Route planning method, device, storage medium and electronic device

Abstract

The present invention discloses a route planning method, device, storage medium and electronic device, wherein the method comprises: dividing the target points into at least two sub-areas according to the distance between the target points and the area division rule; iteratively training the simulated path between the at least two sub-areas according to at least one path evaluation parameter, and determining the first path between the at least two sub-areas that satisfies the at least one path evaluation parameter; generating the target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas. By dividing a large number of target points into regions, a small number of sub-areas are obtained, and the path planning between the sub-areas and the path planning of the target points in each sub-area are formed, the path planning scale is simplified, and the path planning of each part can be processed in parallel to improve the efficiency of path planning.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of logistics technology, and in particular to a route planning method, device, storage medium, and electronic device. Background Art

[0002] With the rapid development of computer technology, the e-commerce industry and the logistics industry have been accepted by the majority of users. Correspondingly, users' requirements for the logistics industry are also getting higher and higher.

[0003] Path planning is a key issue in the field of logistics and is widely used in scenarios such as commodity distribution, warehouse picking, customer visits, and robot mobile navigation. Path planning usually refers to determining the optimal path from the starting point to the end point in the workspace under specific constraints based on certain criteria. It essentially belongs to the category of combinatorial optimization. By making optimal decisions on combinatorial problems, the rational allocation of relevant resources can be achieved, thereby fully saving various expenses (energy consumption, time, etc.) and achieving the goal of reducing costs and increasing efficiency.

[0004] There are two main methods for solving path planning problems, namely, exact solution algorithms and heuristic algorithms. Exact solution algorithms are generally based on strict mathematical principles and can theoretically guarantee the optimal solution within a limited time. However, the amount of calculation is usually large and increases dramatically as the scale of the problem increases. Heuristic algorithms construct solutions based on some empirical rules and continuously improve the solutions during the solution process. They usually have high solution efficiency and can guarantee the quality of the solution to a certain extent. However, the current heuristic algorithms are only applicable to small-scale path planning problems. As the scale of the problem increases, the amount of search in the solution space will increase exponentially, resulting in a long calculation time to obtain a feasible solution, which does not meet the timeliness requirements. Summary of the invention

[0005] The embodiments of the present invention provide a route planning method, device, storage medium and electronic device to achieve efficient large-scale path planning.

[0006] In a first aspect, an embodiment of the present invention provides a route planning method, including:

[0007] Dividing the target points into at least two sub-areas according to the distance between the target points and the area division rule;

[0008] Iteratively training the simulated path between the at least two sub-areas according to at least one path evaluation parameter to determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter;

[0009] A target path between the target points is generated according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

[0010] In a second aspect, an embodiment of the present invention further provides a route planning device, including:

[0011] A sub-region determination module, used to divide the target points into at least two sub-regions according to the distance between the target points and the region division rule;

[0012] A first path determination module, configured to iteratively train the simulated path between the at least two sub-areas according to at least one path evaluation parameter, and determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter;

[0013] The target path determination module is used to generate a target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a route planning method as provided in any embodiment of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a route planning method as provided in any embodiment of the present invention.

[0016] The technical solution adopted in the embodiment of the present invention is to obtain at least two sub-regions according to the distance between each target point and the area division rule, iteratively train the simulated path between the at least two sub-regions according to at least one path evaluation parameter, determine the first path between the at least two sub-regions that satisfies the at least one path evaluation parameter, and generate the target path between the target points according to the second path of each target point in any sub-region and the first path between the at least two sub-regions. By dividing a large number of target points into regions to obtain a small number of sub-regions, path planning between sub-regions and path planning for target points in each sub-region are formed, the scale of path planning is simplified, and the path planning of each part can be processed in parallel to improve the efficiency of path planning. At the same time, in the process of path planning, at least one path evaluation parameter is used as the optimization target to obtain the target path that satisfies at least one path evaluation parameter, so that multi-target path planning can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a route planning method provided in the first embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a seed target point provided by the first embodiment of the present invention;

