A smart mowing cooperative control method and device based on module identification and a medium

Abstract

The application discloses a kind of intelligent mowing collaborative control method, equipment and medium based on module identification, it is related to intelligent control technical field, comprising: acquisition host interface state and extended identity data, identification analysis and control parameter initialization are carried out in combination with current electric quantity and work area information, obtain module identification result and initial control constraint set;Enter the work area and carry out path planning and match detection stop point, obtain first round execution path;Real-time mowing travel and environmental detection collaborative control are carried out according to first round execution path, and area position information and work state information are synchronously recorded, obtain detection work result;Area state evaluation and risk analysis are carried out to detection work result, and control parameter is corrected in real time;Collaborative work scheduling is carried out based on area risk label and corrected control parameter set, and the execution strategy of different areas is adjusted.The application realizes intelligent mowing whole process collaborative control and dynamic optimization based on module identification and environmental detection result.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent lawn mowing collaborative control method, device and medium based on module identification. Background Technology

[0002] As garden maintenance equipment evolves towards automation and intelligence, intelligent lawn mowing equipment has gradually evolved from a single mowing actuator into a composite operating platform integrating autonomous navigation, area operation planning, boundary recognition, and environmental perception. This type of technology typically controls the mowing path, movement posture, and operating rhythm based on work area boundary information, equipment operating status information, and preset operating rules. It can also be combined with extended operating mechanisms to achieve functions such as edge trimming, grass clipping collection, or environmental monitoring. Under this technological system, the equipment needs to complete path organization, status perception, process scheduling, and operation result management in different operating scenarios, thereby meeting the continuous and refined maintenance needs of courtyard lawns, park green spaces, and other similar scenarios.

[0003] Existing conventional methods still face two main technical challenges in practical applications. First, after integrating different extended functions, most control strategies still rely primarily on preset mode switching, lacking sufficient linkage to control constraints arising from changes in external contours, load variations, energy consumption, and operational methods. This results in insufficient coordination between path entry, turning transitions, and boundary operations. Second, in some solutions, environmental monitoring information is primarily used for status recording or subsequent analysis, and has not been fully integrated into real-time control corrections during operations. Consequently, a continuous closed loop is lacking between regional risk identification, differentiated scheduling, and historical model rewriting, impacting dynamic adaptability in complex areas. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent lawn mowing collaborative control method based on module identification to solve the problems of insufficient control constraint linkage after the access of extended functions and the difficulty of environmental detection information to participate in real-time closed-loop control in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an intelligent lawn mowing collaborative control method based on module identification, which includes: collecting host interface status and extended identity data, combining current power and work area information to perform identification analysis and control parameter initialization, and obtaining module identification results and initial control constraint set;

[0008] Based on the module identification results and the initial control constraint set, the operation area is determined and the path is planned and matched with the detection stop point to obtain the first round of execution path;

[0009] Based on the first round of execution path, real-time grass cutting movement and environmental monitoring are coordinated and controlled, and regional location information and operation status information are recorded simultaneously to obtain the detection operation results;

[0010] The regional status assessment and risk analysis of the detection results are carried out, and the operation control parameters are corrected in real time to obtain the regional risk label and the corrected control parameter set;

[0011] Based on regional risk labels and the revised set of control parameters, collaborative operation scheduling is carried out to adjust the execution strategies of different regions and obtain the regional completion status and remaining task list.

[0012] Based on the regional completion status and the remaining task list, tasks are closed out and historical data is updated and written back to the control model to obtain the collaborative control update results.

[0013] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module identification described in this invention, the specific steps for obtaining the module identification result and the initial control constraint set are as follows:

[0014] Perform connection status identification and communication status identification on the host interface status, extract extended identity data and generate extended identity mapping information;

[0015] Based on the extended identity mapping information, constraint mapping processing is performed on the job impact characteristics corresponding to different extended identities to generate extended identity constraint information;

[0016] The extended identity constraint information is organized and matched with the current power consumption and work area information to form work control restriction information that matches the current extended identity;

[0017] The operation control constraint information is merged and registered to form the module identification result and the initial control constraint set.

[0018] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module recognition described in this invention, the steps of determining entry into the work area, planning the path, and matching and detecting stopping points to obtain the first-round execution path are as follows:

[0019] Based on the boundary information of the work area and the information of the restricted area, perform boundary location identification and passable location identification of the work area to generate basic distribution information of the area;

[0020] Based on the regional basic distribution information, combined with the module identification results and the initial control constraint set, the work area is divided into regional attributes to form work areas with different attributes.

