Distribution path planning method of mobile charging pile and related device

Abstract

The embodiment of the invention discloses a distribution path planning method of a mobile charging pile and a related device. The method comprises the following steps: determining K shortest paths for K delivery vehicles to deliver mobile charging piles for V charging stations; one distribution vehicle corresponds to one shortest path; k and V are integers greater than 1; determining first constraint information according to the distribution rule; second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging pile; and according to a principle of minimum distribution cost, based on the K shortest paths, the first constraint information and the second constraint information, determining a distribution path of the K distribution vehicles for distributing the mobile charging piles for the V charging stations. According to the embodiment of the invention, personalized mobile charging pile distribution can be carried out on each charging station according to the service demand of each charging station under the condition that the distribution cost is minimum.

Description

Technical Field

[0001] The present application relates to the field of power data processing, and in particular to a distribution path planning method and related devices for a mobile charging pile. Background Art

[0002] At present, after my country implemented the "dual carbon" strategy, the demand for electric vehicle charging has grown rapidly, and the investment speed of charging facilities cannot keep up with the large-scale development of electric vehicles, and the total amount of charging facilities is insufficient. At the same time, the temporal and spatial distribution of electric vehicle charging demand is uneven, resulting in regional and periodic shortages in the charging service capacity of charging stations, and facing challenges such as low utilization and difficulty in profitability.

[0003] Mobile charging piles are convenient and easy to move. They can not only solve the shortcomings of fixed facilities such as difficult construction and maintenance, low operating income, and limited transformer capacity, but also reduce the waiting time of electric vehicles and alleviate drivers' mileage anxiety. They can even guide electric vehicles to accurately discharge to the "severely affected" distribution transformers in load-concentrated areas, thereby helping to ensure the reliable operation of the distribution network.

[0004] Domestic and foreign scholars have conducted a series of research on the path optimization of mobile charging services, but previous research objects have focused on the location deployment, energy supply, vehicle scheduling, path transfer planning, etc. of mobile charging vehicles, and there has been no discussion on the mobile scheduling of charging facilities in the form of "smart socket + mobile pile". In addition, existing studies rarely consider the impact of mobile charging pile layout and deployment plans on mobile charging service quality and service costs. For example, too few mobile charging piles cannot meet the charging needs of electric vehicle owners, while too many mobile charging piles will reduce equipment utilization and cause resource waste.

[0005] Therefore, in the research on the distribution and transfer of mobile charging piles, how to reasonably plan the distribution route to achieve low distribution cost and high utilization rate of mobile charging piles has become one of the characteristic issues of current research. Summary of the invention

[0006] The embodiments of the present application provide a distribution path planning method and related devices for mobile charging piles, which realize the reasonable and efficient distribution and transfer of mobile charging piles with minimum distribution cost.

[0007] On the one hand, an embodiment of the present application provides a method for planning a delivery path of a mobile charging pile, including:

[0008] Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0009] Determine first constraint information according to the delivery rule;

[0010] Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0011] According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

[0012] On the one hand, an embodiment of the present application provides a distribution path planning device for a mobile charging pile, comprising:

[0013] A determination unit, used to determine K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0014] The determining unit is further used to determine the first constraint information according to the delivery rule;

[0015] A calculation unit, configured to calculate second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0016] The determination unit is further used to determine, according to the principle of minimizing the delivery cost, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the K shortest paths, the first constraint information and the second constraint information.

[0017] On the one hand, an embodiment of the present application provides an electronic device, including:

[0018] A processor adapted to implement one or more computer programs; a computer storage medium storing one or more computer programs, wherein the one or more computer programs are adapted to be loaded and executed by the processor:

[0019] Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0020] Determine first constraint information according to the delivery rule;

[0021] Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0022] According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

[0023] In one aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor of a data processing device, the computer program is used to perform:

[0024] Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0025] Determine first constraint information according to the delivery rule;

[0026] Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0027] According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

[0028] On the one hand, an embodiment of the present application provides a computer program product or a computer program, wherein the computer program product includes a computer program, which may refer to a computer program, and the computer program is stored in a computer storage medium; a processor of an electronic device reads the computer program from the computer storage medium, and the processor executes the computer program, so that the electronic device executes the above-mentioned delivery path planning method for mobile charging piles.

[0029] In the embodiment of the present application, when designing a distribution path for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rule, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, the distribution path for K distribution vehicles to distribute mobile charging piles to the V charging stations is determined based on the K shortest paths, the first constraint information and the second constraint information. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution and transfer of mobile charging piles can be achieved while minimizing the distribution cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 It is a flow chart of a method for planning a distribution path of a mobile charging pile provided in an embodiment of the present application;

[0032] Figure 2 The embodiment of the present application provides a road network topology diagram;

[0033] Figure 3 is a delivery path of a delivery vehicle provided in an embodiment of the present application;

[0034] Figure 4 is a delivery path of another delivery vehicle provided in an embodiment of the present application;

[0035] Figure 5 It is a structural schematic diagram of a distribution path planning device for a mobile charging pile provided in an embodiment of the present application;

[0036] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. It should be noted that in the following description of the present application, all information related to the object in the social application, such as avatar identification, virtual image and name identification, etc., are obtained with the permission of the corresponding object.

