Multi-machine collaborative operation control method and system based on common inductance calculation control technology

Abstract

The invention relates to the technical field of information, and discloses a multi-machine collaborative operation control method and system based on a common inductance calculation control technology, and the method comprises the steps: obtaining task granularity distribution and a resource demand matrix, and carrying out the classification of the task granularity distribution and the resource demand matrix through a classification algorithm, and obtaining an initial task segmentation feature set; based on the optimal segmentation point set, a preliminary task allocation scheme is generated in combination with node calculation heterogeneity, and a task segmentation feature set is dynamically updated through a real-time sensing technology; a real-time task state set is generated through a group information sharing mechanism, a task allocation scheme is optimized through a dynamic programming algorithm, and it is ensured that tasks are reasonably allocated according to priorities and node resources; and the cooperation state matrix is updated through a real-time communication protocol, so that the accuracy and timeliness of a multi-node cooperation execution result are ensured. According to the invention, task allocation can be dynamically adjusted in a complex environment, resource utilization is optimized, the cooperation efficiency of a multi-robot system is improved, and the method can be widely applied to the fields of industrial production, warehouse logistics, disaster rescue and the like.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a multi-machine collaborative operation control method and system based on synergistic computing and control technology, which is particularly suitable for multi-robot task allocation and collaborative operation optimization in the fields of robotic systems, automated production, logistics warehousing, disaster relief, etc. Background Art

[0002] With the continuous advancement of automation technology and robotic systems, especially in the field of multi-robot collaborative operations, efficient task allocation and collaboration have become key technical issues. Multi-robot systems (MRS) are widely used in complex environments such as industrial production, warehousing and logistics, and disaster relief. The tasks in these areas are often highly complex and dynamic, and collaborative execution between robots requires real-time adjustments to varying environmental conditions and task requirements.

[0003] Currently, existing multi-robot task allocation methods mostly rely on static presets or rule-based scheduling strategies, lacking the ability to dynamically adjust to factors such as task complexity, resource requirements, and robot heterogeneity. Traditional methods often struggle to achieve efficient task allocation when faced with complex factors such as frequent task changes, differences in robot computing power and resources, and variations in communication bandwidth, resulting in inefficient task execution and wasted resources.

[0004] In order to solve these problems, researchers have proposed a variety of task allocation methods based on intelligent algorithms in recent years, such as genetic algorithms and particle swarm optimization algorithms. However, these methods usually require a long computing time and are difficult to respond to dynamically changing tasks and environments in real time, which limits their widespread use in practical applications.

[0005] Furthermore, existing technologies also have issues with information sharing and coordination mechanisms. While some methods attempt to optimize task scheduling through information sharing, in real-world environments, communication between robots is often affected by factors such as bandwidth and latency, leading to delayed information synchronization and, in turn, impacting task execution efficiency and collaborative effectiveness.

[0006] Therefore, how to dynamically and in real time allocate tasks in a multi-robot system, taking into account multiple factors such as task complexity, robot heterogeneity, resource requirements, and real-time communication, is a key challenge in current technological development. The main purpose of this invention is to provide a control method and system for multi-robot collaborative operations based on telepathic computing and control technology. By using real-time perception and dynamic optimization of task allocation schemes, this method effectively addresses the existing problems of low multi-robot task allocation efficiency, severe resource waste, and delayed information sharing. Summary of the Invention

[0007] To solve the above technical problems, the present invention adjusts the task allocation scheme in real time according to dynamic factors such as changes in tasks, the computing power of robot nodes, and changes in communication bandwidth, thereby improving the collaborative efficiency of the multi-robot system.

[0008] In a first aspect, the present application provides a control method for multi-machine collaborative operation based on synergistic computing and control technology, the method comprising: Step S1: Obtain the task size distribution and resource requirement matrix, classify the task size distribution and resource requirement matrix using a classification algorithm, and obtain the initial task segmentation feature set; Step S2: extract subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and use cluster analysis technology to determine the optimal segmentation point set; Step S3: Based on the optimal segmentation point set and combined with the node computing heterogeneity, a preliminary task allocation plan is generated; Step S4: Update the task granularity distribution and resource requirement matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; Step S5: Determine the real-time task state set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; Step S6: extract task execution instructions from the optimized task allocation plan, distribute them to each node, and update the collaborative state matrix through the real-time communication protocol to obtain the collaborative execution result.

[0009] In conjunction with the first aspect, in a first implementation of the first aspect of the present application, the process of updating the collaborative state matrix through the real-time communication protocol further includes: Add a local timestamp to the collaborative status data reported by each node; For each item in the collaborative state matrix, the state hysteresis value is calculated based on the difference between the most recent timestamp corresponding to each item and the current system clock; If the state hysteresis value exceeds the set hysteresis tolerance threshold, the fast refill mechanism is triggered, requesting the target node to rebroadcast its current state information to overwrite the outdated data; During the matrix update process, a credibility weighted calculation strategy is adopted for the status information from multiple nodes, where the node credibility is calculated based on its past status reporting frequency, communication success rate and delay jitter statistics; Through the above-mentioned timestamp control and credibility weighted update strategy, the data inconsistency of the collaborative state matrix caused by asynchrony or delay is reduced, and the real-time performance of task scheduling decisions is improved.