[0021] Based on the distribution location of different attribute work areas, the path entry order is arranged and the path direction is matched to form the first round of path draft;

[0022] The first draft path is embedded with the corresponding locations of the detection stops to form the first execution path.

[0023] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module recognition described in this invention, the steps of performing real-time lawn mowing movement and environmental detection collaborative control based on the first round execution path, and simultaneously recording regional location information and operation status information to obtain the detection operation results are as follows:

[0024] Real-time mowing movement control is executed based on the first round of execution path, and detection coordination control is executed when the detection stop point is reached;

[0025] During the detection and collaborative control process, soil environmental information and corresponding regional location information are collected, and the soil environmental information is synchronously associated with the operation status information to form regional environmental association information;

[0026] Identify abnormal states during the detection and coordination process, and register the abnormal locations as locations to be compensated;

[0027] The regional environmental information and the locations to be compensated are merged and registered to form the detection results.

[0028] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module identification described in this invention, the steps of performing regional state assessment and risk analysis on the detection operation results and correcting control parameters in real time to obtain regional risk labels and a set of corrected control parameters are as follows:

[0029] Based on the soil environmental information, regional location information, and operation status information in the test results, regional status assessment and risk analysis are performed to generate regional risk labels.

[0030] Based on the regional risk labels, the operation control parameters are adjusted accordingly to generate the corrected control parameters.

[0031] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module identification described in this invention, the operation control parameters include travel control parameters, turning control parameters, boundary operation parameters, collection operation parameters, and detection operation parameters;

[0032] The process of performing linkage correction on operation control parameters based on regional risk labels refers to performing adaptive correction on at least one of the following: travel control parameters, turning control parameters, boundary operation parameters, collection operation parameters, and detection operation parameters, based on different risk types and risk levels, to form corrected control parameters that match the current regional risk label.

[0033] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module identification described in this invention, the following steps are taken: Cooperative job scheduling is performed based on regional risk labels and a modified set of control parameters to adjust the execution strategies for different regions, thereby obtaining the regional completion status and a list of remaining tasks.

[0034] Based on the regional risk labels and the revised set of control parameters, the priority of tasks in areas where tasks have not yet been completed is rearranged to form a secondary scheduling order.

[0035] Based on the secondary scheduling sequence, a continuous judgment is made on the differences in detection results and workload between adjacent areas;

[0036] When the difference in detection results between adjacent areas exceeds a preset range, a retest is triggered.

[0037] When the change in regional operation load is inconsistent with the predicted load corresponding to the first round of execution path, re-cutting is triggered;

[0038] When there are uncovered zones in the boundary coordination area, boundary retracing is triggered;

[0039] The execution results of re-survey processing, re-cutting processing, and boundary re-tracing processing are merged and registered to form a list of regional completion status and remaining tasks.

[0040] As a preferred embodiment of the intelligent lawn mowing collaborative control method based on module identification described in this invention, the steps of completing tasks and updating historical data according to the regional completion status and remaining task list, and writing back to the control model to obtain the collaborative control update result, are as follows:

[0041] Based on the regional completion status and the remaining task list, determine the status of continuing work, supplementary work, and completed work.

[0042] The module identification results in this operation are linked and organized with the initial control constraint set, the first round of execution path, the detection operation results, the regional risk labels and the corrected control parameter set, as well as the regional completion status and the remaining task list to form operation history association information;

[0043] The historical index is updated and the control parameters are written back to the historical information of the operation to form a collaborative control update result.

[0044] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the intelligent lawn mowing collaborative control method based on module identification as described in the first aspect of the present invention.

[0045] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent lawn mowing collaborative control method based on module identification as described in the first aspect of the present invention.

[0046] The beneficial effects of this invention are as follows: By collecting host interface status and extended identity data and generating module identification results and initial control constraint sets, the corresponding unification of extended identity with travel speed limit information, boundary distance limit information and turning transition limit information is realized, enabling the equipment to complete targeted control initialization according to the current operation configuration, thereby providing a stable basis for path entry and operation coordination; at the same time, by conducting regional status assessment and risk analysis on the detection operation results and correcting control parameters in real time, the continuous driving of soil environmental information and operation status information on collaborative operation scheduling is realized, making re-measurement processing, re-cutting processing and boundary re-traversal processing regionally adaptable, thereby obtaining a more complete regional completion status and collaborative control update results. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a smart lawn mowing collaborative control method based on module recognition.

[0049] Figure 2 This is a flowchart showing the process of combining the module identification results with the initial control constraint set.

[0050] Figure 3 A flowchart for generating the first-round execution path and detection results.