[0038] At present, after the implementation of the "dual carbon" strategy, the demand for electric vehicle charging in my country has grown rapidly, and the speed of investment in charging facilities has been unable to keep up with the large-scale development of electric vehicles, and the total amount of charging facilities is insufficient. At the same time, the temporal and spatial distribution of electric vehicle charging demand is uneven, resulting in regional and periodic shortages in the charging service capacity of charging stations, and facing challenges such as low utilization and difficulty in profitability. The construction of fixed charging facilities is even more restricted by the power infrastructure, land area, and number of parking spaces in the region, and there is an obvious problem of "difficulty in expansion". Mobile charging piles are convenient and easy to move, which can not only solve the shortcomings of difficult construction and maintenance of fixed facilities, small operating benefits, and limited transformer capacity, but also reduce the waiting time of electric vehicles in queues, reduce drivers' mileage anxiety, and even guide electric vehicles to accurately discharge to the "severely affected" distribution transformers in load concentration areas, helping to ensure the reliable operation of the distribution network.

[0039] There are two main types of mobile charging piles at home and abroad: one is the mobile charging service provided by mobile emergency charging vehicles, which consists of vehicles, energy storage battery packs, converters, control cabinets, power supply interface equipment, chargers, fire protection systems, and auxiliary systems such as air conditioning, security, lighting, and reversing images (key typical literature is given); the other is the "smart socket + mobile charging pile" type, where the smart socket has a standardized power supply interface, can adapt to multiple mobile charging piles, and flexibly regulate the charging load. A power company of the State Grid has put 44 units into operation in 18 highway service areas, contributing 20.1% of the charging volume of highway service areas during the National Day holiday. The average daily charging volume of a single mobile charging pile is close to that of a fixed charging pile. Domestic and foreign scholars have conducted many studies on the scheduling of mobile charging piles.

[0040] In terms of the location deployment of mobile charging piles, there are certain similarities with the site selection problem of mobile service facilities (service facilities with the ability to move from one place to another, such as ambulances, mobile food trucks, mobile charging vehicles, etc.). The goal of this type of problem is usually to maximize the service scope to cover more needs. For example, in terms of the location deployment of ambulances, ZhenLu and Lam et al. established a corresponding mathematical model for location relocation and used simulation to verify it. In terms of the location deployment of mobile charging vehicles, ChenHuwei et al. established a multi-objective optimization model by comprehensively considering user needs and the operating costs of mobile charging vehicles, and optimized the location layout of mobile charging vehicles using the chaotic evolution particle swarm algorithm (CEPSO). In terms of the location deployment of battery swap infrastructure construction, Mak et al. took into account the uncertainty of cost and demand, and established two distributed robust optimization models driven by cost optimality and target return on investment.

[0041] In terms of energy replenishment for mobile charging services, Huang Shisheng et al. comprehensively considered the two service modes of mobile charging vehicles and mobile battery exchange stations, proposed a NJN (i.e., selecting the charging order closest to the service vehicle for service first) service rule, used queuing theory to determine the appropriate range of design parameters for such mobile charging systems, and studied the economic configuration of mobile charging battery capacity. Tian Wenqi et al. used the battery exchange mode as the energy replenishment method for mobile charging vehicles, established a multi-objective scheduling model, and used the improved adaptive mutation particle swarm algorithm to solve it, and obtained the optimal charging plan for the next day.

[0042] In terms of path optimization of mobile charging vehicles, Liu Yujun et al. predicted the time and space demand based on the travel chain theory, and studied the allocation method of mobile charging vehicles in detail based on the prediction results. Finally, some management inspirations were given by analyzing simulation examples. Atmaja et al. mainly positioned the function of mobile charging vehicles to provide emergency assistance for overloaded charging stations or electric vehicles with urgent needs. Based on this, mobile charging vehicles were divided into two categories: one is to assist overloaded fixed charging stations without energy storage function, and the other is to charge electric vehicles with urgent charging needs without access to the power grid with energy storage function. An algorithm was developed to optimize the scheduling of these two types of mobile charging vehicles, which helps to reduce the waiting time of each electric vehicle in the queue service of fixed charging piles. Shun Neng Yang et al. used this type of mobile charging vehicle equipped with fast charging poles for reinforcement to reduce the overload burden of fixed charging stations, and constructed a dispatch algorithm for mobile charging vehicles, which effectively reduced the waiting time of car owners in the queue at fixed charging stations.

[0043] In terms of vehicle path transfer based on dynamic demand, existing research can be divided into two categories according to the characteristics of demand: without time window constraints and with time window constraints. At the same time, path transfer based on dynamic demand is divided into two scenarios: without considering future random demand information and considering future random demand information. The former mainly adopts heuristic algorithms to search and update path plans, and judges in real time whether the current optimal plan can be inserted when new demand occurs. The latter mostly reflects the consideration of future random demand in waiting strategies. For example, Ichoua et al. use the probabilistic knowledge of future requests to determine whether the vehicle is waiting in the current area. Barkaoui integrates future customer demand information into dynamic vehicle path decision-making, and mainly uses a co-evolutionary method to generate better waiting strategies in order to maximize the expected number of customers served.

