Mobile energy storage vehicle path planning method and system of improved ant colony algorithm based on positive and negative excitation

Abstract

The invention belongs to the field of path planning, and discloses a mobile energy storage vehicle path planning method and system of an improved ant colony algorithm based on positive and negative excitation, and the method comprises the steps: taking the information of a regional road traffic network and a power distribution network as the input, and building a basic two-dimensional model in combination with the road trafficability affected by extreme weather and the fault state of the power distribution network; and iterating an optimal traveling path of the mobile energy storage vehicle by using an ant colony algorithm based on the established basic two-dimensional model so as to meet the power distribution demand, including forming negative feedback based on the current road trafficability so as to dynamically adjust the pheromone concentration of each path in the ant colony algorithm. According to the method, road information of a traffic department region, road accompanying conditions in extreme weather, extreme weather forecast information of a meteorological department region, power distribution network information of an electric power department region and power distribution network fault conditions in extreme weather are combined, strong correlation factors of regional road congestion in an extreme weather environment are fully considered, and the method has reasonability; and the method is more practical and is higher in applicability.

Description

Technical Field

[0001] The present invention belongs to the field of new generation information technology, and relates to a mobile energy storage vehicle path planning method and system based on an improved ant colony algorithm with positive and negative incentives. Background Art

[0002] In recent years, global climate change has led to frequent extreme weather events, such as storms, floods, ice and snow disasters, etc. These events not only bring huge losses to the social economy, but also pose a serious threat to infrastructure such as road traffic networks and power distribution networks. Under extreme weather conditions, the vulnerability of the distribution network is particularly prominent, and power outages and power outages occur frequently, seriously affecting residents' lives and industrial production. To alleviate such emergencies, mobile energy storage vehicles, as a flexible emergency power supply solution, are increasingly being used to quickly restore power supply in power-off areas. However, extreme weather is often accompanied by poor road conditions, which not only increases the difficulty of mobile energy storage vehicles, but also puts higher requirements on the safe driving of vehicles. At the same time, considering the wide distribution of power outage areas and the differences in the urgency of power supply needs, how to formulate the optimal travel path of mobile energy storage vehicles based on power supply priority while ensuring traffic safety has become a technical problem that needs to be solved urgently.

[0003] In existing path planning research, although a variety of path planning methods have been developed, including traditional graph search algorithms, heuristic search algorithms, sampling-based planning algorithms, intelligent optimization algorithms and other algorithms, most of these studies have not fully considered the road conditions under extreme weather conditions and the power supply priority in the power outage area, and the application scenarios are relatively single, lacking the ability to perform effective path planning in complex and changeable extreme weather environments. Therefore, developing an intelligent path planning method that can comprehensively consider road conditions, power supply priority and the impact of extreme weather has practical application prospects for improving the emergency response capabilities and resource allocation efficiency of mobile energy storage vehicles under extreme weather conditions to meet the urgent needs of power outage areas. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to provide a mobile energy storage vehicle path planning method and system based on an improved ant colony algorithm with positive and negative incentives.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A first aspect of the present invention provides a mobile energy storage vehicle path planning method based on an improved ant colony algorithm with positive and negative incentives, comprising:

[0007] Taking the regional road traffic network and distribution network information as input, a basic two-dimensional model is established in combination with the road trafficability affected by extreme weather and the distribution network fault status; the path is from the initial node to the target node;

[0008] Based on the established basic two-dimensional model, the ant colony algorithm is used to iterate the optimal travel path of the mobile energy storage vehicle to meet the power distribution needs, including the formation of negative feedback based on the current road accessibility to dynamically adjust the pheromone concentration of each path in the ant colony algorithm. Among them, regional road information and road traffic conditions under extreme weather conditions are obtained from the transportation department, extreme weather forecast information is obtained from the meteorological department, and distribution network information and distribution network fault conditions under extreme weather conditions are obtained from the power department. The strong correlation factors of regional road congestion under extreme weather conditions are fully considered, and it has certain adaptability and promotion.

[0009] Preferably, a basic two-dimensional model is established with regional road traffic network and distribution network information as input, combined with road trafficability affected by extreme weather and distribution network fault information, including:

[0010] Based on the regional road traffic network and distribution network information, nodes and edges are divided to establish a topological structure;

[0011] The road passability and distribution network fault information affected by extreme weather are integrated into the topological structure to establish the basic two-dimensional model.

[0012] Preferably, based on the regional road traffic network and distribution network information, nodes and edges are divided to establish a topological structure, including:

[0013] The first physical space is the workable space of the mobile energy storage vehicle in the region;

[0014] The geographical location of the distribution network and power supply sites within the region is regarded as the second physical space;

[0015] Mapping the first physical space and the second physical space to the regional road traffic network;

[0016] A topological structure is established with the road intersections of the regional road traffic network as nodes and the roads as edges connecting the nodes.

