Path planning method and device, equipment and medium

Abstract

The invention provides a path planning method and device, equipment and a medium, and relates to the technical field of path planning, and the method comprises the steps: determining a warehouse in a preset distribution region and the geographic position information of each distribution site; generating a target data set according to the geographical location information; wherein the target data set comprises spatial information corresponding to each distribution station, and the spatial information comprises geographic position information of the distribution station and direction angle information of the distribution station relative to the position of the warehouse; according to the target data set, dividing distribution stations in the preset distribution area into a plurality of class clusters; wherein each cluster comprises a plurality of distribution stations; determining a path set corresponding to the preset distribution area according to the divided class clusters; wherein the path set comprises a path corresponding to each cluster. According to the invention, the problem of low accuracy and efficiency of existing path planning can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of path planning, and in particular to a path planning method, a path planning device, an electronic device, and a machine-readable storage medium. Background Art

[0002] The number of distribution stations is one of the key factors affecting the complexity of solving the Vehicle Routing Planning Problem (VRP), which is directly related to the difficulty of problem solving and the required computing resources.

[0003] In related technologies, in the application scenario of large-scale distribution stations, how to improve the efficiency of path planning calculations and reduce the calculation complexity and cost is one of the problems that need to be solved urgently. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a path planning method, path planning device, equipment and medium to solve the problems of low accuracy and efficiency of path planning in the prior art.

[0005] In order to achieve the above objectives, the present application provides a path planning method in a first aspect, the method comprising:

[0006] Determine the geographic location information of warehouses and distribution sites within the pre-set distribution area;

[0007] Generate a target data set based on each geographic location information; wherein the target data set includes spatial information corresponding to each delivery station, and the spatial information of the delivery station includes geographic location information of the delivery station and directional angle information of the delivery station relative to the location of the warehouse;

[0008] According to the target data set, the delivery sites within the preset delivery area are divided into a plurality of clusters; wherein each cluster includes a plurality of delivery sites;

[0009] According to the divided clusters, a path set corresponding to the preset delivery area is determined; wherein the path set includes the paths corresponding to each cluster.

[0010] In the embodiment of the present application, generating a target data set according to each geographic location information includes:

[0011] Determine a first vector corresponding to the warehouse based on the preset origin and the geographic location information of the warehouse; wherein the first vector represents the geographic location relationship between the warehouse and the preset origin;

[0012] Determine, based on the geographic location information of the warehouse and each delivery site, a second vector corresponding to each delivery site; wherein the second vector corresponding to the delivery site represents the geographic location relationship between the delivery site and the warehouse;

[0013] For each delivery site, the direction angle information corresponding to the delivery site is determined according to the first vector and the second vector corresponding to the delivery site.

[0014] In the embodiment of the present application, for each delivery station, determining the direction angle information corresponding to the delivery station according to the first vector and the second vector corresponding to the delivery station includes:

[0015] For each delivery station, the direction angle information corresponding to the delivery station is determined by a first formula; wherein the first formula is:

[0016]

[0017] Among them, θ n Indicates the direction angle information corresponding to the nth delivery station in the preset delivery area, represents the first vector, represents the second vector corresponding to the nth delivery site in the preset delivery area, * represents the dot product, and |·| represents the modulus of ·.

[0018] In an embodiment of the present application, the delivery sites within the preset delivery area are divided into a plurality of clusters according to the target dataset, including:

[0019] Standardizing the spatial information corresponding to each delivery station to obtain first standardized information corresponding to each delivery station;

[0020] Initialize a preset total number of cluster centers, and determine the current cluster center based on the initialized cluster centers;

[0021] Performing standardization processing on each current cluster center to obtain second standardized information corresponding to each current cluster center;

[0022] Determine a first clustering result based on the first standardized information corresponding to each delivery station and the second standardized information corresponding to each current cluster center; wherein the first clustering result includes the current cluster center corresponding to each delivery station;

[0023] Determining updated cluster centers based on the first clustering result and the geographic location information of each delivery station, replacing the updated cluster centers with the current cluster centers, and returning to the step of performing normalization processing on each current cluster center to determine a second clustering result; wherein the second clustering result includes the current cluster center corresponding to each delivery station;

[0024] Based on the first clustering result and the second clustering result, a plurality of clusters are determined.

