Generation method and generation device for working path of motor sweeper

Abstract

The invention discloses a motor sweeper operation path generation method and device, and the method comprises the steps: constructing a road connected graph based on a to-be-swept region map road boundary and a topological connection relation; coarsening the connected graph according to a preset merging strategy, and merging the fine-grained nodes into logic region nodes; constructing a final cleaning task area based on the coarsened connected graph; planning a sub-region cleaning sequence in combination with the current position of the cleaning vehicle; and constructing a global operation path. According to the invention, whole-process automation of cleaning area division and sequence planning is realized, manual intervention is not needed, and operation efficiency and reliability are improved; complex road topologies such as roundabout and the like are adapted, and sub-region division is more reasonable; a heuristic weight and an improved threshold acceptance algorithm are fused, the solving efficiency and the planning quality are balanced, and the requirements for the real-time performance and the accuracy of outdoor cleaning are met.

Description

Technical Field

[0001] This invention relates to the field of unmanned driving technology, specifically to a method and apparatus for generating the operation path of a sweeper. Background Technology

[0002] In recent years, the commercialization of autonomous driving technology in the field of environmental cleaning has continued to accelerate. Autonomous cleaning vehicles have been gradually applied to outdoor parks, municipal roads, large industrial parks and other scenarios, effectively replacing manual labor in environmental cleaning operations. However, the unstructured characteristics of outdoor scenarios (such as complex road networks and dynamic environmental changes) make multi-sub-area collaborative operations and path planning the core bottleneck restricting system efficiency. Traditional cleaning task planning schemes are no longer able to meet actual needs.

[0003] Traditional cleaning robots primarily focus on structured indoor environments, where road topology is simple (mainly straight roads and right-angle turns), and task planning can be completed based on fixed paths. However, outdoor cleaning tasks face larger spatial scales and more complex road networks, such as roundabouts, multi-road intersections, and asymmetrical lane structures, leading to an exponential increase in task planning complexity. Existing outdoor cleaning solutions still heavily rely on human intervention, specifically: manually surveying the area to be cleaned beforehand, dividing the work zone based on experience, and specifying a fixed cleaning sequence. This approach has significant limitations in large-scale scenarios.

[0004] First, manually dividing task areas is inefficient and highly subjective. In large-scale scenarios such as industrial parks and science parks, manual surveying and area division can take several hours or even days. Moreover, the division results depend on the operator's understanding of the site layout and cleaning logic. The boundaries and areas of the areas divided by different operators vary greatly, making it difficult to ensure scientific accuracy and consistency, which in turn leads to repeated or missed cleaning paths.

[0005] Secondly, static zoning schemes cannot adapt to dynamic environmental changes. In outdoor scenarios, dynamic factors such as temporary construction, temporary vehicle parking, and seasonal vegetation growth occur frequently. Manually pre-set static zone boundaries and cleaning paths may become invalid due to environmental changes. For example, construction areas obstructing traffic can cause the original path to be interrupted, requiring manual readjustment of zones and sequences, which seriously affects the continuity of operations.

[0006] Finally, there is a lack of globally optimized cleaning sequence planning. Manually assigned cleaning sequences are often based on the "proximity principle," failing to consider the global optimality of the entire area's path, which easily leads to back-and-forth travel across areas. For example, after cleaning area A, the cleaning vehicle needs to travel a long distance empty back to area B, increasing the empty travel distance and working time. This not only reduces energy efficiency but also increases operating costs (such as increased energy consumption and accelerated equipment wear and tear).

[0007] In summary, existing task planning schemes for outdoor unmanned cleaning vehicles, due to their reliance on human intervention, inability to adapt to dynamic environments, and lack of global optimization, are insufficient to meet the high-efficiency cleaning needs of large-scale and complex scenarios. There is an urgent need for an intelligent decision-making method based on road topology features to achieve automatic cleaning area division, dynamic adaptation to environmental changes, and global optimal cleaning sequence planning, thereby improving the automation level, operational efficiency, and economy of unmanned cleaning systems. Summary of the Invention

[0008] The purpose of this invention is to provide a method for generating the working path of a sweeper, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: Firstly, this application provides a method for generating a sweeper's operating path, comprising: Based on the road boundaries and topological connectivity of the area to be cleaned, a road connectivity graph is constructed. Based on a preset merging strategy, the road connectivity graph is coarsened, and fine-grained graph nodes are merged into logical region nodes to realize the task division of the cleaning area and obtain a coarsened connectivity graph. Multiple sub-task regions are constructed based on the coarsened connected graph to form the final cleaning task region; Obtain the current location of the unmanned cleaning vehicle, plan the cleaning task execution order for the multiple sub-task areas, and ensure that each sub-area is cleaned only once; Based on the cleaning task execution order, a complete and executable global job path is constructed.

