Robot ground station intelligent monitoring system and method

Abstract

The invention discloses a robot ground station intelligent monitoring system and method, and relates to the technical field of robot monitoring. Strategy space of each robot is defined, a target index is generated for each strategy through a target function, and a utility index is generated for each strategy through a utility function; generating an optimal coefficient for each strategy in combination with the target index and the utility index, in the cooperation set, calculating the total cooperation benefit by the robot according to the optimal coefficient, optimizing benefit distribution through a distribution method and then carrying out resource sharing, and in the non-cooperation set, selecting an optimal strategy by the robot according to the optimal coefficient after solving a Nash equilibrium configuration strategy. And task scheduling is carried out based on the selected optimal strategy. The monitoring system adopts a global optimization strategy, the robot shares resources in the cooperation set and independently selects an optimal strategy in the non-cooperation set, and finally optimization of global tasks and resources is achieved.

Description

Technical Field

[0001] The present invention relates to the field of robot monitoring technology, and in particular to a robot ground station intelligent monitoring system and method. Background Art

[0002] The monitoring system is a system that provides all-round monitoring and intelligent analysis for the robot operating platform. It aims to achieve real-time monitoring, status evaluation, fault warning and intelligent maintenance of robot equipment. The system is mainly used in ground control stations, which communicate data with the robot through wireless or wired networks to collect various status information during the operation of the robot.

[0003] The prior art has the following deficiencies:

[0004] 1. Traditional task scheduling and resource management methods are usually only local optimization. When planning paths, different robots may choose conflicting paths, or delays may occur during task execution due to uneven resource allocation. It is impossible to optimize from a global perspective, resulting in low overall system efficiency.

[0005] 2. The existing monitoring system uses static rules or simple priority decision-making mechanisms to allocate tasks, and lacks the ability to adapt to complex environmental changes. As a result, when facing a dynamic environment, the system has poor flexibility and cannot adjust strategies in real time.

[0006] Based on this, the present invention proposes a robot ground station intelligent monitoring system and method, which adopts a global optimization strategy. The robot shares resources in a cooperative set and independently selects the optimal strategy in a non-cooperative set, ultimately achieving global task and resource optimization. Summary of the invention

[0007] The purpose of the present invention is to provide a robot ground station intelligent monitoring system and method to solve the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a robot ground station intelligent monitoring method, the monitoring method comprising the following steps:

[0009] The monitoring system obtains all robot information in the ground station based on the management platform and defines the strategy space of each robot, classifying all robots with interaction into the cooperative set and all robots without interaction into the non-cooperative set;

[0010] After the robot selects the strategy space, it generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates an optimization coefficient for each strategy by combining the target index and the utility index;

[0011] In the cooperative set, the robot calculates the total benefit of cooperation based on the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy based on the optimization coefficient, and schedules tasks based on the selected optimal strategy, and conducts real-time monitoring and control during the operation of all robots.

[0012] In a preferred embodiment, the target index and the utility index are combined to generate a preferred coefficient for each strategy, including the following steps:

[0013] After obtaining the target index and utility index of the strategy, the optimization coefficient of the strategy is calculated. The expression is: , where select is the optimization coefficient, is the target index, is the utility index, , are the adjustment coefficients of the target index and utility index respectively, and , Both are greater than 0.

[0014] In a preferred embodiment, after the robot selects the strategy space, a target index is generated for each strategy through the target function, including the following steps:

[0015] Obtain the historical target data of the strategy, which includes performance factors and resource factors. Normalize the performance factors and resource factors so that their value ranges are mapped to [0,1]. Obtain the normalized value of the performance factor and the normalized value of the resource factor. Subtract the normalized value of the resource factor from the normalized value of the performance factor to obtain the target index.

[0016] In a preferred embodiment, the utility index is generated for each strategy using the utility function, comprising the following steps:

[0017] The historical utility data of the strategy is obtained. The historical utility data includes the completion factor and the conflict factor. The completion factor and the conflict factor are normalized so that their value ranges are mapped to [0, 1]. The normalized value of the completion factor and the normalized value of the conflict factor are obtained. The normalized value of the factor and the normalized value of the conflict factor are summed to obtain the utility index.

[0018] In a preferred embodiment, in a cooperation set, the robots calculate the total cooperation benefit according to the optimization coefficient, and share resources after optimizing the benefit distribution through the distribution method, including the following steps:

[0019] In the cooperation set, the optimization coefficient and operation cost of the strategy are obtained. After normalizing the operation cost, the optimization coefficient is divided by the normalized value of the operation cost to obtain the total cooperation benefit. After calculating the total cooperation benefit, the average distribution method is adopted to evenly distribute the total cooperation benefit to each robot in the cooperation set. The expression is: , where For the robot's distribution income, is the number of robots in the cooperative set, For the total benefit of cooperation;

[0020] The robots share resources and coordinate task execution in cooperation according to the optimized distribution benefits, and adjust the execution strategy according to the priority of the task.