[0025] In the embodiment of the present application, determining the first clustering result according to the first standardized information corresponding to each delivery site and the second standardized information corresponding to each current cluster center includes:

[0026] For each distribution site, the distance between the distribution site and each current cluster center is determined based on the first standardized information corresponding to the distribution site and the second standardized information corresponding to each current cluster center, and the current cluster center with the smallest distance is determined as the current cluster center corresponding to the distribution site.

[0027] In the embodiment of the present application, the first standardized information and the second standardized information both include standardized latitude information, standardized longitude information, and standardized direction angle information;

[0028] For each delivery station, the distance between the delivery station and each current cluster center is determined based on the first standardized information corresponding to the delivery station and the second standardized information corresponding to each current cluster center, including:

[0029] For each delivery station, the distance between the delivery station and the current cluster center is determined by the second formula; wherein the second formula is:

[0030]

[0031] Among them, d(S′ n ,D′ i ) represents the distance between the nth delivery station and the ith current cluster center in the preset delivery area, S′ n represents the first standardized information corresponding to the nth delivery station in the preset delivery area, D′ i Indicates the second standardized information corresponding to the i-th current cluster center, lat′ n , lng′ n ,θ′ n S′ n Corresponding standardized latitude information, standardized longitude information and standardized direction angle information; lat′ i , lng′ i ,θ′ i D′ i Corresponding standardized latitude information, standardized longitude information and standardized direction angle information.

[0032] In the embodiment of the present application, multiple clusters are determined based on the first clustering result and the second clustering result, including:

[0033] Based on the first clustering result and the second clustering result, determine whether the second clustering result meets the preset convergence condition. If so, multiple clusters are obtained. If not, the second clustering result is replaced with the first clustering result. Based on the first clustering result and the geographical location information of each distribution site, the updated cluster center is determined, and the updated cluster center is replaced with the current cluster center. Return to execute the step of performing standardization processing on each current cluster center separately, determine the second clustering result, and obtain multiple clusters until the second clustering result meets the preset convergence condition.

[0034] A second aspect of the present application provides a path planning device, the device comprising:

[0035] A location information determination module is used to determine the geographical location information of warehouses and various distribution sites within a preset distribution area;

[0036] a data set determination module, configured to generate a target data set based on the respective geographic location information; wherein the target data set includes spatial information corresponding to each delivery station, and the spatial information of the delivery station includes the geographic location information of the delivery station and the directional angle information of the delivery station relative to the location of the warehouse;

[0037] a delivery site division module, configured to divide the delivery sites within the preset delivery area into a plurality of clusters according to the target data set; wherein each cluster includes a plurality of delivery sites;

[0038] The path planning module is used to determine a path set corresponding to the preset delivery area based on the divided clusters; wherein the path set includes the paths corresponding to each cluster.

[0039] A third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the path planning method described in the first aspect when executing the computer program.

[0040] In a fourth aspect, the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the path planning method described in the first aspect.

[0041] The path planning method, apparatus, equipment, and medium provided in this application first add a feature, namely, the direction angle of the distribution site relative to the warehouse, to the distribution sites. The distribution sites are then grouped to obtain multiple clusters. This ensures that the distribution sites within each cluster have similar spatial characteristics (including distance and direction angle). That is, distribution sites with the same direction relative to the warehouse are grouped together as much as possible. Finally, path planning is performed separately for each cluster. By rationally grouping distribution centers and breaking down large-scale problems into multiple sub-problems, this method can effectively improve the accuracy of path planning, reduce the search space, reduce the amount of computation, computational complexity, and computational cost, and avoid computational performance bottlenecks, thereby improving the overall efficiency of path planning and its compatibility with business needs.

[0042] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present application but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0044] Figure 1 The following schematically shows a flow chart of a path planning method according to an embodiment of the present application;

[0045] Figure 2 The following schematically shows a structural block diagram of a path planning device according to an embodiment of the present application;

[0046] Figure 3 The internal structure diagram of the computer device according to the embodiment of the present application is schematically shown.

[0047] Description of Reference Numerals

[0048] A01-Processor; A02-Network interface; A03-Internal memory; A04-Display screen; A05-Input device; A06-Non-volatile storage medium; B01-Operating system; B02-Computer program. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not intended to limit the embodiments of the present application. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application without making creative efforts are within the scope of protection of this application.

