Method and device for determining distribution strategy of mobile charging pile

Abstract

The embodiment of the invention discloses a method and a device for determining a distribution strategy of a mobile charging pile. The method comprises the following steps: determining K driving paths of K distribution vehicles for distributing mobile charging piles for V charging stations based on a dynamic road network; k and V are integers greater than 1; one distribution vehicle corresponds to one driving path; according to the principle of minimum distribution cost, multiple distribution strategies for distributing the mobile charging piles to the V charging stations are predicted based on the K driving paths and the distribution requirements of each charging station; for each distribution strategy, an influence value corresponding to the distribution strategy is determined, and the influence value is used for reflecting the influence degree of mobile charging pile distribution for the V charging stations according to the distribution strategy on the power distribution network; and determining a target distribution strategy from the plurality of distribution strategies based on the influence value corresponding to each distribution strategy, and realizing efficient distribution of the mobile charging piles for each charging station under the condition of considering the influence on the distribution network.

Description

Technical Field

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

[0002] The uneven temporal and spatial distribution of charging demand has led to an increasingly prominent problem of regional and periodic charging service capacity shortages. In recent years, user charging demand has grown rapidly, but the speed of investment in charging facilities has not kept up with the scale of the development of pure electric driving mode, and the total number of charging facilities is insufficient. The contradiction between supply and demand in the peak charging of pure electric driving mode has become increasingly prominent, and long charging queues often occur. The construction of fixed charging facilities is subject to various restrictions and faces problems such as difficulty in expansion and upgrading. Mobile charging piles have received attention. How to distribute suitable mobile charging piles to each charging station has become one of the hot issues currently being studied. Summary of the invention

[0003] The embodiments of the present application provide a method and device for determining a mobile charging pile distribution strategy. Based on a dynamic road network, distribution costs, and the impact on the distribution network, a mobile charging pile distribution strategy is determined for each charging station. This can save costs while achieving reasonable distribution of mobile charging piles for each charging station with minimal impact on the distribution network.

[0004] On the one hand, an embodiment of the present application provides a method for determining a mobile charging pile distribution strategy, including:

[0005] Based on the dynamic road network, M driving paths for M delivery vehicles to deliver mobile charging piles to N charging stations are determined; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving path;

[0006] According to the principle of minimizing the delivery cost, multiple delivery strategies for mobile charging piles for N charging stations are predicted based on M driving routes and the delivery requirements of each charging station;

[0007] For each distribution strategy, determine the impact value corresponding to the distribution strategy according to the evaluation index, and the impact value is used to reflect the impact of distributing mobile charging piles to N charging stations according to the distribution strategy on the distribution network;

[0008] A target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0009] On the one hand, an embodiment of the present application provides a device for determining a distribution strategy, including:

[0010] A determination unit, configured to determine, based on a dynamic road network, M driving paths for M delivery vehicles to deliver mobile charging piles to N charging stations; M and N are both integers greater than 1; and one delivery vehicle corresponds to one driving path;

[0011] A prediction unit, used for predicting multiple distribution strategies for distributing mobile charging piles to N charging stations based on the principle of minimizing distribution cost and the distribution requirements of each charging station according to the M driving routes;

[0012] The determination unit is further used to determine, for each distribution strategy, an impact value corresponding to the distribution strategy according to the evaluation index, wherein the impact value is used to reflect the degree of influence of the distribution of mobile charging piles for N charging stations according to the distribution strategy on the distribution network;

[0013] The determination unit is further configured to determine a target delivery strategy from among the plurality of delivery strategies based on an impact value corresponding to each delivery strategy.

[0014] On the one hand, an embodiment of the present application provides an electronic device, including:

[0015] 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:

[0016] Based on the dynamic road network, M driving paths for M delivery vehicles to deliver mobile charging piles to N charging stations are determined; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving path;

[0017] According to the principle of minimizing the delivery cost, multiple delivery strategies for mobile charging piles for N charging stations are predicted based on M driving routes and the delivery requirements of each charging station;

[0018] For each distribution strategy, determine the impact value corresponding to the distribution strategy according to the evaluation index, and the impact value is used to reflect the impact of distributing mobile charging piles to N charging stations according to the distribution strategy on the distribution network;

[0019] A target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0020] 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:

[0021] Based on the dynamic road network, M driving paths for M delivery vehicles to deliver mobile charging piles to N charging stations are determined; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving path;

[0022] According to the principle of minimizing the delivery cost, multiple delivery strategies for mobile charging piles for N charging stations are predicted based on M driving routes and the delivery requirements of each charging station;

[0023] For each distribution strategy, determine the impact value corresponding to the distribution strategy according to the evaluation index, and the impact value is used to reflect the impact of distributing mobile charging piles to N charging stations according to the distribution strategy on the distribution network;

[0024] A target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0025] 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 method for determining the mobile charging pile distribution strategy.

[0026] In the embodiment of the present application, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, the present application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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.

[0028] Figure 1 It is a flowchart of a method for determining a mobile charging pile distribution strategy provided in an embodiment of the present application;

[0029] Figure 2 The embodiment of the present application provides a schematic diagram of using a weighted directed graph to represent the topology of a transportation network;

[0030] Figure 3It is a flow chart for determining a driving path for a delivery vehicle provided by an embodiment of the present application;

[0031] Figure 4 This is a network topology diagram of power-transportation coupling provided in an embodiment of the present application;

[0032] Figure 5 It is a structural schematic diagram of a distribution strategy determination device provided in an embodiment of the present application;

[0033] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] 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.