[0021] In a preferred embodiment, in a non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the optimization coefficient, including the following steps:

[0022] The condition of Nash equilibrium is that for any robot i, given the choice of other robots' strategies, robot i cannot improve its utility by changing its own strategy, that is:

[0023] , where Represents a robot The optimal coefficient under the corresponding strategy, Represents a robot The strategy of choice, Represents a robot Alternative strategies, That is, the strategies chosen by other robots;

[0024] Through the greedy algorithm, find the optimal strategy combination of each robot when the strategies of other robots are fixed, and obtain the Nash equilibrium strategy;

[0025] After solving the Nash equilibrium, each robot will select the optimal strategy based on the optimization coefficient of its own strategy. After sorting all strategies from large to small according to the optimization coefficient, the strategy ranked first will be selected as the optimal strategy. After the robot determines the optimal strategy, it will perform the task according to the optimal strategy.

[0026] In a preferred embodiment, the calculation logic of the completion factor is as follows: obtain the actual task completion time of the robot after the strategy is selected, divide the actual task completion time by the scheduled task completion time to obtain the task completion efficiency, obtain the number of tasks completed by the robot after the strategy is selected, divide the number of tasks completed by the monitoring time to obtain the task completion rate, and add the task completion rate to the task completion efficiency to obtain the completion factor;

[0027] The calculation logic of the conflict factor is as follows: after obtaining the number of path conflicts with other robots and the number of shared resource conflicts during the robot's task execution after the strategy is selected, the number of robot path conflicts plus the number of shared resource conflicts are added to obtain the conflict amplitude, and the conflict amplitude is divided by the total number of conflicts to obtain the conflict influence coefficient. After obtaining the actual position and expected position of each monitoring point on the robot's moving path, the navigation path deviation at the monitoring point is calculated. After obtaining the navigation path deviation at all monitoring points, the navigation deviation coefficient is calculated. The expression is: , where NDI is the navigation deviation coefficient, For the The navigation path deviation at each monitoring point is is the number of monitoring points on the moving path, and the conflict factor is obtained by summing the conflict influence coefficient and the navigation deviation coefficient.

[0028] In a preferred embodiment, the calculation expression of the performance factor is: , where is the performance factor, The number of nodes covered by the strategy’s tasks, is the number of all nodes in the ground station;

[0029] The calculation expression of the resource factor is:

[0030] , where is the resource factor, Indicates the cumulative time period, Indicates the robot at time The power consumption rate at the moment, Indicates the robot at time The occupancy rate of shared tools at each moment.

[0031] In a preferred embodiment, the calculation expression of the navigation path deviation is: after obtaining the actual position and expected position of each monitoring point on the moving path of the robot, the navigation path deviation at the monitoring point is calculated, and the expression is: , where For the The navigation path deviation at each monitoring point is , is the actual location, , is the expected position.

[0032] A robot ground station intelligent monitoring system, including a set partitioning module, a coefficient calculation module, and a task allocation and scheduling module;

[0033] Set division module: Based on the management platform, all robot information in the ground station is obtained, and the strategy space of each robot is defined. All robots with interaction are classified into cooperative sets, and all robots without interaction are classified into non-cooperative sets.

[0034] Coefficient calculation module: After the robot selects the strategy space, it generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates an optimization coefficient for each strategy by combining the target index and the utility index;

[0035] Task allocation and scheduling module: In the cooperative set, the robot calculates the total benefit of cooperation based on the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy based on the optimization coefficient, and schedules tasks based on the selected optimal strategy, and conducts real-time monitoring and control during the operation of all robots.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] The present invention defines the strategy space of each robot, generates a target index for each strategy through the target function, generates a utility index for each strategy using the utility function, and generates a preferred coefficient for each strategy by combining the target index and the utility index. In the cooperative set, the robot calculates the total benefit of cooperation according to the preferred coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the preferred coefficient, and performs task scheduling based on the selected optimal strategy. The monitoring system adopts a global optimization strategy. The robot finally achieves the optimization of global tasks and resources by sharing resources in the cooperative set and independently selecting the optimal strategy in the non-cooperative set. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0041] Example 1: Please refer to Figure 1 As shown, the present embodiment provides a robot ground station intelligent monitoring method, the monitoring method comprising the following steps:

[0042] The monitoring system obtains all robot information in the ground station based on the management platform, and defines the strategy space of each robot (the strategy space includes task selection, path planning, energy management, resource allocation, etc.), and classifies all robots with interaction into cooperative sets, and all robots without interaction into non-cooperative sets. After the robot selects the strategy space, it generates a target index for each strategy through the objective function, and generates a utility index for each strategy using the utility function. The target index and the utility index are combined to generate an optimization coefficient for each strategy. In the cooperative set, the robot calculates the total benefit of cooperation according to the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the optimization coefficient, and performs task scheduling based on the selected optimal strategy, and performs real-time monitoring and control during the operation of all robots.

