A robot communication control system and method based on a core control module

Abstract

A robot communication control system and method based on a core control module, relating to the field of communication control technology, is disclosed. The system acquires tasks to be processed within a working area and classifies them into different priorities. It then obtains allocation coefficients for different tasks based on robot equipment information, allocates tasks accordingly, acquires and optimizes the robot's channel evaluation coefficients, obtains a communication topology map of the working area and topology coefficients for different robots, performs topology reconstruction using the topology coefficients, acquires predicted topology coefficients for different robots, and performs topology pre-reconstruction for the robots using the predicted topology coefficients. This effectively improves the flexibility of task allocation and allows for dynamic adjustment of the communication connections between different AGVs and robotic arms based on multiple influencing factors.

Description

Technical Field

[0001] This invention relates to the field of communication control technology, specifically a robot communication control system and method based on a core control module. Background Technology

[0002] With the development of intelligent technology, more and more jobs are being replaced by equipment. For example, in warehousing, by deploying different robots in the warehouse and setting up independent modules for control, the efficiency of warehousing, transportation and sorting can be significantly improved. As the scale of the warehouse becomes larger and larger, optimizing the communication of the equipment in it has become a technical problem that urgently needs to be solved.

[0003] In existing technologies, the allocation of warehousing tasks lacks flexibility, resulting in some equipment being frequently used while others are frequently idle. This not only hinders efficiency but also indirectly affects the communication control of frequently used equipment. Furthermore, existing technologies often limit communication optimization for different equipment to a single influencing factor, lacking technical means to coordinate and optimize equipment by integrating multiple different influencing factors. To address the shortcomings of existing technologies, this invention provides a robot communication control system and method based on a core control module. Summary of the Invention

[0004] The purpose of this invention is to provide a robot communication control system and method based on a core control module.

[0005] The objective of this invention can be achieved through the following technical solution: a robot communication control system based on a core control module, comprising the following modules:

[0006] The data twin module is used to acquire the robot's equipment information and the scene information of its working area, and to build the corresponding digital twin model;

[0007] The task allocation module is used to obtain the tasks to be processed in the work area, divide them into different priorities, obtain the allocation coefficient of different tasks to be processed in combination with the robot's equipment information, and allocate tasks to different tasks to be processed according to the allocation coefficient.

[0008] The channel optimization module is used to obtain the robot's channel evaluation coefficients and perform channel optimization based on these coefficients.

[0009] The topology reconstruction module is used to obtain the communication topology map within the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robots using the predicted topology coefficients.

[0010] Furthermore, the process of acquiring the robot's equipment information and the scene information of its working area, and constructing the corresponding digital twin model, includes:

[0011] The robot is an AGV and a robotic arm, the work area is a warehouse, the equipment information includes the core control module and status parameters, and the scene information includes spatial layout and environmental parameters;

[0012] The core control module of the AGV includes an ARM+FPGA dual-core processor, a lidar unit, a positioning unit, a camera unit, a dual-channel transceiver, and a lithium battery pack. The status parameters of the AGV include coordinates, navigation path, speed and acceleration, remaining power, signal strength, and data transmission rate.

[0013] The core control module of the robotic arm includes a vision processing unit, a gripping unit, and an antenna array. The status parameters of the robotic arm include joint angles, joint speeds, forces and torques, signal strength, and data transmission rates.

[0014] The spatial layout of the warehouse includes warehouse boundaries, shelf locations, work areas, ground conditions, and driving areas. The environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality. Several distributed communication base stations are installed in the warehouse.

[0015] Digital twin technology is used to construct a digital twin model based on the obtained spatial layout of the warehouse and the core control module of the robot. The environmental parameters of the warehouse and the status parameters of the robot are uploaded to the digital twin model for synchronization.

[0016] Furthermore, the process of acquiring the tasks to be processed within the work area and classifying them into different priorities includes:

[0017] The pending tasks refer to all transportation and sorting tasks that have not yet been processed in the warehouse. When goods enter the warehouse, they are assigned a corresponding level of importance, and the pending tasks corresponding to goods of different importance are marked with different priorities, including high priority, medium priority, and low priority.