[0019] Figure 3 is a flowchart of a route planning method provided in Embodiment 2 of the present invention;

[0020] Figure 4 is a graph showing changes in evaluation values ​​during an iteration process provided by the second embodiment of the present invention;

[0021] Figure 5 is a schematic diagram of the structure of a route planning device provided in Embodiment 3 of the present invention;

[0022] Figure 6 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0024] Embodiment 1

[0025] Figure 1 This is a flow chart of a route planning method provided in the first embodiment of the present invention. This embodiment can be applied to reasonably plan order transportation routes to reduce the time cost of transportation. The method can be executed by the route planning device provided in the embodiment of the present invention. The route planning device can be implemented in software and / or hardware. The route planning device can be integrated in electronic devices such as computers or servers. The method specifically includes the following steps:

[0026] S110 . Divide the target points into at least two sub-areas according to the distance between the target points and an area division rule.

[0027] S120: Iteratively train the simulated path between the at least two sub-areas according to at least one path evaluation parameter to determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter.

[0028] S130: Generate a target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

[0029] In this embodiment, the target point is a location point to be visited. For example, in the field of logistics, the target point can be a location to be delivered in an area (such as a city or administrative district, etc.). The paths between the target points are planned, and the target path formed can achieve a single visit to each target point and meet the requirements of at least one path evaluation parameter. When the number of target points is large, the traditional planning method for each target point has a large amount of calculation and poor planning efficiency.

[0030] By dividing a large number of target points into multiple sub-areas based on area division rules, path planning between sub-areas and path planning for target points in each sub-area are formed, which simplifies the scale of path planning. At the same time, path planning between sub-areas and path planning for target points in each sub-area can be processed in parallel to improve path planning efficiency.

[0031] Optionally, the target points are divided into at least two sub-areas according to the distance between each target point and the area division rule, including: obtaining a first seed target point, and dividing the target points among the target points whose distance from the first seed target point is less than a distance threshold in the area division rule into a first sub-area corresponding to the first seed target point; among the target points outside the first sub-area, determining a second seed target point according to the first seed target point, and determining the target points belonging to the second sub-area based on the second seed target point, wherein the target points in the first sub-area and the second sub-area do not overlap.

[0032] Among them, the first seed target point among the target points can be determined randomly or according to the starting point of the path. For example, the first seed target point can be the starting point or a target point randomly selected from the target points within a preset distance from the starting point.

[0033] For example, the first seed target point is marked as S0, the distance between each target point and the first seed target point S0 is determined, and the target point Pi whose distance is less than the distance threshold d is determined to belong to the first sub-region R0 corresponding to the first seed target point S0. Therefore, R0=S0+T(S0), where On the basis of determining the first sub-region, the target point outside the first sub-region and closest to the first seed target point S0 can be determined as the second seed target point, and the second sub-region is determined based on the above area division rule, and so on, until all target points are divided.

[0034] For example, see Figure 2 , Figure 2 is a schematic diagram of a seed target point provided by an embodiment of the present invention, wherein: Figure 2The corresponding area includes 1843 target points. According to the above sub-area division method, 198 sub-target points are obtained. Figure 2 Each position point in is a seed target point, and each seed target point corresponds to a sub-region.

[0035] In this embodiment, a large number of target points are divided into a small number of sub-areas according to the distance division rule, which meets the planning requirements of the shortest path on the basis of reducing the path planning scale, and is conducive to obtaining the shortest target path.

[0036] It should be noted that the distance in this embodiment can be the straight-line distance between target points, or the driving distance to the target point.