[0043] This application defines the strategy space of each robot, generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates a preferred coefficient for each strategy by combining the target index and the utility index. In the cooperative set, the robot calculates the total benefit of cooperation according to the preferred coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the preferred coefficient, and performs task scheduling based on the selected optimal strategy. The monitoring system adopts a global optimization strategy. The robot finally optimizes global tasks and resources by sharing resources in the cooperative set and independently selecting the optimal strategy in the non-cooperative set.

[0044] Embodiment 2: The monitoring system obtains all robot information in the ground station based on the management platform and defines the strategy space of each robot (the strategy space includes task selection, path planning, energy management, resource allocation, etc.), including the following steps:

[0045] The monitoring system obtains the status information of all robots in the ground stations in real time through the data interface, including the current position, mission status, remaining power, path planning, task allocation, etc. of each robot, providing basic data for subsequent strategic decision-making and ensuring that all robots can share status information on the same platform;

[0046] For each robot, define its optional strategy space, including the following:

[0047] A list of tasks that each robot can choose to perform. Tasks may include transportation, inspection, maintenance, etc., and decisions are made based on factors such as task priority, time requirements, and resource consumption.

[0048] Each robot needs to choose a path to complete a task. The path planning strategy takes into account factors such as the environment map, obstacles, shortest path, collision avoidance, and traffic congestion.

[0049] The robot needs to choose whether to return to the charging station or perform energy management based on the remaining battery status and the location of the charging facilities. This strategy space also includes power consumption optimization, charging strategy, etc.

[0050] Resource allocation: Robots may share resources (such as tools and equipment) between multiple tasks, and these resources need to be allocated reasonably. Resource allocation strategies may include priority management, time management, etc.

[0051] This ensures that each robot can make decisions from multiple dimensions (task, path, power, resources) to meet the requirements of complex tasks.

[0052] All robots with interaction are classified into a cooperative set, and all robots without interaction are classified into a non-cooperative set, including the following steps:

[0053] First, define what kind of interactions will make the robot fall into the cooperative set. Interactions can be of the following types:

[0054] Shared resources: Multiple robots share certain resources, such as charging stations, object handling tools, sensors, etc.

[0055] Task collaboration: Robots need to work together to complete tasks, such as multiple robots moving items, patrolling areas, repairing equipment, etc.

[0056] Information sharing: Robots need to exchange data or information to form a collaborative network, such as collaborative path planning and sharing of sensor data.

[0057] Space sharing: When robots operate in the same or adjacent physical spaces, problems such as path conflicts and collaborative obstacle avoidance may occur.

[0058] By clarifying the interactive relationships, determine which robots need to collaborate and which robots can work independently.

[0059] After the robot selects the strategy space, it generates a target index for each strategy through the objective function, including the following steps:

[0060] Operation content: The management platform needs to collect the status information of each robot in the ground station in real time, including but not limited to:

[0061] Current task: The task the robot is currently performing, such as transportation, inspection, maintenance, etc.

[0062] Resource usage: Resources used by the robot, such as batteries, charging stations, tools, etc.

[0063] Path planning: Whether the robot's current path planning conflicts with other robots.

[0064] Mission Progress: Each robot's progress in the mission.

[0065] Status of other robots: whether other robots are performing tasks in the same area, and whether their paths overlap or resources are shared with this robot.

[0066] By collecting the task and status information of each robot in real time, an interactive relationship network between robots is established to provide data support for subsequent division and decision-making.

[0067] Operation content: Based on the interactive relationship between robots (such as shared resources, collaborative tasks, information exchange, etc.), an interactive matrix is ​​constructed. Each element of the matrix represents the interactive relationship between two robots:

[0068] The matrix element value is 1: It means that the two robots have an interactive relationship and need to collaborate or share resources. The matrix element value is 0: It means that the two robots do not need to interact and can perform tasks independently.

[0069] Example: Assuming there are four robots R1, R2, R3, and R4, the interaction matrix may be as shown in Table 1:

[0070] R1 R2 R3 R4 R1 0 1 0 0 R2 1 0 1 0 R3 0 1 0 1 R4 0 0 1 0

[0071] Table 1

[0072] In Table 1, R1 interacts with R2, R2 interacts with R3, R3 interacts with R4, etc. The interaction matrix intuitively displays the interaction relationship between each robot, providing a basis for the subsequent division of cooperative sets and non-cooperative sets.

[0073] Based on the interaction matrix, connected component analysis is used to divide the interactive robots into cooperative sets, and the non-interactive robots into non-cooperative sets.

[0074] Cooperation set: If two robots form a connected subgraph through direct or indirect interaction (e.g., indirect connection through other robots), then these robots are classified into the same cooperation set.

[0075] Non-cooperative set: Robots that do not form interactive relationships with other robots will be classified into the non-cooperative set.

[0076] For example: In the interaction matrix above, R1 interacts with R2, R2 interacts with R3, and R3 interacts with R4. Through connected component analysis, it is concluded that robots R1, R2, R3, and R4 form a connected subgraph, so these robots belong to the same cooperative set. If there is a robot R5, which has no interaction with R1, R2, R3, and R4, then R5 will be classified as a non-cooperative set.