[0017] Preferably, based on the established basic two-dimensional model, the ant colony algorithm is used to iterate the optimal travel path of the mobile energy storage vehicle to meet the power distribution demand, including:

[0018] Determine the number of ant populations and the maximum number of iterations;

[0019] Determine the initial node and target node of the ant;

[0020] Determine whether it is the first iteration, if so, initialize the pheromone concentration;

[0021] Each ant enters a round of iteration;

[0022] After this iteration, update one or more of the pheromone concentration, road traffic information, and power distribution priority sequence of each path;

[0023] When the termination condition of the ant colony algorithm is met, output the optimal travel path.

[0024] Preferably, each ant entering a round of iteration includes:

[0025] Each ant starts from the initial node and traverses each node;

[0026] Obtain the path information of each ant traversing each node from the initial node;

[0027] Adjust the pheromone concentration by positive and negative incentives for the path;

[0028] Output the optimal travel path in the current iteration.

[0029] Preferably, when all ants have completed traversing, it further includes:

[0030] Update the pheromone concentration of the path between each node according to the following formula

[0031] τ ij (t + n)=(1 - ρ)·τ ij (t)+Δτ ij

[0032] Among them, ρ(0 < ρ < 1) represents the pheromone evaporation coefficient of the path between node i and node j, and 1 - ρ represents the pheromone persistence coefficient; Δτ ij represents the increment of the pheromone of the path between node i and node j in this iteration;

[0033] Determine Δτ according to the following formula ij

[0034]

[0035] In the formula, x(0 < x < 1) represents its path traffic index, which is divided into three cases: normal passable is 0, congested and impassable is 1, and the traffic condition is uncertain is 0.5; y(0 < y < 1) represents the path priority index determined according to the priority of the vertex of the path with the target node as the vertex; represents the pheromone left by the kth ant on the path between node i and node j in this iteration.

[0036] Preferably, setting the initial pheromone concentration in the ant colony algorithm based on the current road passability includes:

[0037] Set the initial concentration τ of each path pheromone according to the following formula ij (0)

[0038] τ ij (0) = (1 - x + y)·c

[0039] In the formula, c represents a constant; x (0 < x < 1) represents the path passing index, which is divided into three cases: normal passable is 0, congested and impassable is 1, and the passing condition is uncertain is 0.5; y (0 < y < 1) represents the path priority index determined according to the priority of the vertices of the path with the target node as the vertex.

[0040] Preferably, it further includes forming a positive feedback based on the power distribution priority sequence to adjust the pheromone concentration in the ant colony algorithm.

[0041] In the second aspect of the present invention, there is provided a path planning system for a mobile energy storage vehicle based on an improved ant colony algorithm with positive and negative incentives, including:

[0042] A model establishment module, configured to take the regional road traffic network and power distribution network information as input, and establish a basic two-dimensional model in combination with the passability of roads affected by extreme weather and the power distribution network fault status;

[0043] A power distribution module, configured to, based on the established basic two-dimensional model, use the ant colony algorithm to iteratively find the optimal travel path of the mobile energy storage vehicle to meet the power distribution requirements, including setting the initial pheromone concentration in the ant colony algorithm based on the current road passability.

[0044] In the third aspect of the present invention, there is provided a computer device, including a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the above-mentioned path planning method for a mobile energy storage vehicle based on an improved ant colony algorithm with positive and negative incentives.

[0045] In the fourth aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, and is characterized in that when the computer program is executed by a processor, it implements the above-mentioned path planning method for a mobile energy storage vehicle based on an improved ant colony algorithm with positive and negative incentives.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] By real-time monitoring of the road traffic conditions under extreme weather conditions and combining the power supply priorities of power outage areas, the best travel route of the mobile energy storage vehicle can be quickly determined, ensuring that power support is provided to the most needed areas in the shortest time, thereby significantly improving the speed and efficiency of power emergency response;

[0048] Considering the storage capacity limitations of mobile energy storage vehicles, limited power resources should be allocated reasonably, giving priority to serving key facilities and high-priority users, avoiding resource waste, and maximizing the benefits of power supply. Effective power restoration can minimize the impact of power outages on residents' lives and business operations, reduce economic losses, and improve public confidence in the power supply system.

