Intelligent control method for collaborative operation of concrete leveling robots

Abstract

The invention discloses an intelligent control method for collaborative operation of a concrete leveling robot, and the method comprises the steps: obtaining the position state updating data of multiple devices according to an initial operation region division result, carrying out the dynamic distribution of tasks in combination with the workload and mobility of each device, and determining the responsible region and operation sequence of each device; according to the operation rhythm control parameters, operation area boundary data between adjacent devices are obtained, the elevation deviation between adjacent areas is analyzed according to the boundary elevation connection requirement, and the boundary processing priority and the adjustment scheme are determined; according to the boundary processing priority, obtaining operation progress difference data between the devices, and if the progress of a certain device is lagged, dynamically updating a task allocation strategy through data interaction frequency adjustment to obtain an optimized device operation path; and according to the updated region boundary data, obtaining the latest state information of multi-device cooperation, aiming at a region seamless connection target, adjusting the operation rhythm between the devices, and determining a final elevation consistency control parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering construction robot control, and particularly relates to an intelligent control method for collaborative work of a concrete leveling robot. BACKGROUND

[0002] In the field of building construction, concrete leveling technology is an important link to ensure engineering quality and efficiency, especially in complex terrain environments. Intelligent leveling robots have irreplaceable value in improving construction precision and safety. With the growing demand for efficient and precise operations in the construction industry, developing leveling robots that can adapt to various terrains and achieve automated operation has become an urgent need for industry development.

[0003] However, the current mainstream leveling robots or methods often expose the problems of insufficient coordination and lack of overall planning ability when facing large-area and complex terrain operations. Many leveling robots lack effective communication mechanisms when multiple machines collaborate, resulting in uneven division of work areas, with some areas being repeatedly processed while others being ignored, thereby affecting overall construction progress and quality. This problem is not simply a technical backwardness, but is caused by the lack of systematic design for dynamic interaction and task coordination among multiple leveling robots.

[0004] By analyzing the challenges in this field, it can be found that real-time information sharing among multiple leveling robots is the primary bottleneck. In large-area operations, if each leveling robot cannot timely understand the position and state of the other, it is difficult to form a unified work rhythm, which leads to chaos in task allocation. Furthermore, due to the lack of accurate processing of the boundaries of the work area, the elevation connection between adjacent areas often deviates, affecting the overall leveling effect. SUMMARY

[0005] The purpose of the present application is to solve the above-mentioned problems and provide an intelligent control method for collaborative work of a concrete leveling robot.

[0006] The technical solution of the present application is as follows: The present application provides an intelligent control method for collaborative work of a concrete leveling robot. Through a pre-established terrain data model, elevation and slope distribution information is obtained from the work area. For complex terrain adaptation needs, the terrain characteristics are analyzed and the initial work area is divided to obtain the boundary range and elevation reference data of each region. According to the initial work area division result, the position state update data of multiple leveling robots is obtained. Combined with the work load and movement ability of each leveling robot, task dynamic allocation is performed to determine the responsible area and work order of each leveling robot. The real-time position and work progress information is obtained from the sensors of the respective flattening robots, a data interaction network for the cooperation of the multiple flattening robots is constructed through an information real-time sharing mechanism, and unified work rhythm control parameters are obtained; Through the work rhythm control parameters, work area boundary data between adjacent flattening robots is obtained, the elevation deviation between adjacent areas is analyzed for the boundary elevation connection requirement, and the boundary processing priority and adjustment scheme are determined; According to the boundary processing priority, work progress difference data between the flattening robots is obtained, if the progress of a flattening robot lags behind, the task allocation strategy is dynamically updated through data interaction frequency adjustment, and an optimized flattening robot work path is obtained.

[0007] In an embodiment, the control method further comprises: For the optimized flattening robot work path, real-time feedback information of terrain data collection is obtained, terrain changes encountered in the path execution process are analyzed, and it is determined whether the region division scheme needs to be adjusted, updated region boundary data is obtained; According to the updated region boundary data, the latest state information of the cooperation of the multiple flattening robots is obtained, the work rhythm between the flattening robots is adjusted for the seamless connection goal of the region, and the final elevation consistency control parameters are determined; Through the final elevation consistency control parameters, region flatness data after the work of each flattening robot is completed is obtained, whether there is a deviation is analyzed, if the deviation exceeds a preset threshold, a secondary processing of a local region is triggered, and corrected flatness distribution information is obtained.

[0008] In an embodiment, the method for determining the boundary range and the elevation reference data of each region comprises: Through a preset terrain data model, the elevation distribution and the slope distribution of the work area are obtained, and the elevation and slope distribution data are obtained; The elevation and slope distribution data are processed by a digital elevation model, terrain features are extracted, and a terrain feature set is determined; If the slope value in the terrain feature set is greater than a preset threshold, the terrain feature set is marked as a complex terrain region, and a complex terrain region set is obtained; The complex terrain region set is subjected to initial work region division through a K-means clustering algorithm, and an initial work region set is determined; According to the initial work region set, the boundary range of each region is calculated, and boundary range data is obtained; The boundary range data and the elevation distribution data are processed by an interpolation algorithm, and the elevation reference data is generated; The elevation reference data is processed by rasterization to generate an elevation reference raster map of the work area, and a final work region division result is determined.

[0009] In an embodiment, the method for determining the responsible area and the work order of each flattening robot comprises: Obtaining the position state and update data of the plurality of flattening robots, collecting the position information and running status of each flattening robot in real time through a pre-established monitoring system, determining the current working area and state change trend of the flattening robot; According to the state change trend, combining the work load data, using the preset load balancing strategy to analyze the current task amount of each flattening robot, judging whether the flattening robot is in a high load state, if the load exceeds the preset threshold, marking the flattening robot as needing adjustment; For the flattening robot marked as needing adjustment, obtain its moving ability data, compare the moving ability with the distance and path information between the working areas to determine the flattening robot list that can be allocated to a new responsible area; According to the flattening robot list and the area division data, using dynamic programming algorithm to optimize task allocation, obtaining the new responsible area and work order of each flattening robot; Through the update data and state monitoring result, judge whether the newly allocated responsible area matches the current position state of the flattening robot, if the position state and the allocated area do not match, recalculate the moving path and adjust the work order; According to the adjusted work order, obtain the task execution progress of each flattening robot, update the work load data through the real-time monitoring system, and determine the execution effect of task allocation; Through the execution effect data, combining the state monitoring information, continuously tracking the dynamic adjustment needs of the flattening robot in the working area, obtaining the final task allocation and area division result.

[0010] In an embodiment, the method for obtaining the unified work rhythm control parameter comprises: Obtaining real-time position and work progress data from flattening robot sensors, using timestamp synchronization mechanism to obtain standardized data stream; Transmitting the standardized data stream through the data interaction network, using message queue protocol to determine the data consistency between multiple flattening robots; If the data consistency meets the preset threshold, based on the real-time position and work progress, using K-means algorithm to cluster the flattening robot distribution, obtaining the flattening robot grouping result; According to the flattening robot grouping result, dynamically adjusting the task allocation strategy, optimizing resource allocation through linear programming algorithm, generating a task allocation scheme; Extracting the work load of each flattening robot from the task allocation scheme, using time series analysis to predict the work rhythm trend, obtaining the rhythm prediction result; The control parameters of the leveling robots are adjusted by broadcasting the rhythm prediction result through the cooperative network to generate unified operation rhythm control parameters. If the control parameter deviation exceeds the preset threshold, the deviation information is fed back through the data interaction network, the task allocation scheme is iteratively optimized, and the updated control parameters are obtained.