[0035] The existing mobile charging pile path optimization technology is mostly aimed at power battery distribution or mobile charging vehicle scheduling. However, at present, the application scope of the battery swap charging method is relatively small, and only some electric vehicle brands can use it; and mobile charging vehicles are configured in small quantities, are expensive, and have limited charging power, and cannot meet the large-scale charging needs of cities. At the same time, the destination set for the battery swap charging method and the mobile charging vehicle route is the customer's location, which is highly uncertain. In addition, due to the off-grid characteristics of the battery swap charging method and the mobile charging vehicle, their path planning usually does not take into account the status of the distribution network. The uneven distribution of the distribution network load further exacerbates the scenario where the charging facilities of the battery swap charging method and the mobile charging vehicle are in short supply.

[0036] In order to solve the above problems, this application proposes a method for determining the distribution strategy of mobile charging piles, which is aimed at the scenario where charging facilities are in short supply, and is aimed at the problem of optimizing the dispatching path of urban mobile charging piles, such as mobile charging piles. Its characteristics are that the distribution objects are mobile charging piles that support smart sockets, and the distribution destinations are various charging stations; the distribution path optimization takes into account factors such as road conditions, costs, and distribution networks.

[0037] Specifically, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, this application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum.

[0038] The method for determining the mobile charging pile distribution strategy provided in the 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.

[0039] It should be noted that, unless otherwise specified, the method for determining the mobile charging pile distribution strategy provided in the embodiments of the present application is based on the following assumptions: (1) All distribution vehicles are completely homogeneous, the starting point and end point of the distribution route are both service midpoints, and the distribution vehicles are powered by fuel, ignoring a series of problems such as mileage anxiety caused by the distribution vehicles refueling; (2) Considering the road conditions, service route planning is performed based on a dynamic road network model and an optimal route selection algorithm; (3) Each distribution vehicle has only one service route in each distribution cycle and can serve multiple charging stations; (4) The demand of each charging station is only served by one distribution vehicle, and there are no multiple distribution vehicles serving the same charging station at the same time. The situation of mobile charging pile distribution; (5) Multiple delivery vehicles can be dispatched from the service midpoint to serve multiple charging stations, but the total number of distribution facilities cannot exceed the total number of delivery vehicles in the service center; (6) The total number of mobile charging piles delivered by each delivery vehicle must not exceed the rated load of the delivery vehicle, and the demand of any charging station must be less than the rated load of the delivery vehicle; (7) It is assumed that the demand, service time and service soft window of each charging station are known; (8) The time when each delivery vehicle departs from the service center is the start time of the distribution cycle; (9) The mobile charging pile delivery service should be provided to the charging station within the soft time window of the charging station's social conditions. If you arrive early, you must wait, and you will be punished if you exceed the time window constraint.

[0040] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , which is a flow chart of a method for determining a mobile charging pile distribution strategy 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 method for determining the mobile charging pile distribution strategy shown may include the following steps:

[0042] Step S101: Determine K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network.

[0043] Among them, K and V are both integers greater than 1, and one delivery vehicle corresponds to one driving route.

[0044] In one embodiment, determining K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network may include: constructing a traffic network model, and obtaining real-time road network weights based on the traffic network model; and determining M driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the real-time road network weights and optimal path calculation rules.

[0045] In a specific implementation, the real-time road network weight includes the real-time road network weight between any two nodes in the traffic, and the nodes in the traffic include the nodes corresponding to the service center and the nodes corresponding to the charging station; obtaining the real-time road network weight based on the traffic road network model includes:

[0046] If it is determined according to the traffic network model that there is a path between any two nodes, the real-time network weight between the any two nodes is calculated based on the node impedance of the any two nodes and the section impedance between the any two nodes.

[0047] Assuming that any two nodes include a first node and a second node, the road section impedance between the first node and the second node can be calculated based on the traffic conditions, impedance influencing factors and zero-flow travel time between the first node and the second node, and the node impedance of the first node can be calculated based on the traffic conditions between the first node and the second node, the signal period of the first node, the green-to-signal ratio and the road section vehicle arrival rate.

[0048] The following is a detailed introduction of how to construct the dynamic road network and how to determine the real-time road network weight based on the dynamic road network. It should be known that the dynamic road network can also be called a dynamic road network model or a traffic road network model. It is constructed by using a graph theory analysis method and taking into account factors such as road conditions and road resistance. In one embodiment, the construction of the traffic road network model may include the following:

[0049] The graph theory analysis method is used for modeling. The traffic network mathematical model used in this application can be shown as the following formula:

[0050]

[0051] Among them, G is the traffic network topology diagram; V represents the set of all nodes in the traffic, which may include nodes corresponding to charging stations and nodes corresponding to service midpoints, that is, intersections or starting and ending points of roads; L represents the set of all two-way road sections; W is the set of road network weights, which are based on the connection status of each node in the actual traffic road network and the distance length of the road section, and give corresponding weights to the connecting edges between each node. The specific implementation method will be introduced in the subsequent embodiments.