[0049] Update road conditions based on weather information, dynamically adjust route planning, maintain accuracy and safety of route selection even when extreme weather conditions change, and reduce additional risks caused by wrong decisions;

[0050] Specifically, the road conditions under extreme weather conditions, distribution network fault information and distribution priority information are integrated into the basic two-dimensional model, and various realistic factors are fully considered, such as the impact of extreme weather on road driving. At the same time, the initial pheromone concentration in each iteration is adjusted according to the road conditions to obtain the optimal travel path of the mobile energy storage vehicle in this iteration, and then the optimal travel path of the mobile energy storage vehicle is determined. This method has strong environmental adaptability and can operate effectively under different climatic conditions. It provides a universal solution for global power emergency protection and has certain guiding significance for power companies to accurately predict the optimal travel route of mobile energy storage vehicles based on road conditions and distribution priority. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0052] Figure 1 It is a flowchart of a mobile energy storage vehicle path planning method based on an improved ant colony algorithm with positive and negative incentives;

[0053] Figure 2 A schematic diagram of the node topology structure of a basic two-dimensional model in one implementation. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0055] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0057] This example analyzes a certain area, selects 14 road intersections as nodes, and the number of ants is 30; nodes 4, 9, 11, and 14 are distribution network fault nodes, i.e., target nodes, and the distribution priority is 4>9>11>14. The road between nodes 11 and 12 is congested and impassable; the traffic condition between nodes 13 and 14 is uncertain, and the remaining roads are normal and passable. The specific node topology diagram is shown in the figure below: Figure 2 shown.

[0058] Figure 1 A flow chart of mobile energy storage vehicle path planning based on improved ant colony algorithm with positive and negative incentives under extreme weather conditions is given. Figure 1 , a mobile energy storage vehicle path planning method, comprising the following steps:

[0059] S1. Establish a basic two-dimensional model based on the regional road traffic network and distribution network information; the regional road traffic network is the basis for the movement of mobile energy storage vehicles, and the regional distribution network is the basis for the distribution of power grid faults;

[0060] The specific steps for establishing a basic two-dimensional model based on regional road traffic network and distribution network information are as follows:

[0061] S11, determining the modeling object;

[0062] The working space of the mobile energy storage vehicle is used as the modeling object of the regional road traffic network, and the specific power supply location and geographical location information of the distribution network within the regional scope are used as the modeling object of the regional distribution network; among them, all roads within the regional scope that can be used by the mobile energy storage vehicle are the working space of the mobile energy storage vehicle;

[0063] S12, divide the nodes and edges, and draw a topological graph;

[0064] After obtaining the road information of the regional road traffic network through the geographic information system, the intersection of roads is taken as nodes, and the roads are taken as edges connecting the nodes; the geographical location of the power supply site is taken as a node, and the geographical location of the power supply site is connected with the nodes in the road traffic network, the working space of the mobile energy storage vehicle is simulated as a two-dimensional space, and the power supply range responsible for it is divided based on the power supply node, and a topological map is drawn;

[0065] S13, introducing road traffic conditions and distribution network fault information under the influence of extreme weather based on the topological map;

[0066] Since extreme weather has a certain impact on road traffic conditions, and there is also a possibility of uncertain road traffic conditions due to limited regional road information feedback; as an emergency resource, the road traffic conditions of mobile energy storage vehicles must be considered. Good road conditions will provide a reliable basis for the safe driving of mobile energy storage vehicles. Therefore, the road traffic conditions under extreme weather are divided into three situations: normal passable, congested and impassable, and uncertain traffic conditions. These three situations will correspond to different incentives in the subsequent path planning; at the same time, extreme weather will also have a certain impact on the normal operation of the distribution network. Therefore, by obtaining the monitoring operation status information of the distribution network by the power department, the distribution network faults and their specific geographical locations can be obtained, and the latest regional road information and the distribution network faults and their specific geographical locations can be integrated into the topology map. The information that can be integrated is marked on the topology map;

[0067] Furthermore, the priority determination information of the distribution network faults in the power department can also be obtained. The priority determination information is specifically the priority level of the fault among all current distribution network faults, that is, the priority recovery sequence of the fault. At the same time, this priority information is attached to the topology map.

[0068] S2. Based on the established basic two-dimensional model, use the ant colony algorithm to meet the power distribution requirements; among them, the object processed by the ant colony algorithm is the modeling object;

[0069] S21. Determine the initial conditions of the iteration, including various parameters such as population size and maximum number of iterations, and at the same time determine the initial position and target position of the ants;

[0070] Specifically: Assume the number of ants is m, and distribute m ants randomly in the basic two-dimensional model. Each ant takes a random road intersection node in the basic two-dimensional model as the initial node and n distribution network faults as the target nodes, and the target nodes are attached with their priority information;

[0071] S22. Determine whether this iteration is the first iteration. If so, initialize the pheromone concentration;

[0072] Specifically: Take the path from the initial node to the target node, and set the initial pheromone concentration of each path. The formula is:

[0073] τ ij (0)=(1 - x + y)·c

[0074] Among them, c represents a constant, x(0 < x < 1) represents the path traffic index, which is divided into three situations: normal passable is 0, congested and impassable is 1, and uncertain traffic conditions is 0.5, and y(0 < y < 1) represents the path priority index determined according to the priority of the path with the target node as the vertex.