[0044] Domestic and foreign scholars have carried out a series of research work on the path optimization of mobile charging services, but the previous research objects have focused on the location deployment, energy supply, vehicle scheduling, path transfer planning and other aspects of mobile charging vehicles, and there has been no discussion on the mobile scheduling of charging facilities in the form of "smart socket + mobile pile". In addition, existing studies rarely consider the impact of mobile charging pile layout and deployment plan on mobile charging service quality and service cost. For example, too few mobile charging piles cannot meet the charging needs of electric vehicle owners, while too many mobile charging piles will reduce equipment utilization and cause resource waste; in addition, the specific operation mode and path planning algorithm of mobile charging piles will also affect the operation efficiency of mobile charging service system. By reasonably planning the charging service path of mobile charging and discharging vehicles, as well as the charging process and driving path of electric vehicles, it is possible to meet the travel needs of users with less deployment cost, improve the utilization rate of mobile charging piles, improve the operating income of mobile charging pile operators, and achieve the purpose of reducing the impact of charging load on the power grid, assisting in the realization of green electricity consumption, improving charging service quality, and promoting the development of electric vehicles.

[0045] Based on the above problems, the present application provides a distribution path planning method for mobile charging piles. When designing distribution paths for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rules, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, based on the K shortest paths, the first constraint information and the second constraint information, the distribution paths for K distribution vehicles to distribute mobile charging piles to the V charging stations are determined. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution of mobile charging piles is achieved while minimizing distribution costs.

[0046] The delivery route planning method for a mobile charging pile provided in an embodiment of the present application can be executed by an electronic device, which may include a terminal or a server, wherein the terminal may include terminal devices such as mobile phones, laptops, vehicle-mounted terminals, smart wearable devices, etc., and the server may refer to an independent physical server, a server cluster composed of multiple servers, or a cloud server capable of cloud computing.

[0047] It should be noted that, unless otherwise specified, the distribution route planning method for mobile charging piles provided in the embodiment of the present application is implemented based on the following assumptions: in the area composed of V charging stations, each site (that is, each charging station) can provide mobile charging piles or generate demand for mobile charging piles, and based on the prediction of charging demand in the area and the number of mobile charging piles required and supplied by each charging station, the source and destination of the mobile charging piles are both fixed charging stations in the area, and the activities of picking up and delivering mobile charging piles by delivery vehicles can be carried out alternately, but ultimately the needs of each charging station in the entire area must be met. At the same time, the service center and each charging station are subject to time window restrictions. The time window of the service center is assumed to be [a0, b0], indicating that the delivery vehicle cannot leave before a0 or return after b0. Charging station i (any one of the V charging stations, i is an integer greater than or equal to 1 and less than or equal to V) also has a pre-set service time range, also called a time window, assumed to be expressed as [a i , b i ], a i Defines the earliest allowed service start time of charging station i, b i The latest allowed service start time of charging station i is defined, and the time window is defined as a hard time window. If the delivery vehicle arrives at charging station i earlier than a i , then the delivery vehicle needs to wait at charging station i. If the delivery vehicle arrives at charging station i later than b i , then it is not a feasible solution.

[0048] The embodiment of the present application attempts to establish a delivery route for K delivery vehicles to deliver mobile charging piles to the V charging stations based on the service demand information of V charging stations in the area, including the demand for mobile charging piles and the time window. The K delivery vehicles start from the service center, pick up and deliver mobile charging piles to several charging stations, and finally all return to the service center, so that the above conditions are met. The delivery route of the K delivery vehicles is planned to minimize the total delivery route and meet the following requirements: the demand of each charging station must be met, and can only be delivered by one delivery vehicle; each delivery vehicle can only serve one delivery route, and the starting and ending points of the delivery vehicle are both the service center; the cargo capacity of the delivery vehicle is met.

[0049] Based on the above assumptions, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 , which is a flow chart of a method for planning a delivery route for a mobile charging pile provided in an embodiment of the present application. Figure 1 The determination method shown may be executed by an electronic device, and specifically may be executed by a processor of the electronic device. Figure 1 The distribution path planning method of the mobile charging pile shown may include the following steps:

[0051] Step S101, determining K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0052] Wherein, K and V are both integers greater than 1, and one delivery vehicle corresponds to one shortest path. In other words, a shortest path is determined for each delivery vehicle.

[0053] In one embodiment, the implementation of step S101 may include: constructing a road network topology map, the road network topology map includes nodes and connecting edges, the nodes include nodes corresponding to the service center, nodes corresponding to V charging stations, and road network nodes; there is a path between two nodes with connecting edges; based on the road network topology map and the shortest path calculation rules, determine the shortest path for each delivery vehicle.

[0054] The shortest path calculation rule may be the F1oyd shortest path algorithm. In a specific implementation, an adjacency matrix of road weights may be generated based on a road network topology map; and then the shortest path is determined for each delivery vehicle based on the adjacency matrix and the shortest path calculation rule.

[0055] The road network topology can be represented by G(V, E), where E is the set of connected edges (also called edge set), and V is the set of vertices (vertices can also be called nodes). Assuming there are n nodes, V can be represented as {1, 2, ..., n}. Assuming that all roads are two-way roads, the matrix W can be used to represent the adjacency matrix of road weights, which is used to describe the length of each road section and the connection relationship between nodes. The element w in W is ij The value of is shown in the following formula:

[0056]

[0057] In the above formula, l ij represents the length of the road section between node i and node j, and inf means that there is no direct connection between node i and node j (or the two nodes are not adjacent).

