Intelligent operation method and device of operation equipment, operation equipment and storage medium

Abstract

The invention discloses an intelligent operation method and device of operation equipment, the operation equipment and a storage medium. The method comprises the following steps: before operation of operation equipment, controlling an unmanned aerial vehicle to carry out flight scanning on a target operation area so as to collect environmental data of the target operation area; based on the environment data, generating an operation map with geographic coordinates, and planning an operation path of the operation equipment on the operation map; wherein the operation map comprises obstacle information identified and marked based on environment data, and the operation path is planned to avoid obstacles; issuing the operation task containing the operation path to operation equipment; and controlling the operation equipment to run along the operation path and execute the green cutting operation based on the high-precision positioning information of the operation equipment.

Description

Technical Field

[0001] This application relates to the field of intelligent robot technology, specifically to an intelligent operation method, device, operation equipment, and storage medium for a work device. Background Technology

[0002] In existing technologies, intelligent equipment used for vegetation cutting, such as intelligent greening vehicles or lawnmower robots, generally adopts an onboard real-time environmental perception and autonomous decision-making operation mode. A typical technical solution involves integrating sensors such as cameras and lidar onto the equipment itself. These sensors collect real-time environmental information from the work site and utilize onboard real-time positioning and mapping or target detection algorithms to dynamically identify obstacles ahead (such as rocks, tree stumps, potholes, etc.). Simultaneously, the vehicle performs local path planning and obstacle avoidance decisions, controlling the vehicle to perform vegetation cutting operations while avoiding obstacles.

[0003] However, during cutting operations, the high-speed rotating blades of the equipment generate a large amount of flying grass clippings, dust, and plant debris. These contaminants directly adhere to or diffuse onto the detection windows of onboard sensors, such as camera lenses and LiDAR transmitter / receiver windows, severely interfering with their normal operation. This results in the performance of core sensing sensors deteriorating sharply or even failing due to contamination generated by the operation itself, at the very moments when precise environmental perception is most crucial for the task. The direct consequence is that the equipment is highly susceptible to collisions with hard obstacles due to "sensory blindness," causing damage to the blades or vehicle body, or falling into undetected potholes and causing accidents. To avoid collisions, the system often has to limit its travel speed to extremely low levels, severely restricting operational efficiency.

[0004] Therefore, how to fundamentally solve the problem of equipment sensing failure caused by self-contamination during operation, thereby improving operational safety and efficiency, has become a pressing technical challenge in this field. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent operation method, device, greening vehicle, and storage medium for operating equipment, in order to solve the technical problems in the prior art where the on-board sensing sensors are contaminated by grass clippings and dust generated during the operation, causing the real-time environmental sensing capability of the operating equipment to fail, resulting in poor operation safety, equipment damage, and low operation efficiency.

[0006] To achieve the above objectives, the first aspect of this application provides an intelligent operation method for a work equipment, comprising: Before the equipment begins operation, the drone is controlled to perform a flight scan of the target work area in order to collect environmental data of the target work area. Based on environmental data, a work map with geographic coordinates is generated, and the work path of the equipment is planned on the work map; the work map includes obstacle information identified and marked based on environmental data, and the work path is planned to avoid obstacles; Distribute the work tasks containing the work paths to the work equipment; The control equipment travels along the work path and performs the vegetation clearing operation based on its own high-precision positioning information.

[0007] In this embodiment of the application, generating a work map with geographic coordinates based on environmental data includes: processing the environmental data to generate an orthophoto map and a digital surface model of the target work area; fusing the orthophoto map and the digital surface model to generate a work map with geographic coordinates; wherein the work map includes at least a base map layer and an obstacle layer.

[0008] In this embodiment of the application, generating a work map with geographic coordinates based on environmental data further includes: using a deep learning-based target detection network to automatically identify and select obstacles in the orthophoto map; and for each obstacle, assigning corresponding semantic attributes to the obstacle based on the height information of the obstacle in the digital surface model and the image features in the orthophoto map.