[0050] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0051] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0052] The vehicle routing problem (VRP) in the logistics industry is one of the most complex problems in the fields of supply chain, operations research, and management science. In the new retail distribution scenario, factors such as the number of stores to be delivered, the complexity of the constraints, and the expected solution time greatly increase the difficulty of finding the optimal planning solution. The vehicle routing problem involves a series of loading and unloading points (all of which belong to the distribution site). The goal is to organize appropriate driving routes to minimize the total mileage and cost of all vehicles while meeting constraints such as cargo load capacity restrictions, loading and unloading time windows, vehicle capacity restrictions, vehicle mileage restrictions, and vehicle operating time restrictions.

[0053] The computational complexity of vehicle routing increases significantly with the increasing number of delivery points and the scale of the problem, coupled with the complexity of the constraints that must be considered. The number of possible routing combinations increases exponentially with more delivery points. This not only significantly increases the difficulty of finding an optimal or feasible solution within a limited timeframe, but also significantly increases the computational resources required.

[0054] In summary, the number of delivery points is a key factor affecting the complexity of VRP solutions, directly impacting the difficulty of problem solving and the required computing resources. In scenarios involving a large number of delivery points (for example, greater than 2,000), improving the efficiency of route planning while reducing computational complexity and cost is a pressing issue.

[0055] In view of the low accuracy and efficiency of path planning in related technologies, the present application provides a path planning method, device, equipment and medium. Below, in conjunction with the accompanying drawings, the path planning method, device, equipment and medium provided in the present application are described in detail through specific examples and implementation methods.

[0056] Figure 1 The following schematically shows a flow chart of the path planning method according to an embodiment of the present application. Figure 1 As shown, in one embodiment of the present application, a path planning method is provided, which may include the following steps.

[0057] Step 100: Determine the geographical location information of the warehouses and each distribution site within a preset distribution area.

[0058] The preset distribution area refers to a geographical area pre-divided according to specific standards and requirements in the logistics distribution system, which is used to organize and manage distribution tasks in order to optimize distribution efficiency, reduce costs and improve service quality.

[0059] The preset distribution area includes a warehouse and multiple distribution sites, and the geographical location information of the warehouse and the distribution sites includes latitude information and longitude information.

[0060] Specifically, the geographical location information of the warehouse can be expressed as C=(lat0, lng0), where lat0 and lng0 represent the latitude information and longitude information of the geographical location information of the warehouse, respectively.

[0061] The geographical location information of the nth (1≤n≤N) delivery station in the preset delivery area can be expressed as S n =(lat n ,lng n ). Among them, lat n 、lng n respectively represent the latitude and longitude information in the geographic location information of the nth delivery station within the predetermined delivery area, and N represents the total number of delivery stations within the predetermined delivery area. It will be appreciated that all delivery stations within the predetermined delivery area may form a set, and each delivery station has a corresponding serial number within the set.

[0062] Step 200: Generate a target data set based on each geographic location information.

[0063] The target data set includes spatial information corresponding to each distribution site, and the spatial information of the distribution site includes geographical location information of the distribution site and direction angle information of the distribution site relative to the location of the warehouse.

[0064] In the embodiment of the present application, step 200 includes the following steps.

[0065] Step 210: Determine a first vector corresponding to the warehouse based on a preset origin and the geographic location information of the warehouse.

[0066] The first vector represents the geographical location relationship between the warehouse and the preset origin.

[0067] In the embodiment of the present application, the geographical location information of the preset origin can be expressed as O=(0,0), that is, the latitude information and longitude information of the preset origin are both 0.

[0068] It can be understood that in the coordinate system with the preset origin as the origin, the geographical location information of the warehouse can be represented by a vector, that is, as follows: In the embodiments of this application, This is the first vector, which is also the vector representing the distance from the warehouse to the preset origin. Of course, it is understandable that the geographical location information of the nth delivery station in the preset delivery area can also be represented by a vector, that is, as follows:

[0069] Step 220: Determine a second vector corresponding to each distribution site based on the geographic location information of the warehouse and each distribution site.

[0070] The second vector corresponding to the distribution site represents the geographical location relationship between the distribution site and the warehouse.

[0071] In the embodiment of the present application, for the nth delivery station in the preset delivery area, the corresponding second vector can be expressed as It can be understood that the second vector corresponding to the distribution site is a vector representing the distribution site to the warehouse.

[0072] Step 230: For each delivery site, determine the direction angle information corresponding to the delivery site based on the first vector and the second vector corresponding to the delivery site.

[0073] Specifically, a possible implementation of step 230 is as follows.