[0010] In a second aspect, the present application provides a control system for multi-machine collaborative operation based on synergistic computing and control technology, the system comprising: The initial task segmentation unit is used to obtain the task granularity distribution and resource requirement matrix, and classify the task granularity distribution and resource requirement matrix using a classification algorithm to obtain the initial task segmentation feature set; Determine the optimal segmentation unit, which is used to extract subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and use cluster analysis technology to determine the optimal segmentation point set; A preliminary task allocation unit, configured to generate a preliminary task allocation plan based on an optimal segmentation point set and combined with node computing heterogeneity; Dynamic task segmentation unit, used to update the task granularity distribution and resource demand matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; The optimized task allocation unit is used to determine the real-time task status set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; The collaborative execution unit is used to extract task execution instructions from the optimized task allocation plan, distribute them to each node, and update the collaborative state matrix through the real-time communication protocol to obtain the collaborative execution results.

[0011] Compared with the prior art, the beneficial effects of the present invention are at least as follows: 1. The technical solution provided in this application, through classification algorithms, cluster analysis and dynamic adjustment mechanisms, can achieve accurate task allocation based on factors such as task complexity, resource requirements, node computing power, etc.; especially when facing multi-robot collaborative operations, it can quickly respond to task changes, optimize task allocation, and improve the overall efficiency of task execution.

[0012] 2. In the technical solution provided in this application, by introducing real-time perception technology and dynamic programming optimization algorithm, the present invention can timely adjust the task allocation plan according to environmental changes and task progress; it can flexibly adapt to different work scenarios, handle different task priorities, node computing capabilities and bandwidth limitations, and significantly improve the adaptability of the system.

[0013] 3. In the technical solution provided in this application, through dynamic perception and optimization algorithms, the task granularity distribution can be flexibly adjusted according to the current resource and bandwidth conditions, so as to more reasonably utilize the system's computing resources and communication bandwidth; this refined resource scheduling can reduce resource waste and improve the overall resource utilization efficiency of the system.

[0014] 4. In the technical solution provided in this application, by updating the real-time communication protocol and the collaborative state matrix, each node can maintain synchronization during the collaborative operation process and provide timely feedback on the task execution status; in this way, in the multi-robot system, not only the smooth execution of the task is guaranteed, but also the collaborative stability of the system is improved, and task execution interruption caused by communication delays or node failure can be avoided.

[0015] 5. In the technical solution provided in this application, through the sharing and feedback of real-time task status, it is possible to quickly adjust the task execution strategy, optimize the allocation of bandwidth and computing resources, and ensure that tasks can be completed in a timely manner; especially in complex and dynamic environments, it is also possible to significantly reduce the execution time of tasks without affecting the quality of task completion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a control method for multi-machine collaborative operation based on synergistic computing and control technology in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a control system for multi-machine collaborative operation based on synergistic computing and control technology in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a control method and system for multi-machine collaborative operation based on synergistic computing and control technology. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. Example 1

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1, an embodiment of the control method of multi-machine collaborative operation based on synergistic computing and control technology in the embodiment of the present application includes.

[0020] Step S1: Obtain task granularity distribution and resource requirement matrix, use classification algorithm to classify task granularity distribution and resource requirement matrix, and obtain initial task segmentation feature set; wherein the initial task segmentation feature set obtained in step S1 includes: obtaining task granularity distribution, resource requirement matrix and dynamic priority label; using support vector machine algorithm to classify task granularity distribution, resource requirement matrix and dynamic priority label, and determine classification feature vector; generate initial task segmentation feature set based on classification feature vector, wherein the initial task segmentation feature set includes subtask dependency weight and task granularity distribution.

[0021] Specifically, the task granularity distribution reflects the complexity and resource requirements of the task in different subtasks, and the resource requirement matrix describes the requirements of each subtask in different resource dimensions, such as computing resources, memory, bandwidth, etc.; in practical applications, these data come from task analysis and resource evaluation of the robot system, and are obtained in real time through sensors or scheduling systems; in step S1, through sensor data collection and computing models, the task is divided into subtasks of different granularities. The resources required for each subtask are represented in the matrix, and the task priority label is marked to evaluate the urgency and importance of the task.

[0022] After obtaining the task granularity distribution, resource requirement matrix and dynamic priority labels, these data are classified using the support vector machine algorithm in order to determine the feature vectors for task segmentation. The key in this process is to use the classification capability of the support vector machine to classify task data into different categories. The support vector machine generates a classification feature vector by calculating the relationship between task granularity, resource requirement matrix and priority labels. The classification feature vector will reflect the execution characteristics of each task. The classification feature vector actually extracts classification information related to resource requirements, execution difficulty or priority from a large amount of data. Through this information, it is possible to clarify which subtasks have similar resource requirements, execution difficulty or priority, thereby providing a basis for subsequent task optimization.