[0051] Figure 4 A flowchart for generating regional risk labels and revised control parameter sets. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Reference Figures 1-4 This is one embodiment of the present invention, which provides an intelligent lawn mowing collaborative control method based on module identification, comprising the following steps:

[0056] S1. Collect host interface status and extended identity data, combine current power and work area information to perform identification analysis and control parameter initialization, and obtain module identification results and initial control constraint set;

[0057] S1.1 Perform connection status identification and communication status identification on the host interface, extract extended identity data and generate extended identity mapping information;

[0058] Specifically, the physical connectivity and signal interaction status of the host interface are checked item by item to determine whether the host interface is in a valid connection state or a valid communication state. For interfaces that are validly connected and can communicate normally, the extended identity data is read one by one. The corresponding registration is carried out in the manner of "interface location - connection state - communication state - extended identity data". The interface identification results that can stably correspond to the same extended identity data are merged into the same identity category to form extended identity mapping information that can represent the relationship between different interface access objects and their corresponding identities.

[0059] It should be noted that extended identity data is identification information used to characterize the category of the current access object. The identification information can come from the identity identifier content read from the host interface, such as the identity code, identity level range, identity feature value or identity communication field corresponding to different access objects.

[0060] S1.2. Based on the extended identity mapping information, perform constraint mapping processing on the job impact features corresponding to different extended identities to generate extended identity constraint information.

[0061] Furthermore, the identity category of the current access object is determined based on the extended identity mapping information; then the influence rules corresponding to the identity category are retrieved; then it is determined whether the current access object affects the operation coverage boundary, whether it affects the load status during the process, whether it affects the energy consumption status during continuous operation, and whether it affects the execution order and action method; then the above influences are classified into the scope influence category, load influence category, energy consumption influence category, and behavior influence category respectively; finally, the influence contents of each type under the same identity category are summarized to form extended identity constraint information.

[0062] S1.3. The extended identity constraint information is matched with the current power and work area information to form work control restriction information that matches the current extended identity.

[0063] Furthermore, based on the extended identity constraint information, combined with the current battery level reflecting the range conditions and the work area information reflecting the work boundary conditions, access conditions, and regional distribution conditions, the extended identity constraint information is matched item by item and classified hierarchically, so that the extended identity constraint information can be mapped to specific work control requirements together with the current battery level and work area information, forming work control restriction information that matches the current extended identity.

[0064] S1.4 Complete the merging and registration of the operation control restriction information to form the module identification result and the initial control constraint set.

[0065] Specifically, the operation control restriction information is uniformly organized according to the extended identity category, operation area adaptation relationship, and control requirement category; the scattered operation control restriction information is merged according to the control logic corresponding to the same extended identity, and the merged operation control restriction information is associated with the previously formed extended identity mapping information, so that the extended identity recognition content and the control constraint content form a unified correspondence, and finally form the module recognition result and the initial control constraint set.

[0066] S2. Based on the module identification results and the initial control constraint set, the entry determination and path planning of the work area are performed, and the detection stop points are matched to obtain the first round of execution path;

[0067] S2.1 Based on the boundary information and restricted area information of the work area, perform boundary location identification and passable location identification of the work area to generate basic distribution information of the area;

[0068] Specifically, the system reads the content reflecting the scope of the work area boundary from the work area boundary information and the content reflecting prohibited and restricted access locations from the restricted access area information. Based on the work area boundary information, it determines the distribution of boundary locations within the work area and the distribution of impassable locations within the work area based on the restricted access area information. In addition to the distribution of boundary locations and impassable locations, it identifies the distribution of passable locations. The distribution of boundary locations, impassable locations, and passable locations are then uniformly organized to form basic regional distribution information that can characterize the spatial distribution relationship of the work area.

[0069] S2.2. Based on the regional basic distribution information, combined with the module identification results and the initial control constraint set, the work area is divided into regional attributes to form work areas with different attributes.

[0070] Preferably, the boundary location distribution, impassable location distribution, and passable location distribution in the regional basic distribution information are read. The operation requirements corresponding to the current access identity are determined by combining the module identification results. Based on the control boundaries and operation restrictions in the initial control constraint set, the different locations in the regional basic distribution information are classified by attribute. Locations close to the boundary and meeting the boundary operation requirements form boundary attribute regions, locations meeting the regular operation requirements form basic attribute regions, and locations meeting the detection requirements form detection attribute regions. The location ranges corresponding to different attributes are merged and organized to finally form operation regions with different attributes.

[0071] S2.3. Based on the distribution location of different attribute work areas, perform path entry order arrangement and path direction matching processing to form the first round of path draft.