[0074] For each delivery site, the direction angle information corresponding to the delivery site is determined by the first formula.

[0075] Among them, the first formula is:

[0076]

[0077] Among them, θ nIndicates the direction angle information corresponding to the nth delivery station in the preset delivery area, represents the first vector, represents the second vector corresponding to the nth delivery site in the preset delivery area, * represents the dot product, and |·| represents the modulus of ·.

[0078] Among them, the vector Length of mold vector Length of mold vector and The dot product of In the embodiment of the present application, θ n OCS n (i.e. ∠OCS n ).

[0079] It can be understood that in the embodiment of the present application, the target data set can be expressed as S=S1(lat1, lng1, θ1), S2(lat2, lng2, θ2), ..., S n (lat n ,lng n ,θ n ).

[0080] Step 300: Divide the delivery sites within the preset delivery area into a plurality of clusters based on the target data set.

[0081] Each cluster includes multiple distribution sites.

[0082] In the embodiment of the present application, step 300 includes the following steps.

[0083] Step 310 : Standardize the spatial information corresponding to each delivery station to obtain first standardized information corresponding to each delivery station.

[0084] The first standardized information includes standardized latitude information, standardized longitude information and standardized direction angle information.

[0085] Specifically, due to the different metrics between longitude and latitude and directional angles, the embodiment of the present application performs feature normalization processing on spatial information to ensure that data with different metrics can be effectively processed and analyzed, thereby improving the accuracy of subsequent clustering results.

[0086] In the embodiment of the present application, a possible implementation of step 310 is as follows.

[0087] First, based on the spatial information corresponding to each delivery station, a latitude sequence, a longitude sequence, and a direction angle sequence are generated. The latitude sequence includes the latitude information of each delivery station, the longitude sequence includes the longitude information of each delivery station, and the direction angle sequence includes the direction angle information of each delivery station.

[0088] Then, Z-score normalization is performed on the latitude sequence, longitude sequence, and direction angle sequence, respectively, to obtain a standardized latitude sequence, a standardized longitude sequence, and a standardized direction angle sequence. The standardized latitude sequence includes the standardized latitude information of each delivery station obtained by Z-score normalization, the standardized longitude sequence includes the standardized longitude information of each delivery station obtained by Z-score normalization, and the standardized direction angle sequence includes the standardized direction angle information of each delivery station obtained by Z-score normalization.

[0089] In this embodiment, the means of the standardized latitude sequence, the standardized longitude sequence, and the standardized direction angle sequence are all 0, and the standard deviations are all 1.

[0090] It is understandable that the embodiments of the present application may also standardize the spatial information through other processing methods, and the present application does not limit the specific implementation method of standardizing the spatial information.

[0091] Step 320 : Initialize a preset total number of cluster centers, and determine the current cluster center based on the initialized cluster centers.

[0092] The initialized cluster center has latitude information and longitude information, and the current cluster center has corresponding standardized latitude information, standardized longitude information, and standardized direction angle information.

[0093] In the embodiment of the present application, the preset total number is set based on actual conditions and needs. The cluster centers can be determined based on the distribution of delivery stations, so that the initialized cluster centers are well representative and dispersed in the feature space of the entire delivery station set, thereby reducing the number of subsequent iterations and ultimately improving path planning efficiency.

[0094] Specifically, the step 320 may use a K-means++ algorithm, a data visualization method, etc. to initialize the cluster center. This application does not limit the specific implementation method of initializing the cluster center.

[0095] In the embodiment of the present application, the specific implementation method of determining the current cluster center in step 320 is similar to the specific implementation method of determining the spatial information corresponding to the distribution site in step 200, and will not be repeated here.

[0096] Step 330 : performing standardization processing on each current cluster center to obtain second standardized information corresponding to each current cluster center.

[0097] The second standardized information includes standardized latitude information, standardized longitude information and standardized direction angle information.

[0098] In the embodiment of the present application, a possible implementation of step 330 is as follows.

[0099] If the preset total number is greater than or equal to the preset number threshold, then step 330 performs standardization based on each current cluster center. The specific implementation method is similar to the specific implementation method of step 310 and will not be repeated here. If the preset total number is less than the preset number threshold, then based on the mean and error of the above-mentioned latitude sequence, the standardized latitude information corresponding to the current cluster center is determined by the Z-score standardization method. Based on the mean and error of the above-mentioned longitude sequence, the standardized longitude information corresponding to the current cluster center is determined by the Z-score standardization method. Based on the mean and error of the above-mentioned direction angle sequence, the standardized direction angle information corresponding to the current cluster center is determined by the Z-score standardization method.