[0058] Taking nodes i and j as examples, the algorithm principle of F1oyd's shortest path algorithm is introduced in detail. Suppose the shortest path between two nodes i and j is l min , there are multiple nodes v1, v2, v3, v4, ..., v on a path between node i and node j. n , let the distance from node i to node v1 be l1. The distance from node v1 to node v2 is l2, and so on. n The distance to node j is l n+1 , if there is another path between two nodes i and j, the path has multiple nodes which can be represented as v′1, v′2, v′3, v′4, .., v′ n, the corresponding distances can be expressed as l′1, l′2, l′3, l′4, ..., l′ n+1 , and satisfies the following formula:

[0059]

[0060] Then determine the shortest path l between node i and node j min It is expressed as the following formula:

[0061]

[0062] Step S102: Determine first constraint information according to the delivery rule.

[0063] Among them, the distribution rules refer to some requirements used to regulate or constrain K distribution vehicles to distribute mobile charging piles to V charging stations. For example, the distribution rules include that the service center can provide K distribution vehicles to provide mobile charging pile distribution services for V charging stations, where the K distribution vehicles start from and where they return to; for example, the distribution rules can also include that a distribution vehicle can provide services for several charging stations, a charging station can be provided by several distribution vehicles, and what is the maximum load of the distribution vehicle during the entire service process, etc. In actual applications, the distribution rules can be determined according to the requirements of different application scenarios for distribution vehicles and other distribution details.

[0064] In one embodiment, the distribution rules may include flow requirements for distribution services. Specifically, the flow requirements may mean that a distribution vehicle can provide services for several charging stations, and one charging station can be served by several distribution vehicles, etc. Based on this, the first constraint information determined according to the distribution rules may include flow constraints. The flow constraint is used to constrain each distribution vehicle to distribute mobile charging piles to multiple charging stations, but one charging pile can only be served by one distribution vehicle, and the nodes that a distribution vehicle enters and leaves must be the same. The nodes mentioned here include service centers and charging stations. In other words, if a distribution vehicle enters a charging station, the distribution vehicle must also leave the charging station.

[0065] Optionally, the above flow constraint can be expressed by the following formula:

[0066]

[0067] Among them, x ijk is the driving variable of delivery vehicle k from charging station i to charging station j, with a value of 0 or 1, V represents the set of V charging stations, i and j represent charging station i and charging station j in V charging stations, i and j are both greater than or equal to 1 and less than or equal to V. K represents K delivery vehicles, k represents delivery vehicle k, k is greater than or equal to and less than or equal to K.

[0068] If there is a path for delivery vehicle k to travel from charging station i to charging station j, then x ijk The value of is 1, otherwise it is 0. jik The driving variable representing the delivery vehicle k driving from charging station j to charging station i can be inferred based on the following similar expressions and will not be described in detail.

[0069] In another embodiment, the delivery rule may include requirements for the starting point and the end point of the delivery vehicle. Based on this, the first constraint information may include a route constraint, and the route constraint is used to constrain the node where each delivery vehicle starts and the node where it finally returns to to be a service center. Optionally, the route constraint may be expressed by the following formula:

[0070]

[0071] Among them, V0 represents the node set, which includes the service center and V charging stations, x 0jk represents the driving variable of the delivery vehicle from the service center to the charging station j.

[0072] In another embodiment, the distribution rules may include a load requirement for the distribution vehicle during the service process. Based on this, the first constraint information may also include a vehicle load constraint. The vehicle load constraint is used to constrain that at any time, the load of each distribution vehicle should be less than or equal to the maximum load value that the corresponding distribution vehicle can bear. In simple terms, the vehicle load constraint is used to constrain each distribution vehicle from being overloaded during the service process. Optionally, the vehicle load constraint can be expressed by the following formula:

[0073]

[0074] Among them, D i represents the number of mobile charging piles required by charging station i, Q k Represents the maximum load value of delivery vehicle k.

[0075] It should be noted that the first constraint information may include any one or more of the above-mentioned flow constraints, route constraints and vehicle load constraints.

[0076] Step S103: Calculate second constraint information based on the service demand information of each of the V charging stations regarding the mobile charging pile.

[0077] In one embodiment, the service demand information of each charging station may include the time range for providing services to the charging station, that is, as mentioned in the above assumptions, the hard time window corresponding to each charging station. The second constraint information may include the service time constraint of the charging station, and the service time constraint of the charging station is used to indicate that each charging station corresponds to a service time range, and the delivery vehicle that delivers the mobile charging pile to each charging station must arrive within the service time range of the corresponding charging station. Optionally, for each charging station, the service time constraint can be determined by the following steps:

[0078] Step 1031: Determine the delivery vehicle k serving the charging station, the start time of serving the charging station, the travel time of the delivery vehicle k from the previous node of the charging station to the charging station, and determine the service time required for the delivery vehicle k to serve the charging station;

[0079] Step 1032: Perform service time constraint calculation based on the service start time, the travel time, the service time, and the service time range corresponding to the charging station to obtain the service time constraint corresponding to the charging station.

[0080] Among them, performing service time constraint calculation can refer to pre-setting a formula with service time constraints, substituting the start service time, driving time, service time and the service time range corresponding to the charging station into the formula of the service time constraint for calculation, and then the service time constraint corresponding to the charging station can be obtained.