[0052] If a weighted directed graph is used to represent the traffic network topology, see Figure 2 As shown, in Figure 2 It is assumed that the traffic includes four nodes v1-v4, and the value on the connecting line between the nodes represents the road network weight between the two nodes.

[0053] In the embodiment of the present application, the road network weights can be stored in the structure of an adjacency matrix. The adjacency matrix can be referred to in the following formula:

[0054]

[0055] The element w in the matrix ij It can be expressed as:

[0056]

[0057] Among them, w ij Represents node v i and node v j The road network weight between inf Indicates that there is no road section between the two nodes, which can generally be recorded as ∞. When i and j are equal, the road network weight is 0. When there is a road section between the two nodes, the road network weight is a ij .a ij It can be specifically expressed by node impedance (also called node impedance model) and section impedance (also called section impedance model). Node impedance and section impedance can be quantified by weights such as section length and traffic status to express travel cost.

[0058] Based on this embodiment of the present application, any two nodes (when there is no special description, any two nodes can be represented as nodes v i and node v j, these two nodes are mapped to the actual traffic and can correspond to any two charging stations. The road network weight between them (corresponding to the aforementioned first node and second node) can be expressed by the following formula:

[0059]

[0060] Among them, Cv i represents the node impedance of node v i and Rv ij represents the section impedance. The traffic condition is divided by S. When the traffic condition is unobstructed (0 < S ≤ 0.6); when the traffic condition is slow (0.6 < S ≤ 0.8); when the traffic condition is congested (0.8 < S ≤ 1.0); when the traffic condition is severely congested (1.0 < S ≤ 2.0), the section impedance Rv ij can be calculated by the following formula:

[0061]

[0062] Among them, t0 represents the zero-flow travel time, and α and β are impedance influence factors. Generally, α = 0.15 and β = 4 can be taken. That is to say, in the case of known traffic conditions and impedance influence factors, substituting into the above formula can calculate the section impedance between the first node and the second node. For example, if the traffic condition between the first node and the second node is unobstructed, it can be known that the value of traffic condition S falls into the upper branch of the above formula. Substituting the impedance influence factor and the zero-flow travel time into the upper branch for calculation, the section impedance can be obtained. It should be known that

[0063] the node impedance of node v i can be calculated by the following formula:

[0064]

[0065] Among them, c represents the signal cycle, λ represents the green ratio, and q represents the vehicle arrival rate of the section.

[0066] Based on the calculation formula of the node impedance and the calculation formula of the section impedance, the calculation formula of the road network weight a ij is rewritten, and the calculation formula of the road network weight a ij can be obtained as:

[0067]

[0068] Combined with the above embodiments and specific examples, the real-time road network weight can be determined. Further, based on the real-time road network weight and the optimal path calculation rule, K driving paths for K distribution vehicles to distribute mobile charging piles to V charging stations are determined.

[0069] In one embodiment, the optimal path calculation rule indicates that a driving path is determined for each delivery vehicle according to the shortest path. Based on the traffic network model and the optimal path calculation rule, K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined, which may specifically include:

[0070] A service center and V charging stations are mapped as nodes, and for each delivery vehicle, a starting node and an ending node that the delivery vehicle needs to pass through are determined; the service center refers to a provider of delivery vehicles; temporary marks are added to L nodes based on the real-time road network weights, and the temporary mark of each node includes the sum of the road network weights of the shortest path from the starting node to the node, and the previous node of the node in the shortest path; the L nodes refer to the nodes corresponding to the charging stations in the V charging stations that are not assigned to delivery vehicles; a driving path is determined for the delivery vehicle based on the temporary marks of the L nodes.

[0071] Determining a driving path for each delivery vehicle can actually be understood as selecting the shortest path from the starting node to the ending node of each delivery vehicle.

[0072] Specifically, determining the driving path for the delivery vehicle based on the temporary marks of the L charging stations may include the following steps: converting the temporary mark of the starting node into a permanent mark, and adding the starting node to a permanent mark set; determining the temporary mark with the smallest sum of the road network weights as the target temporary point mark according to the temporary point marks of the L nodes, and updating the starting node with the previous node in the target temporary mark; if the updated starting node is the same as the ending node, determining the driving path corresponding to the delivery vehicle with the path composed of the nodes in the permanent set;

[0073] If the updated starting node is different from the ending node, the steps of adding temporary marks to the L charging stations based on the real-time road network weight and subsequent steps are repeated.

[0074] In the embodiment of the present application, a typical shortest path optimization algorithm, namely the Dijkstra algorithm, can be used to determine the shortest path. The characteristic of the algorithm is that it expands around the starting point, adopts a greedy strategy, and traverses to the adjacent node closest to the starting point and has not been visited each time until it expands to the end point, so as to obtain the desired optimal solution. The following is an example to specifically introduce how to determine the driving path for a delivery vehicle.

[0075] Assuming that each charging station and service center can be regarded as a node, the temporary mark set for a node j can be expressed as (D ij , v k ), D ijis the sum of the network weights of the shortest path from the starting node i to the node j. It should be noted that the network weight of each node to itself is 0, and the network weight between nodes without a section is infinite. k The previous node of node j in the shortest path is denoted as k. The algorithm steps for determining the shortest path for a delivery vehicle using the Dijkstra algorithm can be as follows:

[0076] (1) Initialize the algorithm and determine the starting node (which can be recorded as the starting parent node v i ) and the end node (i.e. the end point of the path v e ); In the embodiment of the present application, it is assumed that the starting node and the ending node of each delivery vehicle are the same, both of which are service centers.