[0023] Based on the classification feature vector, an initial task segmentation feature set is generated, which contains subtask dependency weights and task granularity distribution. The initial task segmentation feature set not only reflects the complexity of the task, but also reveals the dependencies between tasks. For example, some subtasks may need to be executed after the predecessor task is completed, while other subtasks can be executed in parallel. The task granularity distribution determines how the tasks are divided according to the amount of resources and execution time required for each subtask. The initial task segmentation feature set enables subsequent task allocation and optimization to be completed more efficiently.

[0024] By combining classification algorithms with task allocation, this application can efficiently handle the complexity of tasks in a multi-robot system, especially in response to situations where tasks are constantly changing during execution; specifically, the classification feature vector can help the system identify the urgency of the task, resource requirements, and tasks that can be executed in parallel, thereby ensuring that task allocation conforms to the real-time resource status of the robot system; this method can effectively solve the problems of unreasonable resource scheduling and untimely response in the multi-robot task allocation process in the prior art, thereby improving the overall efficiency of the system and the accuracy of task execution.

[0025] Step S2: extracting subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and using cluster analysis technology to determine the optimal segmentation point set; wherein, the cluster analysis technology is used in step S2 to determine the optimal segmentation point set, including: extracting subtask dependency weights and task granularity distribution from the initial task segmentation feature set; clustering the subtask dependency weights using cluster analysis technology to obtain subtask dependency clusters; determining task segmentation points based on the subtask dependency clusters and task granularity distribution; generating the optimal segmentation point set based on the task segmentation points, wherein the optimal segmentation point set includes the subtask dependency weights and segmentation point positions.

[0026] Specifically, the task segmentation feature set is obtained from the task granularity distribution, resource requirement matrix and dynamic priority label. It contains information such as the execution complexity, resource requirement and priority of each subtask; among them, the extracted subtask dependency weights reflect the dependency relationship between tasks. For example, some tasks may need to be started after other tasks are completed, while the task granularity distribution provides a fine-grained division of the computing resources required for each subtask.

[0027] After data preparation is completed, cluster analysis technology is used to process the subtask dependency weights. Cluster analysis discovers the intrinsic relationship between tasks by grouping tasks with similar subtask dependency weights into the same category. The purpose is to assign tasks with strong dependencies to the same task group, thereby ensuring that these tasks can be executed in an appropriate order and parallelism. The clustered subtask dependency clusters show the degree of dependency between different tasks and facilitate subsequent task segmentation and scheduling.

[0028] Based on these dependency clusters and task granularity distribution, the task splitting point can be further determined; among them, the selection of task splitting point is crucial, which determines how to find the optimal balance between task granularity, dependency and resource requirements; by analyzing the granularity distribution of tasks within the dependency cluster, the most appropriate splitting point position can be determined, so that when tasks are assigned to different nodes, not only resource utilization can be optimized, but also the dependency relationship of tasks can be ensured. The task splitting point is not just a simple division point, it is the result of multi-dimensional data analysis, covering multiple factors such as time dependency between tasks, resource requirements, computing power and network bandwidth.

[0029] By calculating the set of task segmentation points and combining the dependency weights and resource requirement information of each task segmentation point, the system generates an optimal set of segmentation points. This optimal set of segmentation points provides a reasonable basis for task division, ensuring that tasks can be efficiently executed in a multi-robot system. In practical applications, the selection of task segmentation points not only affects the execution order of tasks, but also affects the collaborative efficiency, resource allocation, and timeliness of task completion of the entire system. This method can effectively avoid resource conflicts, reduce task execution delays, and improve the accuracy of task scheduling and the overall performance of the system.

[0030] The above method can solve the problems of inaccurate task allocation, resource waste, and low collaboration efficiency in the existing technology through reasonable cluster analysis and determination of task segmentation points. For example, in a typical warehousing and logistics scenario, different robots perform different handling tasks. Some of these tasks require high computing resource support, while others mainly rely on physical space and mobility. Without precise task segmentation and resource scheduling, the system may experience problems such as uneven resource allocation and task conflicts. Through the technical solution of the present invention, the system can dynamically adjust task allocation based on task dependencies and resource requirements, avoiding resource waste while ensuring that tasks are executed in a reasonable order and parallelism, which can significantly improve the execution efficiency of the system.

[0031] Through the above method, the present invention can flexibly adjust task allocation and scheduling in a dynamic environment, improve the collaborative efficiency and resource utilization of multi-robot systems, and solve technical problems such as delays in task execution, uneven resource allocation, and poor system adaptability. It has significant technical effects and broad application prospects.