[0081] Assume that for charging station i, the start time of delivery vehicle k serving charging station i is determined to be w ik , if the delivery vehicle does not serve charging station i, then the value is 0. The driving time of delivery vehicle k from the previous node of charging station i (the previous node can be represented by j, which can refer to a charging station or a service center) to charging station node i is represented by t ij , si represents the service time of delivery vehicle k serving charging station i, and the left time window in the time range corresponding to the charging station can be expressed as a i , the right time window can be expressed as b i , then the service time constraint of charging station i can be expressed by the following formula:

[0082]

[0083] Among them, w jk represents the starting service time when the delivery vehicle starts serving node j, a0 represents the left time window in the service time range corresponding to the distribution center, b0 represents the right time window in the service time range corresponding to the distribution center, and M is an arbitrary number that is large enough.

[0084] In another embodiment, the service demand information of each charging station includes the supply and demand quantity of mobile charging piles of the charging station, and the second constraint information also includes the load constraints of the delivery vehicles at each node, and the load constraints are the cargo capacity constraints of the K delivery vehicles in the K shortest paths. Optionally, for each charging station, the cargo capacity constraints of the K delivery vehicles in driving can be determined based on the demand data of the charging station for mobile charging piles, the number of mobile charging piles obtained by the charging station from the delivery vehicles, and the driving variables corresponding to the K delivery vehicles.

[0085] It should be noted that if the supply and demand quantity is a positive number, it indicates the number of mobile charging piles that the charging station needs to provide, and if the supply and demand quantity is negative, it indicates the number of mobile charging piles that the charging station can provide. Assume that the number of mobile charging piles that charging station j obtains from the delivery vehicle can be expressed as P j , L j represents the cargo capacity of the delivery vehicle after serving charging station j. Similarly, L i It represents the cargo capacity of the delivery vehicle after serving charging station i. Similar expressions can be deduced by analogy and will not be repeated here. The cargo capacity constraint of K delivery vehicles during driving can be expressed by the following formula:

[0086]

[0087] In addition, the second constraint information may also include a load constraint on the delivery vehicle when it departs from the service center, which may also become an initial load constraint, and may be expressed as:

[0088]

[0089] Among them, L 0k It represents the cargo capacity of the delivery vehicle k when it leaves the service center, assuming that the charging station j is the first charging station served by the delivery vehicle k after leaving the service center. Yes, the second constraint information may also include: the cargo capacity constraint after the delivery vehicle k provides service for the first charging station j, which can be expressed by the following formula:

[0090]

[0091] In addition, the second constraint information may also include a constraint that the load of the delivery vehicle k must not exceed its maximum load value. As follows:

[0092]

[0093] From the above, it can be seen that by considering the supply and demand of mobile charging piles at each charging station, as well as the service time requirements of each charging station, the second constraint information for the delivery vehicle is determined, so that after the delivery route is subsequently determined, each charging station can be accurately provided with the required number of mobile charging piles and provide services within the specified time content. In this way, the service center can provide charging stations with timely and personalized mobile charging pile delivery services.

[0094] Step S104: According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, determine the delivery paths for the K delivery vehicles to deliver the mobile charging piles to the V charging stations.

[0095] In one embodiment, step S104 may include the following steps: calculating the travel variables between every two nodes, and determining the shortest path value between every two nodes based on K shortest paths; multiplying the travel variables, shortest path values ​​and path costs of every two nodes to obtain a calculation function for the travel cost; based on the first constraint information and the second constraint information, solving the calculation function according to the principle of minimizing the travel cost, and obtaining a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0096] Assuming two nodes, the shortest path value between node i and node j (nodes can include service centers and V charging stations) can be expressed as d ij , the path cost is denoted as c ij , then the calculation function of the travel cost can be expressed as Z:

[0097]

[0098] By taking the various constraints included in the first constraint information and the second constraint information as constraint conditions and solving them according to the principle of minimizing the calculation function, a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations can be obtained. The delivery path may include which delivery vehicle delivers mobile charging piles to which charging stations in what order.

[0099] In the embodiment of the present application, when designing a distribution path for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rule, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, the distribution path for K distribution vehicles to distribute mobile charging piles to the V charging stations is determined based on the K shortest paths, the first constraint information and the second constraint information. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution and transfer of mobile charging piles can be achieved while minimizing the distribution cost.

[0100] To facilitate understanding of the aforementioned delivery route planning method, an example is given below to assist in explanation. Assume that a city with 29 nodes, including a service center, 7 charging stations, other nodes are road network nodes, and 49 roads, is a part of the main roads. The road network topology under this assumption can be as follows: Figure 3 The distribution path rules of mobile charging piles in this area on a typical working day are simulated and analyzed. Figure 3 In the road network topology diagram shown, node 24 is the distribution vehicle collection storage point, that is, the aforementioned service center, and nodes 1, 6, 8, 13, 17, 22, and 25 represent charging stations, that is, there are 7 charging stations. The dispatchable amount of mobile charging piles for these 7 charging stations in a scheduling cycle (that is, the supply and demand quantity mentioned above) can be represented by D, D = {d1, d2, d3, d4, d5, d6, d7}, which is known. The subscript of d represents the corresponding charging station. d greater than 0 indicates the number of mobile charging piles that a charging station needs to be provided, and d less than 0 indicates the number of mobile charging piles that a charging station can provide. There are 7 charging stations and a service center. The supply and demand quantity, service time, and time window of each charging station in a certain scheduling cycle T can be shown in the following Table 1.