[0018] Furthermore, the process of obtaining allocation coefficients for different tasks based on the robot's equipment information, and then allocating tasks according to these coefficients, includes:

[0019] For tasks with different priorities, assign a priority coefficient Q. For transportation tasks, obtain the allocation coefficient S between a single transportation task and a single AGV. a ;

[0020] ;

[0021] D is the preset weight value.sh D max These represent the remaining and maximum battery power of the single AGV, respectively. dq R max These represent the current number of tasks that the single AGV is currently carrying and the maximum number of tasks it can carry, respectively. mb L max These are the shortest distance between the single AGV and the goods corresponding to the single transportation task, and the longest distance that the single AGV can travel, respectively.

[0022] The task allocation refers to obtaining the allocation coefficient between the single transportation task and each other AGV, and allocating it to the AGV with the largest allocation coefficient. Each transportation task is then allocated to the AGV with the largest allocation coefficient.

[0023] For sorting tasks, obtain the allocation coefficient S between a single sorting task and a single robotic arm. b ;

[0024] ;

[0025] P is the preset weight value. d G is the preset gripper matching degree between the goods corresponding to this single sorting task and this single robotic arm. dq G max These represent the current number of tasks loaded by the single robotic arm and the maximum number of tasks it can load, respectively.

[0026] The task allocation refers to obtaining the allocation coefficient between the single sorting task and other robotic arms, and allocating it to the robotic arm with the largest allocation coefficient. Each sorting task is then assigned to the robotic arm with the largest corresponding allocation coefficient.

[0027] Furthermore, the process of obtaining the robot's channel evaluation coefficients and then performing channel optimization based on these coefficients includes:

[0028] An evaluation cycle is set, and at the end of each evaluation cycle, the channel evaluation coefficient L between each AGV and robotic arm and its distributed communication base station is obtained. q ;

[0029] ;

[0030] B is the preset weight value. s B t B w These are the signal-to-noise ratio, throughput, and bit error rate between the corresponding AGV and robotic arm and their respective distributed communication base stations;

[0031] The channel optimization refers to dividing the corresponding channel into different load states according to the channel evaluation coefficient, changing the modulation scheme of the corresponding channel in the high load state to 64QAM, changing the modulation scheme of the corresponding channel in the medium load state to 16QAM, and changing the corresponding channel in the low load state to a LoRa channel.

[0032] Furthermore, the process of obtaining the communication topology map within the working area and acquiring the topology coefficients of different robots, and then using these topology coefficients to reconstruct the topology, includes:

[0033] In the digital twin model, each AGV is used as a topology point, and the channels between each AGV and its distributed communication base station are used as topology edges to construct a corresponding communication topology graph. In the communication topology graph, the topology coefficient T between a single AGV and a single distributed communication base station is obtained. p ;

[0034] ;

[0035] The weights are preset values, Ltb is the shortest distance between the single AGV and the single distributed communication base station, and V is the shortest distance between the single AGV and the single distributed communication base station. s The speed of this single AGV;

[0036] The topology reconstruction refers to obtaining the topology coefficients between the single AGV and each other distributed communication base station, connecting it to the distributed communication base station with the largest topology coefficient, connecting each AGV to the distributed communication base station with the largest corresponding topology coefficient, and updating the communication topology map.

[0037] Furthermore, the process of obtaining the predicted topology coefficients for different robots and using these coefficients to perform topology pre-reconstruction of the robots includes:

[0038] Set a prediction period and continuously obtain the speed of a single AGV after one prediction period and the shortest distance between the single AGV and each distributed communication base station in the digital twin model to obtain the prediction topology coefficient between the single AGV and each distributed communication base station.

[0039] The topology pre-reconstruction refers to connecting the single AGV to the distributed communication base station with the largest predicted topology coefficient when the distributed communication base station with the largest predicted topology coefficient is different from the distributed communication base station currently connected to the single AGV, connecting each AGV to its corresponding distributed communication base station with the largest predicted topology coefficient, and updating the communication topology map.