[0100] Step 340: Determine a first clustering result based on the first standardized information corresponding to each delivery site and the second standardized information corresponding to each current cluster center.

[0101] The first clustering result includes a cluster center sequence and a current cluster center corresponding to each delivery site, and the cluster center sequence includes each current cluster center.

[0102] Specifically, a possible implementation of step 340 is as follows.

[0103] For each distribution site, the distance between the distribution site and each current cluster center is determined based on the first standardized information corresponding to the distribution site and the second standardized information corresponding to each current cluster center, and the current cluster center with the smallest distance is determined as the current cluster center corresponding to the distribution site.

[0104] For each delivery station, the distance between the delivery station and each current cluster center is determined based on the first standardized information corresponding to the delivery station and the second standardized information corresponding to each current cluster center, including:

[0105] For each delivery station, the distance between the delivery station and the current cluster center is determined by the second formula. The second formula is:

[0106]

[0107] Among them, d(S′ n ,D′ i ) represents the distance between the nth delivery station and the ith current cluster center in the preset delivery area, S′ n represents the first standardized information corresponding to the nth delivery station in the preset delivery area, D′ i Indicates the second standardized information corresponding to the i-th current cluster center, lat′ n , lng′ n ,θ′ n S′ n Corresponding standardized latitude information, standardized longitude information and standardized direction angle information; lat′ i , lng′ i ,θ′ i D′ i Corresponding standardized latitude information, standardized longitude information and standardized direction angle information.

[0108] In particular, for each delivery station, if the distance between the delivery station and two or more current cluster centers is equal, all or any one of the two or more current cluster centers are determined as the current cluster center corresponding to the delivery station.

[0109] Step 350 , based on the first clustering result and the geographical location information of each delivery station, determine an updated cluster center, replace the updated cluster center with the current cluster center, and return to execute step 330 to determine a second clustering result.

[0110] In the embodiment of the present application, a possible implementation method of step 350 for determining the updated cluster center based on the first clustering result and the geographical location information of each delivery station is as follows.

[0111] For each current cluster center, the latitude mean and longitude mean are determined based on the geographic location information of each distribution station corresponding to the current cluster center. A new cluster center is generated based on the latitude mean and longitude mean. Based on the new cluster center, the updated cluster center corresponding to the current cluster center is determined.

[0112] The updated cluster center has latitude information, longitude information, and direction angle information. The specific implementation method for determining the updated cluster center corresponding to the current cluster center based on the new cluster center is similar to the specific implementation method for determining the spatial information corresponding to the distribution site in step 200, and will not be repeated here.

[0113] The second clustering result includes a cluster center sequence and the current cluster center corresponding to each delivery station. The cluster center sequence includes each current cluster center. It should be understood that the current cluster center in the cluster center sequence of the second clustering result has a one-to-one correspondence with the current cluster center in the cluster center sequence of the first clustering result.

[0114] Step 360: Determine a plurality of clusters based on the first clustering result and the second clustering result.

[0115] Specifically, a possible implementation of step 360 is as follows.

[0116] Based on the first clustering result and the second clustering result, determine whether the second clustering result meets the preset convergence condition. If so, multiple clusters are obtained. If not, the second clustering result is replaced with the first clustering result. Based on the first clustering result and the geographic location information of each distribution site, an updated cluster center is determined, and the updated cluster center is replaced with the current cluster center. Return to execute step 330 to determine the second clustering result until the second clustering result meets the preset convergence condition and multiple clusters are obtained.

[0117] The preset convergence condition includes: the distance between the sequence of cluster centers in the first clustering result and the sequence of cluster centers in the second clustering result is less than or equal to a preset distance threshold. It should be understood that when the preset convergence condition is met, indicating that the cluster centers determined in two iterations have not changed significantly, the algorithm can be considered to have converged.

[0118] Step 400: Determine a set of routes corresponding to the preset delivery area based on the divided clusters.

[0119] The path set includes paths corresponding to each type of cluster.

