Distributed industrial robot management system

Abstract

The invention relates to the technical field of industrial automation, and discloses a distributed industrial robot management system. The system comprises a plurality of industrial robots with independent working capabilities and communication modules, a central control unit, a distributed communication network, a working condition monitoring module and a task scheduling module. The operation state of the industrial robot is monitored in real time, the task execution sequence is dynamically adjusted according to the state and the task allocation condition, and efficient allocation and scheduling management of tasks are achieved. The system adopts a tree-based allocation algorithm to perform industrial robot task allocation, so that the task allocation efficiency is further improved. Meanwhile, the distributed communication network adopts redundancy design and has a data encryption function, so that high-speed and reliable transmission of data and information and security of transmitted data are ensured. The method has a wide application prospect in the field of industrial automation, and can improve the production efficiency, reduce the production cost and enhance the system reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a distributed industrial robot management system. Background Art

[0002] With the continuous development of industrial automation technology, industrial robots are increasingly used in production lines. However, when faced with large-scale and complex production tasks, traditional centralized robot management systems often have problems such as low task allocation efficiency, high communication delay, and insufficient system reliability. In order to solve these problems, a distributed management system is needed that can efficiently allocate tasks and achieve high-speed and reliable communication between industrial robots and between industrial robots and central control units. The system should have the ability to monitor the operating status of industrial robots in real time, so as to dynamically adjust the task execution order according to actual conditions and ensure the smooth completion of production tasks. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a distributed industrial robot management system to solve the problems of low task allocation efficiency, high communication delay and insufficient system reliability in the prior art.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distributed industrial robot management system, the system comprising: Multiple industrial robots, each with independent working capabilities and communication modules; A central control unit, which is used to receive task instructions, analyze task requirements, and assign tasks to individual industrial robots; Distributed communication network, used to connect the central control unit and each industrial robot to achieve communication transmission of data and information; Working condition monitoring module, used to monitor the operating status of industrial robots in real time; The task scheduling module is used to dynamically adjust the task execution order according to the operating status and task allocation of the industrial robot.

[0005] Preferably, in a possible implementation manner of the first aspect, the industrial robot has positioning and navigation capabilities, and can move to a designated location to perform a task according to path planning; The industrial robot works within its preset working range.

[0006] Preferably, in a possible implementation manner of the first aspect, the task instruction includes a task type, task parameters and execution time, and the task requirement includes a time limit for task completion and required resources.

[0007] Preferably, in a possible implementation manner of the first aspect, the allocating tasks to the respective industrial robots adopts a tree-based allocation algorithm to perform industrial robot task allocation.

[0008] Preferably, in a possible implementation of the first aspect, the tree-based allocation algorithm specifically includes: Calculate the effective task set and calculate the tasks for each industrial robot according to the constraints of the existing tasks of the industrial robot. The set of reachable tasks , satisfy as well as ,in Indicates the target task, Indicates the remaining time required for the current task. Indicates the existing task points. represents the target task point, Indicates the time required to reach the target task point from the current task point. Indicates the remaining time required for the target task. Indicates the remaining time limit of the target task. Represents each industrial robot Scope of work; Split the robot set and build an industrial robot dependency graph based on the industrial robot set and task set , each node Represents an industrial robot, each edge Represents the task dependency between industrial robots, where the task dependency means that the reachable task sets of two industrial robots have an intersection. The industrial robot dependency graph is divided into multiple industrial robot cut sets using a tree decomposition algorithm. Each cut set divides the industrial robot dependency graph in a balanced manner until the number of industrial robots in the connected subgraph is less than a threshold. The search tree is constructed using the industrial robot cut sets. The nodes of the search tree represent the industrial robot sets, and the edges represent the parent-child relationship between the industrial robot sets. There is no task dependency between the industrial robots in the brother nodes of the search tree. Perform task search based on tree decomposition and calculate the upper bound of the number of tasks that can be assigned to the subtree with node N as the root node , and the upper bound and the heuristic function value For comparison, the heuristic function value Represents the minimum number of tasks that need to be assigned so that the subtree is not pruned. If < , then prune the subtree, where the upper bound is calculated The formula is:

[0009] in, Represents all industrial robots in the current subtree, It represents the set with the largest number of elements in the reachable task set of the i-th industrial robot in the subtree. The updating formula of the heuristic function value h is:

[0010] in, Represents the updated heuristic function value represents the jth child node of node N, m represents the total number of child nodes of node N, Indicates the maximum number of tasks that can be assigned to the traversed subtrees. represents the sum of the estimated upper bounds of the unsearched subtrees; Multiple rounds of recursive calls will exclude the allocation schemes that will not be the optimal solution, and finally output the task allocation scheme .