[0037] After the target point is divided into sub-regions, each sub-region is taken as a whole to perform path planning between the sub-regions, wherein the position of the seed target point can be taken as the position of the corresponding sub-region. Optionally, path planning can be performed for each sub-region based on MMMAS (Max-Min Ant System). In the iterative planning of the above sub-regions, at least one path evaluation parameter is used as an evaluation criterion for the planned path. For example, the path evaluation parameters include but are not limited to path length, access priority and total access time. In the above, the planned path is evaluated by the path length, that is, the path planning is performed based on the shortest path principle to determine the target path with the shortest length. The access priority can be pre-set. For example, in the logistics industry, the access priority can be determined based on the order volume and / or delivery time of the target point. The planned path is evaluated by the access priority to determine the path that best fits the access priority. This embodiment performs path planning based on multiple path evaluation parameters, and can generate a first path that meets the above at least one path evaluation parameter.

[0038] The first path is the path between each sub-area. For each target point in each sub-area, the second path can be determined according to the access priority of each target point or the shortest path rule. Exemplarily, the second path can be generated by connecting each target point according to the range priority of the target point in the sub-area; or, according to the distance between each target point, the path combination is performed to generate the shortest second path.

[0039] Optionally, the target path between the target points is generated based on the second path of each target point in any sub-area and the first path between the at least two sub-areas, including: connecting the last target point in the second path of the previous sub-area in the first path with the first target point in the second path of the next sub-area in the first path to generate the target path.

[0040] The technical solution in this embodiment is to obtain at least two sub-regions according to the distance between each target point and the area division rule, iteratively train the simulated path between the at least two sub-regions according to at least one path evaluation parameter, determine the first path between the at least two sub-regions that satisfies the at least one path evaluation parameter, and generate the target path between the target points according to the second path of each target point in any sub-region and the first path between the at least two sub-regions. By dividing a large number of target points into regions to obtain a small number of sub-regions, path planning between sub-regions and path planning for target points in each sub-region are formed, the path planning scale is simplified, and the path planning of each part can be processed in parallel to improve the efficiency of path planning. At the same time, in the process of path planning, at least one path evaluation parameter is used as the optimization target to obtain the target path that satisfies at least one path evaluation parameter, so that multi-target path planning can be achieved.

[0041] Embodiment 2

[0042] Figure 3 : is a flow chart of a route planning method provided by an embodiment of the present invention, which is optimized on the basis of the above embodiment. The method specifically includes:

[0043] S210: Divide the target points into at least two sub-areas according to the distance between the target points and an area division rule.

[0044] S220: Determine at least one simulated path between the at least two sub-areas, and calculate a path evaluation value of each simulated path according to at least one path evaluation parameter.

[0045] S230: Iteratively optimize the simulated path between the at least two sub-areas according to the path evaluation value to generate a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter.

[0046] S240: Generate a target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

[0047] In this embodiment, an iterative model of each sub-region is established, including a path starting point, a pheromone matrix and a distance matrix, wherein the distance matrix includes the distance between any two sub-regions, and the pheromone matrix includes the pheromone between any two sub-regions. When the simulation path includes a local path from one sub-region to another sub-region, the pheromone of the local path will be increased. The more times the local path is used, the higher the pheromone, which can be used to assist the subsequent simulation path. It should be noted that the initial pheromone matrix can be preset.

[0048] Wherein, at least one simulated path from the starting point is determined according to the distance and pheromone between each sub-region. Optionally, determining at least one simulated path between the at least two sub-regions includes: for the current sub-region, determining the probability of each feasible sub-region according to the distance and pheromone between the feasible sub-region of the current sub-region and the current sub-region; determining the next sub-region of the current sub-region according to the probability of each feasible sub-region, and generating the simulated path.

[0049] In this embodiment, the feasibility area of ​​the current sub-area is the sub-area outside the determined path, exemplarily including six sub-areas A, B, C, D, E, and F. The determined path is AB, and the current sub-area is B. Then the feasibility area of ​​the current sub-area B is the sub-area outside A and B included in the determined path, that is, the sub-areas C, D, E, and F that the determined path has not passed through.