[0072] Specifically, the system reads the boundary ranges corresponding to different attribute work areas, and then determines whether any two different attribute work areas have boundary contact, an interval distance less than the preset passage distance, or can be connected by continuous passable positions. If any of the above conditions are met, the corresponding two different attribute work areas are registered as adjacent areas. The system compares the reachability order of different attribute work areas relative to the work start position, and then sorts them according to the work requirements of different attribute work areas. Priority is given to entering different attribute work areas that are close to the work start position and do not depend on the processing results of other areas. Different attribute work areas that depend on the results of previous work or need to be processed in a specific area before entry are registered as subsequent entry areas, thus forming an entry sequence relationship. The system determines whether it is possible to directly enter the next different attribute work area after the previous different attribute work area is completed, and then determines the end position of the previous different attribute work area. The system checks whether there are continuous passable locations between the starting position of the current work area and the starting position of the next work area with different attributes. If the continuous entry condition is met, the corresponding relationship is registered as a work connection relationship. Then, the main travel direction within the area is determined according to the boundary extension direction of each work area with different attributes. The transition direction between areas is determined according to the relationship between adjacent areas and the work connection relationship. The main travel direction within the area and the transition direction between areas are then adjusted to form the path entry order arrangement result and the path direction matching result. The path entry order arrangement result and the path direction matching result are then integrated and sorted to form the first draft path, which includes the path passing positions within the area and the path transition positions between areas. Among them, the path passing positions refer to the continuous travel positions located within each work area with different attributes in the first draft path. The path transition positions refer to the connecting positions when moving from the previous work area with different attributes to the next work area with different attributes in the first draft path.

[0073] S2.4. The first-round path draft is embedded with the corresponding locations of the detection stop points to form the first-round execution path.

[0074] Furthermore, the range of locations corresponding to the detection attribute area is read, and locations within that range that are reachable, allow for short stops, and enable detection actions are selected to form the corresponding locations of detection stop points. These locations are then matched point-by-point with the locations traversed by the path in the initial draft path. This allows the initial draft path to maintain the original path entry order and path direction while adding stopping requirements and order for the locations corresponding to the detection stop points. Subsequently, the path content containing the locations corresponding to the detection stop points is uniformly organized to ultimately form the initial execution path, which includes both continuous operation paths and detection stop arrangements.

[0075] S3. Based on the first round of execution path, perform real-time coordinated control of grass mowing movement and environmental monitoring, and simultaneously record regional location information and operation status information to obtain the detection operation results;

[0076] S3.1 Execute real-time mowing movement control based on the first round execution path, and execute detection coordination control when reaching the detection stop point.

[0077] Specifically, based on the path location, entry order, and detection stop location in the first execution path, the mowing operation proceeds sequentially according to the first execution path, maintaining the correspondence between the current position and the first execution path throughout the operation. When the current position reaches the detection stop, the real-time mowing movement control switches its movement state according to the stopping requirements and order corresponding to the detection stop, transitioning the operation from continuous movement to detection stop state. In the detection stop state, detection and coordination control is performed according to the stopping arrangements corresponding to the first execution path, and then the subsequent path progression relationship is maintained according to the first execution path.

[0078] It should be noted that detection-coordinated control refers to a control process that continuously coordinates and processes real-time mowing movement control, stopping state switching, soil environmental information collection, abnormal state identification, and subsequent movement recovery based on the detection stopping points in the first round of execution path.

[0079] S3.2 During the detection and collaborative control process, soil environmental information and corresponding regional location information are collected, and the soil environmental information is synchronously associated with the operation status information to form regional environmental association information.

[0080] Furthermore, during the detection and collaborative control process, the detection stop points in the first execution path are used as the basis for data collection. Soil environmental information collection is initiated after the real-time mowing movement control reaches the detection stop point. The soil environmental information is obtained from the detection actions in the detection and collaborative control, the corresponding area location information is confirmed by the detection stop point location and the real-time positioning results, and the operation status information is obtained from the real-time mowing movement control process record when reaching the detection stop point. Subsequently, according to the same detection stop point and the same data collection time, the soil environmental information, the corresponding area location information, and the operation status information are bound and registered, so that the soil environmental information can be mapped to the specific operation location and specific operation process, ultimately forming regional environmental association information.

[0081] S3.3 Identify abnormal states during the detection and coordination process, and register the abnormal locations as locations to be compensated.