[0101] Table 1

[0102] Node Number 1 6 8 13 17 22 25 Supply and Demand -5 3 -3 -2 -1 4 4 Service Hours 10 6 6 4 2 8 8 Left time window 500 568 566 600 580 652 680 Right time window 515 590 576 610 595 662 690

[0103] Assume that the delivery vehicle drives at a speed of 60 kilometers per hour and the driving cost is 1 yuan per kilometer. A delivery vehicle can carry up to 6 mobile charging piles at a time, that is, the maximum cargo value of a delivery vehicle is 6.

[0104] Assuming that there are two delivery vehicles, the delivery path planning method for the mobile charging pile provided in the embodiment of the present application is used for calculation, and the delivery path for the two delivery vehicles to deliver the mobile charging piles to the seven charging stations can be determined as shown in Table 2:

[0105] Table 2

[0106]

[0107] Through the above solution and after determining the charging station that each delivery vehicle needs to serve, the delivery route of delivery vehicle 1 is determined according to the road network topology:

[0108] 24->9->6->5->2->1->2->3->6->10->13->14->20->25->24;

[0109] The delivery path of delivery vehicle 2 is:

[0110] 24->9->8->23->26->28->17->18->27->22->23->24

[0111] The time window of each charging station can be represented by a time range, the supply and demand quantity can be represented by D (it can also be represented by the demand quantity), and the vehicle weight can be represented by L. In the road network topology diagram, the time window of each charging station, the demand quantity and the vehicle load of the delivery vehicle, the schematic diagrams of the two delivery routes of the two delivery vehicles are as follows: Figure 3 and Figure 4 shown.

[0112] Through the simulation of specific examples, it can be seen that the time for each delivery vehicle to visit the charging station in the delivery path of the mobile charging pile solved by the embodiment of the present application meets the requirements of each,

[0113] The time window constraints corresponding to each charging station are met, and the supply and demand quantity of mobile charging piles at each charging station are met. At the same time, the embodiment of the present application takes into account that the movement of the delivery vehicle between each node will pass through many road network nodes, and adopts the shortest path optimization method to obtain the shortest path value between each station. Therefore, the path planning method provided in the embodiment of the present application can effectively realize the optimal transfer of mobile charging piles between various charging stations.

[0114] Based on the above-mentioned mobile charging pile delivery path planning method embodiment, the present application embodiment provides a mobile charging pile delivery path planning device, see Figure 5 , which is a schematic diagram of a distribution path planning device for a mobile charging pile provided in an embodiment of the present application. Figure 5 The distribution path planning device of the mobile charging pile shown can run the following units:

[0115] A determination unit 501 is used to determine K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0116] The determining unit 501 is further used to determine the first constraint information according to the delivery rule;

[0117] A calculation unit 502, configured to calculate second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0118] The determination unit 501 is further configured to determine, according to the principle of minimizing the delivery cost, a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations based on the K shortest paths, the first constraint information, and the second constraint information.

[0119] In one embodiment, the first constraint information includes any one or more of a flow constraint, a route constraint, and a vehicle load constraint; the flow constraint is used to constrain each delivery vehicle to deliver mobile charging piles to multiple charging stations, but a charging station can only be served by one delivery vehicle, and a delivery vehicle must enter and leave the same node, and the node includes a charging station and a service center;

[0120] The route constraint is used to constrain the starting node and the final return node of each delivery vehicle to be the service center;

[0121] The vehicle load constraint is used to constrain the load of each delivery vehicle to be less than or equal to the maximum load value that the corresponding delivery vehicle can bear at any time.

[0122] In one embodiment, the service demand information of each charging station includes a time range for providing services to the charging station; the second constraint information includes a service time constraint of the charging station, and the service time constraint of the charging station is used to indicate that each charging station corresponds to a service time range, and a delivery vehicle that delivers a mobile charging pile to each charging station must arrive within the service time range of the corresponding charging station;

[0123] The determining unit 501 is further configured to perform the following steps for each charging station:

[0124] Determine the delivery vehicle k serving the charging station, the start time of serving the charging station, the travel time of the delivery vehicle k from the previous node of the charging station to the charging station, and determine the service time required for the delivery vehicle k to serve the charging station; k is an integer greater than 1 and less than or equal to K;

[0125] A service time constraint calculation is performed based on the service start time, the travel time, the service time, and the service time range corresponding to the charging station to obtain the service time constraint corresponding to the charging station.

[0126] In one embodiment, the service demand information of each charging station includes the supply and demand quantity of the mobile charging piles of the charging station; the second constraint information also includes the load constraint of the delivery vehicle at each node, and the load constraint is the load constraint of K delivery vehicles in the travel of K shortest paths; the determination unit 501 is also used to:

[0127] For each charging station, the cargo capacity constraints of the K delivery vehicles during driving are determined according to the supply and demand quantity of the mobile charging piles at the charging station, the number of mobile charging piles obtained by the charging station from the delivery vehicles, and the driving variables corresponding to the K delivery vehicles.

[0128] In one embodiment, when determining K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations, the determining unit 501 performs the following steps:

[0129] Constructing a road network topology graph, wherein the road network topology graph includes nodes and connecting edges, wherein the nodes include nodes corresponding to the service center, nodes corresponding to the V charging stations, and road network nodes; there are paths between nodes with connecting edges;

[0130] Based on the road network topology and the shortest path calculation rules, the shortest path is determined for each delivery vehicle.