[0011] Preferably, in a possible implementation of the first aspect, the distributed communication network adopts a redundant design and includes multiple communication paths. The communication network also has a data encryption function to protect the security of data transmitted between the central control unit and each industrial robot.

[0012] Preferably, in a possible implementation of the first aspect, the operating condition monitoring module includes sensors and a data analysis unit. The sensors are deployed on each industrial robot to collect the operating parameters of the industrial robot in real time. The data analysis unit is responsible for receiving and monitoring these parameters to obtain the operating status of the industrial robot.

[0013] Preferably, in a possible implementation manner of the first aspect, the operating status of the industrial robot includes workload, energy consumption, fault warning and position information.

[0014] Preferably, in a possible implementation of the first aspect, the task scheduling module dynamically adjusts the task execution order according to the current task execution status, remaining task priority and estimated completion time of the industrial robot.

[0015] Preferably, in a possible implementation manner of the first aspect, the task scheduling module further includes a feedback mechanism, which provides feedback to the central control unit to reallocate tasks when the industrial robot fails suddenly and cannot execute the assigned tasks.

[0016] The beneficial effects of the present invention are: by introducing multiple industrial robots with independent working capabilities and communication modules, as well as a central control unit, a distributed communication network, a working condition monitoring module and a task scheduling module, efficient task allocation and scheduling management are achieved. The system can monitor the operating status of the industrial robot in real time, and dynamically adjust the task execution order according to the operating status of the industrial robot and the task allocation situation. In addition, the system also adopts a tree-based allocation algorithm to allocate tasks for industrial robots, further improving the efficiency of task allocation. At the same time, the distributed communication network adopts a redundant design and has a data encryption function, ensuring the high-speed, reliable transmission of data and information and the security of transmitted data. These beneficial effects make the present invention have a wide range of application prospects in the field of industrial automation, and can improve production efficiency, reduce production costs, and enhance system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A structural diagram of a distributed industrial robot management system is provided for this application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1: Figure 1 As shown, the present invention provides a distributed industrial robot management system, the system comprising: Multiple industrial robots, each with independent working capabilities and communication modules.

[0021] Specifically, industrial robots have positioning and navigation capabilities, and can move to designated locations to perform tasks according to path planning; at the same time, industrial robots work within their preset working range.

[0022] In this embodiment, the industrial robot adopts the TR60-1CR-ES model industrial robot, which has positioning and navigation functions, ensuring that various tasks can be accurately performed in a complex and changeable working environment. When performing tasks, it will move autonomously in the working area according to the preset working range and path planning, and perform industrial operations such as material handling. Each industrial robot is equipped with an independent communication module, which can exchange real-time data and information with other industrial robots and the central control unit in the system.

[0023] The central control unit is used to receive task instructions, analyze task requirements, and assign tasks to individual industrial robots.

[0024] Specifically, task instructions include task type, task parameters and execution time, and task requirements include the time limit for task completion and the required resources.

[0025] The tree-based allocation algorithm is used to allocate tasks to each industrial robot. The tree-based allocation algorithm specifically includes: Calculate the effective task set and calculate the tasks for each industrial robot according to the constraints of the existing tasks of the industrial robot. The set of reachable tasks , satisfy as well as ,in Indicates the target task, Indicates the remaining time required for the current task. Indicates the existing task points. represents the target task point, Indicates the time required to reach the target task point from the current task point. Indicates the remaining time required for the target task. Indicates the remaining time limit of the target task. Represents each industrial robot scope of work.

[0026] Split the robot set and build an industrial robot dependency graph based on the industrial robot set and task set , each node represents an industrial robot, each edge It represents the task dependency between industrial robots. Task dependency means that the reachable task sets of two industrial robots have an intersection. The tree decomposition algorithm is used to split the industrial robot dependency graph into multiple industrial robot cut sets. Each cut set splits the industrial robot dependency graph in a balanced manner until the number of industrial robots in the connected subgraph is less than a threshold. The search tree is constructed using the industrial robot cut sets. The nodes of the search tree represent the sets of industrial robots, and the edges represent the parent-child relationship between the sets of industrial robots. There is no task dependency between the industrial robots in the brother nodes of the search tree.