[0009] In this embodiment of the application, planning the operation path of the operation equipment on the operation map includes: automatically generating an initial operation path covering the operation area based on the user's definition of the operation area and a preset coverage mode; and calling a path planning algorithm to make the initial operation path automatically avoid all obstacles on the operation map and maintain a preset safe distance.

[0010] In this embodiment of the application, planning the operation path of the operation equipment on the operation map further includes: providing a graphical human-computer interaction interface to display the operation map and the initial operation path; and in response to the user's operation on the interaction interface, dragging and dropping the nodes of the initial operation path to form the final operation path.

[0011] In this embodiment of the application, controlling the working equipment to travel along the working path and perform the greening operation based on its own high-precision positioning information includes: the working equipment determining its real-time pose in the geographic coordinate system based on its own high-precision positioning information; comparing the real-time pose with the working path, and generating motion control commands for the working equipment based on the comparison results, so as to make the working equipment travel along the working path.

[0012] In this embodiment of the application, the method further includes: monitoring the environment in front of the working equipment in real time while the working equipment is traveling along the working path and performing the greening operation; and controlling the working equipment to perform an emergency stop and sending an alarm when a sudden obstacle not marked in the working path is detected in the environment in front.

[0013] In this embodiment of the application, the working equipment is a greening vehicle.

[0014] A second aspect of this application provides an intelligent operating device for operating equipment, the device comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement any of the above-mentioned intelligent operation methods for the work device.

[0015] A third aspect of this application provides a work device, the work device comprising: The vehicle body is equipped with an actuator for performing the cutting of vegetation. A high-precision positioning system is used to acquire the real-time position and orientation information of the operating equipment. The controller, which communicates with the high-precision positioning system, is configured as follows: Receive job paths from external task planning terminals; and Based on the comparison between real-time pose information and the work path, motion control commands are generated for the work equipment to travel along the work path.

[0016] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to be configured to perform any of the above-described intelligent operation methods for a work device.

[0017] The technical solution of this application utilizes a drone to conduct aerial scanning of the target area to collect environmental data before the operation of the equipment. Based on this data, a work map with geographic coordinates and marked obstacles is generated, along with a planned obstacle avoidance path, thus constructing a priori work task. On this basis, the task is distributed to the work equipment, which is then controlled to execute the work along a preset path using high-precision positioning information. This method decouples the environmental perception phase from the work execution phase in time and space, forming a work system based on prior mapping. This fundamentally avoids the perception failure problem caused by self-contamination during operation, thereby significantly improving the safety and systemic efficiency of the vegetation clearing operation.

[0018] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The illustration shows a flowchart of an intelligent operation method for a work equipment according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0022] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0023] Figure 1 The illustration shows a schematic flowchart of an intelligent operation method for a work device according to an embodiment of this application. Figure 1 As shown in the figure, this application provides an intelligent operation method for a work equipment, which may include the following steps: Step 101: Before the operation of the equipment, control the drone to perform a flight scan of the target operation area in order to collect environmental data of the target operation area.

[0024] Operating equipment refers to autonomous or semi-autonomous mobile vehicles or robots equipped with vegetation cutting devices such as rotary cutter heads or cutting arms, used to perform weeding and irrigation operations in forest land, slopes, parks and other scenarios.

[0025] In one embodiment, an operator sets an automatic flight path for the UAV to cover the target work area via a ground control station or handheld terminal. The UAV flies along this path and simultaneously collects environmental data using its onboard sensors, including but not limited to sequential optical images acquired by a visual camera, 3D point cloud data acquired by a lidar, and spatial position and attitude information corresponding to each frame of data recorded by the positioning module. The UAV can transmit the data back to the ground station in real time via an onboard image transmission link, or export it via a storage medium after the flight is completed.

