Path planning method and system for cooperative operation of multiple environmental sanitation vehicle regions

Abstract

The invention provides a multi-environmental sanitation vehicle area collaborative operation path planning method and system, and relates to the technical field of environmental sanitation operation planning, and the method comprises the steps: obtaining a cleaning area map corresponding to a to-be-cleaned target area of an environmental sanitation vehicle, the cleaning area map comprising a plurality of environmental sanitation operation areas; discretizing each environmental sanitation operation area into a plurality of cleaning nodes, and constructing a cleaning path network according to all the cleaning nodes; based on the sweeping node transition probability function and the sweeping path network, constructing a path planning model, and constructing a corresponding initial cleaning path for each sanitation vehicle according to the path planning model; and inputting the collaborative path optimization function and the initial cleaning path into the path planning model for successive iteration, and when an iteration termination condition is reached, enabling the path planning model to output an optimized cleaning path corresponding to each sanitation vehicle. According to the method, the punctuality and the working efficiency of sanitation vehicle working can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sanitation operation planning, and in particular to a method and system for planning a path for regional collaborative operations of multiple sanitation vehicles. Background Art

[0002] With the development of cities, people's requirements for the environment are getting higher and higher. Traditional sanitation management methods can no longer meet the information processing needs of sanitation management; managers cannot grasp relevant information in real time and dynamically, and cannot quickly dispatch personnel when encountering emergencies; sanitation departments have increasingly higher requirements for low-cost control, and similarly, the standards for urban environmental sanitation are also getting higher and higher, and the sanitation management system is also tending to develop in a refined manner.

[0003] The Chinese patent with publication number CN117436662A discloses an intelligent optimization and guidance system for sanitation vehicle operation routes. The system calculates the priority coefficient of the cleaning operation by comprehensively considering the emergency label, time limit and distance from the sanitation vehicle of the cleaning operation; generates a job ranking table based on the priority coefficient, and plans the operation route of the sanitation vehicle according to the order of the job ranking table; can give priority to more urgent cleaning operations; when the sanitation vehicle starts to handle the cleaning operation, it monitors the time limit of the cleaning operation, and when the time limit is less than the set time limit threshold, the emergency label of the corresponding cleaning operation is upgraded by one level. However, the above scheme needs to draw the operation route on the electronic map and lacks data support. It is also impossible to adjust the cleaning path according to real-time data and dynamic changes, and it is difficult to ensure the operating efficiency of the sanitation vehicle. Therefore, it is very necessary to provide a container plant area container truck path planning method and system based on Beidou positioning to improve the punctuality and operating efficiency of sanitation vehicle operations. Summary of the invention

[0004] In view of this, the present invention proposes a method and system for planning paths for regional collaborative operations of multiple sanitation vehicles. By obtaining a cleaning area map and discretizing each sanitation operation area into multiple cleaning nodes, and assigning an initial cleaning path to each sanitation vehicle, and adjusting the cleaning path according to real-time data and dynamic changes, the punctuality and efficiency of sanitation vehicle operations can be significantly improved.

[0005] The present invention provides a method for planning a path for regional collaborative operation of multiple sanitation vehicles, the method comprising:

[0006] Acquire a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas;

[0007] Discretize each sanitation operation area into multiple cleaning nodes, and construct a cleaning path network based on all cleaning nodes;

[0008] Based on the cleaning node transfer probability function and the cleaning path network, a path planning model is constructed, and a corresponding initial cleaning path is constructed for each sanitation vehicle according to the path planning model;

[0009] The collaborative path optimization function and the initial cleaning path are input into the path planning model for successive iterations. When the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

[0010] On the basis of the above technical solution, preferably, each sanitation operation area is discretized into a plurality of cleaning nodes, and a cleaning path network is constructed according to all the cleaning nodes, specifically including:

[0011] Dividing each sanitation operation area into a plurality of sanitation operation sub-areas according to a predetermined spatial resolution, wherein each sanitation operation sub-area includes one or more cleaning nodes;

[0012] Determine the connection relationship between any two cleaning nodes and the path weight between any two cleaning nodes according to the geographical distance between any two cleaning nodes, the traffic attributes between any two cleaning nodes, and the job attribute information corresponding to the cleaning nodes;

[0013] According to the connection relationship and path weight between any two cleaning nodes, an adjacency matrix of each cleaning node is generated;

[0014] A cleaning path network is constructed based on the adjacency matrix and the cleaning nodes corresponding to the sanitation operation sub-areas.

[0015] Based on the above technical solution, preferably, the job attribute information includes one or more of the geographical location coordinates of the cleaning node, the garbage density of the cleaning node, the job time window requirement of the cleaning node, the estimated job workload of the cleaning node and the node priority.

[0016] More preferably, constructing a corresponding initial cleaning path for each sanitation vehicle according to the path planning model specifically includes:

[0017] Collecting basic working information of each sanitation vehicle, wherein the basic working information includes the available time window of operating capacity and historical operating data;

[0018] Based on the cleaning node transfer probability function and the cleaning path network, a corresponding cleaning node sequence is allocated to each sanitation vehicle, and the cleaning nodes in the cleaning node sequence are sequentially connected according to timing requirements to obtain an initial cleaning path.

[0019] More preferably, the corresponding cleaning node sequence allocated to each sanitation vehicle based on the cleaning node transfer probability function and the cleaning path network specifically includes:

[0020] According to the cleaning node transition probability function, calculating the transition probability between any two cleaning nodes;

[0021] A corresponding cleaning node sequence is allocated to each sanitation vehicle based on the ant colony optimization algorithm and the transition probability between any two cleaning nodes.