[0027] Perform task search based on tree decomposition and calculate the upper bound of the number of tasks that can be assigned to the subtree with node N as the root node , and the upper bound and the heuristic function value For comparison, the heuristic function value Represents the minimum number of tasks that need to be assigned so that the subtree is not pruned. If < , then prune the subtree, where the upper bound is calculated The formula is:

[0028] in, Represents all industrial robots in the current subtree, It represents the set with the largest number of elements in the reachable task set of the i-th industrial robot in the subtree. The updating formula of the heuristic function value h is:

[0029] in, Represents the updated heuristic function value represents the jth child node of node N, m represents the total number of child nodes of node N, Indicates the maximum number of tasks that can be assigned to the traversed subtrees. Represents the sum of estimated upper bounds of unsearched subtrees.

[0030] Multiple rounds of recursive calls will exclude the allocation schemes that will not be the optimal solution, and finally output the task allocation scheme .

[0031] In this embodiment, the task instructions are obtained externally, including order import, manual input, etc. The received task instructions include task type, task parameters and execution time. After obtaining the task instructions, the central control unit performs task requirement analysis, including analyzing the time limit and required resources for task completion. After analyzing the task requirements, the central control unit uses a tree-based allocation algorithm to allocate tasks to each industrial robot.

[0032] First, the effective task set is calculated. According to the constraints of the existing tasks of the industrial robot, the The set of reachable tasks , satisfy as well as ,in Indicates the target task, Indicates the remaining time required for the current task. Indicates the existing task points. represents the target task point, Indicates the time required to reach the target task point from the current task point. Indicates the remaining time required for the target task. Indicates the remaining time limit of the target task. Represents each industrial robot The above two conditions can ensure that an industrial robot can complete the existing task before accepting the target task, and leave enough time to reach the task point of the target task and complete the target task, while ensuring that the task point of the target task is within the working range of the industrial robot.

[0033] Then, the robot set is segmented and the industrial robot dependency graph is constructed based on the industrial robot set and task set. , the task set is the collection of all tasks, each node Represents an industrial robot, each edge It represents the task dependency between industrial robots. Task dependency means that the reachable task sets of two industrial robots have an intersection. The tree decomposition algorithm is used to split the industrial robot dependency graph into multiple industrial robot cut sets. Each cut set splits the industrial robot dependency graph in a balanced manner until the number of industrial robots in the connected subgraph is less than a threshold. The search tree is constructed using the industrial robot cut sets. The nodes of the search tree represent the sets of industrial robots, and the edges represent the parent-child relationship between the sets of industrial robots. There is no task dependency between the industrial robots in the brother nodes of the search tree.

[0034] For the tree decomposition algorithm, the algorithm specifically receives the following input parameters: the industrial robot dependency graph G, which represents the graph structure of the dependency relationship between industrial robots; the cut set sequence C, which is used to store the generated cut sets; the sequential sequence I, which records the order in which each cut set is generated; the global variable index, which represents the total number of cut sets currently processed; the local variable cur, which represents the sequence number of the point cut set currently being processed. The algorithm outputs the updated cut set sequence C and sequential sequence I.

[0035] The algorithm first constructs the TreeDecomposition function and initializes two variables: Best_h is set to infinity to measure the ability of the cut set to split the connected graph; Best_S is set to an empty set to store the best cut set currently found. Check whether the number of industrial robots in the current connected graph G is less than the preset threshold. In this embodiment, the threshold is set to 4. If so, all industrial robot nodes in G are taken as a cut set and directly stored in C[cur], and the current processing ends. If the number of industrial robots in G is not less than the threshold, the algorithm traverses each node in G. For each node, the algorithm first constructs the set , including All directly connected nodes. Then construct the set , including not with All directly connected nodes. Then according to the set Constructing a new graph . Then the metric h is calculated, and its value is The size plus If the calculated h is less than Best_h, update Best_h to h and Assign it to Best_S. After the traversal is completed, the algorithm stores Best_S as the currently processed cut set in C[cur]. Then a new graph is constructed based on the nodes that are not in Best_S. .for Each subgraph in First, add one to the global variable index to get the new subgraph processing number Sub_index. Then store Sub_index in the sequence sequence Finally, recursively call the TreeDecomposition function and pass in the subgraph , point cut set sequence C, global variable index and new subgraph processing sequence number Sub_index. This embodiment does not solve the optimal cut set in each iteration, but tries to use each node and its surrounding nodes in the dependency graph as a cut set in turn, and records the best cut set currently found. Then, the search tree is constructed using the industrial robot cut set. The nodes of the search tree represent the industrial robot set, and the edges represent the parent-child relationship between the industrial robot sets. Through the tree structure, the optimal allocation subproblem of each brother node can be solved independently, and then the results of the subproblems are merged.