[0026] In another embodiment, for areas with significant topographic relief, such as slopes, a parallel flight path along the slope direction can be used for scanning to ensure effective vertical coverage; for densely vegetated areas, multi-altitude flight can be used to obtain information on the vegetation canopy and underlying structure. The collected environmental data forms the raw data foundation for subsequent map generation and analysis.

[0027] Step 102: Based on environmental data, generate a work map with geographic coordinates and plan the work path of the work equipment on the work map; wherein, the work map contains obstacle information identified and marked based on environmental data, and the work path is planned to avoid obstacles.

[0028] In one embodiment, the processor generates a work map with geographic coordinates based on environmental data, including: processing the environmental data to generate an orthophoto map and a digital surface model of the target work area; fusing the orthophoto map and the digital surface model to generate a work map with geographic coordinates; wherein the work map includes at least a base map layer and an obstacle layer.

[0029] In this embodiment of the application, the processor generates a work map with geographic coordinates based on environmental data, and further includes: using a deep learning-based target detection network to automatically identify and select obstacles in the orthophoto map; for each obstacle, assigning corresponding semantic attributes to the obstacle based on the height information of the obstacle in the digital surface model and the image features in the orthophoto map.

[0030] In this embodiment of the application, the processor plans the operation path of the operation equipment on the operation map, including: automatically generating an initial operation path covering the operation area based on the user's definition of the operation area and a preset coverage mode; and calling a path planning algorithm to make the initial operation path automatically avoid all obstacles on the operation map and maintain a preset safe distance.

[0031] In this embodiment of the application, the processor plans the operation path of the operation equipment on the operation map, and further includes: providing a graphical human-machine interface to display the operation map and the initial operation path; and in response to the user's operation on the interface, dragging and dropping the nodes of the initial operation path to form the final operation path.

[0032] In one embodiment, the ground station software receives raw environmental data transmitted or imported from the UAV exploration unit and, through the collaborative work of its map building module, obstacle recognition and marking module, and interactive path planning module, generates a final, deployable task. Specifically, the process of generating the task map and planned path includes the following steps: (1) Data Preprocessing and Map Generation: The map building module first preprocesses and fuses the environmental data. For visible light image sequences collected by UAVs, distortion correction, feature matching, and stitching are performed to generate orthophoto maps with unified geographic coordinates. For lidar point cloud data, denoising and point cloud classification are performed to separate "ground points" from "non-ground points," and a digital surface model is generated based on all point clouds. A digital elevation model is generated based on the classified "ground points." Subsequently, the orthophoto map, digital surface model (DSM), and digital elevation model (DEM) are fused and aligned at the pixel level or grid level in a unified geographic coordinate system to output a working map with accurate geographic reference coordinates. The map is a two-dimensional raster map or a 2.5-dimensional elevation map, and contains multiple overlayable data layers, mainly including: a base map layer, i.e., an orthophoto map, providing the real visual background of the work area; an obstacle layer, storing the spatial location and attribute information of obstacles obtained from subsequent identification steps; a restricted area layer, storing restricted areas based on DEM analysis, such as slopes exceeding safety thresholds or manually drawn by the user; and a work path layer, storing the final planned trajectory of the work equipment.

[0033] (2) Intelligent obstacle recognition and semantic labeling: Based on the generated orthophoto map, the obstacle recognition and labeling module uses a deep learning-based target detection algorithm to automatically identify and locate typical obstacles in the image, such as rocks, tree stumps, and utility poles. The system not only outputs the bounding boxes or polygons of the obstacles, but also combines the height information provided by the DSM to assign semantic attributes to each identified obstacle. For example, the system can automatically label rocks with a height of more than 20 cm as "impassable - rigid obstacle" and low shrubs as "crossable - flexible vegetation" according to preset rules. Operators can visually verify the results of the system's automatic recognition, modify attributes, delete false alarms, or manually supplement the labeling of missed obstacles on the graphical user interface provided by the software. The confirmed or corrected obstacle information and its semantic attributes are accurately associated with the geographic coordinates of the work map to form an obstacle layer.