[0022] More preferably, the specific expression of the sweep node transition probability function is:

[0023]

[0024] Among them, P ij represents the sweep node transition probability function, represents the pheromone concentration on the edge between cleaning node i and cleaning node j, λ 1 represents the first important parameter, η ij represents the heuristic information parameter of cleaning node i and cleaning node j, λ 2 represents the second importance parameter, k represents all feasible neighbor nodes of node i, O i represents the set of neighbor nodes of the cleaning node i, λ 3 represents the third most important parameter, S ij represents the spatial similarity factor between the cleaning node i and the cleaning node j in the cleaning path network, ω 1 Represents the weight coefficient corresponding to the geographical distance, d ij represents the Euclidean distance between cleaning node i and cleaning node j in the cleaning path network, ω 2 Represents the weight coefficient corresponding to the regional density, ρ i represents the area density of the cleaning node i, ρ j represents the area density of the cleaning node j, ω 3 represents the weight coefficient corresponding to the time window, OL() represents the overlapping length function of the time window, T i represents the operation time window of cleaning node i, T j represents the operation time window of cleaning node j, and Span() represents the time span function of the operation time window of cleaning node i and the operation time window of cleaning node j.

[0025] More preferably, the specific expression of the collaborative path optimization function is:

[0026]

[0027] Among them, F represents the collaborative path optimization function, α represents the weight coefficient corresponding to the topological structure term, u represents the total number of edges in the sweep path network, and N ij represents the edge between cleaning node i and cleaning node j in the cleaning path network, k i represents the cleaning workload of cleaning node i, k j represents the cleaning workload of cleaning node j, g i Indicates the sanitation operation area number to which cleaning node i belongs, g j represents the sanitation operation area number to which cleaning node j belongs, Γ(g i ,g j ) represents the judgment function of the sanitation operation area, β represents the weight coefficient corresponding to the spatial similarity function, S ij represents the spatial similarity function of the cleaning node i and the cleaning node j in the cleaning path network, γ represents the weight coefficient corresponding to the load balancing factor, μ represents the average value of the workload of all sanitation vehicles, and σ represents the standard deviation of the workload of all sanitation vehicles.

[0028] More preferably, the iteration termination condition includes:

[0029] When the path planning model reaches a preset maximum number of iterations, the iterative process of the path planning model is stopped; or when the difference between two consecutive output values ​​of the collaborative path optimization function is lower than a preset optimization threshold in two consecutive iterations, the iterative process of the path planning model is stopped.

[0030] In a second aspect of the present application, a multi-sanitation vehicle regional collaborative operation path planning system is provided, wherein the multi-sanitation vehicle regional collaborative operation path planning system comprises a network construction module, a path construction module and a path optimization module, wherein:

[0031] The network construction module is used to obtain a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas, each sanitation operation area is discretized into a plurality of cleaning nodes, and a cleaning path network is constructed according to all the cleaning nodes;

[0032] The path construction module is used to construct a path planning model based on the cleaning node transfer probability function and the cleaning path network, and construct a corresponding initial cleaning path for each sanitation vehicle according to the path planning model;

[0033] The path optimization module is used to input the collaborative path optimization function and the initial cleaning path into the path planning model for successive iterations. When the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

[0034] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0035] The method and system for planning a path for regional collaborative operation of multiple sanitation vehicles provided by the present invention have the following beneficial effects compared with the prior art:

[0036] (1) By obtaining the cleaning area map and discretizing each sanitation operation area into multiple cleaning nodes, it is ensured that all cleaning areas are fully covered and the omission of operations is reduced. Based on the cleaning node transfer probability function and the path planning model, an initial cleaning path is assigned to each sanitation vehicle to ensure the balance of the operation load of each vehicle and improve the overall resource utilization and comprehensiveness. The path planning model can adjust the cleaning path according to real-time data and dynamic changes through successive iterative optimization to ensure that each cleaning node completes the operation within the specified time window, significantly improving the punctuality and efficiency of sanitation vehicle operations. In the collaborative path optimization process, the coordination between multiple vehicles is considered to avoid path conflicts and resource competition, thereby improving the stability and reliability of sanitation vehicle operation.

[0037] (2) By subdividing the sanitation operation area into multiple sanitation operation sub-areas, and each sanitation operation sub-area contains one or more cleaning nodes, the comprehensiveness and meticulousness of the operation coverage are ensured, important areas are avoided from being missed, and the accuracy of path planning is improved. At the same time, by generating an adjacency matrix and constructing a cleaning path network, structured data support is provided for the path optimization algorithm, so that sanitation vehicles can operate efficiently in the optimized path network, reduce repeated operations and empty driving, and improve resource utilization and operation efficiency. In addition, the calculation model of the path weight takes into account the dynamic traffic conditions and operation priorities, and can adjust the path planning according to real-time changes, ensuring that sanitation operations can respond quickly to environmental changes, further improving operation efficiency and resource allocation flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a flow chart of a method for regional collaborative operation path planning for multiple sanitation vehicles provided by the present invention;

[0040] Figure 2A schematic diagram of the structure of the multi-sanitation vehicle regional collaborative operation path planning system provided by the present invention;

[0041] Figure 3 This is a schematic structural diagram of an electronic device provided by the present invention.

[0042] Explanation of the accompanying drawings: 1. Multi-sanitation vehicle regional collaborative operation path planning system; 11. Network construction module; 12. Path construction module; 13. Path optimization module; 2. Electronic device; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. DETAILED DESCRIPTION

[0043] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The present invention discloses a method for planning a path for regional collaborative operation of multiple sanitation vehicles, referring to Figure 1 The method includes steps S1 to S4.

[0045] Step S1, obtaining a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas.