[0032] Step S3: Based on the optimal split point set, a preliminary task allocation plan is generated in combination with node computing heterogeneity; wherein the preliminary task allocation plan is generated in combination with node computing heterogeneity in step S3, including: obtaining subtask dependency weights and collaborative state matrix from the optimal split point set; obtaining node computing heterogeneity, if the node computing heterogeneity matches the subtask dependency weight, then generating a preliminary task allocation plan based on the subtask dependency weight and the collaborative state matrix, wherein the preliminary task allocation plan includes dynamic priority labels and subtask allocation mapping.

[0033] Specifically, the optimal split point set can determine the split point locations of the tasks and reasonably divide the tasks according to resource requirements, computing load and dependencies; among them, the dependency weight reflects the sequential execution relationship between tasks, and the collaborative state matrix describes the collaboration requirements between tasks and the collaboration capabilities between nodes; in this way, the optimal split point set can provide detailed structured information for subsequent task allocation, so that tasks can be executed in a reasonable order while ensuring dependencies.

[0034] In the process of generating preliminary task allocation plans, node computing heterogeneity refers to the differences in computing power, memory resources, processing speed, etc. among different robot nodes. This difference can directly affect the efficiency and performance of task allocation. In order to reasonably allocate tasks, it is necessary to consider the computing power of each node and match it with the computing requirements of the subtasks. Specifically, the computing power of each node is evaluated in real time through the resource management module, including factors such as the node's CPU load, memory usage, processing speed, and bandwidth utilization. This information is collected in real time through the node monitoring system and reflected in the collaborative status matrix, which provides a comprehensive view of the current status of the node.

[0035] When the node computing heterogeneity matches the subtask dependency weight, the task is assigned to the appropriate node. For example, if a subtask has a large computing requirement and depends on the completion of other tasks before it can be executed, then this task will be assigned to a node with stronger computing power. At this time, the execution order and computing requirements of the subtasks will be optimized according to the computing power of the node to ensure that the task can be executed smoothly without bottlenecks. If the node computing power is insufficient, the execution progress of the task may be delayed, which will reduce the collaborative efficiency of the robot system.

[0036] Based on the above matching relationship, dynamic priority tags are used to identify the priority of tasks. Priority tags are automatically generated by analyzing the urgency and importance of tasks. The priority of tasks affects the scheduling order of tasks, thereby optimizing the overall efficiency of the multi-robot system. In practical applications, tasks with higher priority will be preferentially assigned to nodes with more sufficient computing resources to ensure that the tasks can be completed in a timely manner. At the same time, the generation of priority tags is also constrained by node computing heterogeneity, bandwidth, and resources to avoid task accumulation.

[0037] The subtask allocation map indicates which tasks are assigned to which nodes; based on the optimal split point set, task dependency weights, node computational heterogeneity, and real-time monitoring data, an allocation map is generated for each task; this mapping not only ensures that the task is executed on the appropriate node, but also adjusts the execution timing and order of the tasks according to the node's resource conditions, thereby optimizing the parallelism and response speed of the tasks; for example, in a complex logistics warehousing scenario, multiple robots collaborate to complete the tasks of transporting and sorting items; the present invention can reasonably allocate nodes with appropriate computing power to each subtask based on the task dependencies, the computing power of the robot nodes, and the resource requirements; assuming that a sorting task depends on the completion of the previous transport task, the order of task allocation and node allocation are automatically adjusted according to the execution progress of the former task and the computing power of the robot node, thereby ensuring that the sorting task can be executed at the appropriate time and node without wasting computing resources or bandwidth.

[0038] Through the above method, not only can the problems of task execution delay, uneven computing resources and unreasonable task scheduling in traditional methods be solved, but also the resource utilization efficiency of multi-robot systems and the smoothness of collaborative operations can be improved.

[0039] Step S4: updating the task granularity distribution and resource requirement matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; obtaining the dynamically adjusted task segmentation feature set includes: obtaining environmental change sensitivity and cross-layer communication bandwidth; analyzing environmental change sensitivity through real-time perception technology to update the task granularity distribution; updating the resource requirement matrix based on the cross-layer communication bandwidth; generating a dynamically adjusted task segmentation feature set based on the updated task granularity distribution and resource requirement matrix.

[0040] Updating the task granularity distribution includes: obtaining and constructing a task granularity adjustment function based on the relationship between the current cross-layer communication bandwidth and the preset bandwidth threshold; when the current cross-layer communication bandwidth is greater than or equal to the bandwidth threshold, the task granularity adjustment function outputs a fine-grained instruction to adjust the task granularity distribution toward the fine-grained direction; when the current cross-layer communication bandwidth is less than the bandwidth threshold, the task granularity adjustment function outputs a limited-grained instruction and adjusts the granularity distribution to a medium or coarse-grained distribution through a linear proportional function; based on the output of the task granularity adjustment function, dynamically updating the distribution probabilities of fine granularity, medium granularity and coarse granularity in the task granularity distribution matrix; wherein, the output of the task granularity adjustment function is also linked to the resource demand matrix.

