Distribution planning method and device

Abstract

The present invention discloses a distribution planning method and device, and relates to the field of computer technology. The method comprises: constructing a distribution network model according to order information to be distributed; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the distribution points of the order, and the edges between the nodes are used to represent the connection relationship between the order distribution points; clustering the nodes in the distribution network model to obtain multiple clusters; taking the range covered by each of the multiple clusters as a distribution area to obtain multiple distribution areas. Through the above steps, the distribution area can be dynamically and reasonably divided according to the order information to be distributed, so that the number of distribution orders in each distribution area tends to be balanced, which helps to improve the distribution efficiency as a whole.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a distribution planning method and device. Background Art

[0002] In the logistics distribution scenario, it is necessary to divide the distribution area and assign corresponding delivery personnel to each distribution area. In the prior art, the distribution area is often kept fixed after being divided, and the courier sorts and delivers the goods according to the assigned distribution area.

[0003] In the process of realizing the present invention, the inventors found that there are at least the following problems in the prior art: First, since the number of orders in each delivery area changes dynamically, it is very likely that the delivery quantity and delivery time in different delivery areas will be extremely unbalanced, and the delivery time difference between some couriers may even reach 2 to 3 hours. Second, in the current delivery process, couriers often determine the delivery route based on experience. When the delivery range is small, delivery based on experience may not have much impact on the delivery efficiency. However, when the delivery range becomes larger, delivery based on experience will lead to problems such as unreasonable delivery route planning and low delivery efficiency. Summary of the invention

[0004] In view of this, the present invention provides a distribution planning method and device, which can dynamically and reasonably divide the distribution area according to the order information to be delivered, so that the number of delivery orders in each distribution area tends to be balanced, thereby helping to improve the distribution efficiency as a whole. Furthermore, based on the heuristic algorithm, path planning is performed in the divided distribution area, which can further improve the distribution efficiency.

[0005] To achieve the above objective, according to one aspect of the present invention, a distribution planning method is provided.

[0006] The distribution planning method of the present invention comprises: constructing a distribution network model according to order information to be distributed; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the distribution points of the orders, and the edges between the nodes are used to represent the connection relationship between the order distribution points; clustering the nodes in the distribution network model to obtain a plurality of clusters; taking the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.

[0007] Optionally, constructing a distribution network model based on the order information to be delivered includes: converting the coordinates of the order's delivery points into nodes in the distribution network model, converting the connection relationship between the order's delivery points into edges between the nodes, and converting the comprehensive score of the logistics distribution difficulty between the order's delivery points into the weight of the edges between the nodes.

[0008] Optionally, clustering the nodes in the distribution network model to obtain multiple clusters includes: determining the initial positions of K cluster center points; calculating the distance between each node in the distribution network model and each cluster center point, and assigning the node to the cluster with the smallest distance to achieve cluster update; calculating the position of the updated cluster center point until the iteration stop condition is met; wherein K is an integer greater than 1.

[0009] Optionally, calculating the distance between each node and each cluster center point in the distribution network model includes: calculating the distance between the node and the cluster center point according to the weights of each edge between a node and a cluster center point in the distribution network model and the real path length corresponding to each edge.

[0010] Optionally, the method includes: after obtaining multiple delivery areas, planning order delivery paths within the delivery areas according to a heuristic algorithm, and returning the planned order delivery paths to the user terminal.

[0011] Optionally, the order delivery path planning within the delivery area according to the heuristic algorithm includes: determining a candidate order delivery path within the delivery area, and calculating the total delivery cost of the candidate delivery path according to the total vehicle dispatch cost and the total driving cost corresponding to the candidate order delivery path; taking the minimization of the total delivery cost as the objective function, optimizing the candidate order delivery path until the planned order delivery path is obtained.

[0012] Optionally, the method includes: determining a comprehensive score of the difficulty of logistics delivery between the order delivery points based on at least one of the following influencing factors: the actual path length between the order delivery points, the road conditions between the order delivery points, the difficulty of parking between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points.

[0013] To achieve the above objective, according to another aspect of the present invention, a distribution planning device is provided.

[0014] The distribution device of the present invention includes: a construction module, which is used to construct a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the distribution points of the orders, and the edges between the nodes are used to represent the connection relationship between the order distribution points; a clustering module, which is used to cluster the nodes in the distribution network model to obtain multiple clusters; and is also used to take the range covered by each of the multiple clusters as a distribution area to obtain multiple distribution areas.