[0077] (2) Temporarily mark the nodes that have not been assigned a delivery vehicle. Set the temporary mark of the starting parent node to (0, -1) and the temporary mark of other nodes to (D ij , v i ), if v i and v j Directly connected, then D ij =a ij , then they are not directly connected, then D ij =w inf =∞, and at the same time convert the temporary mark of the parent node into a permanent mark and store it in the permanent mark set S;

[0078] (3) With v i is the mother node, for K nodes D ij Compare and take the smallest point v k Convert it to a permanent mark and store it in the permanent mark set S. Then adjust the temporary marks of other nodes. At this time, the temporary marks of other nodes are set to (D ij , v k ), if a new node v k With any node v j The road network weight D kj ≠w inf , then D ij =D ik +w kj , at this time the parent node is updated to v k ;

[0079] (4) Repeat step (3) until the end point v e Appears in the permanent mark set S, then the set S is the starting node v i To the end point v e The optimal path.

[0080] The above example shows how to determine the optimal route for each delivery vehicle. For any vehicle, the determination method is the same as above. Figure 3 , which is a schematic diagram of setting a driving path for each delivery vehicle provided in an embodiment of the present application.

[0081] Step S102: According to the principle of minimizing the delivery cost, multiple delivery strategies for delivering mobile charging piles to V charging stations are predicted based on the K driving routes and the delivery requirements of each charging station.

[0082] In one embodiment, a plurality of distribution strategies for distributing mobile charging piles to V charging stations are predicted based on K driving routes and the distribution requirements of each charging station, which may include: step S1, constructing an objective function based on the distribution cost and the distribution requirements of each charging station; step S2, setting solution constraints for the objective function, wherein the solution constraints include any one or more of flow constraints, load capacity constraints of the distribution vehicle, and time window constraints for the distribution vehicle to arrive at the distribution charging station; step S3, solving the objective function according to the principle of minimizing the distribution cost and based on the solution constraints to obtain a plurality of distribution strategies.

[0083] Among them, the delivery cost in step S1 may include the fixed fee of the service center, the total transportation cost, and the early waiting cost and late penalty cost of the soft time window. Specifically, it may include: the waiting cost per unit time of the delivery vehicle arriving at the charging station early, which can be calculated using FW tm It means that the penalty cost per unit time for the delivery vehicle to arrive at the charging station late can be expressed as LW tm Indicates that the total transportation cost can be expressed by FC.

[0084] The delivery requirements of each charging station may include but are not limited to: the earliest acceptable service time and the latest acceptable service time for each charging station. Taking charging station j as an example, the earliest acceptable service time can be expressed as

[0085] ET j The latest acceptable service time is denoted as LT j ; The discrete time point at which the delivery vehicle arrives at each charging station. Taking charging station i as an example, the discrete time point at which the delivery vehicle arrives at charging station i can be expressed as T jk .

[0086] The objective function constructed based on the distribution cost and the distribution requirements of each charging station can be expressed by the following formula:

[0087]

[0088] Among them, V and N are the same, indicating the number of charging stations, K and M are the same, indicating the number of delivery vehicles, and c ijrepresents the weighted distance between charging station i and charging station j, which can be set to the road section distance in the embodiment of the present application. ijk is a variable between 0 and 1, indicating that if there is a direct path between charging station i and charging station j and the number of the delivery vehicle is k (that is, among the K delivery vehicles, the driving path of the vehicle with number k includes the route from charging station i to charging station j), then x ijk =1, otherwise 0.

[0089] In step S2, solving the constraint conditions may include any one or more of the flow constraint conditions, the load capacity constraint conditions of the delivery vehicle, and the time window constraint conditions for the delivery vehicle to arrive at the delivery charging station.

[0090] First, the flow constraints are used to agree on the following:

[0091] (1) There is only one path between any two charging stations, which can be expressed by the following formula:

[0092]

[0093] (2) There is no path between each charging station, which can be expressed as: Among them, K represents the number of delivery vehicles, and V represents the number of charging stations;

[0094] (3) Ensure that the number of delivery vehicles entering and leaving the service center is equal, which can be expressed by the following formula:

[0095]

[0096] (4) Ensure that each delivery vehicle has at most one driving path, which can be expressed by the following formula:

[0097]

[0098] Among them, V0 represents the service center, V c It represents a set of V charging stations. It should be noted that in the vehicle routing problem (VRP), when a loop is split into two or more loops, each loop is called a sub-loop. The flow constraint also includes the elimination of sub-loop constraints, which prevents the final dispatch result of the delivery vehicle from including loops that do not pass through the service center, that is, the sub-loop problem. The elimination of sub-loop constraints can be expressed by the following formula:

[0099]

[0100] Among them, u ik and u jkrepresents the continuous variable to be determined to eliminate the sub-loop. If a delivery vehicle k has a path from a charging station i to j, then x ijk =1, the constraint becomes u ik -u jk ≤-1, that is, u jk ≥u ik +1, follow this word loop, u ik It keeps increasing. Assuming that there is a subcircuit that satisfies this constraint, find a subcircuit that does not contain the starting point 1, such as traffic node 2→4→6→2, then u 2k ≥u 6k +1≥u 4k +2≥u 2k +3, this is clearly contradictory.