[0046] In this step, the geographic information data can contact the local municipal department or relevant government agencies to apply for the latest geographic information data, including road networks, building distribution, green space, parks, public facilities, etc. To obtain vector data (such as Shapefile), raster data (such as satellite images) and DEM (digital elevation model), etc. Or use open platforms such as Amap, Baidu Map, OpenStreetMap to obtain basic geographic data, and obtain map data of the required area through API interface or download tools, including roads, landmark buildings, public facilities, etc. Or use drones for high-resolution aerial photography, or purchase the latest satellite images from commercial satellite image providers to obtain the latest terrain and landform information, and identify real-time dynamics such as road changes and construction areas. Sanitation operation data can be obtained through data provided by the sanitation department. Sanitation operation data includes cleaning routes, operation frequency, operation schedule, and historical operation records.

[0047] Furthermore, GIS software (such as ArcGIS, QGIS) is used to convert data from different sources into a unified format, such as GeoJSON, Shapefile, etc. Duplicate or irrelevant data points are deleted to ensure the uniqueness and relevance of the data, correct geographic coordinate errors, and complete missing data attributes. The projection coordinate system, measurement units, and naming conventions of the data are unified to ensure consistency between different data sets. Different types of geographic data (roads, buildings, green spaces, etc.) are superimposed to form a comprehensive geographic information layer. According to the needs of sanitation operations, the entire target area is divided into multiple operation areas, each of which contains specific cleaning tasks and scopes. The sanitation operation data is associated with the geographic information data, such as attaching attributes such as cleaning frequency and operation time to the corresponding geographic area or node.

[0048] In one example, the sanitation operation area division standards include geographic boundary standards and operation demand standards. Geographic boundary standards include dividing the operation area according to natural geographical features (such as rivers, park boundaries) or administrative divisions (such as streets, communities). Operation demand standards include dividing different operation areas according to factors such as cleaning frequency, garbage generation, and traffic flow. Use GIS tools to draw the boundaries of each sanitation operation area to ensure that the coverage of each area is reasonable and non-overlapping. And in each operation area, identify key cleaning nodes, such as garbage bin locations, major intersections, and in front of important buildings. Record the geographic coordinates, cleaning frequency, priority and other attribute information of each cleaning node. Connect each cleaning node according to the road network to form a potential driving route, and assign weights to each path according to factors such as road type, traffic flow, and cleaning difficulty. The path allocation weight reflects the quality of the path. Assign accurate geographic coordinates (longitude, latitude) to each cleaning node to ensure the accuracy of path planning.

[0049] Step S2: discretize each sanitation operation area into multiple cleaning nodes, and construct a cleaning path network based on all the cleaning nodes.

[0050] This step also includes steps S21 to S24.

[0051] Step S21: divide each sanitation operation area into a plurality of sanitation operation sub-areas according to a predetermined spatial resolution, wherein each sanitation operation sub-area includes one or more cleaning nodes.

[0052] In this step, detailed geographic information of the sanitation operation area is obtained, including road networks, building distribution, green areas, etc. A regular grid is covered in GIS to divide the entire operation area into grid units of equal size, each of which is a sanitation operation sub-area. According to actual conditions such as population density, traffic flow, etc., irregular or adaptive partitioning methods can be used to make the sanitation operation sub-area domain more in line with actual needs. According to the location of the cleaning nodes in each sub-area, the nodes are assigned to the corresponding sub-areas to ensure that each sub-area contains one or more cleaning nodes to optimize the operation efficiency.

[0053] Furthermore, according to the operating capacity and characteristics of sanitation vehicles (such as sweeping width, turning radius, etc.), determine the appropriate spatial resolution (for example, the side length of each sub-area is 50 meters). Obtain detailed map data of the sanitation operation area, including road network, block layout, terrain features, etc. Clean and format the acquired map data, remove redundant information, ensure data consistency and availability, and extract the boundary information of the operation area as the basis for the division of sanitation operation sub-areas. Establish a Cartesian coordinate system in the operation area, determine the origin position, and provide a reference framework for grid division. According to the predetermined spatial resolution (ΔX, ΔY), generate equally spaced grids in the operation area, and each grid cell represents a potential sanitation operation sub-area. Identify the boundary grid cells of the sanitation operation area, handle the situation where the boundary and the overall area do not completely overlap, adjust the shape of the boundary grid, ensure the continuity and integrity of the sub-area, and avoid crossing physical obstacles or unreasonable sub-area morphology. For the grid cells near the boundary, refine them to adapt to the actual road and terrain characteristics to ensure the practicality and operability of the sub-area. Set a minimum area threshold for each sub-area to avoid sub-areas that are too small or cannot operate independently. For sub-areas with an area below the minimum threshold, merge according to preset rules (such as merging with the nearest sub-area) to ensure that the merged sub-areas still meet the predetermined spatial resolution requirements while maintaining the regularity and continuity of the sub-areas. In each sanitation operation sub-area, one or more cleaning nodes are assigned according to actual operation needs and cleaning priorities. Ensure that each cleaning node covers a specific operation point in the sub-area to meet the actual needs of sanitation operations. Assign a unique identifier to each sub-area and record its attribute information such as geographic coordinate range, area, and included cleaning nodes. Store the sub-area division results in a standard geographic information system (GIS) format to facilitate subsequent path planning and management.

[0054] Step S22, determining the connection relationship between any two cleaning nodes and the path weight between any two cleaning nodes according to the geographical distance between any two cleaning nodes, the traffic attributes between any two cleaning nodes and the job attribute information corresponding to the cleaning nodes.