[0010] Preferably, the process of constructing the road connectivity graph is as follows: The lanes in the map are abstracted as nodes in a connected graph. Road intersections are modeled as composite nodes that encompass all lanes connected to them, non-intersection lanes are modeled as nodes, and two-way lanes are constructed with only one node. Based on the road topology and connectivity, connecting edges are established between nodes to form a road connectivity graph.

[0011] Preferably, the process of coarsening the road connectivity map includes: Merge parallel lanes and physically adjacent reverse lanes within the same road segment to form the initial nodes of a complete road segment; Iteratively merge non-intersection type continuous road segment nodes along the topological connection direction until an intersection node is encountered, and control the coverage of merged nodes by using a length threshold; After identifying the circular topology and filtering out circular areas that do not conform to the cleaning operation logic, the circular areas that meet the conditions are merged according to the preset priority to form a circular type node. The intersection nodes are merged according to their type. Simple intersection nodes are merged directly, while complex intersection nodes are merged using a combination of main road identification or geometric overlap analysis strategies. Isolated nodes or fragmented regions that do not meet the preset area or length thresholds will be merged into the adjacent largest region node; Perform geometric and topological checks on the coarsened connected graph, mark the cyclic nodes that contain nodes of other regions, and if there are obstacles in such nodes that prevent the cleaning vehicle from passing through, divide the region according to the location of the obstacles.

[0012] Preferably, each node in the coarsened connected graph is mapped to a corresponding sub-task region, covering the geometric range of the original road segment represented by the node; a geometric simplification algorithm is used to simplify the polygonal boundaries of the sub-task regions while keeping the overall shape and area of ​​the region basically unchanged, thus generating the final cleaning task region.

[0013] Preferably, the process of planning the execution order of the cleaning tasks in the sub-task areas includes: Determine the current sub-area of ​​the unmanned cleaning vehicle. If it is outside the area to be cleaned, introduce an additional node to represent the current position and initialize the cleaning cost based on the actual path length from the node to each sub-area. Identify the largest connected component in a coarse connected graph; A cost weight is assigned to each sub-region node. The weight is positively correlated with the total length of the road in the sub-region and negatively correlated with the number of adjacent nodes in the sub-region. The full-area traversal problem is transformed into a traveling salesman problem, and an improved threshold acceptance algorithm is used to solve it, outputting the optimal cleaning order for sub-regions.

[0014] Preferably, the process of constructing the global operation path is as follows: for each sub-task area, a local cleaning path is generated by combining its entry point, exit point and internal road topology; the movement paths between adjacent sub-areas are seamlessly spliced ​​with the local cleaning paths of each area to form a globally optimized cleaning path.

[0015] Secondly, this application provides a device for generating a sweeper's operating path, used to implement the method described in any of the above embodiments, the device comprising: Connectivity Graph Construction Module: Used to construct a road connectivity graph based on the road boundaries and topological connectivity of the area to be cleaned. Connectivity Graph Coarsening Module: Used to coarsen the road connectivity graph based on a preset merging strategy, merging fine-grained graph nodes into logical region nodes to obtain a coarsened connectivity graph, thereby realizing the task division of the cleaning area; Task area generation module: used to construct multiple sub-task areas based on the coarsened connected graph to form the final cleaning task area; Cleaning sequence planning module: used to obtain the current position of the unmanned cleaning vehicle, plan the execution sequence of cleaning tasks for the multiple sub-task areas, and ensure that each sub-area is cleaned only once; Global path construction module: used to construct a complete and executable global job path based on the execution order of the cleaning tasks.

[0016] Thirdly, this application provides an electronic device including at least one processor, the processor being communicatively connected to at least one memory, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in any of the above embodiments.

[0017] Fourthly, this application provides a computer-readable storage medium storing computer instructions that cause a processor to execute the method described in any of the above embodiments.

[0018] Compared with the prior art, the beneficial effects of the present invention are: I. The method achieves full automation of cleaning area division and optimal cleaning sequence planning. It can autonomously complete task zoning and cleaning sequence planning for large-scale, complex environments without human intervention, significantly improving the efficiency and reliability of task planning while ensuring the optimality of the cleaning path from a global perspective.