[0011] In an embodiment, the method for determining the boundary processing priority and adjustment scheme comprises: Boundary data of adjacent leveling robot operation areas are obtained from the leveling robot log through the operation rhythm parameters to obtain a boundary coordinate set; The boundary elevation values between adjacent areas are calculated using the boundary coordinate set to obtain an elevation data table; If the elevation deviation of adjacent areas in the elevation data table exceeds the preset threshold, the deviation correction value is calculated through the linear interpolation algorithm to obtain a corrected elevation set; The priority of boundary connection is analyzed according to the corrected elevation set to obtain a priority ranking table; The adjustment scheme is allocated through the priority ranking table, and the least squares method is used to optimize the boundary elevation connection to obtain an adjustment parameter set; The adjustment parameter set is obtained, the boundary data of the leveling robot operation area is updated, and the updated boundary coordinate set is obtained; The elevation deviation of adjacent areas is recalculated according to the updated boundary coordinate set to obtain final elevation consistency data.

[0012] In an embodiment, the method for obtaining the optimized leveling robot operation path comprises: The progress difference value is determined by preliminarily comparing the current task completion of each leveling robot according to the operation progress data obtained from the leveling robot running log; According to the progress difference value obtained by comparison, the preset threshold is used for screening, and if the difference value of a leveling robot exceeds the threshold, it is marked as a lagging leveling robot to obtain a lagging leveling robot list; For the leveling robots in the lagging leveling robot list, the historical data interaction records are obtained, the correlation between the interaction frequency and the task completion efficiency is analyzed, and the potential influence range of frequency adjustment is determined; Through the analysis result, the data interaction frequency of the lagging leveling robot is dynamically adjusted, and a temporary task allocation scheme is generated in combination with the current task load condition; According to the temporary task allocation scheme, the genetic algorithm is used to optimize the leveling robot operation path, the task execution order of each leveling robot is recalculated, and the improved path planning is determined; The improved path planning data is obtained, the task allocation strategy is updated in real time, and the dynamic adjustment process is completed by synchronizing to the control module of each leveling robot; By continuously monitoring the progress and path execution of the leveling robot, the difference value is compared in a loop, and if a new lagging leveling robot is found, a new round of frequency adjustment and path optimization process is triggered In an embodiment, the method for obtaining the updated regional boundary data comprises: Obtain real-time feedback information from the terrain data acquisition module, preliminarily extract the terrain change characteristics of the leveling robot operation path during execution, classify the change characteristics using a preset classification standard, and obtain the classification results of the terrain changes; According to the classification results of the terrain changes, analyze the range of influence during path execution, combine the current state of the leveling robot operation path, compare the preset terrain adaptation rules, determine whether it is beyond the adaptation range, and determine the deviation degree of path execution; If the deviation degree of path execution exceeds the preset threshold, the adaptability of the regional division scheme is evaluated, the distribution data of the current regional boundary is obtained, the influence of the terrain change characteristics on the boundary distribution is analyzed, and the adjustment requirement of regional division is obtained; According to the adjustment requirement of regional division, combining the terrain change characteristics and the actual situation of the leveling robot operation path, the genetic algorithm is used to recalculate the regional boundary to generate a preliminary boundary adjustment scheme; For the preliminary boundary adjustment scheme, obtain the historical record of leveling robot path optimization, analyze the matching degree of the adjustment scheme and the historical record, compare the preset optimization rules to determine the feasibility of the scheme, and obtain the verified boundary adjustment data; According to the verified boundary adjustment data, real-time update the regional division scheme, synchronize to the control module of the leveling robot operation path, complete the dynamic matching of the path and the boundary, and generate the updated regional boundary information; Through the updated regional boundary information, continuously monitor the execution state of the leveling robot operation path, and compare the terrain change characteristics in the real-time feedback information in a loop. If a new deviation is found, a new round of adjustment process is triggered to determine the final path adaptation scheme In an embodiment, the method for determining the final elevation consistency control parameter comprises: By obtaining the latest regional boundary information and the real-time state data of the leveling robot from the data storage module, a dynamic model of regional division is constructed, and a preliminary boundary division result is obtained; According to the boundary division result, analyze the operation rhythm distribution of the leveling robot in each region, compare the rhythm using a preset threshold, and determine the adjustment direction required for rhythm optimization; For the rhythm optimization direction, combined with real-time state data, the coordination deviation between the flattening robots is judged, if the deviation exceeds the preset range, the optimized flattening robot coordination scheme is obtained by adjusting the operation rhythm parameters; The optimized flattening robot coordination scheme is obtained, the seamless connection degree between the regions is analyzed, the information processing module is used to smooth the connection boundary, and the preliminary control parameters of the elevation consistency are determined; According to the preliminary control parameters, combined with the state monitoring data, the operation effect of the flattening robot after adjustment is analyzed, if the effect does not reach the preset standard, the control parameters are adjusted through iterative calculation to obtain the final elevation consistency parameters; Through the final elevation consistency parameters, the operation instructions of the flattening robot coordination are updated, the data update module is used to distribute the adjusted parameters to each flattening robot, and the dynamic adjustment of rhythm optimization and regional connection is completed; For the result of dynamic adjustment, real-time state monitoring data is obtained, the stability of regional boundary and flattening robot coordination is judged by comparing with historical data, and the continuous optimization scheme of system operation is determined In an embodiment, the method for obtaining the flatness distribution information comprises: Through the elevation consistency parameters, the initial data of the regional flatness of each operation flattening robot is obtained, the data cleaning method is used to remove outliers, and the preliminary processed flatness data set is obtained; According to the preliminary processed flatness data set, the deviation analysis is carried out for the regional flatness distribution, if the analysis result shows that the deviation exceeds the preset threshold line, the local area with deviation is marked, and the regional range needing secondary processing is determined; The data of the marked local area is obtained, the regional division method is used to finely segment these areas, and the detailed division grid information of the local area is generated; Through the detailed division grid information of the local area, the secondary processing method is used to adjust the elevation of each grid unit, and the adjusted local flatness data is obtained; According to the adjusted local flatness data, the flatness distribution of the whole region is recalculated, and the updated flatness distribution information is generated; For the updated flatness distribution information, combined with the deviation analysis method, the verification is carried out again, if there is still a region with deviation exceeding the preset threshold line, the secondary processing process is triggered in a cycle to obtain the final corrected flatness distribution data set; Through the final corrected flatness distribution data set, the elevation consistency parameters and the operation completion degree data are integrated, and the complete regional flatness analysis result is generated.