[0131] In one embodiment, when determining the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the K shortest paths, the first constraint information, and the second constraint information, the determining unit 501 performs the following steps:

[0132] Calculate the travel variable between every two nodes, and determine the shortest path value between every two nodes based on K shortest paths;

[0133] The travel variable, the shortest path value and the path cost of each two nodes are multiplied to obtain the calculation function of the travel cost;

[0134] Based on the first constraint information and the second constraint information, the calculation function is solved according to the principle of minimizing the driving cost to obtain a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0135] In the embodiment of the present application, when designing a distribution path for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rule, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, the distribution path for K distribution vehicles to distribute mobile charging piles to the V charging stations is determined based on the K shortest paths, the first constraint information and the second constraint information. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution and transfer of mobile charging piles can be achieved while minimizing the distribution cost.

[0136] Based on the above-mentioned mobile charging pile delivery path planning method and mobile charging pile delivery path planning device embodiment, the present application also provides an electronic device, see Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 The electronic device shown may include a processor 401, an input interface 402, an output interface 403, and a computer storage medium 404. The processor 401, the input interface 402, the output interface 403, and the computer storage medium 404 may be connected via a bus or other means.

[0137] The computer storage medium 404 may be stored in the memory of the electronic device, the computer storage medium 404 is used to store computer programs, and the processor 401 is used to execute the computer programs stored in the computer storage medium 404. The processor 401 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more computer programs, specifically suitable for loading and executing:

[0138] Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0139] Determine first constraint information according to the delivery rule;

[0140] Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0141] According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

[0142] In the embodiment of the present application, when designing a distribution path for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rule, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, the distribution path for K distribution vehicles to distribute mobile charging piles to the V charging stations is determined based on the K shortest paths, the first constraint information and the second constraint information. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution and transfer of mobile charging piles can be achieved while minimizing the distribution cost.

[0143] The embodiment of the present application also provides a computer storage medium (Memory), which is a memory device of an electronic device for storing programs and data. It is understandable that the computer storage medium here can include both the built-in storage medium of the electronic device and the extended storage medium supported by the electronic device. The computer storage medium provides a storage space, which stores the operating system of the electronic device. In addition, one or more computer programs suitable for being loaded and executed by the processor 401 are also stored in the storage space. It should be noted that the computer storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk storage; optionally, it can also be at least one computer storage medium located away from the aforementioned processor.

[0144] In one embodiment, one or more computer programs stored in the computer storage medium may be loaded and executed by the processor 401:

[0145] Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1;

[0146] Determine first constraint information according to the delivery rule;

[0147] Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations;

[0148] According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

[0149] In one embodiment, the first constraint information includes any one or more of a flow constraint, a route constraint, and a vehicle load constraint;

[0150] The flow constraint is used to constrain each delivery vehicle to deliver mobile charging piles to multiple charging stations, but one charging station can only be served by one delivery vehicle, and the nodes entered and left by a delivery vehicle must be the same, and the nodes include charging stations and service centers;

[0151] The route constraint is used to constrain the starting node and the final return node of each delivery vehicle to be the service center;

[0152] The vehicle load constraint is used to constrain the load of each delivery vehicle to be less than or equal to the maximum load value that the corresponding delivery vehicle can bear at any time.

[0153] In one embodiment, the service demand information of each charging station includes a time range for providing services to the charging station; the second constraint information includes a service time constraint of the charging station, and the service time constraint of the charging station is used to indicate that each charging station corresponds to a service time range, and a delivery vehicle that delivers a mobile charging pile to each charging station must arrive within the service time range of the corresponding charging station; for each charging station, the service time constraint is determined by the following steps:

[0154] Determine the delivery vehicle k serving the charging station, the start time of serving the charging station, the travel time of the delivery vehicle k from the previous node of the charging station to the charging station, and determine the service time required for the delivery vehicle k to serve the charging station; k is an integer greater than 1 and less than or equal to K;

[0155] A service time constraint calculation is performed based on the service start time, the travel time, the service time, and the service time range corresponding to the charging station to obtain the service time constraint corresponding to the charging station.

[0156] In one embodiment, the service demand information of each charging station includes the supply and demand quantity of the mobile charging piles by the charging station; the second constraint information also includes the load constraint of the delivery vehicle at each node, and the load constraint is the load constraint of K delivery vehicles in the travel of K shortest paths; the processor 401 is also used to:

[0157] For each charging station, the cargo capacity constraints of the K delivery vehicles during driving are determined according to the supply and demand quantity of the mobile charging piles at the charging station, the number of mobile charging piles obtained by the charging station from the delivery vehicles, and the driving variables corresponding to the K delivery vehicles.

[0158] In one embodiment, when determining K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations, the processor 401 performs the following steps:

[0159] Constructing a road network topology graph, wherein the road network topology graph includes nodes and connecting edges, wherein the nodes include nodes corresponding to the service center, nodes corresponding to the V charging stations, and road network nodes; there are paths between nodes with connecting edges;

[0160] Based on the road network topology and the shortest path calculation rules, the shortest path is determined for each delivery vehicle.