[0019] Second, it possesses high adaptability to real road topology. In the process of area division, this invention fully considers complex topological relationships such as roundabouts, multi-road intersections, and asymmetrical roads, making the boundaries of the generated sub-areas more consistent with the actual operation logic and mechanical motion constraints of outdoor cleaning, thus improving the rationality and operability of the division results.

[0020] Third, an optimization strategy integrating heuristic weights and threshold acceptance algorithms is proposed. By introducing custom heuristic weights to evaluate the area cleaning cost and combining them with an improved threshold acceptance algorithm for iterative optimization, a high-quality and highly stable cleaning sequence can be output within a reasonable computation time, effectively balancing solution efficiency and planning quality, and meeting the dual requirements of real-time performance and accuracy in outdoor cleaning scenarios. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a sub-process of the method flowchart of an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the execution order of the cleaning area task based on the improved threshold acceptance algorithm according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the initial input map in an embodiment of the present invention; Figure 5 This is a schematic diagram of the cleaning area division results according to an embodiment of the present invention; Figure 6 This is a schematic diagram showing the execution sequence of the cleaning area task in an embodiment of the present invention; Figure 7 This is a schematic diagram of the device module according to an embodiment of the present invention; Figure 8 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1 to 6 This invention provides a technical solution: a method for generating the working path of a sweeper, comprising: S100: Construct a road connectivity map based on the road boundaries and topological connectivity of the area to be cleaned.

[0024] S200: Based on a preset merging strategy, the road connectivity graph is coarsened, and fine-grained graph nodes are merged into logical region nodes to realize the task division of the cleaning area and obtain a coarsened connectivity graph.

[0025] S300: Based on the coarsened connected graph, construct multiple sub-task regions to form the final cleaning task region.

[0026] S400: Obtain the current location of the unmanned cleaning vehicle, plan the cleaning task execution order for the multiple sub-task areas, and ensure that each sub-area is cleaned only once.

[0027] S500: Based on the cleaning task execution sequence, construct a complete and executable global operation path.

[0028] In one embodiment of the present invention, the process of constructing a road connectivity graph in step S100 includes: S110: Abstract all lanes in the map into nodes in a connected graph. Each road intersection is modeled as a composite node, which covers all lanes connected by the intersection and is marked as "intersection type". For lanes in non-intersections, each lane is constructed as a separate node and marked as "lane type". Only one node is constructed to represent two-way lanes.

[0029] S120: Based on the road topology and connectivity, establish connecting edges between nodes to form a road connectivity graph.

[0030] In one embodiment of the present invention, the process of coarsening the road connectivity map in step S200 further includes: S210: Merge parallel lanes and physically adjacent reverse lanes within the same road segment to form a preliminary node of a complete road segment.

[0031] S220: Iteratively merge continuous road segment nodes that are not intersection types along the topological connection direction until an intersection node is encountered, and control the coverage of merged nodes by using a length threshold.

[0032] S230: Identify the ring topology, filter out ring areas that do not conform to the cleaning operation logic, and merge the ring areas that meet the conditions according to the preset priority to form a loop type node.

[0033] Specifically, the system identifies and merges ring topologies. For ring structures such as roundabouts and U-turn lanes, unreasonable candidate areas are first filtered out, that is, areas with a ring structure length exceeding 250 meters, significant differences in road width (the maximum road width is more than three times the minimum width), mixed single and two-way lanes, or internally nested other rings are removed. Then, the rings are merged in ascending order of area, with priority given to merging the ring areas with the smallest area.

[0034] S240: Implement type-based merging for intersection nodes. Simple intersection nodes are merged directly, while complex intersection nodes are merged using a combination of arterial road identification or geometric overlap analysis strategies. First, simple intersection nodes connecting only two adjacent lanes are merged. Then, for complex intersections connecting three or more lanes, merging is performed based on arterial road identification or geometric overlap analysis strategies, prioritizing the merging of intersections with through arterial roads, while controlling the length of the merged node to not exceed 100 meters.

[0035] S250: Merge isolated nodes or fragmented areas that do not meet the preset area or length thresholds into the adjacent largest area node. Specifically, merge the remaining isolated nodes or fragmented areas that do not meet the minimum length of 5 meters or the area threshold of 15 square meters into the adjacent largest area node.