[0041] Specifically, the task granularity distribution and resource requirement matrix are important parameters that describe the resource requirements and complexity between the subtasks of a task. As the task progresses, factors such as environmental changes, node status, and network bandwidth will affect the execution conditions and resource requirements of the task. Therefore, real-time perception technology must be used to analyze and adjust factors such as environmental changes, node status, and network bandwidth to maintain the efficiency of task allocation.

[0042] Sensitivity to environmental changes and cross-layer communication bandwidth are important factors affecting task execution efficiency; sensitivity to environmental changes reflects the impact of dynamic changes in the task execution process on task division and scheduling, while cross-layer communication bandwidth directly determines the rate and delay of data transmission between nodes; changes in these factors will affect the allocation of task granularity; monitoring and analysis of these parameters through real-time perception technology can timely discover potential resource bottlenecks and make corresponding adjustments; specifically, real-time perception technology obtains data on environmental change sensitivity and communication bandwidth through sensors and data acquisition systems, thereby evaluating the current system's operating status, updating the task granularity distribution and resource demand matrix, and ensuring that tasks can be allocated and executed at the appropriate time and under appropriate conditions.

[0043] The task granularity adjustment function dynamically adjusts the task granularity distribution by obtaining the relationship between the current cross-layer communication bandwidth and the preset bandwidth threshold; when the cross-layer communication bandwidth is greater than or equal to the preset bandwidth threshold, the task granularity adjustment function will output fine-grained instructions, enabling tasks to be subdivided under higher bandwidth conditions, and the task granularity distribution will tend to be fine-grained; this fine-grained task allocation can perform more parallel processing when the bandwidth is sufficient, thereby improving the system's processing capability and task execution efficiency; and when the cross-layer communication bandwidth is less than the bandwidth threshold, the task granularity adjustment function will output a granularity limit instruction, and adjust the task granularity distribution to a medium or coarse-grained distribution based on a linear proportional function, which can reduce the high bandwidth occupancy caused by excessively fine task granularity and avoid communication delays and task execution bottlenecks caused by bandwidth limitations.

[0044] By dynamically updating the distribution probabilities of fine, medium, and coarse granularity in the task granularity distribution matrix, the system can automatically adjust the task execution strategy under different bandwidth conditions to adapt to different resource and environmental changes. The task granularity distribution matrix assigns different granularity levels to each subtask and adjusts it based on the current bandwidth conditions and resource requirements, thereby ensuring that tasks can be executed smoothly without adding unnecessary computing burden. This adjustment mechanism not only optimizes the efficiency of task allocation, but also maximizes the utilization of computing resources, avoiding task accumulation or delays caused by bandwidth limitations.

[0045] The dynamically adjusted resource requirement matrix, powered by real-time sensing technology, re-evaluates the resource requirements of each subtask based on the updated task granularity distribution. By recalculating task granularity and resource requirements, the system can more accurately allocate tasks, avoid uneven resource allocation and overload, and ensure that each node executes tasks efficiently within a reasonable resource range.

[0046] Through the above-mentioned adjustment method based on real-time sensing technology, the present invention can cope with dynamically changing task environments and complex resource requirements in multi-robot systems, and improve the flexibility and response speed of task scheduling and allocation; for example, in a multi-robot collaborative warehouse management system, when a robot performs a task, the computing resources and bandwidth it requires depend on the complexity of the current task and the working status of other robot nodes; if the communication bandwidth in the system is congested, the system will automatically adjust the granularity of the task, so that fine-grained tasks are converted into medium or coarse-grained tasks, thereby reducing bandwidth occupancy and ensuring the stability of task execution; when there is sufficient bandwidth, tasks can be performed in a fine-grained form to improve parallel processing efficiency and enhance the overall execution performance of the system.

[0047] Therefore, the above step S4, through the application of real-time perception technology, combined with task granularity adjustment and dynamic update of the resource demand matrix, can effectively solve the problems of unreasonable task scheduling and system response lag caused by bandwidth and computing resource limitations in the existing technology, improve the task execution efficiency and resource utilization of the multi-robot system, and achieve the technical effect of optimizing multi-machine collaborative operations.

[0048] Step S5: Determine the real-time task state set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; wherein the real-time task state set is determined through the group information sharing mechanism in step S5, including: extracting the task granularity distribution and real-time communication delay from the dynamically adjusted task segmentation feature set; distributing the task execution progress, cross-layer synchronization accuracy and real-time communication delay to each node through the group information sharing mechanism; generating the real-time task state set based on the task execution progress and the real-time communication delay, wherein the real-time task state set includes the task execution progress and the abnormal feedback vector.

[0049] In step S5, an optimized task allocation plan is generated, including: extracting the task execution progress and the exception feedback vector from the real-time task status set; if the task execution progress is lower than a preset threshold or the exception feedback vector is not empty, optimizing the subtask dependency weights and bandwidth allocation weights using a dynamic programming algorithm; and generating an optimized task allocation plan based on the optimized subtask dependency weights and bandwidth allocation weights.