[0055] Furthermore, the job attribute information includes one or more of the geographical location coordinates of the cleaning node, the garbage density of the cleaning node, the job time window requirement of the cleaning node, the estimated job workload of the cleaning node, and the node priority.

[0056] In this step, traffic attributes include road type, traffic conditions, and road conditions. Road types include main roads, secondary roads, branch roads, etc. Different road types have different capacity and speed limits; traffic conditions include road congestion, speed limits, number and distribution of traffic lights, etc.; road conditions include one-way streets, two-way streets, road maintenance status, and construction areas. Job attributes include cleaning task priority, time window, and job complexity. Task priorities of different cleaning nodes, such as garbage collection and street sweeping, may have specific job time requirements and need to be completed within the specified time. Job complexity includes the difficulty of cleaning tasks, required resources (such as manpower, equipment), etc.

[0057] According to the traffic attributes, geographical distance and operation attribute information, it is determined whether a connection relationship should be established between any two cleaning nodes, which mainly includes the following steps:

[0058] Use geographic information system (GIS) tools or geographic coordinates to calculate the straight-line distance and actual driving distance between two cleaning nodes. Analyze the road type and traffic conditions connecting the two cleaning nodes to determine the road's capacity and possible travel time. Check the job priority and time window of the two cleaning nodes to ensure that the connection path can meet the job time requirements. Filter out feasible connection paths based on geographic distance and traffic attributes to ensure that there is at least one feasible path connecting each pair of cleaning nodes, for example, excluding paths that are inaccessible due to road construction or traffic restrictions.

[0059] Furthermore, the road network data is used to ensure that the connection relationship follows the actual direction of the road and traffic rules, identify the nodes (intersections) and edges (roads) in the road network, and construct the road network topology. Prioritize the connection of geographically close clean nodes to reduce unnecessary long-distance driving, and ensure that there is a feasible road path between the two nodes, taking into account the road type and access restrictions. Generate possible connection pairs between all clean nodes, and eliminate infeasible or too costly connection pairs based on the road network and traffic restrictions. Path weights can be defined based on the total time required from the start to the end, the total length of the path, and a comprehensive score combining time, distance, and other factors.

[0060] It is understandable that the geographical location and operation attribute information of all cleaning nodes are collected and sorted, and the traffic attribute data of the relevant road network is obtained, including road type, traffic congestion, speed limit, etc. For each pair of cleaning nodes, the actual driving distance and driving time are calculated. According to the road network and traffic attributes, determine whether there is a feasible connection path. Establish feasible connection relationships to form a preliminary set of path candidates. For each candidate path, calculate the path weight based on the geographical distance, traffic attributes and operation attributes. Apply the standardization and weighting model. Integrate all cleaning nodes and their connection relationships and corresponding path weights to form a complete cleaning path network. Check the connectivity of the generated path network to ensure that there are feasible paths connecting all necessary nodes. Evaluate the rationality of the path weights to ensure that the weights reflect the actual operation requirements and traffic conditions. Conduct simulation tests to verify the effectiveness and optimization potential of the path network in actual applications.

[0061] In an example, assuming there are two cleaning nodes A and B, the specific process of determining the connection relationship and path weight between them is as follows:

[0062] Calculated distance: The actual driving distance between cleaning node A and cleaning node B is 2 kilometers.

[0063] Evaluate traffic attributes: The main road between clean node A and clean node B is a secondary road with a speed limit of 50 km / h and a current traffic congestion index of 0.3.

[0064] Consider the job attributes: the job priority of cleaning node A is high, the job priority of cleaning node B is medium, and the job time window is from 8 am to 10 am.

[0065] Calculate path weight (assuming maximum distance is 10 km): Normalized distance: Distance weight = 2 / 10 = 0.2 Distance weight = 2 / 10 = 0.2.

[0066] Standardized traffic congestion index: traffic weight = 0.3 / 1 = 0.3.

[0067] The inverse number of the job priority (high priority is 1, medium is 0.8): job weight = (1+0.8) / 2 = 0.9.

[0068] Comprehensive weight (assuming weight coefficients α = 0.5, β = 0.3, γ = 0.2):

[0069] Comprehensive weight = 0.5×0.2+0.3×0.3+0.2×0.9=0.1+0.09+0.18=0.37 Comprehensive weight = 0.5×0.2+0.3×0.3+0.2×0.9=0.1+0.09+0.18=0.37.

[0070] That is, in the cleaning path network, a connection path with a weight of 0.37 is established between cleaning node A and cleaning node B.

[0071] In this embodiment, by systematically analyzing and integrating geographical distance, traffic attributes and operation attribute information, the connection relationship and path weight between any two cleaning nodes can be effectively determined, which not only ensures the scientificity and rationality of the path network, but also provides a solid foundation for subsequent path optimization and operation scheduling. Reasonable connection relationship and accurate path weight calculation can significantly improve the efficiency and effect of collaborative operation of multiple sanitation vehicles, and realize efficient utilization of resources and comprehensive coverage of operations.

[0072] Step S23: Generate an adjacency matrix of each cleaning node according to the connection relationship and path weight between any two cleaning nodes.

[0073] In this step, all clean nodes are assigned unique numbers (e.g., node 1, node 2, ..., node N). Each node number is mapped to a row and column index in the adjacency matrix. Usually, the node number corresponds to the matrix index one-to-one. First, initialize an N×N matrix A, where N is the total number of clean nodes. For all paths from i=1 to N, and j=1 to N, if i=j, A is usually set to ij = 0, indicating that the distance from the node to itself is zero. Otherwise, set A ij =∞ or other special value indicating "no connection".