[0013] The advantages or beneficial effects of the above technical solutions at least include: The application discloses an intelligent control method for multi-device cooperative operation under complex terrain, which obtains elevation and slope distribution information through a pre-established terrain data model, divides an initial operation area and dynamically allocates tasks. The application constructs a multi-device cooperative data interaction network, shares position and progress information in real time, and obtains unified operation rhythm control parameters. For the boundary elevation connection requirement, the elevation deviation between adjacent areas is analyzed and a processing scheme is determined. The application can also dynamically adjust the area division and device path according to real-time feedback, and ensure seamless connection of the area through the elevation consistency control parameter. Finally, the application effectively solves the problem of accurate control of multi-device cooperative operation under complex terrain by analyzing the flatness data to trigger local secondary processing, and realizes efficient flattening and seamless connection of the operation area. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application.

[0015] Figure 1 A flow chart of the control method of the embodiment of the application is shown; Figure 2 A structural schematic diagram of the flattening robot of the embodiment of the application is shown. DETAILED DESCRIPTION

[0016] Embodiments of the application will be described in more detail by referring to the attached drawings. Although some embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described herein, but rather, these embodiments are provided to more thoroughly and completely understand the application. It should be understood that the drawings and embodiments of the application are only for exemplary purposes, and are not intended to limit the scope of protection of the application.

[0017] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0018] It should be understood that the term "include" and its conjugations, as used herein, are open-ended terms that specify the presence of something and do not exclude the presence of additional something. The term "based on" is intended to mean "based, at least in part, on" unless explicitly stated otherwise. The term "one embodiment" is used herein to refer to at least one embodiment. The term "another embodiment" is used herein to refer to at least one additional embodiment. The term "some embodiments" is used herein to refer to at least one embodiment. Relative terms such as "first" and "second" and the like can be used solely to distinguish one entity from another entity, without necessarily requiring or implying any actual relationship or order between such entities. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0019] It should be noted that the terms "one" and "a" and "multiple" and "plurality" as used herein are illustrative and not limiting, and those skilled in the art will understand that "one" or "a" or "multiple" or "plurality" should be understood as "one or more" unless the context clearly dictates otherwise.

[0020] The names of the messages or information exchanged between the devices in the embodiments of the present application are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0021] As Figures 1-2 The intelligent control method for the concrete leveling robot cooperative work can specifically include: S101, obtaining elevation and slope distribution information from the work area by a pre-established terrain data model, analyzing terrain features and dividing an initial work area for complex terrain adaptation requirements, and obtaining boundary range and elevation reference data of each area.

[0022] The elevation distribution and slope distribution of the work area are obtained by the pre-set terrain data model, and the elevation and slope distribution data are obtained. The elevation and slope distribution data are processed by a digital elevation model to extract terrain features and determine a terrain feature set. If the slope value in the terrain feature set is greater than a pre-set threshold, it is marked as a complex terrain area to obtain a complex terrain area set. The initial work area division is performed on the complex terrain area set by a K-means clustering algorithm to determine an initial work area set. The boundary range of each area is calculated according to the initial work area set to obtain boundary range data. The boundary range data and the elevation distribution data are processed by an interpolation algorithm to generate elevation reference data. The elevation reference data is processed by rasterization to generate an elevation reference raster map of the work area, and the final work area division result is determined.

[0023] Specifically, based on the pre-established terrain data model, assuming the use of a digital elevation model (DEM) format, the spatial resolution is 1 meter, covering an operation area of 1000 meters x 1000 meters, containing an elevation value range of 0 to 500 meters. First, extract the elevation distribution information, process the raster data using the GDAL library of Python to read the DEM file, calculate the elevation value of each grid unit, generate the elevation distribution histogram, and count the area proportion of the elevation interval (such as 0-100 meters, 100-200 meters, etc.). Assuming the results show that 0-100 meters accounts for 30%, 100-200 meters accounts for 50%, and 200-500 meters accounts for 20%. Next, calculate the slope distribution, use the slope analysis tool of ArcGIS to calculate the slope of each grid based on DEM, the formula is atan(√(dz / dx 2 + dz / dy 2 ))×57.29578, where dz / dx and dz / dy are the gradients of elevation in x and y directions, the slope range is 0-45°, the statistical results show that 0-5° accounts for 60%, 5-15° accounts for 30%, and 15-45° accounts for 10%.

[0024] To adapt to complex terrain and analyze terrain features, the K-means clustering algorithm (K=3) is used, taking elevation and slope as features, and the area is divided into flat area (elevation 0-100 meters, slope 0-5°), gentle slope area (elevation 100-200 meters, slope 5-15°) and steep slope area (elevation 200-500 meters, slope 15-45°). The area of each region after clustering is 600 hectares, 300 hectares and 100 hectares respectively. Divide the initial operation area, use the spatial analysis tool of GIS to generate the vector boundary of each region, the boundary is stored in GeoJSON format, containing coordinate point sequence and elevation reference value (take the average elevation in the region, such as flat area elevation 50 meters). Finally, output the boundary range of each region (such as flat area boundary [(0,0), (0,500), (500,500), (500,0)]) and elevation reference data (such as flat area reference elevation 50 meters, gentle slope area 150 meters, steep slope area 300 meters), stored as a database table, to ensure that subsequent operation planning can be optimized based on this data. The whole process is realized through automatic script, the logic is rigorous, the data processing is efficient, and accurate support is provided for complex terrain adaptation.

[0025] S102, according to the initial operation area division result, obtain the position state update data of multiple leveling robots, combine the work load and moving ability of each leveling robot, execute task dynamic allocation, and determine the responsible area and operation order of each leveling robot.

[0026] Among them, such as Figure 2As shown, the leveling robot includes a body 10, an RTK high-precision positioning system 12 is installed on the body 10 for positioning the location of the robot, a wheel 11 is installed below the body 10, and the wheel 11 and the body 10 have a spring suspension therebetween, which can effectively reduce the influence of the terrain on the robot when walking, and a leveling mechanism 20 is also installed at the front end of the body 10, the leveling mechanism includes a leveling plate 22 connected with the body 10, a vibration motor 23 is installed on the leveling plate 22 for leveling the concrete, and a laser receiver 21 is also installed for receiving laser signals from a laser transmitter and feeding back to the control, the laser transmitter is a laser transmitter erected at a position with good field of view outside the construction site, which is used to confirm the horizontal elevation of the robot according to the site elevation line.

[0027] The position state and update data of the plurality of leveling robots are acquired, the position information and operating condition of each leveling robot are collected in real time through a pre-established monitoring system, the current working area and state change trend of the leveling robot are determined. According to the state change trend, in combination with the work load data, a preset load balancing strategy is adopted to analyze the current task amount of each leveling robot, and it is judged whether the leveling robot is in a high load state. If the load exceeds the preset threshold, the leveling robot is marked as needing to be adjusted. For the leveling robot marked as needing to be adjusted, the movement ability data thereof is acquired, and by comparing the movement ability with the distance and path information between the working areas, a list of leveling robots that can be allocated to a new responsible area is determined. According to the leveling robot list and the area division data, a dynamic programming algorithm is used to optimize the task allocation, and the new responsible area and working order of each leveling robot are obtained. Through the update data and the state monitoring result, it is judged whether the newly allocated responsible area matches the current position state of the leveling robot. If the position state and the allocated area do not match, the movement path is recalculated and the working order is adjusted. According to the adjusted working order, the task execution progress of each leveling robot is acquired, the work load data is updated through the real-time monitoring system, and the execution effect of the task allocation is determined. Through the execution effect data, in combination with the state monitoring information, the dynamic adjustment demand of the leveling robot in the working area is continuously tracked, and the final task allocation and area division result are obtained.