[0050] Specifically, the generation of the real-time task state set first extracts the task granularity distribution and real-time communication delay from the dynamically adjusted task segmentation feature set; this feature can reflect the complexity of the task in different subtasks and the delay in data transmission between nodes; through the group information sharing mechanism, the real-time data will be distributed to each node to ensure that all nodes can obtain key information such as the execution progress of the current task, cross-layer synchronization accuracy and real-time communication delay; this information can help nodes adjust the task execution strategy in a timely manner to meet the collaboration needs between different nodes and tasks.

[0051] The real-time task status set includes the task execution progress and exception feedback vectors. The task execution progress reflects the completion status of each subtask, and the exception feedback vector provides potential problems or error signals that may occur during task execution, such as excessive communication delays and node failures. In multi-robot collaboration scenarios, the real-time status of tasks is crucial to the system's scheduling and task allocation. Therefore, the accurate generation and sharing of the real-time task status set can ensure the smooth execution of tasks and adjust the task allocation strategy based on actual feedback to avoid delays in task execution or system crashes.

[0052] The dynamic programming algorithm optimizes subtask dependency weights and bandwidth allocation weights based on the task execution progress and the content of the exception feedback vector. This process first requires evaluating whether the task execution progress meets expectations. For example, some subtasks may be progressing slowly or experiencing delays. If the task execution progress falls below a preset threshold, or the exception feedback vector contains non-empty elements, such as excessive latency or communication interruption, the dynamic programming algorithm adjusts the subtask dependencies and bandwidth allocation strategy to ensure that the task can continue to execute in the most optimal way possible. By optimizing the subtask dependency weights, the algorithm can adjust the execution order between tasks to prevent subtasks with heavy dependencies from blocking the entire task process. Optimizing bandwidth allocation weights ensures that tasks can reasonably allocate bandwidth resources when bandwidth is limited, thereby improving data transmission efficiency and reducing delays.

[0053] After generating an optimized task allocation plan, tasks are allocated based on the optimized subtask dependency weights and bandwidth allocation weights. The optimization plan ensures that tasks can be efficiently executed on appropriate nodes based on the computing power and resource conditions of each node. The computing resources, bandwidth, and task priority of each node are all taken into consideration to maximize the overall efficiency of the system. For example, in a logistics system where multiple robots perform tasks, multiple robots need to work together to carry heavy objects. As the task progresses, insufficient bandwidth may occur, or some robots may be unable to complete the task on time due to insufficient computing power. The present invention can adjust the task allocation of each robot in real time and optimize the bandwidth allocation strategy to ensure that the task can be completed smoothly, thus solving the problems of task execution delays and resource waste caused by bandwidth limitations and node heterogeneity in the existing technology.

[0054] By combining the group information sharing mechanism with the dynamic programming algorithm, the present invention not only improves the flexibility and real-time performance of task allocation, but also effectively enhances the resource utilization and task execution efficiency of the multi-robot system; especially in complex and dynamic environments, the system can quickly respond to changes and adjust the task allocation strategy, thereby ensuring the efficiency and stability of multi-robot collaborative operations.

[0055] Step S6: extracting task execution instructions from the optimized task allocation plan, distributing them to each node, and updating the collaborative state matrix through the real-time communication protocol to obtain a collaborative execution result; in step S6, updating the collaborative state matrix through the real-time communication protocol to obtain a collaborative execution result, including: extracting task execution instructions from the optimized task allocation plan; distributing the task execution instructions to each node through the real-time communication protocol; updating the collaborative state matrix and network delay fluctuations based on the task execution instructions; generating a collaborative execution result based on the updated collaborative state matrix and network delay fluctuations, wherein the collaborative execution result includes the task execution status and the node collaborative status.

[0056] Specifically, task execution instructions are extracted from the optimized task allocation plan, and these instructions are distributed to each node through a real-time communication protocol. The collaborative execution results are further obtained by updating the collaborative state matrix. This process can improve the collaborative efficiency and task completion of the entire system by ensuring the smooth execution and collaborative cooperation of tasks among multiple nodes.

[0057] Specifically, the optimized task allocation plan generated in step S5 takes into account the task's execution progress, subtask dependencies, and resource allocation. It optimizes the order of task execution and bandwidth allocation through a dynamic programming algorithm. Based on this plan, the system determines each subtask's execution priority, the node to which it is assigned, and the task's resource requirements. This information constitutes the task execution instructions, which reflect the resources required for each task or subtask and the time window within which it will execute.

[0058] After receiving the task execution instructions, the system transmits these instructions to each node through the real-time communication protocol. The communication protocol ensures that the instructions can reach each execution node quickly and accurately, ensuring that the execution order of tasks is synchronized with time. In order to cope with the impact of factors such as network delays and bandwidth fluctuations, the real-time communication protocol also involves a confirmation and retransmission mechanism for the feedback information of each node to ensure that all instructions are delivered to each node in a timely and effective manner.