[0050] The probability of moving the current sub-region i to the feasible sub-region j is determined according to the following formula:

[0051]

[0052] Wherein, τ(i, j) is the pheromone between sub-regions i and j, η(i, j) is the heuristic information between sub-regions i and j, and the heuristic information is the inverse of the distance between sub-regions i and j. α and β are weight parameters of pheromone and heuristic information, respectively. Exemplarily, α can be 1, β can be 2, and u is any unexperienced sub-region.

[0053] According to the above probability, the next sub-region of the current sub-region in each simulation path is determined. For example, if the current sub-region is B, the probability of moving to sub-region C, sub-region D, sub-region E, and sub-region F is P, respectively. BC , P BD , P BE , P BF . . In order to ensure that the algorithm does not fall into a local optimal solution, a roulette algorithm is used to determine the next sub-region. The method for determining the next region is as follows: first generate a random number η between 0 and 1, and then select a sub-region with a smaller serial number from the sub-regions to be visited. When the probability of the sub-region to be visited is greater than the random number η, the sub-region to be visited is determined as the next sub-region. When the probability of the sub-region to be visited is less than the random number η, the next sub-region to be visited is determined according to the principle of a smaller serial number. The probability values ​​of the sub-regions to be visited are accumulated. When the accumulated probability value is greater than the random number η, the last accumulated sub-region to be visited is determined as the next sub-region. If the accumulated probability value is greater than the random number but less than the random number η, the next sub-region to be visited continues to be determined until the accumulated probability value is greater than the random number η, and the next sub-region is determined. Exemplarily, when P BCIf the random number is greater than η, then sub-region C is determined as the next sub-region of sub-region B. If not, it can be determined that P BC +P BD Is it a random number n? If so, sub-region D is determined as the next sub-region of sub-region B.

[0054] In the accumulation process, the next sub-region to be visited can be determined based on a preset accumulation order, and each sub-region can be pre-set with a serial number identifier, and the serial number identifier of the sub-region is randomly determined and does not repeat. For example, the serial numbers of sub-regions A, B, C, D, E, and F can be 0, 1, 2, 3, 4, and 5. In the process of comparing the probability value with the random number n, the order of the serial numbers of the sub-regions can be used as the accumulation order of the sub-regions, wherein the order can be from small to large or from large to small. For example, if the serial numbers of the sub-regions to be visited are 2, 3, 4, and 5, then the probability value of the sub-region with the serial number identifier of 2 can be compared with the random number based on the order from small to large, and when the probability value is less than the random number, the sub-region with the serial number identifier of 3 can be used as the next sub-region to be visited in the accumulation, and so on.

[0055] It should be noted that the random number η changes in each iteration and each time the next sub-region is selected to increase the randomness of the simulation path, which is conducive to finding the global optimal solution.

[0056] In each iteration, a large number of ants are set to search for feasible paths simultaneously based on the above method, and each ant determines a simulation path. The multiple simulation paths obtained are evaluated by at least one path evaluation parameter to select the optimal simulation path. Optionally, the access priority evaluation value of the simulation path is determined according to the deviation value between the actual access order of each sub-area in the simulation path and the standard access order. Specifically, the access priority evaluation value of the simulation path can be calculated according to the following formula:

[0057] OBJ1=∑ k q×|k-seq[C k ]|

[0058] Among them, k is the actual access sequence of the current sub-region, seq[C k ] is the standard access order of the current sub-area, and q is the access priority index of the current sub-area.