[0036] S260: Perform geometric and topological checks on the coarsened connected graph, marking cyclic nodes that contain nodes from other regions. If obstacles hindering the passage of cleaning vehicles exist within such nodes, the region is segmented based on the obstacle's location. Specifically, perform geometric and topological checks on the coarsened connected graph to identify whether cyclic nodes contain nodes from other regions. If the identification result is yes, mark such cyclic nodes as C-type nodes. Further detect whether there are facilities hindering the passage of cleaning vehicles within this region. If the detection result is yes, the atomic region is segmented into two smaller sub-regions based on the obstacle's location. Facilities hindering the passage of cleaning vehicles include turnstiles, speed bumps, and manhole covers.

[0037] In one embodiment of the present invention, step S300 further includes: S310: Map each node in the coarsened connected graph independently to a sub-task region that covers the geometry of all original road segments represented by the corresponding node.

[0038] S320: Employs a geometric simplification algorithm to reduce the number of polygon boundary points while maintaining the original overall shape and area of ​​the region, thereby generating a simplified task region.

[0039] In one embodiment of the present invention, such as Figure 5 As shown, step S400 specifically includes: S410: Determine the current sub-area of ​​the unmanned cleaning vehicle. If it is outside the area to be cleaned, introduce an additional node to represent the current position and initialize the cleaning cost based on the actual path length from the node to each sub-area.

[0040] S420: Identify the largest connected component in the coarsened connected graph.

[0041] S430: Assign a cost weight to each sub-region node. The weight is positively correlated with the total road length of the sub-region and negatively correlated with the number of adjacent nodes in the sub-region. The calculation formula is as follows: , in, Weighting of cleaning costs for individual sub-region nodes. This represents the total length of roads covered by the nodes in this sub-region. S440: Transform the full-region traversal problem into a traveling salesman problem, solve it using an improved threshold acceptance algorithm, and output the optimal cleaning order for the sub-region.

[0042] In one embodiment of the present invention, such as Figure 6 As shown, step S500 further includes: S510: For each sub-task area, a local cleaning path is generated by combining its entry and exit points and internal road topology.

[0043] S520: Seamlessly connects the movement paths between adjacent sub-regions with the local cleaning paths of each region to form a globally optimized cleaning path.

[0044] In an embodiment of the present invention, the method of any one of steps S100 to S500 takes the current position of the unmanned cleaning vehicle and the map of the area to be cleaned as input to realize the full automation of the division of the cleaning area and the planning of the optimal cleaning sequence. Figure 6 The numbers in the diagram represent the sequence of the autonomous cleaning vehicle's path, and the black arrows represent the path, connecting areas of different colors. The numbers start from 1 and end at 30, with 1 representing the starting position and 30 representing the ending position. In the embodiments of the present invention, the method of any one of steps S100 to S500 fully considers the complex topological relationships of roundabouts, multi-road intersections, and asymmetrical roads during the area division process, so that the generated sub-area boundaries conform to the actual operation logic and mechanical motion constraints of outdoor cleaning.

[0045] In addition, please see Figure 7 The present invention also provides a sweeper operation path generation device for implementing the method of any of the above embodiments, the device comprising: Used to construct a complete and executable global job path based on the execution order of the cleaning tasks.

[0046] Please see Figure 8 , Figure 8 A schematic diagram of the mechanism of an electronic device 20 that can implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of control devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0047] Electronic device 20 includes at least one processor 21 and a memory, such as read-only memory (ROM) 22 and random access memory (RAM) 23, communicatively connected to at least one processor 21. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 22 or loaded from storage unit 28 into the RAM 13. The RAM 23 may also store various programs and data required for the operation of electronic device 20. The processor 21, ROM 22, and RAM 23 are interconnected via bus 24. Input / output (I / O) interface 25 is also connected to bus 24.

[0048] Multiple components in electronic device 20 are connected to I / O interface 25, including: input unit 26, such as keyboard, mouse, etc.; output unit 27, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 29, such as network card, modem, wireless transceiver, etc. Communication unit 29 allows electronic device 20 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0049] Processor 21 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 21 performs the various methods and processes described above.

[0050] In some embodiments, the methods described above can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 20 via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by processor 21, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, processor 21 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0051] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0052] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0053] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0054] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and Input from the user can be received in any form (including voice input, speech input, or tactile input).