[0028] Specifically, based on the initial job area division result, the system first divides the job area into 100x100 meter grids through a grid division algorithm, assigns a unique identifier to each grid, and records its geographic coordinates and task demand (such as the amount of data to be processed per grid is 50MB). The system obtains real-time position state update data of multiple leveling robots through an API interface. Assume there are three leveling robots A, B, and C, with current coordinates of (20, 30), (50, 60), and (80, 20), respectively, and moving speeds of 5m / s, 4m / s, and 3m / s, respectively, and processing capacities of 10MB / s, 8MB / s, and 6MB / s, respectively. The position data is updated every second through a GPS module and stored in a cloud database. The system uses the WebSocket protocol to ensure real-time performance. Combined with the working load of the leveling robots, the system calculates the current task queue of each leveling robot, for example, A leveling robot has 20MB of pending tasks, B leveling robot has 30MB, and C leveling robot has 15MB. The availability of the leveling robots is evaluated through a load balancing algorithm (weighted round-robin method, with weights based on the inverse ratio of processing capacity and remaining tasks). The improved genetic algorithm is used for dynamic task allocation, with an initial population of 100 allocation schemes, each containing the mapping of leveling robots and grids, and the fitness function being the weighted sum of total moving distance and processing time (weights 0.4 and 0.6). Through crossover (probability 0.8) and mutation (probability 0.1) iterations for 50 generations, the optimal allocation scheme is selected, such as A leveling robot responsible for grid (20, 30) to (40, 50), B leveling robot responsible for (50, 60) to (70, 80), and C leveling robot responsible for (80, 20) to (100, 40). The job order is based on the task priority in the grid (determined by data volume and urgency level, with urgency level score 1-5), and the Dijkstra algorithm is used to calculate the shortest path of the leveling robot in the responsible area to minimize the moving distance.

[0029] For example, the path of A leveling robot is (20, 30)→(30, 40)→(40, 50), with a total distance of about 28.28 meters and an estimated time of 5.66 seconds. The analysis process records the load changes of each leveling robot, and A leveling robot's load decreases to 10MB after processing. The system compares the initial and final loads through logs to verify that the allocation efficiency has improved by 20%. If the grid task volume suddenly increases (such as grid (50, 60) increases to 100MB), the system re-runs the genetic algorithm to adjust the allocation to ensure load balancing. This process is implemented through a distributed computing framework (such as Spark) to automatically process data streams and task scheduling, forming a closed-loop optimization.

[0030] S103, obtain real-time position and work progress information from each flattening robot sensor, build a data interaction network for multi-flattening robot cooperation through information real-time sharing mechanism, and obtain unified work rhythm control parameters.

[0031] Obtain real-time position and work progress data from flattening robot sensors, adopt timestamp synchronization mechanism to obtain standardized data stream. Transmit standardized data stream through data interaction network, adopt message queue protocol to determine data consistency among multi-flattening robots. If data consistency meets preset threshold, adopt K-means algorithm to cluster flattening robot distribution based on real-time position and work progress, and obtain flattening robot grouping result. According to flattening robot grouping result, dynamically adjust task allocation strategy, optimize resource allocation through linear programming algorithm, and generate task allocation scheme. Extract work load of each flattening robot from task allocation scheme, adopt time series analysis to predict work rhythm trend, and obtain rhythm prediction result. Broadcast rhythm prediction result through cooperation network, adjust control parameters of each flattening robot, and generate unified work rhythm control parameters. If control parameter deviation exceeds preset threshold, feedback deviation information through data interaction network, iteratively optimize task allocation scheme, and obtain updated control parameters.

[0032] Specifically, in constructing a system for collaborative operation of multiple leveling robots, position data is first collected in real time using GPS sensors and inertial measurement units (IMUs) on each leveling robot. Assuming leveling robot A is at coordinates (X:100.5, Y:200.3) and leveling robot B is at (X:105.2, Y:198.7), the data collection frequency is 5 times per second. The data is transmitted to the cloud server in JSON format via a 5G network, with transmission latency controlled within 50 milliseconds to ensure real-time performance. Next, progress information is obtained using work progress sensors (e.g., leveling robot A has completed 35% and leveling robot B has completed 42%). The overall progress is calculated using a weighted average algorithm: Total Progress = (Leveling Robot A Progress × Weight 0.6 + Leveling Robot B Progress × Weight 0.4), resulting in a total progress of 37.8%. The progress difference (leveling robot B is 7% ahead) is analyzed to provide a basis for subsequent pace adjustments. Furthermore, a real-time information sharing mechanism is established, employing the MQTT protocol to construct a publish-subscribe model. All leveling robots subscribe to the cloud topic "TaskProgress," updating data every 10 seconds to ensure information synchronization errors are less than 1%. Based on this, a collaborative data interaction network for multiple leveling robots is constructed. Graph theory algorithms are used to calculate the communication topology between leveling robots, establishing connections when the distance between them is less than 50 meters, generating an adjacency matrix, and optimizing data transmission paths using a shortest path algorithm to reduce latency to below 30 milliseconds. Finally, based on the above data, a PID control algorithm is used to calculate unified work rhythm parameters. The target progress is set at 40%, and the current deviation is 2.2%. Using a proportional coefficient Kp=0.5, an integral coefficient Ki=0.1, and a derivative coefficient Kd=0.05, an adjustment amount of 1.3% is calculated, outputting the adjusted work speed command for each leveling robot (e.g., leveling robot A accelerates to 1.2 meters / second), ensuring overall coordination. This process is automated in the cloud, combined with analysis of distance and progress differences between leveling robots, forming a closed-loop control logic to ensure work efficiency and consistency.

[0033] S104. By using the operation rhythm control parameters, obtain the boundary data of the operation area between adjacent leveling robots. Based on the boundary elevation connection requirements, analyze the elevation deviation between adjacent areas and determine the boundary processing priority and adjustment plan.

[0034] The boundary data of the adjacent grading robot work area is obtained from the grading robot log through the work rhythm parameter, and the boundary coordinate set is obtained. The boundary elevation value between adjacent areas is calculated by using the boundary coordinate set, and the elevation data table is obtained. If the elevation deviation of adjacent areas in the elevation data table exceeds the preset threshold, the deviation correction value is calculated by using the linear interpolation algorithm, and the corrected elevation set is obtained. According to the corrected elevation set, the priority of the boundary connection is analyzed, and the priority sorting table is obtained. The adjustment scheme is distributed through the priority sorting table, and the least square method is used to optimize the boundary elevation connection, and the adjustment parameter set is obtained. The boundary data of the grading robot work area is updated by obtaining the adjustment parameter set, and the updated boundary coordinate set is obtained. According to the updated boundary coordinate set, the elevation deviation between adjacent areas is recalculated, and the final elevation consistency data is obtained.