[0015] To achieve the above objective, according to another aspect of the present invention, an electronic device is provided.

[0016] The electronic device of the present invention comprises: one or more processors; and a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the distribution planning method of the present invention.

[0017] To achieve the above objective, according to yet another aspect of the present invention, a computer readable medium is provided.

[0018] The computer-readable medium of the present invention stores a computer program, which implements the distribution planning method of the present invention when executed by a processor.

[0019] An embodiment of the above invention has the following advantages or beneficial effects: by constructing a distribution network model according to the order information to be delivered, clustering the nodes in the distribution network model to obtain multiple clusters, and taking the range covered by each of the multiple clusters as a distribution area to obtain multiple distribution areas. These steps can dynamically and reasonably divide the distribution areas according to the order information to be delivered, so that the delivery order quantity in each distribution area tends to be balanced, which helps to improve the distribution efficiency as a whole.

[0020] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0022] Figure 1 is a schematic diagram of the main process of the distribution planning method according to the first embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of the main flow of the distribution planning method according to the second embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of the structure of a distribution network model constructed according to an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of the positional relationship between nodes and cluster centers according to an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of main modules of a distribution planning device according to a third embodiment of the present invention;

[0027] Figure 6 is a schematic diagram of main modules of a distribution planning device according to a fourth embodiment of the present invention;

[0028] Figure 7is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0029] Figure 8 It is a schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0031] Figure 1 FIG. 1 is a schematic diagram of the main flow of the distribution planning method according to the first embodiment of the present invention. Figure 1 As shown, the distribution planning method of the embodiment of the present invention includes:

[0032] Step S101: construct a distribution network model according to the order information to be delivered.

[0033] The distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the distribution points of the order (the order distribution points may also be called "order delivery locations" or "express delivery locations", etc.), and the edges between the nodes are used to represent the connection relationship between the order delivery points. In addition, the edges between the nodes may also be provided with corresponding weight values.

[0034] In one example, the distribution network model may be represented as G=(V, E), where V represents a set of nodes in the distribution network model, and E represents a set of edges between nodes in the distribution network model.

[0035] In another example, for the convenience of calculation, the distribution network model can also be represented by an adjacency matrix A. In the adjacency matrix A, the element A ij The value of is 1 or 0. ij The value of 1 indicates that there is a connection relationship between node i and node j (that is, there is an edge between node i and node j). ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0036] In another example, for the convenience of calculation, the distribution network model can also be represented by a weighted adjacency matrix A'. In the weighted adjacency matrix A', the element A' ij The value of is the weight value ω ij or 0. Among them, Aij When the value of is a weight value, it means that there is an edge between node i and node j, and the weight value of the edge is ω ij , A ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0037] When constructing a distribution network model, in theory, each order delivery point can be connected. In practice, in order to simplify the network model, for order delivery points in the same community or the same area, the nearest n (n can be set according to demand, such as 2 or 3) order delivery points can be selected for connection, and connections can be made between two communities or two areas, for example, the two order delivery points with the shortest distance between two areas can be selected for connection.

[0038] Step S102: clustering the nodes in the distribution network model to obtain a plurality of clusters; taking the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.

[0039] Exemplarily, a k-means clustering algorithm or other clustering algorithms may be used to cluster the nodes in the distribution network model.

[0040] In an optional embodiment, the k-means clustering algorithm is used to cluster the nodes in the distribution network model to obtain multiple clusters, including: determining the initial positions of K cluster center points; calculating the distance between each node in the distribution network model and each cluster center point, and assigning the node to the cluster with the smallest distance to achieve cluster update; calculating the position of the updated cluster center point until the iteration stop condition is met. Wherein, K is an integer greater than 1. In specific implementation, the value of K can be set flexibly. For example, the value of K can be determined based on the total amount of orders to be delivered and the average value of historical regional delivery volume.

[0041] In an embodiment of the present invention, a distribution network model is constructed according to the order information to be delivered, the nodes in the distribution network model are clustered to obtain a plurality of clusters, and the range covered by each of the plurality of clusters is taken as a distribution area to obtain a plurality of distribution areas. These steps can dynamically and reasonably divide the distribution areas according to the order information to be delivered, so that the delivery order quantity in each distribution area tends to be balanced, which helps to improve the distribution efficiency as a whole.