[0074] For each pair of clean nodes (i, j) in the path planning model, if there is a connection relationship between clean node i and clean node j, the path weight is assigned to the adjacency matrix A ij If there is no connection, keep A ij This is the initial "no connection" value.

[0075] Step S24, constructing a cleaning path network based on the adjacency matrix and the cleaning nodes corresponding to the sanitation operation sub-areas.

[0076] In this embodiment, by subdividing the sanitation operation area into multiple sanitation operation sub-areas, and each sanitation operation sub-area contains one or more cleaning nodes, the comprehensiveness and meticulousness of the operation coverage are ensured, important areas are avoided from being missed, and the accuracy of path planning is improved. At the same time, by generating an adjacency matrix and constructing a cleaning path network, structured data support is provided for the path optimization algorithm, so that sanitation vehicles can operate efficiently in the optimized path network, reduce repeated operations and empty driving, improve resource utilization and operation efficiency, and the calculation model of the path weight takes into account dynamic traffic conditions and operation priorities, and can adjust the path planning according to real-time changes, ensuring that sanitation operations can respond quickly to environmental changes, and further improve operation efficiency and resource allocation flexibility.

[0077] Step S3, constructing a path planning model based on the cleaning node transfer probability function and the cleaning path network, and constructing a corresponding initial cleaning path for each sanitation vehicle according to the path planning model.

[0078] This step also includes steps S31 to S32.

[0079] Step S31, collecting basic working information of each sanitation vehicle, wherein the basic working information includes the available time window of operating capacity and historical operating data.

[0080] In this step, the collection of operation capacity information includes entering basic information such as the cleaning equipment specifications and garbage container capacity of each vehicle into the system, and monitoring the working status of the cleaning equipment in real time through IoT sensors, and recording the use of the equipment. The collection of available time windows includes setting and adjusting the operation schedule of each vehicle in the vehicle management system, including daily working hours, rest periods, and maintenance time. The collection of historical operation data includes the system automatically recording the start and end time, operation location, driving route, and operation volume of each operation, allowing the driver to manually enter or confirm the operation information to supplement the details that the system cannot automatically collect.

[0081] Step S32, based on the cleaning node transfer probability function and the cleaning path network, a corresponding cleaning node sequence is allocated to each sanitation vehicle, and the cleaning nodes in the cleaning node sequence are sequentially connected according to timing requirements to obtain an initial cleaning path.

[0082] In this step, the starting cleaning node of each sanitation vehicle is determined, which is usually its current location or base node. According to the transition probability function, the next cleaning node is selected from the current node, and this process is repeated until all assigned cleaning nodes are included in the sequence. The cleaning nodes are arranged in an appropriate order according to the time window and job priority of each cleaning node. For high-priority or emergency operation nodes, they are given priority in the front of the sequence to ensure timely completion. Traverse the adjacent node pairs in the cleaning node sequence, use the adjacency matrix in the cleaning path network, find the optimal path (such as the shortest path or the minimum weight path) from node A to node B, calculate the travel time and estimated operation time from node A to node B, and ensure that the time to reach node B is within its time window. If it is not satisfied, the node sequence or path needs to be adjusted. If the time check fails, the following strategy is applied for adjustment: adjust the position of node B in the sequence so that it is executed at an earlier or later time period, or select other connection paths, which may sacrifice the driving distance but save time, or extend or shorten the operation time to meet the time window within the possible range.

[0083] In an example, assume that a sanitation vehicle is assigned the following cleaning node sequence: node A, node B, node C, and node D, and each node has a specific time window.

[0084] Based on the transition probability function, the sequence is determined to be A→B→C→D.

[0085] Find the optimal path from A to B, calculate the time required, and ensure that the time to reach B is within its time window. Find the optimal path from B to C, calculate the time required, and ensure that the time to reach C is within its time window. If a conflict is found, such as the time to reach C is earlier than its start time, adjust the sequence or choose an alternative path. Similarly, find the path and verify the time, and adjust it if necessary. Confirm that the optimized path is A→B→C→D. The path has been optimized and verified to meet all job requirements.

[0086] Based on the cleaning node transfer probability function and the cleaning path network, a cleaning node sequence is generated for each sanitation vehicle, and these nodes are connected in sequence to obtain the initial cleaning path. This can effectively optimize the path planning of sanitation operations, which not only improves the accuracy and rationality of the path, but also improves the operation efficiency and resource utilization, ensuring that each vehicle can complete the cleaning task within the specified time.

[0087] In this embodiment, the transition probability between any two cleaning nodes is calculated according to the cleaning node transition probability function; based on the ant colony optimization algorithm and the transition probability between any two cleaning nodes, a corresponding cleaning node sequence is allocated to each sanitation vehicle.

[0088] The specific expression of the sweep node transition probability function is:

[0089]

[0090] Among them, P ij represents the sweep node transition probability function, represents the pheromone concentration on the edge between cleaning node i and cleaning node j, λ 1 represents the first important parameter, η ij represents the heuristic information parameter of cleaning node i and cleaning node j, λ 2 represents the second importance parameter, k represents all feasible neighbor nodes of node i, O i represents the set of neighbor nodes of the cleaning node i, λ 3 represents the third most important parameter, S ij represents the spatial similarity factor between the cleaning node i and the cleaning node j in the cleaning path network, ω 1 Represents the weight coefficient corresponding to the geographical distance, d ij represents the Euclidean distance between cleaning node i and cleaning node j in the cleaning path network, ω2 Represents the weight coefficient corresponding to the regional density, ρ i represents the area density of the cleaning node i, ρ j represents the area density of the cleaning node j, ω 3 represents the weight coefficient corresponding to the time window, OL() represents the overlapping length function of the time window, T i represents the operation time window of cleaning node i, T j represents the operation time window of cleaning node j, and Span() represents the time span function of the operation time window of cleaning node i and the operation time window of cleaning node j.