[0075] S23, each ant performs a new round of iteration;

[0076] Specifically: place the ant at the initial node and add the node to the taboo table to avoid returning to the initial node during path planning;

[0077] Each ant moves according to the state transition probability and traverses all nodes. The probability of ant k transferring from node i to node j is determined according to the following formula:

[0078]

[0079] Where J k (i) = {1,2…n}-tabu k represents the set of nodes that ant k is allowed to choose in the next step; tabu k The taboo table represents the nodes that ant k has walked through. When all nodes are added to the taboo table, k In the process, ant k has completed a node traversal, and the path taken by ant k is a feasible solution for the mobile energy storage vehicle to move forward; τ ij (t) represents the pheromone concentration on the path from node i to node j at time t; η ij (t) represents the heuristic factor from node i to node j at time t, that is, the expected degree of ant transfer from node i to node j at time t, usually the inverse of the distance between node i and node j; α and β represent the relative importance of pheromone concentration and heuristic factor respectively;

[0080] S24, if this iteration is not the first iteration and all ants have completed the traversal of all nodes, then based on the number of ants m, obtain the path information of all ants from the starting node to each target node in the current iteration;

[0081] S25. After all ants complete the node traversal, that is, after one round of search, the pheromone concentration of the path between each node is updated according to the following formula

[0082] τ ij (t+n)=(1-ρ)·τ ij (t)+Δτ ij

[0083] Among them, ρ (0<ρ<1) represents the pheromone evaporation coefficient of the path between node i and node j, 1-ρ represents the persistence coefficient of pheromone; Δτ ij Represents the increment of path pheromone between node i and node j in this iteration.

[0084] S26, performing positive and negative incentives on the travel routes of each ant to obtain an increase in pheromone concentration;

[0085] Specifically, it includes: determining whether the traversal paths of each ant include congested and impassable road traffic conditions and uncertain traffic conditions, and generating negative incentives for them; determining whether the traversal paths of each ant meet the power distribution priority requirements, and generating positive incentives for them. Determine Δτ according to the following formula ij

[0086]

[0087] In the formula, x(0 < x < 1) represents its path traffic index, which is divided into three cases: normal passable is 0, congested and impassable is 1, and uncertain traffic condition is 0.5; y(0 < y < 1) represents the path priority index determined according to the priority of the vertices of the path with the target node as the vertex; represents the pheromone remaining on the path between node i and node j by the k-th ant in this iteration. If it has not passed the relevant path, it is zero. Specifically, it is the following formula

[0088]

[0089] In the formula, Q is a positive constant, and L k represents the sum of the path lengths of the k-th ant in this traversal.

[0090] S27. Among the path information of all ants from the starting node to the target node, select the optimal path information in the current iteration;

[0091] Among them, the optimal path is the traversal path with the smallest L in this iteration k that is, the shortest distance;

[0092] S28. Update the pheromone concentration of all paths, and update the road traffic conditions and power distribution priority information;

[0093] S29. Enter a new round of iteration based on the existing pheromone concentration, road traffic conditions, and power distribution priority information, and determine whether the algorithm termination condition is met. If so, output the optimal path, terminate the algorithm, and use the optimal path as the optimal travel path of the mobile energy storage vehicle for power distribution. Among them, the optimal path is the traversal path with the smallest distance in all iterations; if not, return to step S22; among them, the algorithm termination condition is convergence or reaching the maximum number of iterations.

[0094] In another embodiment of the present invention, the present invention also provides a system including: a model establishment module for establishing a basic two-dimensional model based on the regional road traffic network and power distribution network information;

[0095] A power distribution module for iteratively obtaining the optimal travel path of the mobile energy storage vehicle based on the established basic two-dimensional model to meet the power distribution requirements.

[0096] In another embodiment of the present invention, the present invention further provides an electronic device, comprising: a processor, and a memory for storing executable commands of the processor; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned path planning methods for a mobile energy storage vehicle.

[0097] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here may include a built-in storage medium in a computer device, and of course may also include an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the path planning method for the mobile energy storage vehicle in the above embodiment.