[0042] Figure 2 FIG. 2 is a schematic diagram of the main flow of the distribution planning method according to the second embodiment of the present invention. Figure 2 As shown, the distribution planning method of the embodiment of the present invention includes:

[0043] Step S201: construct a distribution network model based on the order information to be delivered.

[0044] like Figure 3 As shown, the distribution network model constructed by the embodiment of the present invention is composed of nodes and edges between nodes. The nodes are used to represent the distribution points of the order, and the edges between the nodes are used to represent the connection relationship between the order distribution points. In addition, the edges between the nodes can also be provided with corresponding weight values.

[0045] Furthermore, for the convenience of calculation, the distribution network model can be represented by a weighted adjacency matrix A'. In the weighted adjacency matrix A', the element A' ij The value of is the weight value ω ij or 0. Among them, A ij When the value of is a weight value, it means that there is an edge between node i and node j, and the weight value of the edge is ω ij , A ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0046] When constructing a distribution network model, in theory, each order delivery point can be connected. In practice, in order to simplify the network model, for order delivery points in the same community or the same area, the nearest n (n can be set according to demand, such as 2 or 3) order delivery points can be selected for connection, and connections can be made between two communities or two areas, for example, the two order delivery points with the shortest distance between two areas can be selected for connection.

[0047] In an optional example, step S201 may specifically include: converting the delivery point coordinates of the order into nodes in the distribution network model, converting the connection relationship between the order delivery points into edges between the nodes, and converting the comprehensive score of the logistics delivery difficulty between the order delivery points into the weight of the edges between the nodes.

[0048] Among them, the comprehensive score of the difficulty of logistics distribution between the order delivery points can be determined according to at least one of the following influencing factors: the actual path length between the order delivery points, the road conditions between the order delivery points, the parking difficulty between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points. For example, in a specific example, the comprehensive score of the difficulty of logistics distribution between the order delivery points can be determined according to the four influencing factors of the actual path length between the order delivery points, the road conditions between the order delivery points, the parking difficulty between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points. Table 1 shows the scores of these four influencing factors.

[0049] Table 1

[0050]

[0051] Furthermore, in this specific example, the weights of these four influencing factors are w i (i=1,2,3,4), and Each influencing factor is scored as c i (i=1,3,5), and then, the comprehensive score of the logistics delivery difficulty between order delivery point i and order delivery point j is During specific implementation, the weights of these four influencing factors can be flexibly set according to the application scenario.

[0052] Step S202: clustering the nodes in the distribution network model to obtain a plurality of clusters; taking the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.

[0053] In an optional example, step S202 specifically includes: step 1 to step 3.

[0054] Step 1: Determine the initial positions of the K cluster centers.

[0055] In this step, the order delivery point set X = {x i |x i ∈R,i=1,2,...,N}, randomly select K points as cluster centers z1,z2,...,z k , these K clusters can be recorded as W1,W2,...,W k . K is an integer greater than 1. In specific implementation, the value of K can be set flexibly. For example, the value of K can be determined based on the total number of orders to be delivered and the average value of the historical regional delivery volume. In addition, after determining the initial positions of the K cluster center points, the m order delivery points closest to the cluster center point can be selected (the value of m can be set according to demand, such as 2 or 3) and connected to the cluster center point.

[0056] Step 2: Calculate the distance between each node in the distribution network model and the center point of each cluster, and assign the node to the cluster with the smallest distance to achieve cluster update.

[0057] Exemplarily, in this step, the distance between a node and a cluster center point may be calculated according to the weights of each edge between the node and the cluster center point in the distribution network model and the real path length corresponding to each edge.

[0058] In an optional implementation of this example, when there is only one connecting road between a node i and a cluster center point k (the connecting road refers to a road composed of existing edges), the distance between the node i and the cluster center point k can be calculated according to the following formula:

[0059]

[0060] Among them, d ik is the distance between node i and the cluster center k, l r is the actual path length of the rth edge between node i and the cluster center k, C r is the weight of the rth edge between node i and the cluster center k. Furthermore, when there are two or more connecting roads between a node i and a cluster center k, multiple distances can be calculated using the above formula, and the minimum value of the multiple distances is used as the distance between node i and the cluster center k.