[0077] Objective: Through connectivity analysis, the robots are reasonably divided into cooperative sets and non-cooperative sets, ensuring that the robots in each set have a certain cooperative relationship, while the robots outside each set can work independently.

[0078] After the robot selects the strategy space, it generates a target index for each strategy through the objective function, including the following steps:

[0079] Obtain the historical target data of the strategy, which includes performance factors and resource factors. Normalize the performance factors and resource factors so that their value ranges are mapped to [0,1]. Obtain the normalized value of the performance factor and the normalized value of the resource factor. Subtract the normalized value of the resource factor from the normalized value of the performance factor to obtain the target index. The larger the target index, the better the overall performance of the strategy.

[0080] The calculation expression of the performance factor is: , where is the performance factor, The number of nodes covered by the strategy’s tasks, is the number of all nodes in the ground station;

[0081] The larger the performance factor, the greater the task coverage of the robot in the ground station when choosing this strategy, that is, the higher the selection priority of this strategy. The reasons can be explained from the following aspects:

[0082] Task coverage refers to the range or number of tasks covered by the robot when performing tasks in the ground station. A strategy with a high performance factor usually means that the strategy can efficiently complete more tasks, cover more areas or perform more subtasks. An increase in the performance factor usually means that the robot performs better when performing tasks and can more comprehensively meet the task requirements. This allows the robot to more fully exert its capabilities when choosing this strategy, thereby improving the task completion rate and reducing idle time or unnecessary repetitive tasks.

[0083] When designing task performance factors, factors such as task completion rate, time efficiency, and accuracy are usually included. A high performance factor indicates that the robot performs well in these key indicators, that is, it can complete more tasks in a shorter time, efficiently, and accurately. For example, a high task completion rate indicates that the strategy can complete more tasks, a high time efficiency indicates that the tasks are completed quickly, and a high accuracy indicates that there are no errors in the task execution process. These all directly affect the coverage of the task and reflect the superiority of the strategy.

[0084] If the performance factor is large, it means that the strategy can cover a wider range of tasks and optimize the efficiency of task execution. In a multi-robot system, tasks are usually assigned based on multiple factors such as robot capabilities, task requirements, and resource allocation. Choosing a strategy with a high performance factor can enable robots to participate in task execution more efficiently, avoid resource waste and task overlap, and improve overall task coverage.

[0085] Strategies with high performance factors usually involve better spatiotemporal optimization and resource management. For example, when performing tasks, the robot can efficiently plan paths and arrange the task sequence reasonably to ensure maximum task coverage and minimize resource consumption. This optimized strategy not only improves task coverage, but also improves the overall efficiency of the system. Therefore, high performance factors not only reflect the quality of the robot's task completion, but also the basis for its selection priority.

[0086] By setting the performance factor as the key decision basis, the ground station can prioritize those strategies that can effectively execute tasks, reduce conflicts, and improve efficiency based on the performance of the robot strategy. Through the task coverage indicator, when multiple robots work together, they can ensure that they can reasonably allocate tasks to avoid resource competition and task vacancies. The higher the performance factor of the strategy, the more effective it is in improving the ground station's task execution capabilities, and therefore has a higher selection priority.

[0087] A strategy with a high performance factor means greater task completion efficiency, quality, and coverage, which enables the robot to complete more tasks in the ground station and use resources more efficiently. Therefore, choosing such a strategy helps improve the overall system performance and task completion. This strategy can better allocate tasks, reduce conflicts, and improve the effective use of resources, so its priority should be higher.

[0088] The calculation expression of resource factor is: , where is the resource factor, Indicates the cumulative time period, Indicates the robot at time The power consumption rate at the moment, Indicates the robot at time The occupancy rate of shared tools at each moment;

[0089] The larger the resource factor, the greater the energy consumption of the robot in the process of selecting this strategy to perform tasks in the cumulative time period, and the longer the shared tool is occupied, which makes the selection priority of this strategy lower. The reasons can be explained from the following aspects:

[0090] Energy is a limited and precious resource when a robot is performing a task. When a strategy has a large resource factor, it means that the robot will consume more energy during the execution of the strategy. This not only increases the cost of task execution, but may also cause the robot to run out of energy, affecting the execution of subsequent tasks. Therefore, choosing a strategy with a large energy consumption means that the robot will face a higher risk of energy consumption when performing a task, which reduces the priority of the strategy. Strategies with large energy consumption usually mean that the robot is less efficient when performing the same task, perhaps because the path planning is not optimized and the execution process is not efficient, resulting in more energy waste. For example, a task takes a long time to complete, or the robot uses unnecessary additional resources (such as high-speed operation, excessive sensor use, etc.), which exacerbates energy consumption.