[0101] Second, the load capacity constraints of the delivery vehicles are used to constrain the following:

[0102] (1) The constraint is the load change of the slow-charging mobile charging pile and the fast-charging mobile charging pile configured for each delivery vehicle, which can be specifically expressed by the following formula:

[0103]

[0104] Among them, d fh represents the demand for slow charging mobile charging piles at charging station h, d sh represents the demand for fast-charging mobile charging piles at charging station h, Q sihk represents the load of the remaining slow-charging mobile charging piles when a delivery vehicle k delivers a mobile charging pile to charging station i and then delivers a mobile charging pile to charging station h, Q fihk Represents the load of the remaining fast-charging mobile charging piles when delivery vehicle k delivers mobile charging piles to charging station i and then delivers charging facilities to charging station h.

[0105] (2) Constrain the load of slow-charging mobile charging piles and fast-charging mobile charging piles for each delivery vehicle. The total number of mobile charging piles for all delivery vehicles shall not be greater than the total number of existing facilities in the service center. This constraint can be expressed by the following formula:

[0106]

[0107] Among them, Q sijk represents the remaining load of the slow-charging mobile charging pile after the delivery vehicle k serves the charging station i and then serves the charging station j, Q fijk represents the remaining load of the fast-charging mobile charging piles of the delivery vehicle k after serving the charging station i and then serving the charging station j. CVs represents the total number of slow-charging mobile charging piles owned by the service center. CV f Indicates the total number of fast-charging mobile charging piles owned by the service center.

[0108] (3) The load of each delivery vehicle must not exceed its rated load capacity, which can be expressed by the following formula:

[0109]

[0110] Among them, Q ijk It represents the remaining load of the total mobile charging piles of the vehicles after the delivery vehicle k serves the charging station i and then serves the charging station j. CV represents the rated load capacity of each delivery vehicle.

[0111] Third, time window constraints can be used to constrain the following:

[0112] (1) The arrival time of each delivery vehicle delivering mobile charging piles to the charging station must be within the scheduling service cycle, which can be expressed by the following formula:

[0113]

[0114] Among them, T t Represents a scheduling service cycle, T jk represents the discrete time point when delivery vehicle k arrives at charging station i.

[0115] (2) The time when each delivery vehicle departs from the service center and arrives at a charging station is related to the time taken from the service center to the charging station, which can be expressed by the following formula:

[0116]

[0117] Among them, c ij The weighted distance between node i and node j can be equal to the road section between the two points. For example, node i here can refer to the service center and node j can refer to charging station j.

[0118] (3) Constrain the time when a delivery vehicle departs from charging station i and arrives at charging station j, that is, the sum of the time when the delivery vehicle starts serving charging station i, the time it serves charging station i, and the time it takes to travel from charging station i to charging station j, including the constraint that the delivery vehicle must wait if it arrives at the charging station early. The constraint content in this part can be expressed by the following formula:

[0119]

[0120] Among them, t ik represents the service time of delivery vehicle k at charging station i, ET i represents the earliest acceptable arrival time of charging station i.

[0121] (4) The service time of a delivery vehicle at a charging station is related to the number of mobile charging piles delivered to the charging station. The content of this constraint can be reflected by the following formula:

[0122]

[0123] Among them, d fi represents the demand for slow-charging mobile charging piles at charging node i, d si represents the demand for fast charging mobile charging piles at charging node i, T se Represents the time it takes to deliver a single mobile charger to a charging station.

[0124] Step S103: for each distribution strategy, determine the impact value corresponding to the distribution strategy.

[0125] A delivery strategy may include when each of the K delivery vehicles sets out to deliver mobile charging piles, the number of slow-charging mobile charging piles and the number of fast-charging mobile charging piles configured for each delivery vehicle, and the like. The specific arrangements may be made based on the actual application scenario, and the embodiments of the present application do not impose any limitation thereto.

[0126] It should be known that a mobile charging device without energy storage needs to be connected to a distribution network for service. In order to minimize the impact on the distribution network, after multiple distribution strategies are determined in step S102, it is necessary to measure the impact value of each distribution strategy on the distribution network, and finally select the optimal distribution strategy from the multiple distribution strategies based on the impact value, and distribute mobile charging piles to V charging stations according to the optimal distribution strategy.

[0127] In one embodiment, determining the impact value corresponding to the distribution strategy may include: obtaining a network topology diagram of power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes and connection relationships between each node; and determining the impact value corresponding to the distribution strategy based on the network topology diagram.

[0128] Among them, the topological structure diagram of power-transportation integration can be shown as follows: Figure 4 As shown, in Figure 4 China Mobile Charging Service Center refers to the service center mentioned above.

[0129] Optionally, determining the impact value corresponding to the distribution strategy based on the network topology diagram may include: determining the resistance of each branch, the current of each branch and the voltage of each charging node under the distribution strategy based on the network topology diagram; determining the impact value corresponding to the distribution strategy according to the resistance of each branch, the current of each branch and the voltage of each charging node.