[0061] After calculating the distance between each node and the center point of each cluster, the node can be assigned to the cluster with the smallest distance. For example, assuming there are three clusters a, b, and c, the distance between node 1 and the center point of cluster a is 35, the distance between node 1 and the center point of cluster b is 25, and the distance between node 1 and the center point of cluster c is 40, then node 1 is assigned to cluster b.

[0062] Step 3: Calculate the position of the updated cluster center point until the iteration stop condition is met.

[0063] In this step, the position of the cluster center point can be calculated according to the coordinates of each node in the updated cluster, and the following formula can be used for specific calculation:

[0064]

[0065] Among them, z k is the location of the cluster center, n k is the number of nodes contained in the updated cluster, x represents the cluster W k Any node in .

[0066] The iteration stop condition can be flexibly set. For example, the iteration stop condition can be set to: no nodes are assigned to different cluster centers; or the iteration stop condition can be set to: the position of the cluster center point remains unchanged or the error is less than a specified threshold.

[0067] Step S203: Plan the order delivery route in the delivery area according to a heuristic algorithm.

[0068] Exemplarily, the order delivery path planning in the delivery area according to the heuristic algorithm includes: determining a candidate order delivery path in the delivery area, calculating the total delivery cost of the candidate delivery path according to the total vehicle dispatch cost and the total driving cost corresponding to the candidate order delivery path; taking the minimum total delivery cost as the objective function, optimizing the candidate order delivery path until the planned order delivery path is obtained. In specific implementation, heuristic algorithms such as simulated annealing algorithm and variable domain dispersed search algorithm can be used for order delivery path planning.

[0069] In an actual application scenario, while delivering orders, it is also necessary to meet the customer's return and delivery needs. When planning the delivery route for this actual application scenario, the following objective function can be set:

[0070]

[0071] Furthermore, when planning the delivery route for this actual application scenario, constraints as shown in Table 2 can be set.

[0072] Table 2

[0073]

[0074]

[0075] The following is an explanation of the parameters involved in the objective function and constraints, as well as the meaning of the constraints.

[0076] V: represents the point set of express delivery, V = V'∪{0}, V' = (1,2,...n).

[0077] i,j: represents the index of the order delivery point.

[0078] k: represents the index of the delivery vehicle.

[0079] K t : Represents the set of types t of delivery vehicles.

[0080] T: represents the set of delivery vehicles.

[0081] t: Index representing the type of delivery vehicle.

[0082] Q t : represents the vehicle capacity of delivery vehicle type t.

[0083] Z tk : represents the dispatch cost of the kth vehicle of delivery vehicle type t.

[0084] e ij : represents the driving cost between order delivery point i and order delivery point j.

[0085] p i : represents the reverse demand (i.e., return demand) of the customer corresponding to order delivery point i.

[0086] d i : represents the shipment demand of customer i corresponding to order delivery point i.

[0087] α ijtk : It means that if the k-th vehicle of delivery vehicle type t finishes delivering to order delivery point i and then continues to order delivery point j, it is 1, otherwise it is 0.

[0088] γ itk : It means that the kth vehicle of delivery vehicle type t is responsible for order delivery point i, which is 1, otherwise it is 0.

[0089] ε i : represents the pickup quantity of the delivery vehicles at order delivery point i and order delivery point j.

[0090] : Represents the delivery volume of the delivery vehicles at order delivery point i and order delivery point j.

[0091] The objective function indicates that we want to obtain the order delivery path with the minimum sum of the regional dispatch cost and the driving cost. Constraint (1) is used to ensure that all customers are served by delivery vehicles; constraint (2) is used to ensure that there is only one vehicle serving between point i and point j; constraint (3) is used to ensure that the delivery vehicle is not overloaded; constraints (4) and (5) are used to constrain the load of delivery and pickup trucks; constraint (6) is used to ensure that the delivery vehicle returns to the distribution center after completing the delivery task; constraint (7) is used to constrain the total number of delivery vehicles; constraints (8) to (11) are used to represent symbol ranges.

[0092] Step S204: Return the planned order delivery path to the user terminal.

[0093] For example, the planned order delivery path may include: a sequence of orders to be delivered arranged in the order of delivery. For example, assuming that there are 200 orders to be delivered in a delivery area and there are 4 delivery vehicles, the sequence of orders arranged in the order of delivery for each delivery vehicle may be returned to the user terminal.