[0120] In the embodiments of the present application, for each cluster, the path corresponding to that cluster can be generated using methods such as the A* algorithm, Dijkstra's algorithm, the Rapid Random Tree (RRT) algorithm, the Genetic Algorithm, the Ant Colony Algorithm, and the Simulated Annealing Algorithm. In specific applications, the appropriate method can be selected based on actual needs. After determining the paths corresponding to each cluster, they are aggregated (i.e., the set of paths corresponding to the predetermined delivery area) to obtain the relatively optimal available solution.

[0121] It can be seen that the driving path under the logistics distribution based on a single warehouse usually starts from the warehouse, and the vehicles load and unload goods in the order from near to far, showing the characteristic of radial distribution in space. The path planning method provided in the embodiment of the present application first adds the feature of the direction angle relative to the warehouse to the distribution site, and then uses the clustering algorithm to group the distribution sites to obtain multiple clusters. This ensures that the distribution sites in each cluster have similar spatial characteristics (including distance and direction angle), that is, the distribution sites with the same direction relative to the warehouse are grouped as much as possible, and finally, path planning is performed for each cluster separately. This method can effectively improve the accuracy of path planning by reasonably grouping the distribution centers and breaking down the large-scale problem into multiple sub-problems, and can also reduce the search space, reduce the amount of calculation, calculation complexity and calculation cost, avoid calculation performance bottlenecks, and thus improve the overall efficiency of path planning and the matching degree with business needs.

[0122] Figure 1 FIG. 1 is a flow chart of a path planning method in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0123] Figure 2 The structure block diagram of the path planning device of the embodiment of the present application is schematically shown. Figure 2 As shown, in one embodiment of the present application, a path planning device is provided, and the path planning device may include the following functional modules.

[0124] The location information determination module is used to determine the geographical location information of warehouses and various distribution sites within a preset distribution area.

[0125] The data set determination module is configured to generate a target data set based on the geographic location information. The target data set includes the spatial information corresponding to each delivery station, and the spatial information of the delivery station includes the geographic location information of the delivery station and the directional angle information of the delivery station relative to the location of the warehouse.

[0126] The delivery station division module is configured to divide the delivery stations in the preset delivery area into a plurality of clusters according to the target data set, wherein each cluster includes a plurality of delivery stations.

[0127] The route planning module is used to determine a set of routes corresponding to the preset delivery area based on the divided clusters, wherein the set of routes includes a route corresponding to each cluster.

[0128] In an embodiment of the present application, the data set determination module may include:

[0129] The first vector determining unit is configured to determine a first vector corresponding to the warehouse based on a preset origin and the geographical location information of the warehouse, wherein the first vector represents the geographical location relationship between the warehouse and the preset origin.

[0130] The second vector determining unit is configured to determine a second vector corresponding to each distribution station based on the geographical location information of the warehouse and each distribution station, wherein the second vector corresponding to the distribution station represents the geographical location relationship between the distribution station and the warehouse.

[0131] The direction angle information determining unit is configured to determine, for each delivery site, the direction angle information corresponding to the delivery site based on the first vector and the second vector corresponding to the delivery site.

[0132] In the embodiment of the present application, the direction angle information determination unit is specifically configured to:

[0133] For each delivery site, the direction angle information corresponding to the delivery site is determined using the first formula.

[0134] Among them, the first formula is:

[0135]

[0136] Among them, θ n Indicates the direction angle information corresponding to the nth delivery station in the preset delivery area, represents the first vector, represents the second vector corresponding to the nth delivery site in the preset delivery area, * represents the dot product, and |·| represents the modulus of ·.

[0137] In an embodiment of the present application, the delivery site division module may include:

[0138] The first standardization unit is used to perform standardization processing on the spatial information corresponding to each distribution site to obtain first standardized information corresponding to each distribution site.

[0139] The initialization unit is used to initialize a preset total number of cluster centers and determine the current cluster center based on the initialized cluster centers.

[0140] The second standardization unit is used to perform standardization processing on each current cluster center to obtain second standardized information corresponding to each current cluster center.

[0141] The clustering unit is configured to determine a first clustering result based on the first standardized information corresponding to each delivery station and the second standardized information corresponding to each current cluster center, wherein the first clustering result includes the current cluster center corresponding to each delivery station.

[0142] The iteration unit is configured to determine an updated cluster center based on the first clustering result and the geographic location information of each delivery station, replace the updated cluster center with the current cluster center, and return to execute the second normalization unit to determine a second clustering result. The second clustering result includes the current cluster center corresponding to each delivery station.

[0143] The cluster generating unit is configured to determine a plurality of clusters based on the first clustering result and the second clustering result.