[0040] The embodiments of the present invention also include a robot communication control method based on a core control module, comprising the following steps:

[0041] Step S1: Obtain the robot's equipment information and the scene information of its working area, and construct the corresponding digital twin model;

[0042] Step S2: Obtain the tasks to be processed in the work area and divide them into different priorities. Combine the robot's equipment information to obtain the allocation coefficients for different tasks to be processed, and allocate tasks to different tasks to be processed according to the allocation coefficients.

[0043] Step S3: Obtain the channel evaluation coefficients of the robot, optimize the channel based on the channel evaluation coefficients, obtain the communication topology map in the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robot using the predicted topology coefficients.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention divides goods into different priorities and combines the equipment information of AGVs and robotic arms in the warehouse to obtain the allocation coefficient between each AGV and robotic arm and each task to be processed and to allocate tasks, which can effectively improve the flexibility of task allocation and ensure that each piece of goods is allocated to the most suitable AGV and robotic arm in a timely manner.

[0046] By constructing a communication topology map and obtaining the topology coefficients of different AGVs and robotic arms, and using the topology coefficients to reconstruct the topology, the communication connection of different AGVs and robotic arms can be dynamically adjusted in combination with multiple influencing factors. By obtaining the predicted topology coefficients and performing topology pre-reconstruction, the communication connection that will change can be optimized in advance, which is conducive to ensuring that the communication connection is always connected to the distributed communication base station with the lowest latency and the highest transmission efficiency. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0048] like Figure 1 As shown, a robot communication control system based on a core control module includes the following modules:

[0049] The data twin module is used to acquire the robot's equipment information and the scene information of its working area, and to build the corresponding digital twin model;

[0050] The task allocation module is used to obtain the tasks to be processed in the work area, divide them into different priorities, obtain the allocation coefficient of different tasks to be processed in combination with the robot's equipment information, and allocate tasks to different tasks to be processed according to the allocation coefficient.

[0051] The channel optimization module is used to obtain the robot's channel evaluation coefficients and perform channel optimization based on these coefficients.

[0052] The topology reconstruction module is used to obtain the communication topology map within the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robots using the predicted topology coefficients.

[0053] It should be further explained that, in the specific implementation process, the process of acquiring the robot's equipment information and the scene information of its working area, and constructing the corresponding digital twin model, includes:

[0054] In an embodiment of the present invention, the robot refers to an AGV and a robotic arm in the same working area, the working area refers to the warehouse in which the robot is located, the equipment information includes the core control module and status parameters of the AGV and the robotic arm, and the scene information includes the spatial layout and environmental parameters of the warehouse.

[0055] For AGVs, the core control module includes an ARM+FPGA dual-core processor for path planning and communication acceleration, a LiDAR unit for obstacle detection, a positioning unit for obtaining the AGV's position, a camera unit for shelf recognition, a dual-channel transceiver for communication, and a lithium battery pack.

[0056] For the robotic arm, its core control module includes an FPGA-accelerated vision processing unit for target identification, a gripping unit for grasping boxes and bags of goods, and an antenna array for communication.

[0057] The status parameters of the AGV include coordinates, navigation path, speed and acceleration, remaining battery power, signal strength, and data transmission rate; the status parameters of the robotic arm include joint angle, joint speed, force and torque, signal strength, and data transmission rate.

[0058] For a warehouse, its spatial layout includes warehouse boundaries, shelf locations, work areas, ground conditions, and driving areas. The environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality. The warehouse is also equipped with several distributed communication base stations to form a MESH network covering the entire area and supports dynamic spectrum switching.

[0059] Using digital twin technology, a corresponding digital twin model is constructed based on the obtained spatial layout of the warehouse and the core control module of the robot. The environmental parameters of the warehouse and the status parameters of the robot are then uploaded to the constructed digital twin model for synchronization.

[0060] It should be further explained that, in the specific implementation process, the process of acquiring the tasks to be processed within the work area and classifying them into different priorities includes:

[0061] The tasks to be processed refer to the various transportation and sorting tasks that have not yet been processed in the warehouse. A single transportation and sorting task is divided into several sub-tasks performed by AGVs and robotic arms. The transportation task is divided into path planning + obstacle avoidance + shelf docking, and the sorting task is divided into visual recognition + grasping trajectory planning + placement verification.