[0094] In specific implementation, when the courier completes the delivery task and returns to the terminal, or encounters a situation where the customer refuses to accept the delivery, a trigger instruction can be input through the user terminal to execute step S203 again and re-plan the route.

[0095] In the embodiment of the present invention, the above steps can dynamically and reasonably divide the delivery area according to the order information to be delivered, so that the delivery order quantity in each delivery area tends to be balanced, which helps to improve the delivery efficiency as a whole. Furthermore, based on the heuristic algorithm, path planning is performed in the divided delivery area to further improve the delivery efficiency.

[0096] Figure 4 Schematic diagram of the positional relationship between nodes and cluster centers according to an embodiment of the present invention. Figure 4 Describes in detail how to calculate the distance between nodes and cluster centers.

[0097] like Figure 4 As shown, there are two connecting roads between node 3 in the distribution network model and the cluster center shown in the figure. One connecting road consists of the edge between node 3 and node 1, and the edge between node 1 and the cluster center. The other connecting road consists of the edge between node 3 and node 2, and the edge between node 2 and the cluster center. When calculating the distance between node 3 and the cluster center, the following formula can be used:

[0098] d 3z =min{l 31 ·C 31 +l 1z ·C 1z ,l 32 ·C 32 +l 2z ·C 2z}

[0099] Among them, d 3z represents the distance between node 3 and the center of the cluster, l 31 represents the true path length of the edge between node 3 and node 1, C 31 represents the weight of the edge between node 3 and node 1, l 1z represents the true path length of the edge between node 1 and the center of the cluster, C 1z represents the weight of the edge between node 1 and the center of the cluster, l 32 represents the true path length of the edge between node 3 and node 2, C 32 represents the weight of the edge between node 3 and node 2, l 2z represents the true path length of the edge between node 2 and the center of the cluster, C 2z The above formula indicates that the minimum distance between the two connected roads is taken as the distance d between node 3 and the cluster center. 3z .

[0100] Figure 5FIG. 2 is a schematic diagram of the main modules of the distribution planning device according to the third embodiment of the present invention. Figure 5 As shown, the distribution planning device 500 of the embodiment of the present invention includes: a construction module 501 and a clustering module 502.

[0101] The construction module 501 is used to construct a distribution network model according to the order information to be distributed.

[0102] The distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the distribution points of the order (the order distribution points may also be called "order delivery locations" or "express delivery locations", etc.), and the edges between the nodes are used to represent the connection relationship between the order delivery points. In addition, the edges between the nodes may also be provided with corresponding weight values.

[0103] In one example, the distribution network model may be represented as G=(V, E), where V represents a set of nodes in the distribution network model, and E represents a set of edges between nodes in the distribution network model.

[0104] In another example, for the convenience of calculation, the distribution network model can also be represented by an adjacency matrix A. In the adjacency matrix A, the element A ij The value of is 1 or 0. ij The value of 1 indicates that there is a connection relationship between node i and node j (that is, there is an edge between node i and node j). ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0105] In another example, for the convenience of calculation, the distribution network model can also be represented by a weighted adjacency matrix A'. In the weighted adjacency matrix A', the element A' ij The value of is the weight value ω ij or 0. Among them, A ij When the value of is a weight value, it means that there is an edge between node i and node j, and the weight value of the edge is ω ij , A ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0106] During specific implementation, in order to simplify the network model, for order delivery points in the same community or the same area, the n (n can be set according to needs, such as 2 or 3) order delivery points closest to it can be selected for connection, and connections can be made between two communities or two areas. For example, the two order delivery points with the shortest distance between two areas can be selected for connection.

[0107] The clustering module 502 is used to cluster the nodes in the distribution network model to obtain a plurality of clusters; and to take the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.

[0108] Exemplarily, a k-means clustering algorithm or other clustering algorithms may be used to cluster the nodes in the distribution network model.

[0109] In an optional embodiment, the clustering module 502 uses the k-means clustering algorithm to cluster the nodes in the distribution network model to obtain multiple clusters including: the clustering module 502 determines the initial positions of K cluster center points; the clustering module 502 calculates the distance between each node in the distribution network model and each cluster center point, and assigns the node to the cluster with the smallest distance to achieve cluster update; the clustering module 502 calculates the position of the updated cluster center point until the iteration stop condition is met. Wherein, K is an integer greater than 1. In specific implementation, the value of K can be set flexibly. For example, the value of K can be determined based on the total amount of orders to be delivered and the average value of the historical regional delivery volume.