[0059] After the task execution instructions are transmitted through the real-time communication protocol, the collaborative state matrix is ​​updated to reflect the status of task execution and the collaboration between nodes. The collaborative state matrix is ​​a real-time dynamic data structure that describes the current execution status, resource usage, and task progress of each node. It includes not only the execution progress of each subtask, but also the collaboration efficiency, communication delay, and bandwidth utilization between nodes. Based on the collaborative state matrix, dynamic adjustments can be made during the task execution process to ensure that the task can be completed smoothly as planned.

[0060] By updating the collaborative state matrix, fluctuations in network latency can be reflected, allowing timely corrections to task execution. For example, if a node fails to receive a task instruction on time due to network latency, the collaborative state matrix will record this delay and trigger a rapid recovery mechanism to ensure that the node synchronizes as quickly as possible and continues task execution. This mechanism can respond to delays and error feedback in a timely manner to ensure the smooth execution of the overall task.

[0061] In a multi-robot system, the results of collaborative execution not only provide feedback on the completion status of the task but also include the collaborative efficiency between nodes, which directly affects the efficiency and resource utilization of the overall operation. In an actual logistics task, if a node's task execution is blocked, the updated collaborative state matrix can be used to identify the node's status and adjust resource or task allocation to prevent other nodes from wasting time or resources due to waiting. Therefore, the collaborative execution results not only reflect the completion status of task execution but also provide a basis for optimization and adjustment, enabling the multi-robot system to complete complex tasks efficiently and stably. For example, in a robotic warehouse management scenario, multiple robots need to work together to move goods. During the execution process, if a robot experiences data transmission delays due to bandwidth limitations, the real-time communication protocol will reallocate bandwidth through a feedback mechanism and update the collaborative state matrix so that the execution order of tasks can be adjusted according to the current network status. By analyzing the collaborative execution results, the system can promptly identify problems and take adjustment measures to ensure that the collaborative efficiency of the entire system is not affected.

[0062] By updating the collaborative state matrix through a real-time communication protocol, the present invention effectively solves problems such as task execution delay, resource conflict, and unstable node collaboration in the prior art; this technical means can reflect the status of task execution in real time, dynamically adjust the task allocation plan, and improve the adaptability and execution efficiency of the multi-robot system. Especially in complex and changing working environments, this technical solution has demonstrated significant technical advantages.

[0063] Through the coordination between the above steps, the present invention can dynamically adjust task allocation in complex environments, optimize resource utilization, and improve the collaborative efficiency of multi-robot systems. Example 2

[0064] The above describes the control method of multi-machine collaborative operation based on synergy computing and control technology in the embodiment of the present application. The following describes the control system of multi-machine collaborative operation based on synergy computing and control technology in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a control system for multi-machine collaborative operation based on synergistic computing and control technology includes:

[0065] The initial task segmentation unit is used to obtain the task granularity distribution and resource requirement matrix, and classify the task granularity distribution and resource requirement matrix using a classification algorithm to obtain the initial task segmentation feature set; Determine the optimal segmentation unit, which is used to extract subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and use cluster analysis technology to determine the optimal segmentation point set; A preliminary task allocation unit, configured to generate a preliminary task allocation plan based on an optimal segmentation point set and combined with node computing heterogeneity; Dynamic task segmentation unit, used to update the task granularity distribution and resource demand matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; The optimized task allocation unit is used to determine the real-time task status set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; The collaborative execution unit is used to extract task execution instructions from the optimized task allocation plan, distribute them to each node, and update the collaborative state matrix through the real-time communication protocol to obtain the collaborative execution results.

[0066] Through the collaborative cooperation of the above components, the collaborative efficiency of the multi-robot system is further improved.

[0067] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, this application will not further describe various possible combinations. In addition, the various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the concept of the present invention, they should also be regarded as the content disclosed by the present invention.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0070] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for multi-machine collaborative operation based on synergistic computing and control technology, characterized in that: The method comprises: Step S1: Obtain the task size distribution and resource requirement matrix, classify the task size distribution and resource requirement matrix using a classification algorithm, and obtain the initial task segmentation feature set; Step S2: extract subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and use cluster analysis technology to determine the optimal segmentation point set; Step S3: Based on the optimal segmentation point set and combined with the node computing heterogeneity, a preliminary task allocation plan is generated; Step S4: Update the task granularity distribution and resource requirement matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; Step S5: Determine the real-time task state set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; Step S6: extract task execution instructions from the optimized task allocation plan, distribute them to each node, and update the collaborative state matrix through the real-time communication protocol to obtain the collaborative execution result.

2. The method according to claim 1, characterized in that The initial task segmentation feature set obtained in step S1 includes: Obtain task size distribution, resource requirement matrix, and dynamic priority labels; Use the support vector machine algorithm to classify the task granularity distribution, resource requirement matrix and dynamic priority labels to determine the classification feature vector; An initial task segmentation feature set is generated based on the classification feature vector, wherein the initial task segmentation feature set includes subtask dependency weights and task granularity distribution.