[0130] Specifically, determining the impact value corresponding to the distribution strategy according to the resistance of each branch, the current of each branch and the voltage of each charging node includes: calculating the active network loss corresponding to the distribution strategy based on the resistance of each branch, the current of each branch and the time variation;

[0131] According to the voltage of each charging node and the preset node voltage offset extreme value calculation rule, the node voltage offset extreme value under the distribution strategy is calculated; according to the voltage of each charging node and the preset daily voltage offset calculation formula, the daily voltage offset of each charging node is calculated, and the daily voltage offset cumulative value corresponding to the distribution strategy is calculated according to the daily voltage offset of each charging node; the impact value corresponding to the distribution strategy is determined according to any one or more of the active network loss, the node voltage offset extreme value and the daily voltage offset cumulative value corresponding to the distribution strategy.

[0132] For example, suppose r ij Indicates branch l ij The resistance, i ij,t is the branch l at time t ij The current flowing, Δt represents the time change, T represents a dispatch service cycle, and the active network loss in a distribution strategy can be calculated by the following formula:

[0133]

[0134] Assume u j,t represents the voltage of charging node j in period t. The voltage offset calculation formula of each charging node is as follows:

[0135]

[0136] Substituting the voltage of charging node j at time t into the above formula can obtain the voltage offset of the charging node. Furthermore, the daily voltage offset of each charging node is calculated to obtain the cumulative value of the daily voltage offset corresponding to the distribution strategy, which can be specifically expressed as the following formula:

[0137]

[0138] Step S104: determining a target delivery strategy from among the multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0139] The impact value corresponding to each distribution strategy can reflect the degree of impact of the distribution of mobile charging piles according to the distribution strategy on the distribution network. Further, a target distribution strategy is selected from multiple distribution strategies based on the impact values ​​corresponding to various distribution strategies. Optionally, the distribution strategy with the smallest impact value can be used as the target distribution strategy, and then mobile charging piles are distributed to V charging stations according to the target distribution strategy, which can achieve efficient and fast distribution of mobile charging piles with minimal impact on the distribution network.

[0140] In the embodiment of the present application, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, the present application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum.

[0141] Based on the above-mentioned method embodiment for determining the distribution strategy of the mobile charging pile, the present application embodiment provides a device for determining the distribution strategy, see Figure 5 , which is a schematic diagram of a distribution strategy determination device provided in an embodiment of the present application. Figure 5 The distribution strategy determination device shown can run the following units:

[0142] A determination unit 501 is used to determine, based on a dynamic road network, K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; and one delivery vehicle corresponds to one driving path;

[0143] A prediction unit 502 is used to predict multiple distribution strategies for distributing mobile charging piles to V charging stations based on the principle of minimizing distribution cost and the distribution requirements of each charging station according to the K driving routes;

[0144] The determining unit 501 is further used to determine, for each distribution strategy, an impact value corresponding to the distribution strategy, where the impact value is used to reflect the degree of influence of mobile charging pile distribution for V charging stations according to the distribution strategy on the distribution network;

[0145] The determining unit 501 is further configured to determine a target delivery strategy from among the multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0146] In one embodiment, the determining unit 501 performs the following steps when determining the impact value corresponding to the distribution strategy:

[0147] Obtain a network topology diagram of power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes and connection relationships between the nodes; and determine the impact value corresponding to the distribution strategy based on the network topology diagram.

[0148] In one embodiment, when determining the impact value corresponding to the distribution strategy based on the network topology diagram, the determining unit 501 performs the following steps:

[0149] Based on the network topology diagram, the resistance of each branch, the current of each branch and the voltage of each charging node under the distribution strategy are determined; and the impact value corresponding to the distribution strategy is determined according to the resistance of each branch, the current of each branch and the voltage of each charging node.

[0150] In one embodiment, when determining the impact value corresponding to the distribution strategy according to the resistance of each branch, the current of each branch, and the voltage of each charging node, the determining unit 501 performs the following steps:

[0151] Calculating the active network loss corresponding to the distribution strategy based on the resistance of each branch, the current of each branch and the time variation;

[0152] Calculating the node voltage offset extreme value under the distribution strategy according to the voltage of each charging node and a preset node voltage offset extreme value calculation rule;

[0153] Calculate the daily voltage offset of each charging node according to the voltage of each charging node and the preset daily voltage offset calculation formula, and calculate the daily voltage offset cumulative value corresponding to the distribution strategy according to the daily voltage offset of each charging node;

[0154] The impact value corresponding to the distribution strategy is determined according to any one or more of the active network loss, the node voltage offset extreme value, and the daily voltage offset accumulated value corresponding to the distribution strategy.

[0155] In one embodiment, when determining the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy, the determining unit 501 performs the following steps: determining the delivery strategy corresponding to the smallest impact value as the target delivery strategy.

[0156] In one embodiment, when determining K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network, the determining unit 501 performs the following steps:

[0157] A traffic network model is constructed, and real-time network weights are obtained based on the traffic network model; and K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined based on the real-time network weights and optimal path calculation rules.