[0055] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0056] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for generating the working path of a sweeper, characterized in that, include: Based on the road boundaries and topological connectivity of the area to be cleaned, a road connectivity graph is constructed. Based on a preset merging strategy, the road connectivity graph is coarsened, and fine-grained graph nodes are merged into logical region nodes to realize the task division of the cleaning area and obtain a coarsened connectivity graph. Multiple sub-task regions are constructed based on the coarsened connected graph to form the final cleaning task region; Obtain the current location of the unmanned cleaning vehicle, plan the cleaning task execution order for the multiple sub-task areas, and ensure that each sub-area is cleaned only once; Based on the cleaning task execution order, a complete and executable global job path is constructed.

2. The method for generating a sweeper's operating path according to claim 1, characterized in that, The process of constructing the road connectivity graph is as follows: The lanes in the map are abstracted as nodes in a connected graph. Road intersections are modeled as composite nodes that encompass all lanes connected to them, non-intersection lanes are modeled as nodes, and two-way lanes are constructed with only one node. Based on the road topology and connectivity, connecting edges are established between nodes to form a road connectivity graph.

3. The method for generating a sweeper's operating path according to claim 1, characterized in that: The process of coarsening the road connectivity graph includes: Merge parallel lanes and physically adjacent reverse lanes within the same road segment to form the initial nodes of a complete road segment; Iteratively merge non-intersection type continuous road segment nodes along the topological connection direction until an intersection node is encountered, and control the coverage of merged nodes by using a length threshold; After identifying the circular topology and filtering out circular areas that do not conform to the cleaning operation logic, the circular areas that meet the conditions are merged according to the preset priority to form a circular type node. The intersection nodes are merged according to their type. Simple intersection nodes are merged directly, while complex intersection nodes are merged using a combination of main road identification or geometric overlap analysis strategies. Isolated nodes or fragmented regions that do not meet the preset area or length thresholds will be merged into the adjacent largest region node; Perform geometric and topological checks on the coarsened connected graph, mark the cyclic nodes that contain nodes of other regions, and if there are obstacles in such nodes that prevent the cleaning vehicle from passing through, divide the region according to the location of the obstacles.

4. The method for generating a sweeper's operating path according to claim 3, characterized in that: The process of constructing multiple sub-task regions based on the coarsened connected graph to form the final cleaning task region includes: Each node in the coarsened connected graph is mapped to a corresponding sub-task region, covering the geometric range of the original road segment represented by the node; A geometric simplification algorithm is used to simplify the polygonal boundaries of sub-task areas while keeping the overall shape and area of ​​the region basically unchanged, thus generating the final cleaning task area.

5. The method for generating a sweeper's operating path according to claim 4, characterized in that: Obtaining the current location of the unmanned cleaning vehicle and planning the execution order of cleaning tasks for the multiple sub-task areas includes: Determine the current sub-area of ​​the unmanned cleaning vehicle. If it is outside the area to be cleaned, introduce an additional node to represent the current position and initialize the cleaning cost based on the actual path length from the node to each sub-area. Identify the largest connected component in a coarse connected graph; A cost weight is assigned to each sub-region node. The weight is positively correlated with the total length of the road in the sub-region and negatively correlated with the number of adjacent nodes in the sub-region. The full-area traversal problem is transformed into a traveling salesman problem, and an improved threshold acceptance algorithm is used to solve it, outputting the optimal cleaning order for sub-regions.

6. The method for generating a sweeper's operating path according to claim 5, characterized in that: The process of constructing the global cleaning path is as follows: for each sub-task area, a local cleaning path is generated by combining its entry and exit points and internal road topology; the movement paths between adjacent sub-areas are seamlessly spliced ​​with the local cleaning paths of each area to form a globally optimized cleaning path.

7. A device for generating a sweeper's operating path, used to implement the method described in any one of claims 1 to 6, characterized in that, The device includes: Connectivity Graph Construction Module: Used to construct a road connectivity graph based on the road boundaries and topological connectivity of the area to be cleaned. Connectivity Graph Coarsening Module: Used to coarse the road connectivity graph based on a preset merging strategy, merging fine-grained graph nodes into logical region nodes to obtain a coarsened connectivity graph, thereby realizing the task division of the cleaning area; Task area generation module: used to construct multiple sub-task areas based on the coarsened connected graph to form the final cleaning task area; Cleaning sequence planning module: used to obtain the current position of the unmanned cleaning vehicle, plan the execution sequence of cleaning tasks for the multiple sub-task areas, and ensure that each sub-area is cleaned only once; Global path construction module: used to construct a complete and executable global job path based on the execution order of the cleaning tasks.

8. An electronic device, characterized in that, The method includes at least one processor, which is communicatively connected to at least one memory, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to perform the method of any one of claims 1 to 6.

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