determination strategy, the method further includes: A third route is determined, which is a route for multiple first UAVs to fly from a fourth region to a fifth region. The fourth region is any of the first regions involved in the first route, and the fifth region is a first region in the first route that is adjacent to the fourth region and located after the fourth region. A comprehensive potential field is established based on the first information in the fourth region and the first information in the fifth region to adjust the third routes of multiple first UAVs. The comprehensive potential field includes one or more of the following: obstacle potential field, terrain potential field, moving obstacle potential field, meteorological disturbance potential field, target point potential field, energy efficiency optimization potential field, and trajectory smoothing potential field. The obstacle potential field is used to adjust the flight routes of multiple first UAVs based on the obstacle dimension. The terrain potential field is used to adjust the flight routes of multiple first UAVs based on the terrain dimension. The moving obstacle potential field is used to adjust the flight routes of multiple first UAVs based on the moving obstacle dimension. The meteorological disturbance potential field is used to adjust the flight routes of multiple first UAVs based on the meteorological dimension. The target point potential field is used to adjust the flight routes of multiple first UAVs based on the position dimension of the fifth region. The energy efficiency optimization potential field is used to adjust the flight routes of multiple first UAVs based on the energy utilization dimension. The trajectory smoothing potential field is used to adjust the flight routes of multiple first UAVs based on the trajectory smoothing dimension.

[0170] In one implementation, generating initial routes for each first UAV based on the first information includes: Obtain the first battery level of each of the first drones, where the first battery level is the remaining battery level of the first drone; Based on the first information of each first region, a first value is determined for each first region, and the first value is used to characterize the value generated when multiple first drones pass through the first region. The initial route is generated for each of the first drones based on the first battery level of each of the first drones and the first value of the plurality of first regions.

[0171] The specific execution steps can be found in the various steps of the above route planning method embodiment, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0172] It should be noted that the electronic devices in the embodiments of this application include: servers, terminals, or other devices besides terminals.

[0173] The above electronic device structure does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or arrange them differently. For example, an input unit may include a Graphics Processing Unit (GPU) and a microphone, and a display unit may use a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar display panels. User input units include at least one of a touch panel and other input devices. A touch panel is also called a touchscreen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be elaborated further here.

[0174] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0175] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0176] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described route planning method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0177] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0178] This application also provides a computer program product. When the computer program product is executed by a processor, it implements the various processes of the above-described route planning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0179] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described route planning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0180] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0182] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0183] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A route planning method, characterized in that, include: First information of multiple first regions is obtained, and an initial route is generated for each first UAV based on the first information. The first region is the region where route planning is to be performed. The first information is the relevant information of the first region. The first UAV is the UAV whose flight route is to be planned. The initial route is the flight route randomly generated by each first UAV in the first region. Based on a preset route determination strategy, a first route is determined from multiple initial routes, wherein the first route is the flight path to be taken by multiple first UAVs; When multiple first UAVs are located in the second area, information to be updated is obtained and the first information of each first area is updated based on the information to be updated to obtain the second information of each first area. The second area is the area in the first route, and the information to be updated is the area information obtained by multiple first UAVs on their way to the second area. Based on the second information of each first region, an attraction weight is determined for each first region, so as to adjust the flight path of a plurality of first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the plurality of first drones.

2. The method according to claim 1, characterized in that, The method of determining the first route from multiple initial routes based on a preset route determination strategy includes: Determine multiple third regions in the initial route and a first area corresponding to each of the third regions, wherein the third region is the first region involved in the initial route, and the first area is the coverage area when multiple first UAVs pass through the third region; Based on the first information of each of the third regions, a first target included in each of the third regions and a first probability of each first target are determined. The first target is a preset regional interest object, and the first probability is the probability of the first target appearing in the third region. Based on the first area, the first target, and the first probability of each of the third regions, a first score corresponding to each of the initial routes is determined by a preset route scoring strategy. The first route is determined based on the initial route where the first score is greater than the first threshold.