[0158] In one embodiment, the optimal path calculation rule indicates that a driving path is determined for each delivery vehicle according to the shortest path; when the determination unit 501 determines K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the traffic network model and the optimal path calculation rule, the determination unit 501 performs the following steps:

[0159] Mapping the service center and V charging stations as nodes, and determining the starting node and the ending node that each delivery vehicle needs to pass through; the service center refers to the provider of the delivery vehicle;

[0160] Temporary marks are added to L nodes based on the real-time road network weights, and the temporary mark of each node includes the sum of the road network weights of the shortest path from the starting node to the node, and the previous node of the node in the shortest path; the L nodes refer to the nodes corresponding to the charging stations among the V charging stations that are not assigned to delivery vehicles; and a driving path is determined for the delivery vehicle based on the temporary marks of the L nodes.

[0161] In one embodiment, the determining unit 501 performs the following steps when determining a driving path for the delivery vehicle based on the temporary markings of the L charging stations:

[0162] Convert the temporary mark of the starting node into a permanent mark, and add the starting node to the permanent mark set;

[0163] According to the temporary point marks of the L nodes, the temporary mark with the smallest sum of the road network weights is determined as the target temporary point mark, and the previous node in the target temporary mark is used to update the starting node;

[0164] If the updated starting node is the same as the ending node, the path composed of the nodes in the permanent set is used to determine the driving path corresponding to the delivery vehicle;

[0165] If the updated starting node is different from the ending node, the steps of adding temporary marks to the L charging stations based on the real-time road network weight and subsequent steps are repeated.

[0166] In one embodiment, the determining unit 501 performs the following steps when acquiring the real-time road network weight based on the traffic road network model:

[0167] If it is determined according to the traffic network model that there is a path between any two nodes, the real-time network weight between the any two nodes is calculated based on the node impedance of the any two nodes and the section impedance between the any two nodes.

[0168] In one embodiment, the determination device also includes a calculation unit 503, and the arbitrary two nodes include a first node and a second node. The calculation unit 503 is used to calculate the impedance of the road section between the first node and the second node based on the traffic conditions, impedance influencing factors and zero-flow travel time between the first node and the second node; and calculate the node impedance of the first node based on the traffic status between the first node and the second node, the signal period of the first node, the green-to-signal ratio and the vehicle arrival rate of the road section.

[0169] In one embodiment, the prediction unit 502 performs the following steps when predicting multiple delivery strategies for delivering mobile charging settings to V charging stations based on the K driving routes and the delivery requirements of each charging station according to the principle of minimizing the delivery cost:

[0170] In the case where K delivery vehicles deliver mobile charging piles to V charging stations according to their corresponding driving routes, an objective function is constructed based on the delivery cost and the delivery requirements of each charging station;

[0171] Setting solution constraints for the objective function, the solution constraints including any one or more of a flow constraint, a load capacity constraint of the delivery vehicle, and a time window constraint for the delivery vehicle to arrive at the delivery charging station;

[0172] The objective function is solved according to the principle of minimizing the distribution cost and based on the constraint conditions to obtain a variety of distribution strategies.

[0173] In the embodiment of the present application, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, the present application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum.

[0174] Based on the above-mentioned method for determining the distribution strategy of a mobile charging pile and the embodiment of the device for determining the distribution strategy, 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.

[0175] 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:

[0176] Based on the dynamic road network, determine K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving path;

[0177] According to the principle of minimizing the delivery cost, multiple delivery strategies for mobile charging piles for V charging stations are predicted based on K driving routes and the delivery requirements of each charging station;

[0178] For each distribution strategy, determine the impact value corresponding to the distribution strategy, where the impact value is used to reflect the impact of distributing mobile charging piles to V charging stations according to the distribution strategy on the distribution network;

[0179] A target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0180] In the embodiment of the present application, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, the present application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum.

[0181] 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.

[0182] In one embodiment, one or more computer programs stored in the computer storage medium may be loaded and executed by the processor 401:

[0183] Based on the dynamic road network, determine K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving path;

[0184] According to the principle of minimizing the distribution cost, multiple distribution strategies for distributing mobile charging piles to V charging stations are predicted based on K driving routes and the distribution requirements of each charging station; for each distribution strategy, an impact value corresponding to the distribution strategy is determined, and the impact value is used to reflect the degree of influence of distributing mobile charging piles to V charging stations according to the distribution strategy on the distribution network; based on the impact value corresponding to each distribution strategy, a target distribution strategy is determined from multiple distribution strategies.

[0185] In the embodiment of the present application, first, based on the dynamic road network, M driving routes for distributing mobile charging piles to N charging stations are stipulated, and then, according to the principle of minimum distribution cost, multiple distribution strategies for distributing mobile charging piles to N charging stations are predicted based on the M driving routes and the distribution requirements of each charging station. It can be seen that the determination of these multiple distribution strategies comprehensively considers the traffic conditions, the distribution requirements of each charging station, and the distribution cost, so that a reasonable distribution strategy can be determined while minimizing the distribution cost. In addition, for each power distribution strategy, the present application will also determine the impact value of the distribution strategy on the distribution network based on the evaluation index. The impact value is used to reflect the degree of impact on the distribution network if mobile charging piles are distributed to N charging stations according to this distribution strategy; then, based on the impact value of each distribution strategy, the target distribution strategy is selected from multiple distribution strategies. In this way, it is possible to ensure that the distribution strategy is reasonable while keeping the impact on the distribution network to a minimum.