[0110] In the device of the embodiment of the present invention, a distribution network model is constructed according to the order information to be delivered through a construction module, and the nodes in the distribution network model are clustered through a clustering module to obtain multiple clusters. The range covered by each of the multiple clusters is used as a distribution area to obtain multiple distribution areas. The distribution areas can be dynamically and reasonably divided according to the order information to be delivered, so that the delivery order quantity in each distribution area tends to be balanced, which helps to improve the distribution efficiency as a whole.

[0111] Figure 6 Schematic diagram of the main modules of the distribution planning device according to the fourth embodiment of the present invention. Figure 6 As shown, the distribution planning device 600 of the embodiment of the present invention includes: a construction module 601, a clustering module 602, and a path planning module 603.

[0112] The construction module 601 is used to construct a distribution network model according to the order information to be distributed.

[0113] The distribution network model constructed by the embodiment of the present invention is composed of nodes and edges between nodes. The nodes are used to represent the distribution points of the order, and the edges between the nodes are used to represent the connection relationship between the order distribution points. In addition, the edges between the nodes can also be provided with corresponding weight values.

[0114] Furthermore, for the convenience of calculation, the distribution network model can be represented by a weighted adjacency matrix A'. In the weighted adjacency matrix A', the element A' ij The value of is the weight value ωij or 0. Among them, A ij When the value of is a weight value, it means that there is an edge between node i and node j, and the weight value of the edge is ω ij , A ij The value of 0 indicates that there is no connection between node i and node j (that is, there is no edge between node i and node j).

[0115] When constructing a distribution network model, in theory, each order delivery point can be connected. In practice, in order to simplify the network model, for order delivery points in the same community or the same area, the nearest n (n can be set according to demand, such as 2 or 3) order delivery points can be selected for connection, and connections can be made between two communities or two areas, for example, the two order delivery points with the shortest distance between two areas can be selected for connection.

[0116] In an optional example, the construction module 601 constructs a distribution network model based on the order information to be delivered, which may specifically include: the construction module 601 converts the order delivery point coordinates into nodes in the distribution network model, the construction module 601 converts the connection relationship between the order delivery points into edges between the nodes, and converts the comprehensive score of the logistics delivery difficulty between the order delivery points into the weight of the edges between the nodes.

[0117] The comprehensive score of the difficulty of logistics distribution between the order delivery points can be determined based on at least one of the following influencing factors: the actual path length between the order delivery points, the road conditions between the order delivery points, the parking difficulty between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points. For example, in a specific example, the comprehensive score of the difficulty of logistics distribution between the order delivery points can be determined based on the four influencing factors of the actual path length between the order delivery points, the road conditions between the order delivery points, the parking difficulty between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points.

[0118] The clustering module 602 is used to cluster the nodes in the distribution network model to obtain a plurality of clusters; and is also used to take the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.

[0119] In an optional example, the clustering module 602 clusters the nodes in the distribution network model to obtain a plurality of clusters, specifically including: step 1 to step 3.

[0120] Step 1: The clustering module 602 determines the initial positions of K cluster center points.

[0121] In this step, the clustering module 602 can select the order delivery point set X = {x i|x i ∈R,i=1,2,...,N}, randomly select K points as cluster centers z1,z2,…,z k , these K clusters can be recorded as W1, W2, …, W k . K is an integer greater than 1. In specific implementation, the value of K can be set flexibly. For example, the value of K can be determined based on the total number of orders to be delivered and the average value of the historical regional delivery volume. In addition, after determining the initial positions of the K cluster center points, the m order delivery points closest to the cluster center point can be selected (the value of m can be set according to demand, such as 2 or 3) and connected to the cluster center point.

[0122] Step 2: The clustering module 602 calculates the distance between each node in the distribution network model and the center point of each cluster, and allocates the node to the cluster with the smallest distance to achieve cluster update.

[0123] Exemplarily, in this step, the clustering module 602 may calculate the distance between a node and a cluster center point according to the weights of each edge between the node and the cluster center point in the distribution network model and the actual path length corresponding to each edge.