[0059] In this embodiment, the access priority index of the current sub-region is determined according to the access priority index of each target point in the current sub-region, wherein the access priority index of the target point is a value of 0-1. Optionally, the access priority index of the current sub-region is the average of the access priority indexes of each target point in the current sub-region. Correspondingly, the standard access order of the current sub-region is determined according to the access priority index of the current sub-region, wherein the access priority is positively correlated with the access priority index, and the higher the access priority index, the higher the access priority. It can be that each sub-region is sorted from large to small according to the access priority index, and the sorting result is the recommended access order of the sub-region. Exemplarily, the sub-regions are sorted according to the access priority index, and the sorting results are A, B, C, D, E, F. It can be known that the recommended access order of sub-regions A, B, C, D, E, F is 0, 1, 2, 3, 4, 5. When the simulation path is ACBDEF, it can be known that the actual access order of sub-areas A, B, C, D, E, and F is 0, 2, 1, 3, 4, and 5. According to the deviation between the actual access order and the recommended access order of each sub-area, the access priority evaluation value of the simulation path can be obtained, where the deviation of the access order is the absolute value of the difference between the actual access order and the recommended access order. For example, the order deviation of sub-areas A, D, E, and F is 0, and the deviations of sub-areas B and C are 1 respectively. It should be noted that the smaller the access priority evaluation value, the higher the similarity between the actual access order and the recommended access order.

[0060] Optionally, the path length of the simulation path can be obtained according to the distance between any two adjacent sub-areas in the simulation path. Specifically, the path length of the simulation path is OBJ2=∑ i，j dis[i][j], where ∑ i，j dis[i][j] is the distance between the connected sub-regions i and j in the simulation path.

[0061] In this embodiment, the simulated path can be evaluated based on any path evaluation parameter, and can also be evaluated based on two or more path evaluation parameters. Exemplarily, taking path length and access priority as an example, according to the weight information of the path length and access priority of the simulated path, the path length and access priority evaluation values ​​are weighted and calculated to generate the path evaluation value of the simulated path.

[0062] Exemplarily, the path evaluation value of the simulation path can be OBJ sum =OBJ1+ε×OBJ2, where ε is the weight parameter of the second path evaluation parameter (e.g., path length) relative to the first path evaluation parameter (e.g., access priority). Where ε=σ×ε0, ε0 can be the ratio of OBJ1 to OBJ2, for example:

[0063] Among them, m is the number of sub-regions, σ is determined according to the emphasis of the first path evaluation parameter and the second path evaluation parameter. If the first path evaluation parameter is satisfied first, then σ is less than 1; if the second path evaluation parameter is satisfied first, then σ is greater than 1; if the first path evaluation parameter and the second path evaluation parameter are in the same position, then σ is 1.

[0064] In this embodiment, the simulation path is evaluated by one or more path evaluation parameters to obtain the path evaluation value of each simulation path, and the optimal simulation path in the current iteration is determined based on the path evaluation value. The smaller the path evaluation value, the better the simulation path. The path evaluation value with the smallest path evaluation value may be determined as the optimal path evaluation value, and accordingly, the simulation path corresponding to the optimal path evaluation value is the optimal simulation path in the current iteration. The pheromone matrix is ​​updated according to the optimal simulation path to perform iterative optimization of the next path.

[0065] Optionally, the simulated path between the at least two sub-areas is iteratively optimized according to the path evaluation value, including: updating the pheromones between the sub-areas according to the optimal path evaluation value in the current iteration or the optimal path evaluation value in the historical iteration; determining at least one new simulated path according to the updated pheromones, and iteratively optimizing the path based on the at least one new simulated path. The updated value of the pheromone can be determined according to the optimal path evaluation value in the current iteration, or according to the optimal path evaluation value in the historical iteration. Exemplarily, when the current number of iterations is 10, the optimal path evaluation value obtained in the 10th iteration is the optimal path evaluation value in the current iteration (OBJ sum ) iter , the optimal path evaluation value in the 1st to 10th iteration is the optimal path evaluation value in the historical iteration (OBJ sum ) global .

[0066] Accordingly, the corresponding elements in the pheromone matrix are updated according to the local paths between the connected sub-regions in the optimal simulation path. For example, when the optimal simulation path is ABCD, it is determined that the local paths in the optimal simulation path are AB, BC and CD, then the corresponding element in the pheromone matrix is ​​τ A，B , τ B，C and τ C，D .