[0062] When goods are received into the warehouse, they are assigned a corresponding level of importance, including high importance, medium importance, and low importance. The tasks to be processed corresponding to goods of different importance are divided into different priorities, including high priority, medium priority, and low priority.

[0063] Specifically, tasks corresponding to goods of high importance are designated as high priority, tasks corresponding to goods of medium importance are designated as medium priority, and tasks corresponding to goods of low importance are designated as low priority.

[0064] It should be further explained that, in the specific implementation process, the process of obtaining allocation coefficients for different tasks based on the robot's equipment information, and then allocating tasks according to these allocation coefficients, includes:

[0065] A priority coefficient Q is assigned to each pending task with different priorities. Different calculation methods are used for transportation tasks and sorting tasks to obtain their corresponding allocation coefficients. Taking a single transportation task as an example, the allocation coefficient between the single transportation task and a single AGV is obtained and denoted as S. a ;

[0066] ;

[0067] The weights are preset, and the sum of the three is equal to 1, D sh D max These represent the remaining and maximum battery power of the single AGV, respectively. dq R max These represent the current number of tasks that the single AGV is currently carrying and the maximum number of tasks it can carry, respectively. mb L maxThese are the shortest distance between the single AGV and the goods corresponding to the single transportation task, and the longest distance that the single AGV can travel, respectively.

[0068] The task allocation refers to obtaining the allocation coefficient between the single transportation task and each other AGV, and allocating it to the AGV with the largest allocation coefficient. Each transportation task is then allocated to the AGV with the largest allocation coefficient.

[0069] Taking a single sorting task as an example, the allocation coefficient between the single sorting task and a single robotic arm is obtained and denoted as S. b ;

[0070] ;

[0071] The weights are preset, and the sum of the two is equal to 1, P d G is the preset gripper matching degree between the goods corresponding to this single sorting task and this single robotic arm. dq G max These represent the current number of tasks loaded by the single robotic arm and the maximum number of tasks it can load, respectively.

[0072] The task allocation refers to obtaining the allocation coefficient between the single sorting task and each other robotic arm, and allocating it to the robotic arm with the largest allocation coefficient. Each sorting task is then allocated to the robotic arm with the largest corresponding allocation coefficient.

[0073] It should be further explained that, in the specific implementation process, the process of obtaining the robot's channel evaluation coefficients and performing channel optimization based on these coefficients includes:

[0074] An evaluation cycle is set, and at the end of each evaluation cycle, the channel evaluation coefficient L between each AGV and robotic arm and its distributed communication base station is obtained. q ;

[0075] ;

[0076] in, The weights are preset, and the sum of the three is equal to 1, B s B t B w These are the signal-to-noise ratio, throughput, and bit error rate between the AGV and the robotic arm and their distributed communication base stations;

[0077] The channel optimization refers to dividing the corresponding channel into different load states based on the channel evaluation coefficient, including high load state, medium load state, and low load state.

[0078] If L q If the load is greater than 80%, it is marked as a high-load state, and the modulation scheme of its corresponding channel is changed to 64QAM. If the load is less than 40%, it is marked as a high-load state. q If L is ≤80%, it is marked as a medium load state, and the modulation scheme of its corresponding channel is changed to 16QAM. q If the load is ≤40%, it will be marked as a low load state and its corresponding channel will be changed to a LoRa channel.

[0079] It should be further explained that, in the specific implementation process, the process of obtaining the communication topology map within the working area, acquiring the topology coefficients of different robots, and using the topology coefficients to reconstruct the topology includes:

[0080] In the digital twin model, each AGV is treated as a topology point, and the channels between each AGV and its distributed communication base station are treated as topology edges. Based on this, a corresponding communication topology graph is constructed. In the communication topology graph, the topology coefficient T between a single AGV and a single distributed communication base station is obtained. p ;

[0081] ;

[0082] The weights are preset, and the sum of the four values ​​equals 1, D sh R represents the remaining power of this single AGV. dq V represents the number of tasks currently being loaded by the single AGV, Ltb represents the shortest distance between the single AGV and the single distributed communication base station, and V represents the number of tasks currently being loaded by the single AGV. s The speed of this single AGV;

[0083] The topology reconstruction refers to obtaining the topology coefficients between the single AGV and each other distributed communication base station, connecting it to the distributed communication base station with the largest topology coefficient, connecting each AGV to the distributed communication base station with the largest corresponding topology coefficient, and updating the communication topology map.