Claims

1. A method for determining a mobile charging pile distribution strategy, characterized in that: include: Based on the dynamic road network, determine K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route; According to the principle of minimizing the delivery cost, multiple delivery strategies for mobile charging piles for V charging stations are predicted based on K driving routes and the delivery requirements of each charging station; For each distribution strategy, determine the impact value corresponding to the distribution strategy, where the impact value is used to reflect the impact of distributing mobile charging piles to V charging stations according to the distribution strategy on the distribution network; A target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

2. The method according to claim 1, characterized in that The determining the impact value corresponding to the distribution strategy includes: Obtaining a network topology diagram of power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes, and connection relationships between the nodes; Based on the network topology diagram, determine the resistance of each branch, the current of each branch and the voltage of each charging node under the distribution strategy; The impact value corresponding to the distribution strategy is determined according to the resistance of each branch, the current of each branch and the voltage of each charging node.

3. The method according to claim 2, characterized in that The determining of the impact value corresponding to the distribution strategy according to the resistance of each branch, the current of each branch and the voltage of each charging node includes: Calculating the active network loss corresponding to the distribution strategy based on the resistance of each branch, the current of each branch and the time variation; Calculating the node voltage offset extreme value under the distribution strategy according to the voltage of each charging node and a preset node voltage offset extreme value calculation rule; Calculate the daily voltage offset of each charging node according to the voltage of each charging node and the preset daily voltage offset calculation formula, and calculate the daily voltage offset cumulative value corresponding to the distribution strategy according to the daily voltage offset of each charging node; The impact value corresponding to the distribution strategy is determined according to any one or more of the active network loss, the node voltage offset extreme value, and the daily voltage offset accumulated value corresponding to the distribution strategy.

4. The method according to claim 1, characterized in that The method of determining K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network includes: Constructing a traffic network model, and if it is determined according to the traffic network model that there is a path between any two nodes, then calculating the real-time network weight between the any two nodes based on the node impedance of the any two nodes and the section impedance between the any two nodes; Based on the real-time road network weights and the optimal path calculation rules, K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations are determined.

5. The method according to claim 4, characterized in that The optimal path calculation rule indicates that a driving path is determined for each delivery vehicle according to the shortest path; the determining of K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations based on the real-time road network weights and the optimal path calculation rule includes: Mapping the service center and V charging stations as nodes, and determining the starting node and the ending node that each delivery vehicle needs to pass through; the service center refers to the provider of the delivery vehicle; Add temporary marks to L nodes based on the real-time road network weights, where the temporary mark of each node includes the sum of the road network weights of the shortest path from the starting node to the node and the previous node of the node in the shortest path; the L nodes refer to the nodes corresponding to the charging stations among the V charging stations that are not assigned with delivery vehicles; A driving path is determined for the delivery vehicle based on the temporary markings of the L nodes.

6. The method according to claim 5, characterized in that The step of determining a driving path for the delivery vehicle based on the temporary markings of the L charging stations includes: Convert the temporary mark of the starting node into a permanent mark, and add the starting node to the permanent mark set; According to the temporary point marks of the L nodes, the temporary mark with the smallest sum of the road network weights is determined as the target temporary point mark, and the previous node in the target temporary mark is used to update the starting node; If the updated starting node is the same as the ending node, the path composed of the nodes in the permanent set is used to determine the driving path corresponding to the delivery vehicle; If the updated starting node is different from the ending node, the steps of adding temporary marks to the L charging stations based on the real-time road network weight and subsequent steps are repeated.

7. The method according to claim 4, characterized in that The real-time road network weight includes the real-time road network weight between any two nodes in the traffic, and the nodes in the traffic include the nodes corresponding to the service center and the nodes corresponding to the charging station; The arbitrary two nodes include a first node and a second node, and the method further includes: Calculate the impedance of the road section between the first node and the second node based on the traffic conditions, impedance impact factor and zero-flow travel time between the first node and the second node; The node impedance of the first node is calculated based on the traffic status between the first node and the second node, the signal period of the first node, the green-to-signal ratio, and the vehicle arrival rate of the road section.

8. The method according to claim 1, characterized in that According to the principle of minimizing the delivery cost, multiple delivery strategies for delivering mobile charging to V charging stations are predicted based on K driving routes and the delivery requirements of each charging station, including: In the case where K delivery vehicles deliver mobile charging piles to V charging stations according to their corresponding driving routes, an objective function is constructed based on the delivery cost and the delivery requirements of each charging station; Setting solution constraints for the objective function, the solution constraints including any one or more of a flow constraint, a load capacity constraint of the delivery vehicle, and a time window constraint for the delivery vehicle to arrive at the delivery charging station; The objective function is solved according to the principle of minimizing the distribution cost and based on the constraint conditions to obtain a variety of distribution strategies.

9. A device for determining a distribution strategy, characterized in that: include: A determination unit, configured to determine, based on a dynamic road network, K driving paths for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route; A prediction unit, used for predicting multiple distribution strategies for distributing mobile charging piles to V charging stations based on the principle of minimizing distribution cost and based on K driving routes and distribution requirements of each charging station; The determination unit is further used to determine, for each distribution strategy, an impact value corresponding to the distribution strategy, where the impact value is used to reflect the degree of influence of the distribution of mobile charging piles for V charging stations according to the distribution strategy on the distribution network; The determination unit is further configured to determine a target delivery strategy from among the plurality of delivery strategies based on an impact value corresponding to each delivery strategy.

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