[0091] Furthermore, the heuristic information parameter of the cleaning node i and the cleaning node j can be the actual driving distance d from the cleaning node i to the cleaning node j. ij That is, the greater the heuristic information, the shorter the actual driving distance from the cleaning node i to the cleaning node j, and the higher the probability of achieving transfer.

[0092] The specific expression of the collaborative path optimization function is:

[0093]

[0094] Among them, F represents the collaborative path optimization function, α represents the weight coefficient corresponding to the topological structure term, u represents the total number of edges in the sweep path network, and N ij represents the edge between cleaning node i and cleaning node j in the cleaning path network, k i represents the cleaning workload of cleaning node i, k j represents the cleaning workload of cleaning node j, g i Indicates the sanitation operation area number to which cleaning node i belongs, g j represents the sanitation operation area number to which cleaning node j belongs, Γ(g i ,g j ) represents the judgment function of the sanitation operation area, β represents the weight coefficient corresponding to the spatial similarity function, S ij represents the spatial similarity function between cleaning node i and cleaning node j in the cleaning path network, γ represents the weight coefficient corresponding to the load balancing factor, μ represents the average workload of all sanitation vehicles, and σ represents the standard deviation of the workload of all sanitation vehicles.

[0095] The paths constructed by all ants are evaluated, and the path quality is calculated based on the path length, time cost and synergy effect.

[0096] Adjust pheromone concentration according to path quality:

[0097]

[0098] in, represents the pheromone concentration on the edge between cleaning node i and cleaning node j, ε represents the pheromone volatilization rate, represents the change in pheromone concentration on the edge between cleaning node i and cleaning node j, m represents the number of paths, and L represents the path length.

[0099] Step S4, inputting the collaborative path optimization function and the initial cleaning path into the path planning model for successive iterations, and when the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

[0100] In this embodiment, the initial cleaning path of each sanitation vehicle is input into the path planning model as the starting point of optimization, and the cleaning node sequence and initial path of each vehicle are input into the path planning model. The total cost of the initial path (such as total distance, total time) is calculated. Minor adjustments are made to the current initial cleaning path, such as node exchange, insertion or deletion, to generate new candidate paths, and path adjustments are made between multiple sanitation vehicles to optimize resource allocation and job coverage. The performance of each candidate path on the collaborative path optimization function is evaluated, and it is ensured that the candidate path meets constraints such as job capacity and time window. The candidate solution with the optimal objective function value and satisfying all constraints is selected. Once the termination condition is met, the path planning model outputs the optimized cleaning path for each sanitation vehicle and updates the current path to the selected optimal candidate path.

[0101] In an example, suppose there are two sanitation vehicles (vehicle 1 and vehicle 2), and their initial cleaning paths are as follows:

[0102] Car 1: Node A → Node B → Node C.

[0103] Car 2: Node D → Node E → Node F.

[0104] Through the collaborative path optimization function and successive iterations, the optimized path planning is as follows:

[0105] First iteration: Car 1 tries to swap nodes B and D, generating a new path A→D→C. Car 2 adjusts the path to node B→node E→node F. Evaluate the improved path, and the total distance is reduced.

[0106] Second iteration:

[0107] Car 1 exchanges node C with node E, generating a path A→D→E. Car 2 is adjusted to node B→node C→node F. The total time is further optimized.

[0108] The third round of iteration found that further exchange did not improve significantly, so the iteration was terminated.

[0109] Final optimization path:

[0110] Car 1: Node A → Node D → Node E.

[0111] Car 2: Node B → Node C → Node F.

[0112] The path planning model reaches the preset maximum number of iterations, and the iterative process of the path planning model is stopped; or in two consecutive iterations, the difference between the output values ​​of the two consecutive collaborative path optimization functions is lower than the preset optimization threshold, and the iterative process of the path planning model is stopped.

[0113] In this embodiment, by obtaining a cleaning area map and discretizing each sanitation operation area into multiple cleaning nodes, it is ensured that all cleaning areas are fully covered, reducing operation omissions, and based on the cleaning node transfer probability function and path planning model, an initial cleaning path is assigned to each sanitation vehicle to ensure balanced load on each vehicle and improve overall resource utilization and comprehensiveness. The path planning model can adjust the cleaning path according to real-time data and dynamic changes through successive iterative optimization to ensure that each cleaning node completes the operation within the specified time window, significantly improving the punctuality and efficiency of sanitation vehicle operations. In the collaborative path optimization process, coordination between multiple vehicles is considered to avoid path conflicts and resource competition, thereby improving the stability and reliability of sanitation vehicle operation.

[0114] Based on the above method, a multi-sanitation vehicle regional collaborative operation path planning system is proposed. Figure 2 The multi-sanitation vehicle regional collaborative operation path planning system 1 includes a network construction module 11, a path construction module 12 and a path optimization module 13, wherein:

[0115] The network construction module 11 is used to obtain a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas, each sanitation operation area is discretized into a plurality of cleaning nodes, and a cleaning path network is constructed according to all the cleaning nodes;

[0116] The path construction module 12 is used to construct a path planning model based on the cleaning node transfer probability function and the cleaning path network, and to construct a corresponding initial cleaning path for each sanitation vehicle according to the path planning model;

[0117] The path optimization module 13 is used to input the collaborative path optimization function and the initial cleaning path into the path planning model for successive iterations. When the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

[0118] In one example, the network construction module 11 is used to divide each sanitation operation area into multiple sanitation operation sub-areas according to a predetermined spatial resolution, wherein each sanitation operation sub-area includes one or more cleaning nodes; determine the connection relationship between any two cleaning nodes and the path weight between any two cleaning nodes according to the geographical distance between any two cleaning nodes, the traffic attributes between any two cleaning nodes, and the operation attribute information corresponding to the cleaning nodes; generate an adjacency matrix for each cleaning node according to the connection relationship and the path weight between any two cleaning nodes; and construct a cleaning path network based on the adjacency matrix and the cleaning nodes corresponding to the sanitation operation sub-areas. .