[0091] Strategies with high resource consumption will affect the sustainability of robot task execution. For example, under a strategy with high energy consumption, the robot may interrupt the task due to battery exhaustion before the task is completed, resulting in task interruption or failure. In this way, the task completion rate is low and the efficiency is low, making the strategy no longer preferred. If a strategy involves a long occupation time of shared tools, it means that the strategy may occupy or compete for the resources of other robots or tasks during the task execution. This not only affects the overall execution efficiency of the task, but may also lead to a bottleneck of system resources, thereby reducing the overall effectiveness of the system. Conflict of shared resources: In a multi-robot system, multiple robots may need to use the same tool or resource (such as sensors, workbenches, actuators, etc.). If a strategy causes the tool to be occupied for a long time, it may prevent other robots from using the tool in time, causing resource conflicts and even delays or failures of the task. Strategies that occupy shared tools for a long time usually mean that task scheduling is not efficient enough and may cause bottlenecks in resource scheduling. Robots that occupy shared resources for a long time may hinder the execution of tasks by other robots, reduce overall collaborative efficiency, and ultimately affect the coverage and completion of tasks.

[0092] When the resource factor is large, it means that the cumulative effect of resource consumption during the task is more significant. If a robot that performs tasks for a long time continues to choose a strategy with high resource consumption, it will lead to the following problems:

[0093] As the task execution time increases, resource-intensive strategies may cause the robot's battery to deplete quickly and be unable to support the completion of long-term or multi-stage tasks. This may cause task execution to be interrupted or the robot cannot return to the charging station on time for replenishment, thus affecting the entire task chain. When a robot relies on more energy and shared tools, its independence and flexibility are limited. For example, if a robot relies too much on a shared tool (such as a specific sensor, equipment, etc.), it may be restricted to certain specific areas or task types during the task and cannot flexibly adjust its task execution strategy.

[0094] Strategies with high resource factors usually reflect a less efficient execution process, and there may be room for optimization. A larger resource factor means that the robot does not fully optimize its resource usage when performing tasks. For example, the task path planning may be unreasonable, resulting in unnecessary movement and energy consumption; the task sequence is not optimal, resulting in unreasonable use of shared tools, etc. Choosing a strategy with a larger resource factor actually means that the sustainability of task execution is poor, and the long-term occupation of resources will gradually reduce system performance. In a multi-robot system, in order to improve the overall task execution efficiency and reduce resource waste, it is necessary to give priority to strategies that can efficiently utilize resources (energy, time, shared tools, etc.). Strategies with smaller resource factors can usually better balance the completion of tasks and the use of resources, allowing robots to continuously and efficiently perform tasks without frequent charging or waiting for the release of resources.

[0095] Therefore, if a strategy has a large resource factor, it means that it will consume more resources (especially energy and shared tools) when executing tasks, which reduces the efficiency of task execution and increases resource conflicts and risks. This means that the priority of the strategy should be lowered.

[0096] Using the utility function to generate a utility index for each strategy includes the following steps:

[0097] Obtain the historical utility data of the strategy, which includes the completion factor and the conflict factor. Normalize the completion factor and the conflict factor so that their value ranges are mapped to [0,1]. Obtain the normalized value of the completion factor and the normalized value of the conflict factor. Sum the normalized values ​​of the factors and the normalized values ​​of the conflict factor to obtain the utility index. The larger the utility index, the higher the overall benefit of the strategy.

[0098] The calculation logic of the completion factor is as follows: obtain the actual task completion time of the robot after the strategy is selected, divide the actual task completion time by the scheduled task completion time to obtain the task completion efficiency, obtain the number of tasks completed by the robot after the strategy is selected, divide the number of tasks completed by the monitoring time to obtain the task completion rate, and add the task completion rate to the task completion efficiency to obtain the completion factor;

[0099] The larger the completion factor, the higher the task completion rate and efficiency after selecting the strategy, that is, the higher the selection priority of the strategy, specifically:

[0100] The larger the completion factor, the higher the task completion rate and efficiency of the robot after selecting the strategy, which makes the strategy's selection priority higher. The reasons can be explained from the following aspects:

[0101] The task completion rate reflects the proportion of tasks that the robot can complete on time and with high quality when performing tasks. The larger the completion factor, the more tasks the robot can complete in a shorter time and with high quality. This usually means that the strategy has strong efficiency and reliability in the task execution process, which makes the strategy have a higher priority in task allocation. A high completion factor means that the robot is more efficient and reliable when performing tasks, and can complete more tasks or complete a higher proportion of tasks within a given time. A larger completion factor means that the strategy has a lower task failure rate. The robot can encounter fewer mid-way failures or task interruptions, improving the overall task completion rate.

[0102] Task completion efficiency includes not only the proportion of task completion, but also the speed of task completion and resource utilization efficiency. Strategies with large completion factors usually mean that the robot can complete the task in a shorter time and consume fewer resources during task execution. Efficient task execution improves the overall effectiveness of the system and saves time and resources. Strategies with high task completion efficiency can ensure that tasks are completed as quickly as possible, thereby improving overall scheduling efficiency and reducing task waiting time. Large task completion factors usually also mean more efficient resource use. For example, optimized path planning, reasonable energy management, fair task allocation, etc., all of which can reduce unnecessary waste of resources.