[0067] Among them, updating the elements in the pheromone matrix can be or Among them, τ is the pheromone before updating, and τ′ is the pheromone after updating.

[0068] In this embodiment, in the iterative process of path planning, the first preset number of iterations in the early stage of the iteration adopts the current iteration optimal path to update the pheromone, while the second preset number of iterations in the middle and late stages alternately adopts the current iteration optimal path and the historical iteration optimal path to update the pheromone. The sum of the first preset number of times and the second preset number of times is the total number of iterations. Exemplarily, in the middle and late stages of the iteration process, the pheromone is updated based on the optimal path evaluation value in the current iteration in the qth iteration, and the pheromone is updated based on the optimal path evaluation value in the historical iteration in the q+1th iteration, so as to avoid the occurrence of a local optimal solution in the iterative process and improve the accuracy of path iteration optimization.

[0069] Optionally, the updating of the pheromone matrix also includes the volatilization of the pheromone, wherein the volatilization coefficient may be 0.02, that is, the original pheromone minus 0.02 times the original pheromone.

[0070] Optionally, after the pheromone matrix is ​​updated, the updated pheromone is corrected according to the maximum pheromone and the minimum pheromone. When the updated pheromone is greater than the maximum pheromone, the updated pheromone is modified to the maximum pheromone. When the updated pheromone is less than the minimum pheromone, the updated pheromone is modified to the minimum pheromone to increase the possibility of finding the global optimal solution.

[0071] in, τ min =l×τ max , c and l are constants.

[0072] At least one new simulation path is determined according to the updated pheromone matrix and distance matrix, and the above steps are repeated to evaluate the at least one new simulation path to achieve iterative optimization of the path until the number of iterations is reached.

[0073] For example, see Figure 4 , Figure 4 This is a diagram of changes in evaluation values ​​during the iteration process provided by the second embodiment of the present invention. In the diagram, the first target may be an access priority evaluation value, the second target may be a path length, and the overall target is a path evaluation value of the path. Figure 4 The total target value in the algorithm decreases rapidly and monotonically with the increase of the number of iterations, that is, the path evaluation value of the path decreases with the increase of the number of iterations, thus achieving continuous optimization of the path.

[0074] The technical solution provided by this embodiment creates multiple simulation paths in each iteration, evaluates the simulation paths according to at least one path evaluation parameter, and determines the optimal evaluation value and the optimal simulation path, so as to optimize the next iteration until the first path that satisfies at least one path evaluation parameter is obtained. The first path and the second path of each sub-area are used to obtain the target path that traverses each target point, which solves the problem of long path planning time when the number of target points is large, improves the path planning efficiency, and at the same time, the generated target path satisfies multiple path evaluation parameters, realizing the path planning of multiple evaluation targets.

[0075] Embodiment 3

[0076] Figure 5 3 is a schematic diagram of the structure of a route planning device provided in Embodiment 3 of the present invention, the device includes a sub-area determination module 310, a first path determination module 320 and a target path determination module 330, wherein:

[0077] A sub-region determination module 310, configured to divide the target points into at least two sub-regions according to the distance between the target points and the region division rule;

[0078] A first path determination module 320, configured to iteratively train the simulated path between the at least two sub-areas according to at least one path evaluation parameter, and determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter;

[0079] The target path determination module 330 is used to generate a target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

[0080] Optionally, the sub-region determining module 310 is used to:

[0081] Acquire a first seed target point, and divide the target points whose distance from the first seed target point is less than the distance threshold in the area division rule into a first sub-area corresponding to the first seed target point;

[0082] Among the target points outside the first sub-region, second seed target points are determined according to the first seed target points, and target points belonging to the second sub-region are determined based on the second seed target points, wherein the target points in the first sub-region and the second sub-region do not overlap.