[0161] In one embodiment, when the processor 401 determines the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the K shortest paths, the first constraint information, and the second constraint information, the processor 401 performs the following steps:

[0162] Calculate the travel variable between every two nodes, and determine the shortest path value between every two nodes based on K shortest paths;

[0163] The travel variable, the shortest path value and the path cost of each two nodes are multiplied to obtain the calculation function of the travel cost;

[0164] Based on the first constraint information and the second constraint information, the calculation function is solved according to the principle of minimizing the driving cost to obtain a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0165] In the embodiment of the present application, when designing a distribution path for V charging stations, the K shortest paths for K distribution vehicles to distribute mobile charging piles to the V charging stations can be calculated first. Further, the first constraint information is determined according to the distribution rule, and the second constraint information is calculated based on the service demand information of each charging station in the V charging stations about the mobile charging piles. Then, according to the principle of minimum distribution cost, the distribution path for K distribution vehicles to distribute mobile charging piles to the V charging stations is determined based on the K shortest paths, the first constraint information and the second constraint information. It can be seen from the above method that when determining the distribution path, not only the distribution cost is considered, but also the service demand information of each charging station for the mobile charging pile is considered. It can be achieved that while ensuring the lowest distribution cost, mobile charging piles that meet its service demand information can be distributed to each charging station, that is, personalized mobile charging piles can be distributed to each charging station. In this way, reasonable and efficient distribution and transfer of mobile charging piles can be achieved while minimizing the distribution cost.

Claims

1. A distribution path planning method for a mobile charging pile, characterized in that: include: Determine the K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1; Determine first constraint information according to the delivery rule; Calculating second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations; According to the principle of minimizing the delivery cost, based on the K shortest paths, the first constraint information and the second constraint information, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

2. The method according to claim 1, characterized in that The first constraint information includes any one or more of a flow constraint, a route constraint, and a vehicle load constraint; The flow constraint is used to constrain each delivery vehicle to deliver mobile charging piles to multiple charging stations, but one charging station can only be served by one delivery vehicle, and the nodes entered and left by a delivery vehicle must be the same, and the nodes include charging stations and service centers; The route constraint is used to constrain the starting node and the final return node of each delivery vehicle to be the service center; The vehicle load constraint is used to constrain the load of each delivery vehicle to be less than or equal to the maximum load value that the corresponding delivery vehicle can bear at any time.

3. The method according to claim 2, characterized in that The service demand information of each charging station includes a time range for providing services to the charging station; the second constraint information includes a service time constraint of the charging station, and the service time constraint of the charging station is used to indicate that each charging station corresponds to a service time range, and a delivery vehicle for delivering a mobile charging pile to each charging station must arrive within the service time range of the corresponding charging station; For each charging station, the service time constraint is determined by the following steps: Determine the delivery vehicle k serving the charging station, the start time of serving the charging station, the travel time of the delivery vehicle k from the previous node of the charging station to the charging station, and determine the service time required for the delivery vehicle k to serve the charging station; k is an integer greater than 1 and less than or equal to K; A service time constraint calculation is performed based on the service start time, the travel time, the service time, and the service time range corresponding to the charging station to obtain the service time constraint corresponding to the charging station.

4. The method according to claim 3, characterized in that The service demand information of each charging station includes the supply and demand quantity of the mobile charging piles of the charging station; the second constraint information also includes the load constraint of the delivery vehicle at each node, and the load constraint is the load constraint of K delivery vehicles in the driving of K shortest paths; the method also includes: For each charging station, the cargo capacity constraints of the K delivery vehicles during driving are determined according to the supply and demand quantity of the mobile charging piles at the charging station, the number of mobile charging piles obtained by the charging station from the delivery vehicles, and the driving variables corresponding to the K delivery vehicles.

5. The method according to claim 1, characterized in that The step of determining K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations includes: Constructing a road network topology graph, wherein the road network topology graph includes nodes and connecting edges, wherein the nodes include nodes corresponding to the service center, nodes corresponding to the V charging stations, and road network nodes; there are paths between nodes with connecting edges; Based on the road network topology and the shortest path calculation rules, the shortest path is determined for each delivery vehicle.

6. The method according to claim 1, wherein determining, based on the K shortest paths, the first constraint information and the second constraint information, a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations comprises: Calculate the travel variable between every two nodes, and determine the shortest path value between every two nodes based on K shortest paths; The travel variable, the shortest path value and the path cost of each two nodes are multiplied to obtain the calculation function of the travel cost; Based on the first constraint information and the second constraint information, the calculation function is solved according to the principle of minimizing the driving cost to obtain a delivery path for K delivery vehicles to deliver mobile charging piles to V charging stations.

7. A distribution path planning device for a mobile charging pile, characterized in that: include: A determination unit, used to determine K shortest paths for K delivery vehicles to deliver mobile charging piles to V charging stations; one delivery vehicle corresponds to one shortest path; K and V are both integers greater than 1; The determining unit is further used to determine the first constraint information according to the delivery rule; A calculation unit, configured to calculate second constraint information based on service demand information about the mobile charging pile of each charging station in the V charging stations; The determination unit is further used to determine, according to the principle of minimizing the delivery cost, the delivery paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the K shortest paths, the first constraint information and the second constraint information.

8. An electronic device, characterized in that: include; a processor adapted to implement one or more computer programs; A computer storage medium storing one or more computer programs, wherein the one or more computer programs are suitable for being loaded by a processor and executing the traffic signal control method according to any one of claims 1 to 8.

9. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer storage medium storing a computer program, wherein the computer program implements the method according to any one of claims 1 to 6 when executed by a processor.

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