[0035] Specifically, the work area boundary data between adjacent grading robots is obtained through the work rhythm control parameter, and the elevation deviation is analyzed according to the boundary elevation connection requirement, and the specific implementation method of the boundary processing priority and adjustment scheme is as follows: First, the work area boundary of adjacent grading robots A and B is calculated by using the work rhythm control parameter, such as setting the grading robot work speed to 5 kilometers per hour and the work time interval to 10 minutes. It is assumed that the boundary coordinates of the work area of the grading robot A are (100.5, 200.5), and the boundary coordinates of the grading robot B are (100.8, 200.5). The distance between the two points is 0.3 meters, and the data within the range of 0.5 meters of the boundary area is determined as the analysis object. Then, according to the boundary elevation connection requirement, the elevation data in the boundary area is extracted, it is assumed that the boundary elevation of the area of the grading robot A is 50.2 meters, and the boundary elevation of the area of the grading robot B is 50.8 meters. The elevation deviation is calculated by using the difference algorithm, which is 0.6 meters, and compared with the preset deviation threshold of 0.4 meters, it is determined that the deviation exceeds the threshold and needs to be processed in priority. Subsequently, the influence degree of the elevation deviation between adjacent areas is analyzed, and the slope requirement of the business association (assuming that the maximum slope is 2%) is combined to calculate the slope caused by the current deviation, which is 0.6 / 0.3=2%, reaching the slope upper limit, which needs to be adjusted immediately to ensure the smoothness of the work. Finally, the boundary processing priority and adjustment scheme are determined, and the adjustment scheme is automatically generated by the system according to the deviation value and the elevation adjustment rule (preferentially adjusting the area with higher elevation), and the elevation of the area of the grading robot B is smoothly adjusted from 50.8 meters to 50.5 meters, with an adjustment amplitude of 0.3 meters, ensuring that the deviation is reduced to 0.3 meters, which is lower than the threshold. At the same time, the elevation adjustment value of each point in the boundary is calculated by using the smooth interpolation algorithm (such as linear interpolation) to form a smooth transition, and the adjustment scheme is automatically pushed to the grading robot control system for execution. Through the above method, a complete logical chain is formed from data acquisition to scheme generation, which ensures the boundary elevation connection symbolization according to the work requirement.

[0036] S105, according to the boundary processing priority, obtain the work progress difference data between the flattening robots, if the progress of a flattening robot lags behind, then through data interaction frequency adjustment, dynamically update the task allocation strategy, and obtain the optimized flattening robot work path.

[0037] By obtaining the work progress data from the flattening robot operation log, the current task completion of each flattening robot is preliminarily compared to determine the progress difference value. According to the progress difference value obtained by comparison, a preset threshold is used for screening, if the difference value of a flattening robot exceeds the threshold, it is marked as a lagging flattening robot, and a lagging flattening robot list is obtained. For the flattening robots in the lagging flattening robot list, obtain their historical data interaction records, analyze the correlation between interaction frequency and task completion efficiency, and judge the potential influence range of frequency adjustment. Through the analysis result, the data interaction frequency of the lagging flattening robot is dynamically adjusted, and a temporary task allocation scheme is generated combined with the current task load. According to the temporary task allocation scheme, the genetic algorithm is used to optimize the flattening robot work path, the task execution order of each flattening robot is recalculated, and the improved path planning is determined. Obtain the improved path planning data, real-time update the task allocation strategy, synchronize to each flattening robot control module, complete the dynamic adjustment process. Through continuous monitoring of the flattening robot work progress and path execution, the difference value is compared in a loop, if a new lagging flattening robot is found, a new round of frequency adjustment and path optimization process is triggered.

[0038] Specifically, in processing the work progress difference between the flattening robots and optimizing the path, first, the work data of multiple flattening robots is analyzed by a boundary processing priority algorithm. Assuming that there are three flattening robots A, B, and C, their work progress is 75%, 82%, and 60% respectively. By calculating the progress difference value (for example, the difference between A and C is 15%, and the difference between B and C is 22%), it is determined that the progress of flattening robot C is significantly lagging behind, and the priority is the highest, so the task allocation needs to be adjusted. Then, for the flattening robot with lagging progress, through a data interaction frequency adjustment mechanism, the real-time monitoring system collects the state data of the flattening robot every 5 minutes, analyzes the work load of flattening robot C, and finds that its hourly task processing capacity is 50 units, while A and B are 80 units and 75 units respectively. The calculation shows that the efficiency of C is 37.5% lower than that of A, so the interaction frequency is dynamically adjusted to every 3 minutes, and the data feedback density is increased to more accurately grasp the state. Subsequently, based on the difference data and the interaction frequency, a weighted task allocation algorithm is used to reduce the task quantity of flattening robot C by 20%, i.e. from the original 100 units to 80 units, and the reduced task quantity is proportionally allocated to A and B (A increases by 12 units and B increases by 8 units). Through simulation analysis of the new allocation scheme, the expected completion time of the three flattening robots is 10 hours, 10.2 hours, and 10.5 hours respectively, and the difference is reduced to within 0.5 hours, indicating that the balance is improved. Finally, using a path optimization algorithm (such as Dijkstra algorithm), combined with the position coordinates of the flattening robots (assuming A is at (0, 0), B is at (5, 5), and C is at (10, 0)) and the distribution of task points, the shortest work path is recalculated, and the path length of A flattening robot is shortened from the original 15 units to 13 units, B from 18 units to 16 units, and C from 20 units to 17 units, with a total path optimization rate of 12%, thereby improving the overall work efficiency. The above process is completed through system automatic calculation and real-time data interaction, ensuring that the logic is rigorous and the task allocation and path optimization are closely related.

[0039] S106, for the optimized flattening robot work path, real-time feedback information of terrain data collection is obtained, terrain changes encountered in the path execution process are analyzed, and it is judged whether the regional division scheme needs to be adjusted, and updated regional boundary data is obtained.

[0040] The real-time feedback information is acquired from the terrain data acquisition module, the terrain change characteristics in the execution process of the grading robot operation path are preliminarily extracted, the change characteristics are classified by using a preset classification standard, and a classification result of the terrain change is obtained. According to the classification result of the terrain change, the influence range in the path execution process is analyzed, the current state of the grading robot operation path is combined, the preset terrain adaptation rule is compared, whether the adaptation range is exceeded is judged, and the deviation degree of the path execution is determined. If the deviation degree of the path execution exceeds the preset threshold value, the adaptability of the regional division scheme is evaluated, the distribution data of the current regional boundary is acquired, the influence of the terrain change characteristics on the boundary distribution is analyzed, and the adjustment requirement of the regional division is obtained. According to the adjustment requirement of the regional division, the terrain change characteristics and the actual situation of the grading robot operation path are combined, the genetic algorithm is used to recalculate the regional boundary, and a preliminary boundary adjustment scheme is generated. For the preliminary boundary adjustment scheme, the historical record of the grading robot path optimization is acquired, the matching degree of the adjustment scheme and the historical record is analyzed, the preset optimization rule is compared, the feasibility of the scheme is judged, and the verified boundary adjustment data is obtained. According to the verified boundary adjustment data, the regional division scheme is updated in real time, is synchronized to the control module of the grading robot operation path, the dynamic matching of the path and the boundary is completed, and the updated regional boundary information is generated. Through the updated regional boundary information, the execution state of the grading robot operation path is continuously monitored, the terrain change characteristics in the real-time feedback information are cyclically compared, and if a new deviation is found, a new round of adjustment process is triggered, and a final path adaptation scheme is determined.