[0124] In an optional implementation of this example, when there is only one connecting road between a node i and a cluster center point k (the connecting road refers to a road composed of existing edges), the clustering module 602 can calculate the distance between the node i and the cluster center point k according to the following formula:

[0125]

[0126] Among them, d ik is the distance between node i and the cluster center k, l r is the actual path length of the rth edge between node i and the cluster center k, C r is the weight of the rth edge between node i and the cluster center k. Further, when there are two or more connecting roads between a node i and a cluster center k, the clustering module 602 can calculate multiple distances through the above formula, and use the minimum value of the multiple distances as the distance between node i and the cluster center k.

[0127] After calculating the distance between each node and the center point of each cluster, the node can be assigned to the cluster with the smallest distance. For example, assuming there are three clusters a, b, and c, the distance between node 1 and the center point of cluster a is 35, the distance between node 1 and the center point of cluster b is 25, and the distance between node 1 and the center point of cluster c is 40, then node 1 is assigned to cluster b.

[0128] Step 3: The clustering module 602 calculates the position of the updated cluster center point until the iteration stop condition is met.

[0129] In this step, the clustering module 602 may calculate the location of the cluster center point according to the coordinates of each node in the updated cluster, specifically using the following formula:

[0130]

[0131] Among them, z k is the location of the cluster center, n k is the number of nodes contained in the updated cluster, x represents the cluster W k Any node in .

[0132] The iteration stop condition can be flexibly set. For example, the iteration stop condition can be set to: no nodes are assigned to different cluster centers; or the iteration stop condition can be set to: the position of the cluster center point remains unchanged or the error is less than a specified threshold.

[0133] The path planning module 603 is used to plan the order delivery path in the delivery area according to the heuristic algorithm, and return the planned order delivery path to the user terminal.

[0134] Exemplarily, the path planning module 603 performs order delivery path planning in the delivery area according to the heuristic algorithm, including: the path planning module 603 determines a candidate order delivery path in the delivery area, and calculates the total delivery cost of the candidate delivery path according to the total vehicle dispatch cost and the total driving cost corresponding to the candidate order delivery path; taking the minimum total delivery cost as the objective function, the candidate order delivery path is optimized until the planned order delivery path is obtained. In specific implementation, the path planning module 603 can use heuristic algorithms such as simulated annealing algorithm and variable domain dispersed search algorithm to plan the order delivery path.

[0135] In the device of the embodiment of the present invention, the construction module and the clustering module can dynamically and reasonably divide the delivery area according to the order information to be delivered, so that the delivery order quantity in each delivery area tends to be balanced, which helps to improve the delivery efficiency as a whole. Furthermore, the path planning module performs path planning in the divided delivery area based on the heuristic algorithm, which can further improve the delivery efficiency.

[0136] Figure 7 An exemplary system architecture 700 is shown to which the delivery planning method or delivery planning device according to the embodiment of the present invention can be applied.

[0137] like Figure 7As shown, system architecture 700 may include terminal devices 701, 702, 703, network 704 and server 705. Network 704 is used to provide a medium for communication links between terminal devices 701, 702, 703 and server 705. Network 704 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0138] The user can use the terminal devices 701, 702, 703 to interact with the server 705 through the network 704 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 701, 702, 703, such as logistics distribution management applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0139] The terminal devices 701 , 702 , and 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0140] The server 705 may be a server that provides various services, such as a background management server that provides support for logistics distribution management applications or websites browsed by users using terminal devices 701, 702, and 703. The background management server may analyze and process the received distribution planning request and other data, and feed back the processing results (such as the distribution area division results) to the terminal device.

[0141] It should be noted that the distribution planning method provided in the embodiment of the present invention is generally executed by the server 705 , and accordingly, the distribution planning device is generally arranged in the server 705 .

[0142] It should be understood that Figure 7 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0143] Reference below Figure 8 , which shows a schematic diagram of the structure of a computer system 800 of an electronic device suitable for implementing an embodiment of the present invention. Figure 8 The computer system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0144] like Figure 8As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the system 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0145] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage section 808 as needed.

[0146] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, the above-mentioned functions defined in the system of the present invention are executed.