[0084] It should be further explained that, in the specific implementation process, the process of obtaining the predicted topology coefficients of different robots and using these coefficients to perform topology pre-reconstruction of the robots includes:

[0085] Set a prediction period, continuously obtain the speed of a single AGV after one prediction period in the digital twin model, as well as the shortest distance between the single AGV and each distributed communication base station, and then obtain the prediction topology coefficient between the single AGV and each distributed communication base station.

[0086] The topology pre-reconstruction refers to determining whether the distributed communication base station with the largest predicted topology coefficient is the same as the distributed communication base station currently being communicated with by the single AGV. If so, no operation is performed on the single AGV.

[0087] If not, then connect the single AGV communication to the distributed communication base station with the largest predicted topology coefficient, connect each AGV communication to its corresponding distributed communication base station with the largest predicted topology coefficient, and update the communication topology map.

[0088] The embodiments of the present invention also include a robot communication control method based on a core control module, comprising the following steps:

[0089] Step S1: Obtain the robot's equipment information and the scene information of its working area, and construct the corresponding digital twin model;

[0090] Step S2: Obtain the tasks to be processed in the work area and divide them into different priorities. Combine the robot's equipment information to obtain the allocation coefficients for different tasks to be processed, and allocate tasks to different tasks to be processed according to the allocation coefficients.

[0091] Step S3: Obtain the channel evaluation coefficients of the robot, optimize the channel based on the channel evaluation coefficients, obtain the communication topology map in the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robot using the predicted topology coefficients.

[0092] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A robot communication control system based on a core control module, characterized in that, Includes the following modules: The data twin module is used to acquire the robot's equipment information and the scene information of its working area, and to build the corresponding digital twin model; The task allocation module is used to obtain the tasks to be processed in the work area, divide them into different priorities, obtain the allocation coefficient of different tasks to be processed in combination with the robot's equipment information, and allocate tasks to different tasks to be processed according to the allocation coefficient. The channel optimization module is used to obtain the robot's channel evaluation coefficients and perform channel optimization based on these coefficients. The topology reconstruction module is used to obtain the communication topology map within the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robots using the predicted topology coefficients. The process of obtaining the robot's topology coefficients and reconstructing its topology includes: In the digital twin model, each AGV is used as a topology point, and the channels between each AGV and its distributed communication base station are used as topology edges to construct a corresponding communication topology graph. In the communication topology graph, the topology coefficient T between a single AGV and a single distributed communication base station is obtained. p ; ； D is the preset weight value. sh R represents the remaining power of this single AGV. dq L represents the number of tasks currently being loaded onto this single AGV. tb V represents the shortest distance between the single AGV and the single distributed communication base station. s The speed of this single AGV; The topology reconstruction refers to obtaining the topology coefficients between the single AGV and each other distributed communication base station, connecting it to the distributed communication base station with the largest topology coefficient, connecting each AGV to the distributed communication base station with the largest topology coefficient corresponding to it, and updating the communication topology map. The process of obtaining the robot's predicted topology coefficients and performing topology pre-reconstruction includes: Set a prediction period and continuously obtain the speed of a single AGV after one prediction period and the shortest distance between the single AGV and each distributed communication base station in the digital twin model to obtain the prediction topology coefficient between the single AGV and each distributed communication base station. The topology pre-reconstruction refers to connecting the single AGV to the distributed communication base station with the largest predicted topology coefficient when the distributed communication base station with the largest predicted topology coefficient is different from the distributed communication base station currently connected to the single AGV, connecting each AGV to its corresponding distributed communication base station with the largest predicted topology coefficient, and updating the communication topology map.