[0103] In a multi-robot collaborative system, strategies with larger completion factors can effectively improve the overall execution efficiency of tasks and reduce conflicts between tasks. For task scheduling systems, giving priority to strategies with large completion factors helps ensure that the overall system performance is maximized. When robots select tasks, they will give priority to strategies that can complete tasks efficiently and save time and resources. Strategies with high completion factors can usually avoid duplication and conflict of tasks. For example, robots can complete tasks in a shorter time, so that other robots can be assigned new tasks in time, avoiding scheduling conflicts caused by task delays. When robots choose more efficient strategies, the allocation of resources (such as energy, computing power, shared tools, etc.) will also become more reasonable. Resources can be fully utilized without being wasted under some inefficient strategies.

[0104] Strategies with high completion factors can not only show better execution results in the current task, but also accumulate advantages in the long-term task execution process. Robots that choose strategies with high completion factors can accumulate more successful task experience and provide more data support for future task scheduling and optimization. High completion factors also indicate that the strategy is highly reliable. Robots can successfully complete tasks through this strategy, reducing the time for backtracking, adjustment, and repair due to errors or failures. High completion factors enable each task in the task chain to be smoothly connected, and robots can effectively complete a series of tasks without affecting the entire task sequence due to the failure or delay of individual tasks.

[0105] In a multi-robot system, tasks usually have clear goals and time limits. A larger completion factor means that the strategy selected by the robot is more able to help achieve these goals and meet the expectations of the system. The successful completion rate and high efficiency of the task meet the system goals and improve the overall performance of the system. A strategy with a large completion factor means a higher success rate for the task and the results of the task execution are closer to expectations. This is because the strategy can effectively avoid task failures caused by path planning errors, improper task scheduling, or unreasonable resource allocation.

[0106] The larger the completion factor, the higher the completion rate and efficiency of the robot. A high completion factor usually reflects a higher success rate of task completion, faster task execution, and more efficient resource utilization. Due to these advantages, strategies with high completion factors can improve the execution effect of tasks and the overall effectiveness of the system, so they should be given higher priority in task scheduling and resource allocation.

[0107] The calculation logic of the conflict factor is as follows: obtain the number of path conflicts with other robots and the number of shared resource conflicts (including shared tools, shared charging, etc.) during the robot's task execution after the strategy is selected, that is, the number of times the robot needs to share tools with other robots at the same time and the number of times it charges at a shared charging station at the same time), add the number of robot path conflicts to the number of shared resource conflicts to obtain the conflict amplitude, divide the conflict amplitude by the total number of conflicts (the sum of the conflict amplitudes of all strategies) to obtain the conflict influence coefficient, obtain the actual position and expected position of each monitoring point on the robot's moving path, and calculate the navigation path deviation at the monitoring point. The expression is: , where For the The navigation path deviation at each monitoring point is , is the actual location, , For the expected position, after obtaining the navigation path deviation at all monitoring points, the navigation deviation coefficient is calculated, and the expression is: , where NDI is the navigation deviation coefficient, For the The navigation path deviation at each monitoring point is is the number of monitoring points on the moving path, and the conflict factor is obtained by summing the conflict influence coefficient and the navigation deviation coefficient.

[0108] The larger the conflict factor, the more frequent the conflict between the robot and other robots will be during the task execution after the strategy is selected, and the easier it is for the robot to deviate from the expected movement path during the task execution, that is, the lower the selection priority of the strategy. Specifically:

[0109] The larger the Conflict Factor, the more frequently the robot conflicts with other robots during the task execution, and the more likely it is to deviate from the expected movement path during the task execution. Therefore, the selection priority of this strategy is lower. The reasons can be explained from the following aspects:

[0110] A high conflict factor means that the robot frequently conflicts with other robots or the environment when performing tasks. Conflicts can cause interruptions, delays, or resource competition during task execution, thereby reducing the overall efficiency of task execution. The following are the negative effects of conflicts:

[0111] Conflicts may cause tasks to be paused or restarted. For example, when two robots arrive at the same location at the same time, they need to be paused or rescheduled, which prolongs the execution time of the task. Frequent conflicts will increase the uncertainty of task execution time. Robots need to deal with conflicts frequently, which may affect the smooth progress of the entire task and reduce the predictability and reliability of task completion. Therefore, strategies with large conflict factors are usually manifested as frequent task interruptions and rescheduling during execution, which affects the smooth completion of the task, thereby reducing the priority of the strategy.

[0112] In a multi-robot system, conflicts not only affect the execution of tasks, but also may lead to the waste of resources (such as energy, time, shared tools, etc.). For example:

[0113] Multiple robots may need to share the same resource (such as a workbench, sensor, actuator, etc.). When conflicts occur frequently, the efficiency of resource allocation and utilization is reduced, which may cause the robot to wait and reschedule frequently, thereby wasting time and energy. Frequent conflicts may force the robot to take unnecessary avoidance actions or replan the path, which will consume more energy. Especially in the obstacle avoidance process, the robot may need to change the path and increase the additional driving distance, resulting in energy waste. These wastes not only affect the efficiency of the task, but also lead to a decrease in the overall performance of the system. Therefore, a strategy with a large conflict factor requires more resources to make up for the loss caused by the conflict, which reduces the priority of the strategy.