[0144] In the embodiment of the present application, the clustering unit is specifically used to:

[0145] For each distribution site, the distance between the distribution site and each current cluster center is determined based on the first standardized information corresponding to the distribution site and the second standardized information corresponding to each current cluster center, and the current cluster center with the smallest distance is determined as the current cluster center corresponding to the distribution site.

[0146] For each delivery station, the distance between the delivery station and each current cluster center is determined based on the first standardized information corresponding to the delivery station and the second standardized information corresponding to each current cluster center, including:

[0147] For each delivery station, the distance between the delivery station and the current cluster center is determined by the second formula. The second formula is:

[0148]

[0149] Among them, d(S′ n ,D′ i ) represents the distance between the nth delivery station and the ith current cluster center in the preset delivery area, S′ n represents the first standardized information corresponding to the nth delivery station in the preset delivery area, D′ iIndicates the second standardized information corresponding to the i-th current cluster center, lat′ n , lng′ n ,θ′ n S′ n Corresponding standardized latitude information, standardized longitude information and standardized direction angle information. lat′ i , lng′ i ,θ′ i D′ i Corresponding standardized latitude information, standardized longitude information and standardized direction angle information.

[0150] In the embodiment of the present application, the cluster generation unit is specifically used to:

[0151] Based on the first clustering result and the second clustering result, determine whether the second clustering result meets the preset convergence condition. If so, multiple clusters are obtained. If not, the second clustering result is replaced with the first clustering result. Based on the first clustering result and the geographic location information of each distribution site, an updated cluster center is determined, and the updated cluster center is replaced with the current cluster center. Return to execute the second standardization unit to determine the second clustering result until the second clustering result meets the preset convergence condition and multiple clusters are obtained.

[0152] Since the path planning device provided in the embodiment of the present application is a virtual device corresponding to the path planning method of the above embodiment, it can also solve the problem of low accuracy and efficiency of path planning in the prior art.

[0153] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the path planning method described in the above embodiment is implemented.

[0154] The electronic device provided in the embodiment of the present application, since it includes a processor capable of running the path planning method of the aforementioned embodiment, can also solve the problem of low accuracy and efficiency of path planning in the prior art.

[0155] An embodiment of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the path planning method described in the above embodiment.

[0156] The machine-readable storage medium provided in the embodiment of the present application stores instructions for enabling a machine to execute the path planning method of the above embodiment, and therefore can also solve the problem of low accuracy and efficiency of path planning in the prior art.

[0157] Figure 3 The internal structure diagram of the computer device of the embodiment of the present application is schematically shown. Figure 3 As shown, in one embodiment of the present application, a computer device is provided, which can be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. The processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor A01, a path planning method is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0158] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0159] In one embodiment, the path planning device provided by the present application can be implemented in the form of a computer program. The computer program can be used in Figure 3 The computer device is run on the computer device shown. The memory of the computer device can store various program modules that constitute the path planning device, and the computer program composed of each program module enables the processor to execute the steps of the path planning method of each embodiment of the present application described in this specification.

[0160] Figure 3 The computer device shown can be Figure 2 In the path planning device shown, the location information determination module executes step 100 , the data set determination module executes step 200 , the delivery site division module executes step 300 , and the path planning module executes step 400 .

[0161] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0166] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0167] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0168] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0169] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A path planning method, characterized in that: The method comprises: Determine the geographic location information of warehouses and distribution sites within the pre-set distribution area; Generate a target data set based on each geographic location information; wherein the target data set includes spatial information corresponding to each delivery station, and the spatial information of the delivery station includes geographic location information of the delivery station and directional angle information of the delivery station relative to the location of the warehouse; According to the target data set, the delivery sites within the preset delivery area are divided into a plurality of clusters; wherein each cluster includes a plurality of delivery sites; According to the divided clusters, a path set corresponding to the preset delivery area is determined; wherein the path set includes the paths corresponding to each cluster.

2. The method according to claim 1, characterized in that Generate target datasets based on various geographic location information, including: Determine a first vector corresponding to the warehouse based on the preset origin and the geographic location information of the warehouse; wherein the first vector represents the geographic location relationship between the warehouse and the preset origin; Determine, based on the geographic location information of the warehouse and each delivery site, a second vector corresponding to each delivery site; wherein the second vector corresponding to the delivery site represents the geographic location relationship between the delivery site and the warehouse; For each delivery site, the direction angle information corresponding to the delivery site is determined according to the first vector and the second vector corresponding to the delivery site.