[0034] (3) Interactive Global Path Planning: The interactive path planning module plans the operation path of the equipment based on the previously generated operation map that includes information on obstacles and restricted areas. The user first defines the target operation area on the base map, and the software can automatically generate a basic coverage path according to the preset operation mode, such as the "bow-shaped" full coverage mode. When generating the path, the path planning algorithm will automatically read the information of the obstacle layer and the restricted area layer to ensure that the generated path avoids all obstacles marked as "impassable" and all restricted areas, and maintains a user-configurable safe distance between the path centerline and the obstacle boundary, for example, the default safe distance is 50 cm. The planned path will be displayed on the map in real time.

[0035] (4) Manual Review and Route Optimization: The system provides flexible human-computer interaction tools. Operators can visually review and manually fine-tune the automatically generated routes. For example, they can directly drag and drop key points on the route through the graphical interface to adjust the local direction to cope with complex terrain not fully represented by the map or to meet specific operational requirements. In addition, operators can divide different sub-operation areas on the map and set operational parameters for each sub-area independently, such as mowing height and travel speed. The final optimized route is saved to the operation route layer of the operation map, which, together with the base map, obstacles, restricted areas, and other information, constitutes a complete operation task package with geographic coordinates.

[0036] Step 103: Distribute the work task containing the work path to the work equipment.

[0037] Step 104: Control the operating equipment to travel along the operating path and perform the greening operation based on its own high-precision positioning information.

[0038] In one embodiment, controlling the work equipment to travel along the work path and perform vegetation clearing work based on its own high-precision positioning information includes: the work equipment determining its real-time pose in the geographic coordinate system based on its own high-precision positioning information; comparing the real-time pose with the work path; and generating motion control commands for the work equipment based on the comparison results, so as to make the work equipment travel along the work path.

[0039] In one embodiment, the method further includes: monitoring the environment in front of the working equipment in real time while the working equipment is traveling along the working path and performing the greening operation; and controlling the working equipment to perform an emergency stop and sending an alarm when a sudden obstacle not marked in the working path is detected in the environment in front.

[0040] In one embodiment, the task package generated and ultimately confirmed by the ground station includes a task map with geographic coordinates, a planned route, and task parameters. This package is then sent to the onboard controller of the work unit via a wireless network or physical storage medium. Once the work equipment enters the work area and starts, its onboard control system loads the task package. The system first aligns and initializes the work equipment's own positioning system (e.g., RTK-GPS) with the task map coordinate system based on the map coordinate information in the task package, ensuring that the vehicle "knows" its exact location on the map and the route it needs to follow.

[0041] After the task is loaded and initialized, the work equipment enters autonomous operation mode. Relying on its onboard high-precision positioning and attitude determination system, the work equipment acquires its centimeter-level accurate position (latitude, longitude, and elevation) and attitude information such as heading, roll, and pitch angles in the global geographic coordinate system in real time at high frequency. The path tracking controller begins operation. It continuously compares the real-time acquired vehicle position with the predefined planned path in the task package. The controller uses path tracking algorithms, such as pure tracking algorithms, to calculate steering control commands: the algorithm dynamically selects a "pre-aiming point" on the planned path ahead of the vehicle, and then calculates a steering angle that will cause the vehicle's front wheels to steer, thus accurately driving towards the pre-aiming point along a smooth arc trajectory. Simultaneously, the speed controller, for example using a PID control algorithm, calculates the control quantity of the drive motor based on the desired speed set for this section of the planned path and the vehicle's actual speed feedback, ensuring the vehicle maintains a stable and desirable operating speed. Driven by the path tracking controller, the work equipment's mobile chassis strictly follows the planned path. Meanwhile, the actuators, such as rotary cutters or cutting arms, begin work according to the area parameters set in the task package, such as mowing height or global commands, to clear vegetation in the path-covered area. Throughout the entire operation, the degraded safety protection modules of the equipment, such as forward-facing visible light / depth cameras or low-line-count LiDAR, remain active and monitoring. This module does not serve as the basis for path tracking but acts as a "last line of defense." Its data processing flow is independent of the main control loop, detecting in real time whether there are any unmarked, dynamically appearing obstacles (such as suddenly appearing animals or abandoned tools) in front of the vehicle. Once such a sudden obstacle is detected and a collision risk is determined, the module will immediately send a high-priority interrupt signal to the main controller, triggering an emergency stop for the entire vehicle, and simultaneously send an alarm containing location and image information to the remote operator via the vehicle's wireless communication module, awaiting manual intervention.