[0036] Finally, task search is performed based on tree decomposition, and the upper bound of the number of tasks that can be assigned to the subtree with node N as the root node is calculated. , and the upper bound and the heuristic function value For comparison, the heuristic function value Represents the minimum number of tasks that need to be assigned so that the subtree is not pruned. If < , then prune the subtree and update the heuristic function value.

[0037] The framework of the entire search process of this embodiment includes: traversing each industrial robot in the industrial robot set W , for every industrial robot , computing industrial robots The relationship between the task set S and save the result in the variable Then construct the industrial robot dependency graph G. This graph G is used to represent a certain dependency relationship between industrial robots. Then, traverse each connected subgraph g in the graph G. For each connected subgraph g, use the TreeDecomposition function to decompose it into a tree and get the decomposed result Next, the decomposed results Use the search tree structure to index and get the search tree Then, for each connected subgraph g, call the DFSearch function to search, and merge the search results with the current optimal task allocation solution Opt. Finally, return the optimal task allocation solution Opt as the output of the algorithm.

[0038] The DFSearch function inputs the current node number N, the unassigned task set S, and the industrial robot that has not been searched in the Nth subtree. , the heuristic function value h used to trim the search space, initialize the optimal task allocation solution Opt to 0, indicating that no task allocation solution has been found yet. Then calculate the upper bound UB(N) of the number of tasks that can be assigned to the subtree with node N as the root node. This upper bound is used to preliminarily determine whether the current branch is worth further searching. If the calculated upper bound UB(N) is less than the heuristic function value h, it means that even if the current branch is fully searched, no better solution can be obtained, so it directly returns 0, indicating that the current branch has no solution. If the industrial robot set If it is not empty, then for each industrial robot For each industrial robot , calculate the minimum set of tasks that it can complete.

[0039] For each minimum set of tasks Q, call ,in represents the remaining task set after removing set Q from task set S, Indicates removing an industrial robot from the industrial robot collection The remaining industrial robots are assembled, Represents the updated heuristic function value. Add the result of the recursive call to the number of tasks in the current MVT set Q , compare with the current optimal solution Opt, and take the larger value to update Opt. If it is empty, then for all child nodes of the current node N Traverse. For each child node , recursively call , and accumulate the returned results into the optimal solution Opt.

[0040] Finally, the optimal task allocation plan Opt is returned, and the central control unit allocates tasks to each industrial robot according to the optimal task allocation plan.

[0041] Distributed communication network is used to connect the central control unit and various industrial robots to realize communication transmission of data and information.

[0042] Specifically, the distributed communication network adopts a redundant design and includes multiple communication paths. The communication network also has data encryption functions to protect the security of data transmitted between the central control unit and each industrial robot.

[0043] In this embodiment, a communication network with redundant design is first constructed, and a dual network card redundant backup solution is adopted. The solution adopts a "primary-backup" network card strategy, that is, only one network card is working at the same time, and the other network card is temporarily not working as a backup network card. When the main network card or line fails, the system will automatically switch to the backup network card to ensure that the network of the central control unit and each industrial robot can continue to communicate. This dual network card redundant backup still presents the characteristics of a single network card for the application program, and the two network cards share a physical address and IP address. In this embodiment, dual physical network cards are bound to a virtual network card through bonding technology. During the configuration process, the configuration file is read, including the network interface name, network card interrupt number, working mode, etc. to be bound, and then the system network interface is obtained, the network interface structure is obtained through the API interface, and backed up. Next, a virtual network interface is created, and the network card receiving interrupt service function parameters are replaced with a new virtual network interface structure to realize the control of different physical network cards to send and receive data. In addition, a detection thread is created to detect the link connection status of each physical network card so that the network card can be switched when necessary.