[0041] Specifically, during the process of collecting terrain data and adjusting the area division for the optimized leveling robot's working path, the system first automatically collects terrain data every 10 minutes through the sensor network deployed on the leveling robot, including information such as slope, obstacle density, and soil hardness. Suppose that a leveling robot D finds that the slope suddenly increases from 5 degrees to 15 degrees and the obstacle density increases from 0.2 per square meter to 0.8 per square meter during path execution, the system uploads this data to the cloud analysis platform and uses the terrain adaptability assessment algorithm to calculate that the current path's traversal difficulty index has increased from the original 3.5 to 7.8, exceeding the safety threshold of 6.0, and determines that there is a potential risk in path execution. Next, the system automatically triggered the terrain change analysis module. By comparing historical data, it was found that the slope change rate in the area reached 200% in the past 24 hours, which is an abnormal fluctuation, requiring a reassessment of the area division scheme. Therefore, the system invoked a boundary dynamic adjustment algorithm to reduce the original boundary radius from 500 meters to 300 meters to avoid high-risk terrain. Simultaneously, the system analyzed the flatness of the surrounding terrain, finding that the slope in the eastern area was only 3 degrees and the obstacle density was 0.1 obstacles per square meter, making it suitable for re-division. Finally, the system generated updated area boundary data, shifting the working range of the leveling robot D 200 meters eastward, forming new boundary coordinates from (X: 450, Y: 320) to (X: 750, Y: 320). Simulation calculations confirmed that the new area's accessibility index had decreased to 2.9, meeting safety requirements. The entire process was completed automatically by the system, ensuring a close connection between terrain changes and area division logic, while also providing data support for subsequent path planning.

[0042] S107. Based on the updated regional boundary data, obtain the latest status information of multiple leveling robots working together, adjust the operation rhythm among the leveling robots for the goal of seamless regional connection, and determine the final elevation consistency control parameters.

[0043] The latest regional boundary information and real-time state data of the collaborative flattening robots are obtained from the data storage module to construct a dynamic model of regional division and obtain a preliminary boundary division result. According to the boundary division result, the work rhythm distribution of the flattening robots in each region is analyzed, a preset threshold is used for rhythm comparison, and the adjustment direction required for rhythm optimization is determined. In view of the rhythm optimization direction, the collaborative deviation between the flattening robots is judged in combination with the real-time state data, if the deviation exceeds a preset range, the work rhythm parameters are adjusted to obtain an optimized collaborative scheme of the flattening robots. The optimized collaborative scheme of the flattening robots is obtained, the seamless connection degree between regions is analyzed, the information processing module is used to smooth the connection boundary, and the preliminary control parameters of the height consistency are determined. According to the preliminary control parameters, the work effect of the flattening robots after adjustment is analyzed in combination with the state monitoring data, if the effect does not reach a preset standard, the control parameters are adjusted through iterative calculation to obtain final height consistency parameters. Through the final height consistency parameters, the work instructions of the collaborative flattening robots are updated, the data updating module is used to issue the adjusted parameters to each flattening robot, and the dynamic adjustment of rhythm optimization and regional connection is completed. In view of the result after dynamic adjustment, real-time state monitoring data is obtained, the stability of the regional boundary and the collaborative flattening robots is judged by comparison with historical data, and a sustained optimization scheme of system operation is determined.

[0044] Specifically, based on the updated regional boundary data, first, the latest boundary vector data is automatically loaded by the geographic information system (GIS), assuming that the boundary data covers 116.3 degrees east to 116.5 degrees east and 39.8 degrees north to 40.0 degrees north, the system calculates the working coverage range of each grading robot in the region using spatial analysis algorithms, generates a coverage grid with a resolution of 0.5 meters x 0.5 meters, and ensures that the boundary data and the grading robot position are accurately matched. Next, the latest state information of multiple grading robots is obtained, and the system collects grading robot data in real time through the Internet of Things interface, such as the working speed of grading robot A is 2.5 meters / second, the working speed of grading robot B is 2.2 meters / second, and the working height error is ±0.03 meters and ±0.05 meters respectively. Combined with the state data, the system analyzes the coordination efficiency between the grading robots, calculates the working overlap rate of grading robots A and B as 15%, and finds that grading robot A leads grading robot B by about 3 seconds through timestamp comparison, and needs to adjust the pace to reduce overlap. Subsequently, for the purpose of seamless connection of the region, the system uses dynamic programming algorithm to adjust the working pace of the grading robots, sets the speed of grading robot A to 2.3 meters / second and the speed of grading robot B to 2.4 meters / second, calculates the adjusted overlap rate to 5%, and confirms that the error at the boundary connection is controlled within ±0.02 meters through simulation analysis. Finally, the elevation consistency control parameters are determined, the system calculates the elevation curve based on the elevation data collected by the grading robots (the average elevation of grading robot A is 50.25 meters and the average elevation of grading robot B is 50.28 meters), and the least squares method is used to fit the elevation curve. The consistency control parameters are the elevation deviation threshold ±0.015 meters, and the working height instruction of the grading robot is adjusted to ensure that the elevation consistency error in the region is less than 0.01 meters, and auxiliary verification is performed in association with business data such as soil moisture distribution (moisture range 20% to 30%) to ensure that the parameters adapt to different terrain conditions. Through the above automatic process, the system realizes the whole link processing from data updating to parameter optimization, the logic is rigorous and the result is quantifiable.

[0045] S108, through the final elevation consistency control parameters, obtain the regional flatness data after the working of each grading robot is completed, analyze whether there is deviation, if the deviation exceeds the preset threshold, trigger the secondary processing of the local region, and obtain the corrected flatness distribution information.

[0046] The initial data of the regional flatness is obtained from each job grading robot through the elevation consistency parameter, the abnormal values are removed by using the data cleaning method, and the flatness data set after preliminary processing is obtained. According to the flatness data set after preliminary processing, deviation analysis is carried out on the regional flatness distribution, if the analysis result shows that the deviation exceeds the preset threshold line, the local area with deviation is marked, and the area range needing secondary processing is determined. The local area data marked is obtained, and the local area is finely segmented by combining the regional division method, and the detailed division grid information of the local area is generated. Through the detailed division grid information of the local area, the elevation of each grid unit is adjusted by using the secondary processing method, and the adjusted local flatness data is obtained. According to the adjusted local flatness data, the flatness distribution of the whole region is recalculated, and the updated flatness distribution information is generated. According to the updated flatness distribution information, the deviation analysis method is used for verification again, if there is still an area with deviation exceeding the preset threshold line, the secondary processing process is triggered in a loop, and the final corrected flatness distribution data set is obtained. Through the final corrected flatness distribution data set, the elevation consistency parameter and the job completion degree data are integrated to generate the complete regional flatness analysis result.