[0083] Optionally, the first path determination module 320 includes:

[0084] a simulation path determining unit, configured to determine at least one simulation path between the at least two sub-areas;

[0085] a path evaluation value determination unit, configured to calculate a path evaluation value of each simulated path according to at least one path evaluation parameter;

[0086] The first path determination unit is used to iteratively optimize the simulated path between the at least two sub-areas according to the path evaluation value, and generate a first path between the at least two sub-areas that meets the at least one path evaluation parameter.

[0087] Optionally, the path evaluation parameters include path length and access priority;

[0088] Optionally, the path evaluation value determination unit is used to:

[0089] respectively calculating the path length and access priority evaluation value of the simulated path;

[0090] The path length and the access priority evaluation value are weightedly calculated according to the weight information of the path length and the access priority to generate the path evaluation value.

[0091] Optionally, the access priority evaluation value of the simulated path is determined based on a deviation value between an actual access order of each sub-region in the simulated path and a standard access order, wherein the standard access order of any sub-region is determined based on the access priority of each target point in the sub-region.

[0092] Optionally, the simulation path determination unit is used to:

[0093] For the current sub-region, determining the probability of each feasible sub-region according to the distance between the feasible sub-region of the current sub-region and the current sub-region and the pheromone;

[0094] The next sub-region of the current sub-region is determined according to the probability of each of the feasible sub-regions, and the simulation path is generated.

[0095] Optionally, the first path determination unit is used to:

[0096] According to the optimal path evaluation value in the current iteration or the optimal path evaluation value in the historical iteration, the pheromone between each sub-area is updated;

[0097] At least one new simulation path is determined according to the updated pheromone, and path optimization is iteratively performed based on the at least one new simulation path.

[0098] Optionally, the target path determination module 330 is used to:

[0099] The last target point in the second path of the previous sub-area in the first path is connected with the first target point in the second path of the next sub-area in the first path to generate the target path, wherein the second path of each target point in any sub-area is determined according to the access priority of each target point or the shortest path rule.

[0100] The route planning device provided in the embodiment of the present invention can execute the route planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the route planning method.

[0101] Embodiment 4

[0102] Figure 6 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Figure 6 A block diagram of an electronic device 412 suitable for implementing embodiments of the present invention is shown. Figure 6 The electronic device 412 shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. The device 412 is typically an electronic device that performs image classification functions.

[0103] like Figure 6 As shown, the electronic device 412 is in the form of a general purpose computing device. The components of the electronic device 412 may include, but are not limited to: one or more processors 416, a storage device 428, and a bus 418 connecting different system components (including the storage device 428 and the processor 416).

[0104] Bus 418 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0105] The electronic device 412 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 412, including volatile and non-volatile media, removable and non-removable media.

[0106] The storage device 428 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 430 and / or cache memory 432. The electronic device 412 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 434 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a read-only optical disk (Compact Disc-Read Only Memory, CD-ROM), a digital video disk (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 418 via one or more data medium interfaces. The storage device 428 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0107] A program 436 having a set (at least one) of program modules 426 may be stored, for example, in a storage device 428, such program modules 426 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. Program modules 426 generally perform the functions and / or methods of the embodiments described herein.

[0108] The electronic device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, camera, display 424, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 412, and / or communicate with any device that enables the electronic device 412 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 422. In addition, the electronic device 412 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 420. As shown, the network adapter 420 communicates with other modules of the electronic device 412 through a bus 418. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.

[0109] The processor 416 executes various functional applications and data processing by running the programs stored in the storage device 428, such as implementing the route planning method provided in the above embodiment of the present invention.

[0110] Embodiment 5

[0111] Embodiment 5 of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the route planning method provided in the embodiment of the present invention is implemented.

[0112] Of course, the computer-readable storage medium provided by the embodiment of the present invention stores a computer program which is not limited to the method operation described above, but can also execute the route planning method provided by any embodiment of the present invention.