[0147] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may 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 of computer-readable storage media may include, but are not limited to: 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 the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0148] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a building module and a clustering module. The names of these modules do not constitute a limitation on the modules themselves in some cases, for example, the building module may also be described as a "module for building a distribution network model".

[0150] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: constructing a distribution network model according to the order information to be delivered; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the delivery points of the order, and the edges between the nodes are used to represent the connection relationship between the order delivery points; clustering the nodes in the distribution network model to obtain multiple clusters; taking the range covered by each of the multiple clusters as a distribution area to obtain multiple distribution areas.

[0151] According to the technical solution of the embodiment of the present invention, a distribution network model is constructed according to the order information to be delivered, the nodes in the distribution network model are clustered to obtain multiple clusters, and the range covered by each of the multiple clusters is used as a distribution area to obtain multiple distribution areas. These steps can dynamically and reasonably divide the distribution areas according to the order information to be delivered, so that the delivery order quantity in each distribution area tends to be balanced, which helps to improve the distribution efficiency as a whole.

[0152] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distribution planning method, characterized in that: The method comprises: Constructing a distribution network model according to the order information to be delivered; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the delivery points of the order, and the edges between the nodes are used to represent the connection relationship between the order delivery points; Clustering the nodes in the distribution network model to obtain a plurality of clusters, including: determining the initial positions of K cluster center points; calculating the distance between each node in the distribution network model and each cluster center point, and assigning the node to the cluster with the smallest distance to update the cluster; calculating the position of the updated cluster center point until an iteration stop condition is met; wherein K is an integer greater than 1, which is determined based on the total amount of orders to be delivered and the average value of historical regional delivery volume; taking the range covered by each of the plurality of clusters as a delivery area to obtain a plurality of delivery areas; After obtaining multiple delivery areas, order delivery path planning is performed in the delivery area according to a heuristic algorithm, and the planned order delivery path is returned to the user terminal, including: determining a candidate order delivery path in the delivery area, and calculating the total delivery cost of the candidate order delivery path according to the total vehicle dispatching cost and the total driving cost corresponding to the candidate order delivery path; taking the minimum total delivery cost as the objective function, optimizing the candidate order delivery path until the planned order delivery path is obtained.

2. The method according to claim 1, characterized in that The construction of a distribution network model according to the order information to be distributed includes: The coordinates of the order's delivery points are converted into nodes in the distribution network model, the connection relationship between the order's delivery points is converted into edges between nodes, and the comprehensive score of the logistics delivery difficulty between the order's delivery points is converted into the weight of the edges between nodes.

3. The method according to claim 1, characterized in that: The calculating of the distance between each node and each cluster center point in the distribution network model comprises: According to the weights of the edges between a node and a cluster center in the distribution network model and the real path lengths corresponding to the edges, the distance between the node and the cluster center is calculated.

4. The method according to claim 2, characterized in that: The method includes: determining a comprehensive score of the difficulty of logistics delivery between the order delivery points based on at least one of the following influencing factors: the actual path length between the order delivery points, the road conditions between the order delivery points, the parking difficulty between the order delivery points, and the weight of the cargo on the vehicle between the order delivery points.

5. A distribution planning device, characterized in that: The distribution planning device comprises: A construction module is used to construct a distribution network model according to the order information to be delivered; wherein the distribution network model is composed of nodes and edges between nodes; the nodes are used to represent the delivery points of the order, and the edges between the nodes are used to represent the connection relationship between the order delivery points, including: determining the initial positions of K cluster centers; calculating the distance between each node and each cluster center in the distribution network model, and assigning the node to the cluster with the smallest distance to achieve cluster update; calculating the position of the updated cluster center until the iteration stop condition is met; wherein K is an integer greater than 1, which is determined based on the total number of orders to be delivered and the average value of the historical regional delivery volume; A clustering module, used for clustering the nodes in the distribution network model to obtain a plurality of clusters; and also used for taking the range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas; The path planning module is used to plan order delivery paths in the delivery areas according to a heuristic algorithm after obtaining multiple delivery areas, and return the planned order delivery paths to the user terminal, including: determining a candidate order delivery path in the delivery area, calculating the total delivery cost of the candidate order delivery path according to the total vehicle dispatching cost and the total driving cost corresponding to the candidate order delivery path; optimizing the candidate order delivery path with the minimum total delivery cost as the objective function until the planned order delivery path is obtained.

6. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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