[0047] Specifically, the regional flatness data is obtained through the elevation consistency control parameter. First, the three-dimensional point cloud data of the working area is collected by using the laser radar scanning grading robot. Assuming that the area is 1000 square meters and the point cloud density is 100 points per square meter, 100,000 point coordinates and elevation data are generated. The control parameters include the elevation reference value (such as 0.00 meters) and the allowed deviation threshold ±0.05 meters. The least square method is used to fit the point cloud data, the difference between the actual elevation of each point and the reference elevation is calculated, and the flatness distribution graph is generated.

[0048] When analyzing the deviation, the point cloud data is traversed, the mean and standard deviation of the elevation difference are calculated, and it is assumed that the mean is 0.03 meters and the standard deviation is 0.02 meters. The elevation difference of each point is compared with the threshold value ±0.05 meters, and if more than 30% of the points in a certain area (such as a 100 square meter sub-area) have an elevation difference exceeding the threshold value.

[0049] For example, among the 50 points, 20 points have an elevation difference greater than 0.05 meters, and it is determined that the deviation of the area exceeds the standard. When the secondary processing is triggered, the automatic grading robot is called, the optimization path is generated based on the deviation distribution, the area with the largest elevation difference is preferentially processed, and the interpolation algorithm (such as Kriging interpolation) is used to recalculate the processed elevation, and the point cloud data is updated.

[0050] Finally, the flatness distribution is recalculated to obtain the corrected elevation mean (e.g., 0.01 meters) and standard deviation (e.g., 0.015 meters), and a new flatness distribution map is generated to ensure that the elevation difference of all areas is controlled within ±0.05 meters. If there are still areas with deviations exceeding the standard, secondary processing is performed iteratively until the threshold requirement is met. The entire process is integrated through an automated system, and Python scripts are used for data processing, with NumPy libraries for matrix operations and Matplotlib for generating visual distribution maps, ensuring logical rigor and no manual intervention.

[0051] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0052] Those skilled in the art should understand that the above embodiments are only for clearly illustrating the present application, and are not intended to limit the scope of the present application. Based on the above disclosure, other changes or modifications can also be made by those skilled in the art, and these changes or modifications are still within the scope of the present application.

Claims

1. An intelligent control method for collaborative operation of concrete leveling robots, characterized in that: By using a pre-established terrain data model, elevation and slope distribution information is obtained from the work area. To adapt to complex terrain, terrain features are analyzed and the initial work area is divided, and the boundary range and elevation benchmark data of each area are obtained. Based on the initial work area division results, the position status update data of multiple leveling robots are obtained. Combined with the workload and mobility of each leveling robot, the task is dynamically allocated to determine the area of ​​responsibility and work sequence of each leveling robot. Based on the distribution of leveling robots after dynamic task allocation, real-time position and work progress information are obtained from the sensors of each leveling robot. Through a real-time information sharing mechanism, a data interaction network for multi-leveling robot collaboration is constructed to obtain unified work rhythm control parameters. By controlling the work rhythm parameters, the boundary data of the work area between adjacent leveling robots is obtained. Based on the boundary elevation connection requirements, the elevation deviation between adjacent areas is analyzed to determine the boundary processing priority and adjustment plan. Based on the boundary processing priority, the data on the difference in work progress between leveling robots is obtained. If the progress of a certain leveling robot is lagging behind, the task allocation strategy is dynamically updated by adjusting the data interaction frequency to obtain the optimized work path of the leveling robot.

2. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: The control method further includes: For the optimized leveling robot's operation path, real-time feedback information from terrain data collection is obtained, terrain changes encountered during path execution are analyzed, it is determined whether the area division scheme needs to be adjusted, and updated area boundary data is obtained. Based on the updated regional boundary data, obtain the latest status information of multiple leveling robots working together, adjust the operation rhythm among the leveling robots to achieve the goal of seamless regional connection, and determine the final elevation consistency control parameters. By using the final elevation consistency control parameters, the flatness data of the area after each leveling robot has completed its operation is obtained, and the presence of deviations is analyzed. If the deviation exceeds the preset threshold, secondary processing of the local area is triggered to obtain the corrected flatness distribution information.

3. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: The methods for determining the boundary extent and elevation datum data of each region include: By using a pre-set terrain data model, the elevation and slope distribution of the work area are obtained, resulting in elevation and slope distribution data. Digital elevation models are used to process elevation and slope distribution data, extract terrain features, and determine the set of terrain features. If the slope value in the terrain feature set is greater than a preset threshold, it is marked as a complex terrain region, and a set of complex terrain regions is obtained. The K-means clustering algorithm is used to divide the complex terrain region into initial work areas and determine the initial work area set. Based on the initial set of work areas, calculate the boundary range of each area to obtain the boundary range data; Interpolation algorithms are used to process boundary range data and elevation distribution data to generate elevation benchmark data; By rasterizing the elevation benchmark data, an elevation benchmark raster map of the work area is generated, and the final work area division result is determined.

4. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: in, The methods for determining the area of ​​responsibility and work sequence for each leveling robot include: The system acquires the position status and update data of multiple leveling robots, and collects the position information and operating status of each leveling robot in real time through a pre-established monitoring system to determine the current working area and status change trend of the leveling robot. Based on the trend of status changes and combined with workload data, a preset load balancing strategy is used to analyze the current workload of each leveling robot and determine whether the leveling robot is in a high-load state. If the load exceeds the preset threshold, it is marked as a leveling robot that needs to be adjusted. For leveling robots marked as needing adjustment, their mobility data is obtained. By comparing the mobility with the distance and path information between the work area and the work area, a list of leveling robots that can be reassigned to the new work area is determined. Based on the list of leveling robots and the area division data, a dynamic programming algorithm is used to optimize the task allocation, resulting in a new area of ​​responsibility and work sequence for each leveling robot. By updating data and status monitoring results, it is determined whether the newly assigned area of ​​responsibility matches the current position status of the leveling robot. If the position status does not match the assigned area, the movement path is recalculated and the work sequence is adjusted. Based on the adjusted work sequence, obtain the task execution progress of each leveling robot, update the workload data through the real-time monitoring system, and determine the execution effect of the task allocation; By combining performance data with status monitoring information, the dynamic adjustment needs of the leveling robot within the work area are continuously tracked to obtain the final task allocation and area division results.

5. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: The method for obtaining the unified work rhythm control parameters includes: Real-time position and work progress data are obtained from the leveling robot's sensors, and a standardized data stream is obtained using a timestamp synchronization mechanism; Standardized data streams are transmitted through a data interaction network, and a message queue protocol is used to ensure data consistency among multiple leveling robots. If the data consistency meets the preset threshold, the distribution of leveling robots is clustered using the K-means algorithm based on real-time location and work progress to obtain the grouping results of leveling robots; Based on the grouping results of the leveling robots, the task allocation strategy is dynamically adjusted, and resource allocation is optimized through linear programming algorithm to generate a task allocation scheme. The workload of each leveling robot is extracted from the task allocation scheme, and the operation rhythm trend is predicted by time series analysis to obtain the rhythm prediction result; By broadcasting rhythm prediction results through a collaborative network, the control parameters of each leveling robot are adjusted to generate unified operation rhythm control parameters. If the deviation of the control parameters exceeds the preset threshold, the deviation information is fed back through the data interaction network, the task allocation scheme is iteratively optimized, and the updated control parameters are obtained.

6. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: The method for determining the boundary processing priority and adjustment scheme includes: By obtaining the boundary data of adjacent leveling robot operation areas from the leveling robot log using the operation rhythm parameters, a set of boundary coordinates is obtained. The boundary elevation values ​​between adjacent areas are calculated using the boundary coordinate set, resulting in an elevation data table; If the elevation deviation between adjacent areas in the elevation data table exceeds a preset threshold, the deviation correction value is calculated using a linear interpolation algorithm to obtain the corrected elevation set. Based on the priority of boundary connection analysis of the modified elevation set, a priority ranking table is obtained; The adjustment scheme is assigned by prioritizing the sorting table, and the boundary elevation connection is optimized by least squares method to obtain the set of adjustment parameters; Obtain the set of adjustment parameters, update the boundary data of the leveling robot's working area, and obtain the updated boundary coordinate set; The elevation deviations of adjacent areas are recalculated based on the updated boundary coordinate set to obtain the final elevation consistency data.

7. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: in, The optimized methods for obtaining the leveling robot's work path include: By obtaining work progress data from the leveling robot's operation log, a preliminary comparison is made of the current task completion status of each leveling robot to determine the progress difference value. Based on the progress difference value obtained from the comparison, a preset threshold is used for screening. If the difference value of a certain leveling robot exceeds the threshold, it is marked as a lagging leveling robot, and a list of lagging leveling robots is obtained. For the leveling robots in the list of lagging leveling robots, obtain their historical data interaction records, analyze the correlation between interaction frequency and task completion efficiency, and determine the potential impact range of frequency adjustment. Based on the analysis results, the data interaction frequency of the lagging leveling robot is dynamically adjusted, and a temporary task allocation scheme is generated in combination with the current task load. Based on the temporary task allocation scheme, a genetic algorithm is used to optimize the working path of the leveling robot, recalculate the task execution order of each leveling robot, and determine the improved path planning. The improved path planning data is obtained, the task allocation strategy is updated in real time, and synchronized to the control modules of each leveling robot to complete the dynamic adjustment process. By continuously monitoring the leveling robot's operation progress and path execution, and cyclically comparing the differences, if a new lagging leveling robot is found, a new round of frequency adjustment and path optimization process is triggered.

8. The intelligent control method for collaborative operation of concrete leveling robots according to claim 1, characterized in that: The method for obtaining the updated region boundary data includes: Real-time feedback information is obtained from the terrain data acquisition module. The terrain change characteristics during the execution of the leveling robot's work path are initially extracted. The change characteristics are classified according to the preset classification criteria to obtain the classification results of terrain changes. Based on the classification results of terrain changes, the scope of influence during path execution is analyzed. Combined with the current state of the leveling robot's working path, the deviation of the path execution is determined by comparing it with the preset terrain adaptation rules. If the deviation of the path execution exceeds the preset threshold, an adaptive assessment is performed on the regional division scheme to obtain the distribution data of the current regional boundaries, analyze the impact of terrain change characteristics on the boundary distribution, and obtain the adjustment requirements for regional division. Based on the adjustment requirements of the regional division, combined with the terrain change characteristics and the actual situation of the leveling robot's operation path, a genetic algorithm is used to recalculate the regional boundaries and generate a preliminary boundary adjustment plan. For the preliminary boundary adjustment scheme, the historical records of the leveling robot path optimization are obtained, the matching degree between the adjustment scheme and the historical records is analyzed, and the feasibility of the scheme is judged by comparing it with the preset optimization rules, so as to obtain the verified boundary adjustment data. Based on the verified boundary adjustment data, the area division scheme is updated in real time and synchronized to the control module of the leveling robot's operation path to complete the dynamic matching of the path and the boundary, and generate updated area boundary information. By updating the area boundary information, the execution status of the leveling robot's work path is continuously monitored. The terrain change features in the real-time feedback information are compared cyclically. If a new deviation is found, a new round of adjustment process is triggered to determine the final path adaptation solution.

9. The intelligent control method for collaborative operation of concrete leveling robots according to claim 2, characterized in that: The method for determining the final elevation consistency control parameters includes: By obtaining the latest regional boundary information and real-time status data of the leveling robot collaboration from the data storage module, a dynamic model of regional division is constructed to obtain preliminary boundary division results. Based on the boundary division results, the operation rhythm distribution of the leveling robot in each area is analyzed, and the rhythm is compared using a preset threshold to determine the adjustment direction required for rhythm optimization. Based on the rhythm optimization direction and real-time status data, the coordination deviation between leveling robots is judged. If the deviation exceeds the preset range, the optimized coordination scheme of leveling robots is obtained by adjusting the operation rhythm parameters. The optimized leveling robot collaborative scheme was obtained, the degree of seamless connection between areas was analyzed, the connection boundary was smoothed using the information processing module, and the preliminary control parameters for elevation consistency were determined. Based on the preliminary control parameters and combined with the status monitoring data, the operation effect of the leveling robot after adjustment is analyzed. If the effect does not meet the preset standard, the control parameters are adjusted through iterative calculation to obtain the final elevation consistency parameters. By updating the collaborative operation instructions of the leveling robots based on the final elevation consistency parameters, the data update module sends the adjusted parameters to each leveling robot to complete the dynamic adjustment of rhythm optimization and area connection. Based on the results of dynamic adjustments, real-time status monitoring data is obtained. By comparing the data with historical data, the stability of the coordination between the area boundary and the leveling robot is determined, and a continuous optimization scheme for system operation is identified.

10. The intelligent control method for collaborative operation of concrete leveling robots according to claim 9, characterized in that: The method for obtaining the flatness distribution information includes: Using elevation consistency parameters, initial regional flatness data are obtained from each leveling robot. Outliers are removed by data cleaning methods to obtain a pre-processed flatness dataset. Based on the pre-processed flatness dataset, a deviation analysis is performed on the regional flatness distribution. If the analysis results show that the deviation exceeds the preset threshold, the local areas with deviations are marked, and the range of areas requiring secondary processing is determined. The marked local area data is obtained, and these areas are finely segmented using the region division method to generate detailed grid information of the local area. By using detailed grid information of local areas, a secondary processing method is used to adjust the elevation of each grid cell to obtain the adjusted local flatness data. Based on the adjusted local flatness data, the flatness distribution of the entire area is recalculated to generate updated flatness distribution information; The updated flatness distribution information is verified again using the deviation analysis method. If there are still areas where the deviation exceeds the preset threshold, the secondary processing flow is triggered repeatedly to obtain the final corrected flatness distribution dataset. By integrating elevation consistency parameters and work completion data with the final corrected flatness distribution dataset, a complete regional flatness analysis result is generated.

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