2. The robot communication control system based on a core control module according to claim 1, characterized in that, The process of building a digital twin model includes: The robot is an AGV and a robotic arm, the work area is a warehouse, the equipment information includes the core control module and status parameters, and the scene information includes spatial layout and environmental parameters; The core control module of the AGV includes an ARM+FPGA dual-core processor, a lidar unit, a positioning unit, a camera unit, a dual-channel transceiver, and a lithium battery pack. The status parameters of the AGV include coordinates, navigation path, speed and acceleration, remaining power, signal strength, and data transmission rate. The core control module of the robotic arm includes a vision processing unit, a gripping unit, and an antenna array. The status parameters of the robotic arm include joint angles, joint speeds, forces and torques, signal strength, and data transmission rates. The spatial layout of the warehouse includes warehouse boundaries, shelf locations, work areas, ground conditions, and driving areas. The environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality. Several distributed communication base stations are installed in the warehouse. Digital twin technology is used to construct a digital twin model based on the obtained spatial layout of the warehouse and the core control module of the robot. The environmental parameters of the warehouse and the status parameters of the robot are uploaded to the digital twin model for synchronization.

3. A robot communication control system based on a core control module according to claim 2, characterized in that, The process of acquiring tasks to be processed and prioritizing them includes: The pending tasks refer to all transportation and sorting tasks that have not yet been processed in the warehouse. When goods enter the warehouse, they are assigned a corresponding level of importance, and the pending tasks corresponding to goods of different importance are marked with different priorities, including high priority, medium priority, and low priority.

4. A robot communication control system based on a core control module according to claim 3, characterized in that, The process of obtaining the allocation coefficients for different tasks and allocating them includes: For tasks with different priorities, assign a priority coefficient Q. For transportation tasks, obtain the allocation coefficient S between a single transportation task and a single AGV. a ; ； D is the preset weight value. max R is the maximum power consumption of this single AGV. max L represents the maximum number of tasks that this single AGV can handle. mb L max These are the shortest distance between the single AGV and the goods corresponding to the single transportation task, and the longest distance that the single AGV can travel, respectively. The task allocation refers to obtaining the allocation coefficient between the single transportation task and each other AGV, and allocating it to the AGV with the largest allocation coefficient. Each transportation task is then allocated to the AGV with the largest allocation coefficient. For sorting tasks, obtain the allocation coefficient S between a single sorting task and a single robotic arm. b ; ； P is the preset weight value. d G is the preset gripper matching degree between the goods corresponding to this single sorting task and this single robotic arm. dq G max These represent the current number of tasks loaded by the single robotic arm and the maximum number of tasks it can load, respectively. The task allocation refers to obtaining the allocation coefficient between the single sorting task and other robotic arms, and allocating it to the robotic arm with the largest allocation coefficient. Each sorting task is then assigned to the robotic arm with the largest corresponding allocation coefficient.

5. A robot communication control system based on a core control module according to claim 4, characterized in that, The process of obtaining the robot's channel evaluation coefficients and performing channel optimization includes: An evaluation cycle is set, and at the end of each evaluation cycle, the channel evaluation coefficient L between each AGV and robotic arm and its distributed communication base station is obtained. q ; ； B is the preset weight value. s B t B w These are the signal-to-noise ratio, throughput, and bit error rate between the corresponding AGV and robotic arm and their respective distributed communication base stations; The channel optimization refers to dividing the corresponding channel into different load states according to the channel evaluation coefficient, changing the modulation scheme of the corresponding channel in the high load state to 64QAM, changing the modulation scheme of the corresponding channel in the medium load state to 16QAM, and changing the corresponding channel in the low load state to a LoRa channel.

6. A robot communication control method based on a core control module, implemented based on a robot communication control system based on a core control module according to any one of claims 1-5, characterized in that, The method includes: Step S1: Obtain the robot's equipment information and the scene information of its working area, and construct the corresponding digital twin model; Step S2: Obtain the tasks to be processed in the work area and divide them into different priorities. Combine the robot's equipment information to obtain the allocation coefficients for different tasks to be processed, and allocate tasks to different tasks to be processed according to the allocation coefficients. Step S3: Obtain the channel evaluation coefficients of the robot, optimize the channel based on the channel evaluation coefficients, obtain the communication topology map in the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction of the robot using the predicted topology coefficients.

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