3. The method according to claim 1, characterized in that In step S2, cluster analysis technology is used to determine the optimal segmentation point set, including: Extract subtask dependency weights and task granularity distribution from the initial task segmentation feature set; Cluster analysis technology is used to cluster subtask dependency weights to obtain subtask dependency clusters; Determine the task splitting point based on subtask dependency clusters and task granularity distribution; An optimal segmentation point set is generated based on the task segmentation points, where the optimal segmentation point set includes subtask dependency weights and segmentation point positions.

4. The method according to claim 1, wherein In step S3, a preliminary task allocation plan is generated based on the node computing heterogeneity, including: Obtain subtask dependency weights and collaborative state matrix from the optimal split point set; Obtain node computation heterogeneity. If the node computation heterogeneity matches the subtask dependency weight, generate a preliminary task allocation plan based on the subtask dependency weight and the collaborative state matrix. The preliminary task allocation plan includes dynamic priority labels and subtask allocation mapping.

5. The method according to claim 1, wherein The dynamically adjusted task segmentation feature set obtained in step S4 includes: Obtaining sensitivity to environmental changes and cross-layer communication bandwidth; Analyze the sensitivity of environmental changes through real-time perception technology and update the task granularity distribution; Update the resource requirement matrix based on the cross-layer communication bandwidth; Based on the updated task granularity distribution and resource requirement matrix, a dynamically adjusted task segmentation feature set is generated.

6. The method according to claim 1, characterized in that The step S5 determines the real-time task status set through the group information sharing mechanism, including: Extract task granularity distribution and real-time communication delay from the dynamically adjusted task segmentation feature set; Distribute task execution progress, cross-layer synchronization accuracy, and real-time communication delay to each node through a group information sharing mechanism; A real-time task status set is generated based on the task execution progress and the real-time communication delay, wherein the real-time task status set includes the task execution progress and an abnormality feedback vector.

7. The method according to claim 1, characterized in that The step S5 generates an optimized task allocation plan, including: Extract task execution progress and exception feedback vectors from the real-time task status set; If the task execution progress is lower than the preset threshold or the abnormal feedback vector is not empty, the dynamic programming algorithm is used to optimize the subtask dependency weights and bandwidth allocation weights; Based on the optimized subtask dependency weights and bandwidth allocation weights, an optimized task allocation plan is generated.

8. The method according to claim 1, characterized in that In step S6, the collaborative state matrix is ​​updated through the real-time communication protocol to obtain the collaborative execution result, including: Extracting task execution instructions from the optimized task allocation plan; Distribute task execution instructions to each node through real-time communication protocols; Update the collaborative state matrix and network delay fluctuations based on task execution instructions; Based on the updated collaborative state matrix and network delay fluctuations, a collaborative execution result is generated, wherein the collaborative execution result includes the task execution state and the node collaborative state.

9. The method according to claim 5, characterized in that Update task granularity distribution, including: Obtain and construct a task granularity adjustment function based on the relationship between the current cross-layer communication bandwidth and the preset bandwidth threshold; When the current cross-layer communication bandwidth is greater than or equal to the bandwidth threshold, the task granularity adjustment function outputs a fine-grained instruction to adjust the task granularity distribution toward the fine-grained direction; When the current cross-layer communication bandwidth is less than the bandwidth threshold, the task granularity adjustment function outputs a granularity limit instruction and adjusts the granularity distribution to a medium or coarse granularity distribution through a linear proportional function; Based on the output of the task granularity adjustment function, the distribution probabilities of fine granularity, medium granularity and coarse granularity in the task granularity distribution matrix are dynamically updated; wherein, the output of the task granularity adjustment function is also linked to the resource demand matrix.

10. A control system for multi-machine collaborative operation based on synergistic computing and control technology, used to implement the control method for multi-machine collaborative operation based on synergistic computing and control technology as described in any one of claims 1 to 9, characterized in that: The system comprises: The initial task segmentation unit is used to obtain the task granularity distribution and resource requirement matrix, and classify the task granularity distribution and resource requirement matrix using a classification algorithm to obtain the initial task segmentation feature set; Determine the optimal segmentation unit, which is used to extract subtask dependency weights and task granularity distribution based on the initial task segmentation feature set, and use cluster analysis technology to determine the optimal segmentation point set; A preliminary task allocation unit, configured to generate a preliminary task allocation plan based on an optimal segmentation point set and combined with node computing heterogeneity; Dynamic task segmentation unit, used to update the task granularity distribution and resource demand matrix through real-time perception technology to obtain a dynamically adjusted task segmentation feature set; The optimized task allocation unit is used to determine the real-time task status set through the group information sharing mechanism, and use the dynamic programming algorithm to optimize the subtask dependency weights and bandwidth allocation weights to generate an optimized task allocation plan; The collaborative execution unit is used to extract task execution instructions from the optimized task allocation plan, distribute them to each node, and update the collaborative state matrix through the real-time communication protocol to obtain the collaborative execution results.

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