[0114] A large conflict factor also means that the robot is more likely to deviate from the expected navigation path. Path deviation usually increases the complexity and uncertainty of task execution, leading to the following problems:

[0115] When a robot deviates from the path, it usually requires more calculations and adjustments to recalibrate its position, which not only increases the time it takes to execute the task, but can also cause the path planning to no longer be optimal. A robot that deviates from the path may take a longer path, resulting in longer task completion times, which in turn affects the overall task scheduling, especially when multiple tasks are performed alternately. Delays can affect the scheduling of other tasks. Frequent path deviations increase the difficulty of executing tasks and cause the robot to perform tasks at a reduced speed and efficiency, so this strategy should be given a lower priority.

[0116] In a multi-robot system, task scheduling is usually optimized based on factors such as the robot's current position, task requirements, and available resources. Strategies with large conflict factors mean that the robot's scheduling is unstable when performing tasks, which increases the complexity of task scheduling. The following are scheduling problems caused by conflicts:

[0117] Due to frequent conflicts, the system needs to frequently adjust robot task allocation and path planning, which increases the computational complexity of the scheduling system. If conflicts occur frequently, robots may not be able to share resources efficiently, increasing resource competition during the scheduling process and reducing the overall efficiency of the system. Therefore, strategies with large conflict factors usually cause additional burdens on the scheduling system, making the priority of the strategy relatively low.

[0118] Frequent conflicts and path deviations not only affect the execution of the current task, but also have a negative impact on the long-term performance of the system. For example:

[0119] Strategies with frequent conflicts may lead to constant interruption and rescheduling of tasks, and the efficiency of robots in executing tasks is always low, affecting the productivity and effectiveness of the overall system. The instability caused by conflicts makes it impossible for robots to maintain efficient collaboration in the long term, but increases the complexity and uncertainty of the system, affecting the collaborative effect of the system in a multi-task, multi-robot environment. Therefore, strategies with large conflict factors will lead to a decline in the overall performance of the system, reduce the predictability and reliability of task completion, and further weaken the priority of the strategy.

[0120] The larger the conflict factor, the more frequently the robot conflicts with other robots or resources when performing tasks, resulting in unstable task execution, waste of resources, and increased path deviation. Frequently conflicting strategies not only affect the efficiency and reliability of task execution, but also increase the scheduling complexity of the system. Therefore, this strategy should be given a lower priority because it will reduce the overall system efficiency, increase resource consumption, and waste time.

[0121] Combining the target index and the utility index to generate the optimization coefficient for each strategy includes the following steps:

[0122] After obtaining the target index and utility index of the strategy, the optimization coefficient of the strategy is calculated. The expression is: , where select is the optimization coefficient, is the target index, is the utility index, , are the adjustment coefficients of the target index and utility index respectively, and , Both are greater than 0.

[0123] In the cooperation set, the robot calculates the total cooperation benefit according to the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method, including the following steps:

[0124] In the cooperation set, the optimization coefficient and operation cost of the strategy are obtained. After normalizing the operation cost, the optimization coefficient is divided by the normalized value of the operation cost to obtain the total cooperation benefit. After calculating the total cooperation benefit, the average distribution method is adopted to evenly distribute the total cooperation benefit to each robot in the cooperation set. The expression is: , where For the robot's distribution income, is the number of robots in the cooperative set, For the total benefit of cooperation.

[0125] The robots share resources and coordinate task execution in cooperation according to the optimized distribution benefits. The purpose of resource sharing and task scheduling is to ensure efficient completion of tasks, avoid resource conflicts, and adjust the execution strategy according to the priority of tasks, including the following steps:

[0126] Resource sharing: Based on the allocated benefits and optimization coefficients, robots reasonably share limited resources such as batteries, computing resources, sensors, etc. during task execution.

[0127] Task scheduling: The robot performs task scheduling based on the optimized strategy optimization coefficient and benefits. Task scheduling needs to consider the synergy between robots to ensure that resource conflicts and task conflicts are avoided during task execution.

[0128] Coordination and cooperation: When performing tasks, robots share resources and coordinate task progress to ensure smooth completion of tasks. At the same time, robots adjust strategies according to real-time task requirements to optimize the efficiency of task execution.

[0129] In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the optimization coefficient and performs task scheduling based on the selected optimal strategy, including the following steps:

[0130] In a non-cooperative set, the interaction between robots is based on independent strategy selection, so it is necessary to determine the optimal strategy of each robot by solving the Nash equilibrium. Nash equilibrium means that in a task, among the strategy combinations selected by all participants, no participant can obtain higher benefits by unilaterally changing his or her strategy. The steps to solve Nash equilibrium are:

[0131] Each robot has a set of optional strategies, and the strategy space includes task selection, path planning, energy management, etc. The combination of all strategies forms the strategy space of the game.