[0113] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0114] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable source code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0115] Source code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0116] Computer source code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The source code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0117] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A route planning method, characterized in that: include: According to the distance between each target point and the area division rule, the target point is divided into at least two sub-areas, wherein the sub-area includes multiple target points, and the target point is a location to be delivered in an area; Iteratively training the simulated path between the at least two sub-areas according to at least one path evaluation parameter to determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter; Determine a second path for each target point in each sub-area according to the access priority or the shortest path rule of each target point in each sub-area; A target path between the target points is generated according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

2. The method according to claim 1, characterized in that The target points are divided into at least two sub-areas according to the distance between the target points and the area division rule, including: Acquire a first seed target point, and divide the target points whose distance from the first seed target point is less than the distance threshold in the area division rule into a first sub-area corresponding to the first seed target point; Among the target points outside the first sub-region, second seed target points are determined according to the first seed target points, and target points belonging to the second sub-region are determined based on the second seed target points, wherein the target points in the first sub-region and the second sub-region do not overlap.

3. The method according to claim 1, characterized in that The iterative training of the simulated path between the at least two sub-areas according to at least one path evaluation parameter to determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter comprises: Determine at least one simulated path between the at least two sub-areas, and calculate a path evaluation value of each simulated path according to at least one path evaluation parameter; The simulated path between the at least two sub-areas is iteratively optimized according to the path evaluation value to generate a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter.

4. The method according to claim 1 or 3, characterized in that: The path evaluation parameters include path length and access priority, and the step of calculating the path evaluation value of each simulated path according to at least one path evaluation parameter includes: respectively calculating the path length and access priority evaluation value of the simulated path; The path length and the access priority evaluation value are weightedly calculated according to the weight information of the path length and the access priority to generate the path evaluation value.

5. The method according to claim 4, characterized in that The access priority evaluation value of the simulated path is determined according to the deviation value between the actual access order of each sub-area in the simulated path and the standard access order, wherein the standard access order of any sub-area is determined according to the access priority of each target point in the sub-area.

6. The method according to claim 3, characterized in that: The determining of at least one simulated path between the at least two sub-areas comprises: For the current sub-region, determining the probability of each feasible sub-region according to the distance between the feasible sub-region of the current sub-region and the current sub-region and the pheromone; The next sub-region of the current sub-region is determined according to the probability of each of the feasible sub-regions, and the simulation path is generated.

7. The method according to claim 6, characterized in that Iteratively optimizing the simulated path between the at least two sub-areas according to the path evaluation value includes: According to the optimal path evaluation value in the current iteration or the optimal path evaluation value in the historical iteration, the pheromone between each sub-area is updated; At least one new simulation path is determined according to the updated pheromone, and path optimization is iteratively performed based on the at least one new simulation path.

8. The method according to claim 1, characterized in that The generating the target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas comprises: The target path is generated by connecting the last target point in the second path of the previous sub-area in the first path with the first target point in the second path of the next sub-area in the first path, wherein the second path of each target point in any sub-area is determined according to the access priority of each target point or the shortest path rule.

9. A route planning device, characterized in that: include: A sub-area determination module, used to divide the target points into at least two sub-areas according to the distance between the target points and the area division rule, wherein the sub-area includes a plurality of target points, and the target points are locations to be delivered in an area; A first path determination module, configured to iteratively train the simulated path between the at least two sub-areas according to at least one path evaluation parameter, and determine a first path between the at least two sub-areas that satisfies the at least one path evaluation parameter; A second path determination module, used to determine a second path for each target point in each sub-area according to the access priority of each target point in each sub-area or the shortest path rule; The target path determination module is used to generate a target path between the target points according to the second path of the target points in any sub-area and the first path between the at least two sub-areas.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the route planning method as described in any one of claims 1-8 is implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the route planning method as described in any one of claims 1 to 8 is implemented.

Images