[0119] In one example, the job attribute information includes one or more of the geographic location coordinates of the cleaning node, the garbage density of the cleaning node, the job time window requirement of the cleaning node, the estimated job workload of the cleaning node, and the node priority.

[0120] In one example, the path construction module 12 is used to collect basic working information of each sanitation vehicle, wherein the basic working information includes the available time window of the working capacity and the historical working data; based on the cleaning node transfer probability function and the cleaning path network, a corresponding cleaning node sequence is assigned to each sanitation vehicle, and the cleaning nodes in the cleaning node sequence are connected in sequence according to the timing requirements to obtain an initial cleaning path.

[0121] In one example, the path construction module 12 is used to calculate the transition probability between any two cleaning nodes according to the cleaning node transition probability function; and to allocate a corresponding cleaning node sequence to each sanitation vehicle based on the ant colony optimization algorithm and the transition probability between any two cleaning nodes.

[0122] In one example, the specific expression of the sweep node transition probability function is:

[0123]

[0124] Among them, P ij represents the sweep node transition probability function, represents the pheromone concentration on the edge between cleaning node i and cleaning node j, λ 1 represents the first important parameter, η ij represents the heuristic information parameter of cleaning node i and cleaning node j, λ 2 represents the second importance parameter, k represents all feasible neighbor nodes of node i, O i represents the set of neighbor nodes of the cleaning node i, λ 3 represents the third most important parameter, S ij represents the spatial similarity factor between the cleaning node i and the cleaning node j in the cleaning path network, ω1 Represents the weight coefficient corresponding to the geographical distance, d ij represents the Euclidean distance between cleaning node i and cleaning node j in the cleaning path network, ω 2 Represents the weight coefficient corresponding to the regional density, ρ i represents the area density of the cleaning node i, ρ j represents the area density of the cleaning node j, ω 3 represents the weight coefficient corresponding to the time window, OL() represents the overlapping length function of the time window, T i represents the operation time window of cleaning node i, T j represents the operation time window of cleaning node j, and Span() represents the time span function of the operation time window of cleaning node i and the operation time window of cleaning node j.

[0125] In an example, the specific expression of the collaborative path optimization function is:

[0126]

[0127] Among them, F represents the collaborative path optimization function, α represents the weight coefficient corresponding to the topological structure term, u represents the total number of edges in the sweep path network, and N ij represents the edge between cleaning node i and cleaning node j in the cleaning path network, k i represents the cleaning workload of cleaning node i, k j represents the cleaning workload of cleaning node j, g i Indicates the sanitation operation area number to which cleaning node i belongs, g j represents the sanitation operation area number to which cleaning node j belongs, Γ(g i ,g j ) represents the judgment function of the sanitation operation area, β represents the weight coefficient corresponding to the spatial similarity function, S ij represents the spatial similarity function between cleaning node i and cleaning node j in the cleaning path network, γ represents the weight coefficient corresponding to the load balancing factor, μ represents the average workload of all sanitation vehicles, and σ represents the standard deviation of the workload of all sanitation vehicles.

[0128] In one example, the iteration termination condition includes the path planning model reaching a preset maximum number of iterations, stopping the iteration process of the path planning model; or in two consecutive iterations, the difference between two consecutive collaborative path optimization function output values ​​is lower than a preset optimization threshold, stopping the iteration process of the path planning model.

[0129] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3As shown, the electronic device 2 may include: at least one processor 21 , at least one network interface 24 , a user interface 23 , a memory 25 , and at least one communication bus 22 .

[0130] The communication bus 22 is used to realize the connection and communication between these components.

[0131] The user interface 23 may include a display screen (Display) and a camera (Camera), and the optional user interface 23 may also include a standard wired interface and a wireless interface.

[0132] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0133] Among them, the processor 21 may include one or more processing cores. The processor 21 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 25, and calling data stored in the memory 25. Optionally, the processor 21 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 21 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 21, and it can be implemented separately through a chip.

[0134] Among them, the memory 25 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 25 includes a non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 25 may also be optionally at least one storage device located away from the aforementioned processor 21. As Figure 3 As shown, the memory 25 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for regional collaborative operation of multiple sanitation vehicles.

[0135] exist Figure 3 In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 21 can be used to call an application program stored in the memory 25 for a method for regional collaborative operation of multiple sanitation vehicles. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0136] A computer-readable storage medium stores instructions, which, when executed by one or more processors, enable the computer to execute one or more methods in the above-mentioned embodiments.