[0098] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0099] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A mobile energy storage vehicle path planning method based on an improved ant colony algorithm with positive and negative incentives, characterized in that: Including: Taking the regional road traffic network and distribution network information as input, combining the passability of roads affected by extreme weather and the distribution network fault information to establish a basic two-dimensional model; the basic two-dimensional model includes several nodes. Based on the established basic two-dimensional model, using the ant colony algorithm to iterate the optimal travel path of the mobile energy storage vehicle to meet the power distribution demand, including setting the initial pheromone concentration in the ant colony algorithm based on the current road passability.

2. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 1 is characterized in that: The step of taking the regional road traffic network and distribution network information as input, combining the passability of roads affected by extreme weather and the distribution network fault information to establish a basic two-dimensional model includes: Based on the regional road traffic network and distribution network information, dividing nodes and edges to establish a topological structure. Integrating the passability of roads affected by extreme weather and the distribution network fault information into the topological structure to establish the basic two-dimensional model.

3. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 2 is characterized in that: Based on the regional road traffic network and distribution network information, dividing nodes and edges to establish a topological structure includes: Taking the workable space of mobile energy storage vehicles in the region as the first physical space. Taking the geographical locations of the distribution network and power supply sites in the region as the second physical space. Mapping the first physical space and the second physical space onto the regional road traffic network. Taking the road intersections of the regional road traffic network as nodes and the roads as edges connecting the nodes to establish a topological structure.

4. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 1 is characterized in that: The step of based on the established basic two-dimensional model, using the ant colony algorithm to iterate the optimal travel path of the mobile energy storage vehicle to meet the power distribution demand includes: Determining the number m of the ant population. Determining the initial node and target node of the ants. Judging whether it is the first iteration, if so, initializing the pheromone concentration. Each ant enters a round of iteration. After this iteration ends, updating one or more of the pheromone concentration, road traffic information, and power distribution priority sequence of each path. When the termination condition of the ant colony algorithm is met, outputting the optimal travel path.

5. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 4 is characterized in that: The step of each ant entering a round of iteration includes: Each ant starts from the initial node and traverses each node. Obtaining the path information of each ant traversing each node from the initial node. Performing positive and negative incentives on the path to adjust the pheromone concentration. Outputting the optimal travel path in the current iteration.

6. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 5 is characterized in that: When all ants have completed traversing, it further includes: Updating the pheromone concentration of the paths between each node according to the following formula t ij (t+n)=(1-ρ)·τ ij (t)+Δτ ij Where ρ represents the pheromone evaporation coefficient of the path between node i and node j, 0<ρ<1; 1-ρ represents the persistence coefficient of pheromone; Δτ ij represents the increment of the path pheromone between node i and node j in this iteration, n is the number of target nodes for distribution network faults, τ ij (t) represents the pheromone concentration on the path from node i to node j at time t; Determine Δτ according to the following formula ij Wherein, x represents its path passing index, and 0 < x < 1; it is divided into three cases: normal passable is 0, congested impassable is 1, and the passing condition is uncertain is 0.5; y represents the path priority index determined according to the priority of the vertices of the path with the target node as the vertex, and 0 < y < 1; It represents the pheromone left by the k-th ant on the path between node i and node j in this iteration.

7. The mobile energy storage vehicle path planning method based on the improved ant colony algorithm with positive and negative incentives according to claim 1 is characterized in that: The step of setting the initial pheromone concentration in the ant colony algorithm based on the current road passability includes: Set the initial pheromone concentration τ for each path according to the following formula ij (0) t ij (0)=(1-x+y)·c In the formula, c represents a constant; x represents its path passability index, 0 < x < 1; it is divided into three cases: normal passable is 0, congested and impassable is 1, and the passability condition is uncertain is 0.5; y represents the path priority index determined according to the priority of the vertex of the path with the target node as the vertex, 0 < y < 1.

8. A mobile energy storage vehicle path planning system based on an improved ant colony algorithm with positive and negative incentives, characterized in that: Including: A model establishment module, which is used to take the regional road traffic network and distribution network information as input, and combine the passability of roads affected by extreme weather and the distribution network fault status to establish a basic two-dimensional model. A power distribution module, which is used to based on the established basic two-dimensional model, use the ant colony algorithm to iterate the optimal travel path of the mobile energy storage vehicle to meet the power distribution demand, including setting the initial pheromone concentration in the ant colony algorithm based on the current road passability.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a mobile energy storage vehicle path planning method based on an improved ant colony algorithm with positive and negative incentives as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a mobile energy storage vehicle path planning method based on an improved ant colony algorithm with positive and negative incentives as described in any one of claims 1 to 7 are implemented.

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