[0082] Specifically, during the collaborative detection process, the detection docking points and docking requirements in the first round of execution path are read, along with the collected regional location information, soil environment information, and operational status information. The regional location information is checked for consistency with the detection docking points, the operational status information is checked for compliance with the docking requirements, and the collected soil environment information is checked for validity. If any discrepancies are found in the location consistency check, status compliance check, or validity check, the discrepancy is identified as an abnormal state, and the corresponding regional location information is identified as an abnormal location. Finally, the abnormal locations are registered according to the order of the detection docking points to form locations to be compensated.

[0083] S3.4. Merge and register the regional environmental information with the locations to be compensated to form the detection results.

[0084] Specifically, based on regional environmental association information and the location to be compensated, the registration order is based on the order of the detection stop points. The soil environmental information, corresponding regional location information, and operation status information in the regional environmental association information are matched with the location to be compensated. When the corresponding regional location information falls into the location to be compensated, the corresponding regional environmental association information is registered as the detection content that requires subsequent compensation processing. When the corresponding regional location information does not fall into the location to be compensated, the corresponding regional environmental association information is registered as normal detection content. Finally, the normal detection content and the detection content that requires subsequent compensation processing are uniformly merged according to the order of the detection stop points to form the detection operation results.

[0085] S4. Conduct regional status assessment and risk analysis on the detection results and correct the operation control parameters in real time to obtain regional risk labels and the corrected set of control parameters.

[0086] S4.1 Based on the soil environmental information, regional location information, and operation status information in the detection operation results, perform regional status assessment and risk analysis to form regional risk labels.

[0087] Furthermore, the regional location information is matched with the work area range in the work area information, and the regional location information is matched with the path location content in the first round of execution path. When the regional location information falls within the work area range and corresponds to the travel position and detection point stop position in the first round of execution path, the corresponding work area location is determined as the evaluation location. The soil environmental information corresponding to the same evaluation location is compared item by item with the soil state determination conditions. When the soil environmental information meets the soil state determination conditions, the evaluation location is registered as the normal soil state. When the soil environmental information does not meet the soil state determination conditions, the evaluation location is registered as the corresponding soil deviation state, and the soil state is determined according to the degree of deviation of the soil environmental information from the soil state determination conditions. The work state information corresponding to the same evaluation location is compared item by item with the work state determination conditions. When the work state information meets the work state determination conditions, the evaluation location is registered as the work stable state. When the work state information does not meet the work state determination conditions, the evaluation location is registered as the corresponding work deviation state, thus obtaining the work impact state.

[0088] The soil condition and operation impact status at the same evaluation location are registered accordingly, and the risk type is determined according to the risk assessment rules. When the soil condition is a soil deviation state, the evaluation location is marked as soil anomaly. When the operation impact status characterization path following record does not meet the operation status judgment conditions, the evaluation location is marked as traffic instability. When the operation impact status characterization operation load record does not meet the operation status judgment conditions, the evaluation location is marked as operation load anomaly. When the operation impact status characterization traversal record does not meet the operation status judgment conditions, the evaluation location is marked as repeated disturbance risk.

[0089] When an evaluation location has only a single risk type and the degree of deviation is in the low-level range, it is classified as a low-level risk; when an evaluation location has a single risk type and the degree of deviation is in the medium-level range, or when multiple risk types overlap, it is classified as a medium-level risk; when an evaluation location has multiple risk types overlap and at least one risk type has a degree of deviation in the high-level range, it is classified as a high-level risk. The evaluation location, risk type marker, and risk level marker are merged according to the order of the detection stop points to form a regional risk label.

[0090] It should be noted that the low-level, medium-level, and high-level ranges are risk level determination ranges jointly determined based on the initial control constraint set, detection operation results, and operation history association information. The state determination conditions and control boundaries are determined based on the initial control constraint set, and the deviation degree corresponding to the evaluation position is determined based on the detection operation results. Then, based on the control correction methods required to complete the operation in the operation history association information, the deviation range that can be corrected by a single operation control parameter is determined as the low-level range, the deviation range that requires the linkage correction of multiple operation control parameters is determined as the medium-level range, and the deviation range that requires triggering retesting, recutting, or boundary retracing is determined as the high-level range.

[0091] S4.2. Based on the regional risk label, perform linkage correction processing on the operation control parameters to form the corrected control parameters.

[0092] Specifically, based on the evaluation location in the regional risk label, the evaluation location is mapped to the first-round execution path and the initial control constraint set. Adjustable operation control parameters within the allowed range of operation control restrictions at the current evaluation location are extracted to form the operation control parameter range. The operation control parameters requiring adjustment are determined based on the risk type, and the adjustment range is determined based on the risk level. Within the limits of the operation control restrictions, operation control parameters with operational connections at the same evaluation location are adjusted synchronously to ensure that the travel control requirements, turning control requirements, boundary operation requirements, collection operation requirements, and detection operation requirements at the same evaluation location match the regional risk label. The adjusted operation control parameters are then integrated according to the regional location information and the order of detection stop points to form the corrected control parameters.