[0042] In one embodiment, the operating equipment is a green-cutting vehicle.

[0043] This application achieves a fundamental improvement in the safety and efficiency of the entire vegetation clearing process by constructing a decoupled workflow of "UAV-based preliminary exploration and mapping - manual remote path planning - precise execution by the equipment." By moving the critical environmental perception step, which is susceptible to contamination, forward and having the UAV complete the task before operation, the application avoids the perception failure problem caused by grass clippings and dust contaminating the vehicle-mounted sensors during operation, fundamentally eliminating the inherent contradiction of "operational behavior interfering with its own perception" in traditional solutions. The equipment strictly tracks the pre-planned obstacle avoidance path based on global high-precision positioning information, without relying on complex decisions based on real-time environmental perception. This allows the vehicle to maintain stable and predictable operation behavior even in harsh working environments with dense vegetation and flying dust, significantly reducing the risk of collisions with obstacles and falls into potholes, ensuring equipment and operational safety. At the same time, path planning based on a global map can systematically generate optimized coverage trajectories without omissions or repetitions, avoiding path redundancy and inefficiency caused by traditional local random or reactive planning, providing fundamental support for achieving high-efficiency and high-quality vegetation clearing operations.

[0044] In a specific embodiment, the intelligent operation method for the operating equipment of this application is specifically executed collaboratively through the following three stages: Phase 1: Prior surveying and data acquisition: Among them, preliminary surveying refers to the process of using drones to conduct aerial scans and collect environmental data of the target area before the actual operation of the equipment.

[0045] (1) The operator controls the UAV to fly automatically or manually over the target work area.

[0046] (2) The UAV simultaneously acquires high-definition images and / or laser point cloud data.

[0047] (3) Data is transmitted in real time or exported to the mission planning unit after flight.

[0048] The second stage, map generation and path planning: (1) The task planning software automatically stitches together and generates a map of the work area with coordinates based on UAV data.

[0049] (2) The software automatically identifies and marks significant obstacles, and the operator can check, correct, and supplement the markings in the interface. The operator uses the software tools to plan the optimal working path for the greening vehicle on a "clean" map. The system can automatically ensure that a set safe distance is maintained between the path and obstacles.

[0050] The third phase, task assignment and precise execution: (1) The generated map and planned route file are sent to the greening vehicle via network or storage device.

[0051] (2) The greening vehicle enters the work area and loads the map and path file.

[0052] (3) The path tracking controller of the grass cutting vehicle starts working. By comparing its own RTK / GPS positioning coordinates with the preset path, it generates control commands to drive the vehicle to strictly follow the planned path and perform grass cutting operations.

[0053] (4) The downgraded safety protection module is always active to deal with emergencies. Operators can monitor the progress of the operation through status feedback.

[0054] In one embodiment, an intelligent operation device for a work equipment is provided, comprising: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the aforementioned intelligent operation method for the work equipment.

[0055] In one embodiment, a work device is provided, the work device comprising: The vehicle body is equipped with an actuator for performing the cutting of vegetation. A high-precision positioning system is used to acquire the real-time position and orientation information of the operating equipment. The controller, which communicates with the high-precision positioning system, is configured as follows: Receive job paths from external task planning terminals; and Based on the comparison between real-time pose information and the work path, motion control commands are generated for the work equipment to travel along the work path.