[0044] At the same time, this embodiment uses the AES encryption algorithm. The AES encryption algorithm is a symmetric encryption algorithm, that is, the same key is used in the encryption and decryption process. First, a common key is negotiated between the central control unit and each industrial robot. Specifically, the industrial robot sends a key negotiation frame to the central control unit, the central control unit replies to the key negotiation frame, and the two parties obtain the AES key through a specific algorithm based on the content of the negotiation frame. Once the key negotiation is successful, the central control unit and the industrial robot can use the AES key to encrypt and decrypt the data. During the encryption process, the plaintext and the key are encrypted by the AES algorithm to generate a ciphertext. The ciphertext is then transmitted through the communication network. After the receiving end receives the ciphertext, it uses the same AES key to decrypt it through the AES algorithm to restore the plaintext. In addition, a CRC check code is added during the encryption process. The CRC check code is a check code used to detect data transmission errors. At the sending end, after the plaintext is CRC-checked, the check code is sent out together with the message. At the receiving end, a CRC check is performed on the received ciphertext. If the check passes, it means that no error occurred during the data transmission process. If the check fails, it means that an error may have occurred or the data has been tampered with during the transmission process. In this case, the data is discarded or the sender is required to resend it.

[0045] The working condition monitoring module is used to monitor the operating status of the industrial robot in real time.

[0046] Specifically, the working condition monitoring module includes sensors and data analysis units. The sensors are deployed on each industrial robot to collect the operating parameters of the industrial robot in real time. The data analysis unit is responsible for receiving and monitoring these parameters to obtain the operating status of the industrial robot. The operating status of the industrial robot includes workload, energy consumption, fault warning and location information.

[0047] In this embodiment, the working condition monitoring module includes sensors and a data analysis unit. Sensors are installed on each industrial robot to capture the operating parameters of the industrial robot in real time. The operating parameters include motor speed, joint angle, load weight and operating speed, reflecting the actual working conditions when performing tasks. These parameters are then transmitted to the data analysis unit, which evaluates whether the industrial robot is overloaded by comparing it with the preset workload limit to prevent mechanical loss and failure. At the same time, the energy consumption data reveals the energy utilization status during the execution process, and the data analysis unit evaluates the energy efficiency level based on this.

[0048] In addition, the data analysis unit compares the continuous monitoring of the operating parameters with the preset operating parameter thresholds, identifies potential fault signs and issues early warnings. In this embodiment, the preset threshold of the motor speed is 3000 rpm, the preset threshold of the joint angle is -90° to +90°, the preset threshold of the load weight is 80% of the maximum load, and the preset threshold of the operating speed is 1.5 m / s. The position information records the coordinates of the industrial robot in the work area in real time. Combined with the path planning and task allocation information, the data analysis unit monitors its movement trajectory and execution status to ensure that the industrial robot follows the predetermined plan and completes various tasks efficiently and accurately.

[0049] The task scheduling module is used to dynamically adjust the task execution order according to the operating status and task allocation of the industrial robot.

[0050] Specifically, the task scheduling module dynamically adjusts the order of task execution based on the current task execution status of the industrial robot, the priority of the remaining tasks, and the estimated completion time. The task scheduling module also includes a feedback mechanism. When an industrial robot fails suddenly and cannot execute the assigned task, it will feedback to the central control unit to reallocate the task.

[0051] In this embodiment, the task scheduling module monitors and records the current task execution status of each industrial robot, including task progress and completed workload. At the same time, the task scheduling module analyzes the operating scope and task completion status of each industrial robot, and then dynamically adjusts the task execution order of its tasks to be executed, thereby improving the efficiency of task execution. In addition, by predicting the completion time of each task, the task scheduling module can plan the overall workflow to avoid conflicts and delays between tasks. When the estimated completion time of a task changes, the task scheduling module will immediately adjust the execution order of related tasks to ensure the stability and efficiency of the entire system.