3. The method according to claim 2, characterized in that, Determining the first route based on the initial route where the first score is greater than the first threshold includes: The initial route whose first score is greater than the first threshold is determined as the second route, and there are multiple second routes; Determine the number of conflict zones between the second route and each of the initial routes, wherein the number of conflict zones is the number of identical first zones that exist between the second route and each of the initial routes; Based on the number of conflict zones, each of the second routes is voted on to obtain the first number of votes for each of the second routes. The second route whose first vote count is greater than the second threshold is identified as the first route.

4. The method according to claim 1, characterized in that, The step of determining the attraction weight of each first region based on the second information of each first region includes: Based on the second information of each first region, a plurality of first targets included in each first region and a first type of each first target are determined, wherein the first target is a preset regional interest object and the first type is the type of the first target; Based on the first target and the first type in each of the first regions, the attraction weight of each of the first regions is determined by a preset attraction weight determination formula; The first region where the attraction weight is greater than a third threshold is determined as the target region, and the flight routes of multiple first UAVs are updated based on the target region, wherein the target region is the area to be flown to by the multiple first UAVs.

5. The method according to claim 1, characterized in that, After determining the first route from the multiple initial routes based on the preset route determination strategy, the method further includes: A third route is determined, which is a route for multiple first UAVs to fly from a fourth region to a fifth region. The fourth region is any of the first regions involved in the first route, and the fifth region is a first region in the first route that is adjacent to the fourth region and located after the fourth region. A comprehensive potential field is established based on the first information in the fourth region and the first information in the fifth region to adjust the third routes of multiple first UAVs. The comprehensive potential field includes one or more of the following: obstacle potential field, terrain potential field, moving obstacle potential field, meteorological disturbance potential field, target point potential field, energy efficiency optimization potential field, and trajectory smoothing potential field. The obstacle potential field is used to adjust the flight routes of multiple first UAVs based on the obstacle dimension. The terrain potential field is used to adjust the flight routes of multiple first UAVs based on the terrain dimension. The moving obstacle potential field is used to adjust the flight routes of multiple first UAVs based on the moving obstacle dimension. The meteorological disturbance potential field is used to adjust the flight routes of multiple first UAVs based on the meteorological dimension. The target point potential field is used to adjust the flight routes of multiple first UAVs based on the position dimension of the fifth region. The energy efficiency optimization potential field is used to adjust the flight routes of multiple first UAVs based on the energy utilization dimension. The trajectory smoothing potential field is used to adjust the flight routes of multiple first UAVs based on the trajectory smoothing dimension.

6. The method according to claim 1, characterized in that, The step of generating initial routes for each first UAV based on the first information includes: Obtain the first battery level of each of the first drones, where the first battery level is the remaining battery level of the first drone; Based on the first information of each first region, a first value is determined for each first region, and the first value is used to characterize the value generated when multiple first drones pass through the first region. The initial route is generated for each of the first drones based on the first battery level of each of the first drones and the first value of the plurality of first regions.

7. A route planning device, characterized in that, include: The first acquisition module is used to acquire first information of multiple first areas and generate an initial route for each first UAV based on the first information. The first area is an area to be planned, the first information is relevant information of the first area, the first UAV is a UAV whose flight route is to be planned, and the initial route is a flight route randomly generated by each first UAV in the first area. The first determining module is used to determine a first route from a plurality of initial routes based on a preset route determination strategy, wherein the first route is a flight route to be taken by a plurality of the first UAVs. The first update module is used to acquire information to be updated and update the first information of each first area based on the information to be updated when multiple first drones are located in the second area, so as to obtain the second information of each first area. The second area is an area in the first route, and the information to be updated is the area information acquired by multiple first drones on their way to the second area. A first adjustment module is used to determine the attraction weight of each first region based on the second information of each first region, so as to adjust the flight path of multiple first drones based on the attraction weight, wherein the attraction weight is used to characterize the degree of attraction of the first region to the multiple first drones.

8. An electronic device, characterized in that, The device includes: Processor; and A memory configured to store computer-executable instructions configured to be executed by the processor, the executable instructions including steps for performing the route planning method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is used to store computer-executable instructions that cause the computer to perform the route planning method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the route planning method according to any one of claims 1 to 6.

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