[0093] S5. Based on the regional risk labels and the revised control parameter set, perform collaborative operation scheduling, adjust the execution strategies of different regions, and obtain the regional completion status and remaining task list.

[0094] S5.1 Based on the regional risk labels and the revised set of control parameters, the priority of tasks in areas where tasks have not yet been completed is rearranged to form a secondary scheduling order.

[0095] Specifically, based on the regional risk labels and the revised control parameter set, the evaluation positions in the regional risk labels are mapped to the travel positions and detection point stopping positions in the first round of execution path. Combining the normal detection content and the detection content that requires subsequent compensation processing in the detection operation results, the location range in the first round of execution path that has not yet met the operation requirements is identified, forming areas where the operation has not been completed. Based on the risk type label, risk level label, revised control parameters, and adjacent area connection relationship corresponding to the areas where the operation has not been completed, the operation priority of the areas where the operation has not been completed is rearranged, so that areas with higher risk levels, requiring compensation processing and that can be entered continuously are prioritized, and areas with lower risk levels or that need to wait for the results of previous processing are arranged later, forming a secondary scheduling order.

[0096] S5.2. Based on the secondary scheduling sequence, make a continuity judgment on the differences in detection results and workload between adjacent areas.

[0097] Furthermore, areas with incomplete tasks that are consecutive in the secondary scheduling sequence and have a positional connection in the first round of execution path are identified as adjacent areas. Soil environmental information and task status information corresponding to the adjacent areas are extracted from the detection task results. The deviation between soil environmental information is used to determine the difference in detection results, and the deviation between the contents reflecting the task load in the task status information is used to determine the difference in task load. The difference in detection results and the difference in task load are then compared with the area risk label and the corrected control parameter set to determine whether the adjacent areas can be executed consecutively according to the secondary scheduling sequence.

[0098] S5.3 When the difference in detection results between adjacent areas exceeds the preset range, a retest is triggered;

[0099] When the change in regional operation load is inconsistent with the predicted load corresponding to the first round of execution path, re-cutting is triggered;

[0100] When there are uncovered zones in the boundary coordination area, boundary retracing is triggered;

[0101] Furthermore, the differences in detection results between adjacent areas are compared with the preset range in the initial control constraint set. When the differences in detection results between adjacent areas exceed the preset range, the corresponding location of the detection stop point in the adjacent area is registered as a retesting object and the retesting process is triggered.

[0102] It should be noted that, based on the work area information and work areas with different attributes, the regional attributes and location connections of adjacent areas are determined respectively. The allowable control conditions related to the detection work are extracted from the work control restriction information. The strictness of the judgment of the difference in detection results is determined according to the risk type mark and risk level mark of the adjacent areas. The regional attributes, location connections, allowable control conditions and judgment strictness of the adjacent areas are sorted out to form the range of allowable differences in detection results between adjacent areas. The range of allowable differences in detection results between adjacent areas is used as the preset range.

[0103] The system determines the regional workload change based on the workload changes reflected in the work status information. Simultaneously, it determines the predicted workload corresponding to the first-round execution path based on the first-round execution path and the work control restrictions in the initial control constraint set. When the regional workload change is inconsistent with the predicted workload corresponding to the first-round execution path, the corresponding region is registered as a re-cutting object and re-cutting processing is triggered. The system reads the boundary coordination regions corresponding to the boundary work requirements in different attribute work regions and verifies the coverage of the executed path range of the boundary coordination region against the required execution location range of the boundary coordination region. When there are uncovered areas in the boundary coordination region, the uncovered areas are registered as boundary re-travel processing objects and boundary re-travel processing is triggered. The boundary coordination region refers to the boundary-adjacent work region determined based on the boundary location distribution and traversable location distribution in the regional basic distribution information, combined with the module identification results and the work control restrictions in the initial control constraint set. The boundary coordination region is formed by continuously merging traversable locations that meet the boundary proximity condition and work control restriction condition after excluding inaccessible locations. This boundary coordination region is used for subsequent boundary path execution, boundary coverage judgment, and boundary re-travel processing.

[0104] S5.4. Complete and register the execution results of re-survey processing, re-cutting processing, and boundary re-traversal processing to form a list of regional completion status and remaining tasks.