[0056] In this embodiment of the application, the working equipment is a greening vehicle.

[0057] This application also provides a machine-readable storage medium storing instructions that cause a machine to perform the above-described intelligent operation method for a work device.

[0058] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0063] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0064] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0066] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for intelligent operation of a work equipment, characterized in that, The method includes: Before the work equipment is put into operation, the drone is controlled to perform a flight scan of the target work area in order to collect environmental data of the target work area; Based on the environmental data, a work map with geographic coordinates is generated, and the work path of the work equipment is planned on the work map; wherein, the work map contains obstacle information identified and marked based on the environmental data, and the work path is planned to avoid the obstacles; The task containing the work path is sent to the work equipment; The control system uses the equipment to travel along the work path and perform the cutting operation based on its own high-precision positioning information.

2. The intelligent operation method for operating equipment according to claim 1, characterized in that, The step of generating a work map with geographic coordinates based on the environmental data includes: The environmental data is processed to generate an orthophoto map and a digital surface model of the target work area; The orthophoto map is fused with the digital surface model to generate the operation map with geographic coordinates; wherein the operation map includes at least a base map layer and an obstacle layer.

3. The intelligent operation method for operating equipment according to claim 2, characterized in that, The process of generating a work map with geographic coordinates based on the environmental data further includes: A deep learning-based object detection network is used to automatically identify and select obstacles in the orthophoto image. For each obstacle, a corresponding semantic attribute is assigned to the obstacle based on the height information of the obstacle in the digital surface model and the image features in the orthophoto.

4. The intelligent operation method for operating equipment according to claim 1, characterized in that, The step of planning the operation path of the operation equipment on the operation map includes: Based on the user's definition of the work area, an initial work path covering the work area is automatically generated based on a preset coverage mode; The path planning algorithm is invoked to automatically avoid all obstacles on the work map and maintain a preset safe distance.

5. The intelligent operation method for operating equipment according to claim 4, characterized in that, The step of planning the operation path of the operation equipment on the operation map also includes: A graphical human-computer interaction interface is provided to display the operation map and the initial operation path; In response to the user's operation on the interactive interface, the nodes of the initial job path are dragged and adjusted to form the final job path.

6. The intelligent operation method for operating equipment according to claim 1, characterized in that, The control of the operating equipment, based on its own high-precision positioning information, to travel along the operating path and perform the vegetation clearing operation includes: The operating equipment determines its real-time pose in the geographic coordinate system based on its own high-precision positioning information. The real-time pose is compared with the work path, and motion control commands for the work equipment are generated based on the comparison results so that the work equipment can travel along the work path.

7. The intelligent operation method for operating equipment according to claim 1, characterized in that, The method further includes: During the process of the working equipment traveling along the working path and performing the greening operation, the environment in front of the working equipment is monitored in real time; If a sudden obstacle not marked in the work path is detected in the environment ahead, the work equipment is controlled to perform an emergency stop and send an alarm.

8. The intelligent operation method for operating equipment according to claim 1, characterized in that, The equipment used for this operation is a green-cutting vehicle.

9. An intelligent operating device for operating equipment, characterized in that, The device includes: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the intelligent operation method of the work equipment according to any one of claims 1 to 8.

10. A working device, characterized in that, The operating equipment includes: The vehicle body is equipped with an actuator for performing the cutting of vegetation. A high-precision positioning system is used to acquire the real-time pose information of the working equipment itself; The controller, which is communicatively connected to the high-precision positioning system, is configured as follows: Receive job paths from external task planning terminals; and Based on the comparison between the real-time pose information and the work path, motion control commands are generated for the work equipment to make the work equipment travel along the work path.

11. The operating equipment according to claim 10, characterized in that, The equipment used for this operation is a green-cutting vehicle.

12. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, the instruction causes the processor to be configured to perform the intelligent operation method for the operating equipment according to any one of claims 1 to 8.

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