[0137] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0138] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0143] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the technical field that are not recorded in the present disclosure.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for regional collaborative operation path planning of multiple sanitation vehicles, characterized in that: The method comprises: Acquire a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas; Discretize each sanitation operation area into multiple cleaning nodes, and construct a cleaning path network based on all cleaning nodes; Based on the cleaning node transfer probability function and the cleaning path network, a path planning model is constructed, and a corresponding initial cleaning path is constructed for each sanitation vehicle according to the path planning model; The collaborative path optimization function and the initial cleaning path are input into the path planning model for successive iterations. When the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

2. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 1, characterized in that: The method of discretizing each sanitation operation area into multiple cleaning nodes and constructing a cleaning path network based on all the cleaning nodes specifically includes: Dividing each sanitation operation area into a plurality of sanitation operation sub-areas according to a predetermined spatial resolution, wherein each sanitation operation sub-area includes one or more cleaning nodes; Determine the connection relationship between any two cleaning nodes and the path weight between any two cleaning nodes according to the geographical distance between any two cleaning nodes, the traffic attributes between any two cleaning nodes, and the job attribute information corresponding to the cleaning nodes; According to the connection relationship and path weight between any two cleaning nodes, an adjacency matrix of each cleaning node is generated; A cleaning path network is constructed based on the adjacency matrix and the cleaning nodes corresponding to the sanitation operation sub-areas.

3. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 2, characterized in that: The job attribute information includes one or more of the geographical location coordinates of the cleaning node, the garbage density of the cleaning node, the job time window requirement of the cleaning node, the estimated job workload of the cleaning node, and the node priority.

4. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 1, characterized in that: The constructing a corresponding initial cleaning path for each sanitation vehicle according to the path planning model specifically includes: Collecting basic working information of each sanitation vehicle, wherein the basic working information includes the available time window of operating capacity and historical operating data; Based on the cleaning node transfer probability function and the cleaning path network, a corresponding cleaning node sequence is allocated to each sanitation vehicle, and the cleaning nodes in the cleaning node sequence are sequentially connected according to timing requirements to obtain an initial cleaning path.

5. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 4, characterized in that: The corresponding cleaning node sequence allocated to each sanitation vehicle based on the cleaning node transfer probability function and the cleaning path network specifically includes: According to the cleaning node transition probability function, calculating the transition probability between any two cleaning nodes; A corresponding cleaning node sequence is allocated to each sanitation vehicle based on the ant colony optimization algorithm and the transition probability between any two cleaning nodes.

6. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 1, characterized in that: The specific expression of the sweep node transfer probability function is: Among them, P ij represents the sweep node transition probability function, represents the pheromone concentration on the edge between cleaning node i and cleaning node j, λ1 represents the first importance parameter, η ij represents the heuristic information parameter of the sweep node i and the sweep node j, λ2 represents the second importance parameter, k represents all feasible neighbor nodes of the sweep node i, O i represents the set of neighbor nodes of the cleaning node i, λ3 represents the third importance parameter, S ij represents the spatial similarity factor between the cleaning node i and the cleaning node j in the cleaning path network, ω1 represents the weight coefficient corresponding to the geographical distance, and d ij represents the Euclidean distance between the cleaning node i and the cleaning node j in the cleaning path network, ω2 represents the weight coefficient corresponding to the regional density, and ρ i represents the area density of the cleaning node i, ρ j represents the regional density of the cleaning node j, ω3 represents the weight coefficient corresponding to the time window, OL() represents the overlapping length function of the time window, T i represents the operation time window of cleaning node i, T j represents the operation time window of cleaning node j, and Span() represents the time span function of the operation time window of cleaning node i and the operation time window of cleaning node j.

7. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 6, characterized in that: The specific expression of the collaborative path optimization function is: Among them, F represents the collaborative path optimization function, α represents the weight coefficient corresponding to the topological structure term, u represents the total number of edges in the sweep path network, and N ij represents the edge between cleaning node i and cleaning node j in the cleaning path network, k i represents the cleaning workload of cleaning node i, k j represents the cleaning workload of cleaning node j, g i Indicates the sanitation operation area number to which cleaning node i belongs, g j represents the sanitation operation area number to which cleaning node j belongs, Γ(g i ,g j ) represents the judgment function of the sanitation operation area, β represents the weight coefficient corresponding to the spatial similarity function, S ij represents the spatial similarity function of the cleaning node i and the cleaning node j in the cleaning path network, γ represents the weight coefficient corresponding to the load balancing factor, μ represents the average value of the workload of all sanitation vehicles, and σ represents the standard deviation of the workload of all sanitation vehicles.

8. The method for regional collaborative operation of multiple sanitation vehicles as claimed in claim 1, characterized in that: The iteration termination conditions include: When the path planning model reaches a preset maximum number of iterations, the iterative process of the path planning model is stopped; or when the difference between two consecutive output values ​​of the collaborative path optimization function is lower than a preset optimization threshold in two consecutive iterations, the iterative process of the path planning model is stopped.

9. A multi-sanitation vehicle regional collaborative operation path planning system, characterized in that: The multi-sanitation vehicle regional collaborative operation path planning system (1) comprises a network construction module (11), a path construction module (12) and a path optimization module (13), wherein: The network construction module (11) is used to obtain a cleaning area map corresponding to a target area to be cleaned by a sanitation vehicle, wherein the cleaning area map includes a plurality of sanitation operation areas, each sanitation operation area is discretized into a plurality of cleaning nodes, and a cleaning path network is constructed based on all the cleaning nodes; The path construction module (12) is used to construct a path planning model based on the cleaning node transfer probability function and the cleaning path network, and to construct a corresponding initial cleaning path for each sanitation vehicle according to the path planning model; The path optimization module (13) is used to input the collaborative path optimization function and the initial cleaning path into the path planning model for successive iterations, and when the iteration termination condition is reached, the path planning model outputs the optimized cleaning path corresponding to each sanitation vehicle.

10. An electronic device, characterized in that: The electronic device (2) comprises a processor (21), a memory (25), a user interface (23) and a network interface (24), wherein the memory (25) is used to store instructions, the user interface (23) and the network interface (24) are used to communicate with other devices, and the processor (21) is used to execute the instructions stored in the memory (25) so that the electronic device (2) executes the method according to any one of claims 1 to 8.

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