3. The method according to claim 2, characterized in that For each delivery station, determining the direction angle information corresponding to the delivery station based on the first vector and the second vector corresponding to the delivery station includes: For each delivery station, the direction angle information corresponding to the delivery station is determined by a first formula; wherein the first formula is: Among them, θ n Indicates the direction angle information corresponding to the nth delivery station in the preset delivery area, represents the first vector, represents the second vector corresponding to the nth delivery site in the preset delivery area, * represents the dot product, and |·| represents the modulus of ·.

4. The method according to claim 1, wherein According to the target data set, the delivery sites within the preset delivery area are divided into multiple clusters, including: Standardizing the spatial information corresponding to each delivery station to obtain first standardized information corresponding to each delivery station; Initialize a preset total number of cluster centers, and determine the current cluster center based on the initialized cluster centers; Performing standardization processing on each current cluster center to obtain second standardized information corresponding to each current cluster center; Determine a first clustering result based on the first standardized information corresponding to each delivery station and the second standardized information corresponding to each current cluster center; wherein the first clustering result includes the current cluster center corresponding to each delivery station; Determining updated cluster centers based on the first clustering result and the geographic location information of each delivery station, replacing the updated cluster centers with the current cluster centers, and returning to the step of performing normalization processing on each current cluster center to determine a second clustering result; wherein the second clustering result includes the current cluster center corresponding to each delivery station; Based on the first clustering result and the second clustering result, a plurality of clusters are determined.

5. The method according to claim 4, characterized in that Determining a first clustering result according to the first standardized information corresponding to each delivery site and the second standardized information corresponding to each current cluster center includes: For each distribution site, the distance between the distribution site and each current cluster center is determined based on the first standardized information corresponding to the distribution site and the second standardized information corresponding to each current cluster center, and the current cluster center with the smallest distance is determined as the current cluster center corresponding to the distribution site.

6. The method according to claim 5, characterized in that The first standardized information and the second standardized information both include standardized latitude information, standardized longitude information, and standardized direction angle information; For each delivery station, the distance between the delivery station and each current cluster center is determined based on the first standardized information corresponding to the delivery station and the second standardized information corresponding to each current cluster center, including: For each delivery station, the distance between the delivery station and the current cluster center is determined by the second formula; wherein the second formula is: Among them, d(S ′ n ,D i ′ ) represents the distance between the nth delivery station and the ith current cluster center in the preset delivery area, S ′ n represents the first standardized information corresponding to the nth delivery station in the preset delivery area, D i ′ Indicates the second standardized information corresponding to the i-th current cluster center, lat ′ n 、lng ′ n ,θ ′ n S ′ n Corresponding standardized latitude information, standardized longitude information and standardized direction angle information; lat i ′ 、lng i ′ ,θ i ′ D ′ ′ Corresponding standardized latitude information, standardized longitude information and standardized direction angle information.

7. The method according to claim 4, characterized in that Based on the first clustering result and the second clustering result, a plurality of clusters are determined, including: Based on the first clustering result and the second clustering result, determine whether the second clustering result meets the preset convergence condition. If so, multiple clusters are obtained. If not, the second clustering result is replaced with the first clustering result. Based on the first clustering result and the geographical location information of each distribution site, the updated cluster center is determined, and the updated cluster center is replaced with the current cluster center. Return to execute the step of performing standardization processing on each current cluster center separately, determine the second clustering result, and obtain multiple clusters until the second clustering result meets the preset convergence condition.

8. A path planning device, characterized in that: The device comprises: A location information determination module is used to determine the geographical location information of warehouses and various distribution sites within a preset distribution area; a data set determination module, configured to generate a target data set based on the respective geographic location information; wherein the target data set includes spatial information corresponding to each delivery station, and the spatial information of the delivery station includes the geographic location information of the delivery station and the directional angle information of the delivery station relative to the location of the warehouse; a delivery site division module, configured to divide the delivery sites within the preset delivery area into a plurality of clusters according to the target data set; wherein each cluster includes a plurality of delivery sites; The path planning module is used to determine a path set corresponding to the preset delivery area based on the divided clusters; wherein the path set includes the paths corresponding to each cluster.

9. 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 computer program, the path planning method according to any one of claims 1 to 7 is implemented.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the path planning method according to any one of claims 1 to 7.

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