[0105] Furthermore, the execution results of retesting, recutting, and boundary retracing are mapped to the locations to be compensated in the detection operation results, the travel locations in the first round of execution paths, and the uncovered areas in the boundary coordination region, respectively. Locations that have completed retesting and whose detection result differences have returned to the preset range are registered as detection completed. Locations that have completed recutting and whose regional operation load changes are consistent with the predicted load corresponding to the first round of execution paths are registered as operation completed. Locations that have completed boundary retracing and whose uncovered areas have been covered are registered as boundary completed. Locations that still do not meet the requirements for detection, operation, or boundary coverage are marked as incomplete and are assigned to the remaining tasks according to the secondary scheduling order. The detection completed status, operation completed status, boundary completed status, and incomplete markers are integrated according to the regional location information to form a list of regional completed statuses and remaining tasks.

[0106] S6. Based on the regional completion status and the remaining task list, perform task completion and historical data updates, and write back to the control model to obtain the collaborative control update results.

[0107] S6.1. Based on the regional completion status and the remaining task list, determine the status of continuing operations, supplementary operations, and completed operations.

[0108] Specifically, based on the regional completion status and the remaining task list, and using the current power level and the corrected control parameter set as the criteria for judgment; when there are still unfinished tasks in the regional completion status and the current power level meets the work requirements corresponding to the remaining task list, the tasks corresponding to the remaining task list are judged as continuing work; when there are unfinished tasks in the regional completion status but the current power level or the corrected control parameter set cannot support the continuous completion of the remaining task list, the tasks corresponding to the remaining task list are judged as supplementary work; when there are no unfinished tasks in the regional completion status and the remaining task list is empty, the current work is judged as ending work.

[0109] S6.2. The module identification results in this operation are associated with the initial control constraint set, the first execution path, the detection operation results, the regional risk labels and the corrected control parameter set, as well as the regional completion status and the remaining task list, to form operation history association information.

[0110] Specifically, using the travel position and the corresponding detection stop position in the first execution path as the association benchmark, the module identification results are mapped to the initial control constraint set and then to the first execution path; the detection operation results are mapped to the corresponding detection stop positions in the first execution path; the regional risk labels and the corrected control parameter set are mapped to the regional location information in the detection operation results; and the regional completion status and the remaining task list are mapped to the evaluation positions in the regional risk labels and the corrected control parameter set. Finally, the data is merged and registered according to the same operation area and the same operation sequence to form operation history association information.

[0111] S6.3 Perform historical index update and control parameter write-back processing on the historical association information of the operation to form collaborative control update results.

[0112] Furthermore, based on historical operation information, a correspondence is established according to the operation area, extended identity, travel position, corresponding location of detection stop point, evaluation position, and operation sequence. The detection operation results, regional risk labels and corrected control parameter sets, regional completion status and remaining task list formed under the same operation area and the same extended identity are grouped into the same historical index position, and the historical index is updated. Based on the regional completion status, the corrected control parameters that meet the operation completion requirements are selected and written into the control model record associated with the corresponding operation area and extended identity. At the same time, the corrected control parameters corresponding to the unfinished content in the remaining task list are retained as content to be optimized, and the control parameter write-back process is completed to form the collaborative control update result.

[0113] This embodiment also provides a computer device applicable to the intelligent lawn mowing collaborative control method based on module recognition, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent lawn mowing collaborative control method based on module recognition as proposed in the above embodiment.

[0114] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0115] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the intelligent lawn mowing collaborative control method based on module identification as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0116] In summary, this invention achieves unified correspondence between extended identity and travel speed limit information, boundary distance limit information, and turning transition limit information by collecting host interface status and extended identity data and generating module identification results and initial control constraint sets. This enables the equipment to complete targeted control initialization according to the current operation configuration, thus providing a stable basis for path entry and operation coordination. At the same time, by conducting regional status assessment and risk analysis on the detection operation results and correcting control parameters in real time, it realizes continuous driving of collaborative operation scheduling by soil environmental information and operation status information. This makes re-measurement processing, re-cutting processing, and boundary re-traversal processing regionally adaptable, thereby obtaining a more complete regional completion status and collaborative control update results.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent lawn mowing collaborative control based on module recognition, characterized in that, include: Collect host interface status and extended identity data, combine current power and work area information to perform identification analysis and control parameter initialization, and obtain module identification results and initial control constraint set; Based on the module identification results and the initial control constraint set, the operation area is determined and the path is planned and matched with the detection stop point to obtain the first round of execution path; Based on the first round of execution path, real-time grass cutting movement and environmental monitoring are coordinated and controlled, and regional location information and operation status information are recorded simultaneously to obtain the detection operation results; The regional status assessment and risk analysis of the detection results are carried out, and the operation control parameters are corrected in real time to obtain the regional risk label and the corrected control parameter set; Based on regional risk labels and the revised set of control parameters, collaborative operation scheduling is carried out to adjust the execution strategies of different regions and obtain the regional completion status and remaining task list. Based on the regional completion status and the remaining task list, tasks are closed out and historical data is updated and written back to the control model to obtain the collaborative control update results.

2. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 1, characterized in that, The specific steps for obtaining the module identification result and the initial control constraint set are as follows: Perform connection status identification and communication status identification on the host interface status, extract extended identity data and generate extended identity mapping information; Based on the extended identity mapping information, constraint mapping processing is performed on the job impact characteristics corresponding to different extended identities to generate extended identity constraint information; The extended identity constraint information is organized and matched with the current power consumption and work area information to form work control restriction information that matches the current extended identity; The operation control constraint information is merged and registered to form the module identification result and the initial control constraint set.

3. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 2, characterized in that, The steps for determining entry into the work area, planning routes, and matching and detecting stop points to obtain the first-round execution path are as follows: Based on the boundary information of the work area and the information of the restricted area, perform boundary location identification and passable location identification of the work area to generate basic distribution information of the area; Based on the regional basic distribution information, combined with the module identification results and the initial control constraint set, the work area is divided into regional attributes to form work areas with different attributes. Based on the distribution location of different attribute work areas, the path entry order is arranged and the path direction is matched to form the first round of path draft; The first draft path is embedded with the corresponding locations of the detection stops to form the first execution path.

4. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 3, characterized in that, The process involves real-time coordinated control of lawn mowing movement and environmental monitoring based on the initial execution path, simultaneously recording regional location information and operation status information to obtain the detection operation results. The specific steps are as follows: Real-time mowing movement control is executed based on the first round of execution path, and detection coordination control is executed when the detection stop point is reached; During the detection and collaborative control process, soil environmental information and corresponding regional location information are collected, and the soil environmental information is synchronously associated with the operation status information to form regional environmental association information; Identify abnormal states during the detection and coordination process, and register the abnormal locations as locations to be compensated; The regional environmental information and the locations to be compensated are merged and registered to form the detection results.

5. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 4, characterized in that, The specific steps for conducting regional status assessment and risk analysis on the detection results and adjusting control parameters in real time to obtain regional risk labels and a set of corrected control parameters are as follows: Based on the soil environmental information, regional location information, and operation status information in the test results, regional status assessment and risk analysis are performed to generate regional risk labels. Based on the regional risk labels, the operation control parameters are adjusted accordingly to generate the corrected control parameters.

6. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 5, characterized in that, The operation control parameters include travel control parameters, steering control parameters, boundary operation parameters, collection operation parameters, and detection operation parameters; The process of performing linkage correction on operation control parameters based on regional risk labels refers to performing adaptive correction on at least one of the following: travel control parameters, turning control parameters, boundary operation parameters, collection operation parameters, and detection operation parameters, based on different risk types and risk levels, to form corrected control parameters that match the current regional risk label.

7. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 6, characterized in that, The collaborative job scheduling based on regional risk labels and the modified control parameter set, adjusting the execution strategies for different regions, and obtaining the regional completion status and remaining task list are as follows: Based on the regional risk labels and the revised set of control parameters, the priority of tasks in areas where tasks have not yet been completed is rearranged to form a secondary scheduling order. Based on the secondary scheduling sequence, a continuous judgment is made on the differences in detection results and workload between adjacent areas; When the difference in detection results between adjacent areas exceeds a preset range, a retest is triggered. When the change in regional operation load is inconsistent with the predicted load corresponding to the first round of execution path, re-cutting is triggered; When there are uncovered zones in the boundary coordination area, boundary retracing is triggered; The execution results of re-survey processing, re-cutting processing, and boundary re-tracing processing are merged and registered to form a list of regional completion status and remaining tasks.

8. The intelligent lawn mowing collaborative control method based on module recognition as described in claim 7, characterized in that, The steps for completing tasks and updating historical data based on the regional completion status and remaining task list, and then writing back to the control model to obtain the collaborative control update result, are as follows: Based on the regional completion status and the remaining task list, determine the status of continuing work, supplementary work, and completed work. The module identification results in this operation are linked and organized with the initial control constraint set, the first round of execution path, the detection operation results, the regional risk labels and the corrected control parameter set, as well as the regional completion status and the remaining task list to form operation history association information; The historical index is updated and the control parameters are written back to the historical information of the operation to form a collaborative control update result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent lawn mowing collaborative control method based on module identification as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent lawn mowing collaborative control method based on module identification as described in any one of claims 1 to 8.

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