[0052] In this embodiment, the task scheduling module also includes a feedback mechanism. When an industrial robot suddenly fails or cannot continue to perform the assigned task, the fault information will be fed back to the central control unit, and the central control unit will reallocate tasks according to the current system status and remaining tasks.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A distributed industrial robot management system, characterized in that: The system comprises: Multiple industrial robots, each with independent working capabilities and communication modules; A central control unit, which is used to receive task instructions, analyze task requirements, and assign tasks to individual industrial robots; Distributed communication network, used to connect the central control unit and each industrial robot to achieve communication transmission of data and information; Working condition monitoring module, used to monitor the operating status of industrial robots in real time; The task scheduling module is used to dynamically adjust the task execution order according to the operating status and task allocation of the industrial robot.

2. The distributed industrial robot management system according to claim 1, characterized in that: The industrial robot has positioning and navigation capabilities and can move to a designated location to perform tasks according to path planning; The industrial robot works within its preset working range.

3. The distributed industrial robot management system according to claim 1, characterized in that: The task instruction includes the task type, task parameters and execution time, and the task requirement includes the time limit for task completion and the required resources.

4. The distributed industrial robot management system according to claim 1, characterized in that: The task allocation to each industrial robot adopts a tree-based allocation algorithm to perform the task allocation to the industrial robots.

5. The distributed industrial robot management system according to claim 4, characterized in that: The tree-based allocation algorithm specifically includes: Calculate the effective task set and calculate the tasks for each industrial robot according to the constraints of the existing tasks of the industrial robot. The set of reachable tasks , satisfy as well as ,in Indicates the target task, Indicates the remaining time required for the current task. Indicates the existing task points. represents the target task point, Indicates the time required to reach the target task point from the current task point. Indicates the remaining time required for the target task. Indicates the remaining time limit of the target task. Represents each industrial robot Scope of work; Split the robot set and build an industrial robot dependency graph based on the industrial robot set and task set , each node represents an industrial robot, each edge Represents the task dependency between industrial robots, where the task dependency means that the reachable task sets of two industrial robots have an intersection. The industrial robot dependency graph is divided into multiple industrial robot cut sets using a tree decomposition algorithm. Each cut set divides the industrial robot dependency graph in a balanced manner until the number of industrial robots in the connected subgraph is less than a threshold. The search tree is constructed using the industrial robot cut sets. The nodes of the search tree represent the industrial robot sets, and the edges represent the parent-child relationship between the industrial robot sets. There is no task dependency between the industrial robots in the brother nodes of the search tree. Perform task search based on tree decomposition and calculate the upper bound of the number of tasks that can be assigned to the subtree with node N as the root node , and the upper bound and the heuristic function value For comparison, the heuristic function value Represents the minimum number of tasks that need to be assigned so that the subtree is not pruned. If < , then prune the subtree, where the upper bound is calculated The formula is: in, Represents all industrial robots in the current subtree, It represents the set with the largest number of elements in the reachable task set of the i-th industrial robot in the subtree. The updating formula of the heuristic function value h is: in, represents the updated heuristic function value, represents the jth child node of node N, m represents the total number of child nodes of node N, Indicates the maximum number of tasks that can be assigned to the traversed subtrees. represents the sum of the estimated upper bounds of the unsearched subtrees; Multiple rounds of recursive calls will exclude the allocation schemes that will not be the optimal solution, and finally output the task allocation scheme .

6. The distributed industrial robot management system according to claim 1, characterized in that: The distributed communication network adopts a redundant design and includes multiple communication paths. The communication network also has a data encryption function to protect the security of data transmitted between the central control unit and each industrial robot.

7. The distributed industrial robot management system according to claim 1, characterized in that: The working condition monitoring module includes sensors and a data analysis unit. The sensors are deployed on each industrial robot to collect the operating parameters of the industrial robot in real time. The data analysis unit is responsible for receiving and monitoring these parameters to obtain the operating status of the industrial robot.

8. The distributed industrial robot management system according to claim 7, characterized in that: The operating status of the industrial robot includes workload, energy consumption, fault warning and location information.

9. The distributed industrial robot management system according to claim 1, characterized in that: The task scheduling module dynamically adjusts the task execution order according to the current task execution status of the industrial robot, the priority of the remaining tasks and the expected completion time.

10. The distributed industrial robot management system according to claim 9, characterized in that: The task scheduling module also includes a feedback mechanism, which provides feedback to the central control unit to reallocate tasks when an industrial robot fails suddenly and cannot perform assigned tasks.

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