[0132] All robots will choose a strategy that maximizes their own utility while considering the strategies of other robots. Solving the Nash equilibrium means finding a strategy combination under which each robot cannot obtain greater utility by changing its strategy alone. Specifically, the condition of Nash equilibrium is: for any robot i, given the choice of other robots' strategies, robot i cannot improve its own utility by changing its own strategy, that is:

[0133] , where For all robots It is a universal quantification symbol used to indicate that the expression that follows applies to all robots. The object or element represented, Represents a robot The optimal coefficient under the corresponding strategy, Represents a robot The strategy of choice, Represents a robot Alternative strategies, That is, the strategy chosen by other robots.

[0134] It means that in Nash equilibrium, for each participant (robot or player), when the strategies of other participants are fixed, choosing the current strategy will not be worse than choosing any other strategy. In other words, in equilibrium, no participant has the motivation to unilaterally change their strategy, because if they change their strategy, their utility will not increase, and may even decrease. Therefore, equilibrium is stable, and the strategy choices of all individuals are optimal in equilibrium.

[0135] Then, through the greedy algorithm, the optimal strategy combination of each robot is found when the strategies of other robots are fixed, and the Nash equilibrium strategy is obtained;

[0136] The steps of the greedy algorithm are as follows: In each round, the robot selects the current local optimal strategy without considering the future decisions or strategy adjustments of other robots. For example, the robot will select the optimal strategy based on the current environment and the strategies of other robots, even if this choice may cause the subsequent choice to no longer be optimal. Repeat the greedy algorithm until the strategy selected by each robot no longer changes, or the change is very small. At this point, the algorithm has reached convergence and finally obtains a strategy combination, namely the Nash equilibrium strategy;

[0137] After solving the Nash equilibrium, each robot will select the optimal strategy based on the optimization coefficient of its own strategy. After sorting all strategies from large to small according to the optimization coefficient, the strategy ranked first will be selected as the optimal strategy. After the robot determines the optimal strategy, it will perform the task according to the optimal strategy.

[0138] After selecting the optimal strategy, the robots will be scheduled according to the requirements of the task. Task scheduling is the process of ensuring that the robots execute the tasks according to the optimal strategy. The scheduling process needs to consider the priority of the task, the availability of resources, and the coordination between robots.

[0139] Real-time monitoring and control of all robot operations, including the following steps:

[0140] Based on real-time data, monitor various status parameters of the robot, such as:

[0141] Task status (executing, completed, failed);

[0142] Energy status (power remaining, energy consumption);

[0143] Motion state (speed, path deviation, acceleration);

[0144] Environmental status (obstacle detection, environmental changes);

[0145] Goal: Gain real-time visibility into the robot’s health, task progress, and environment interactions to make timely decisions.

[0146] Track the progress of tasks, evaluate whether the current tasks are proceeding as planned, and adjust task parameters or plans based on real-time data. Ensure that tasks are completed on time and promptly detect problems such as task delays and execution failures. Monitor the robot status in real time and use data analysis techniques (such as machine learning and rule detection) to detect anomalies. Once a failure or anomaly occurs (such as low battery, task interruption, collision, etc.), immediately issue an alarm and initiate emergency handling procedures. Minimize robot failure time and ensure the stability of the robot in task execution.

[0147] Generate and adjust control instructions based on real-time monitoring data and task progress. Including: controlling the motion path to adjust energy management (such as charging or replacing batteries), adjusting task priorities or reallocating tasks, optimizing the robot's operating strategy, and ensuring that the robot is as efficient and stable as possible when performing tasks. If multiple robots perform tasks together, conduct real-time communication and collaboration between robots. Including sharing task information, coordinating motion paths, adjusting task priorities, etc. Ensure smooth multi-robot collaboration, avoid conflicts and resource conflicts, and achieve collaborative task completion.

[0148] Monitor the energy consumption of each robot, calculate the remaining power, and dynamically adjust the robot's energy management strategy according to task requirements. If the power is insufficient, it can automatically request charging, battery replacement, and other operations. Ensure that the robot has enough energy during the task and improve energy utilization efficiency. Adjust the robot's path planning and motion strategy based on real-time environmental information. For example, avoid new obstacles, dynamically plan paths according to environmental changes, etc. Ensure that the robot can adapt to dynamic environmental changes and effectively avoid obstacles. Record and store robot operation data in real time, including task execution, energy consumption, fault conditions, etc. Generate real-time reports and display them to operators through the management platform. Provide real-time monitoring data to help operators make decisions and provide basic data for subsequent optimization and analysis.

[0149] According to the real-time data and feedback information of the robot operation, the robot's behavior is optimized through algorithms. For example, by analyzing historical data to optimize strategies, adjust task allocation, path planning, etc. Through continuous feedback and optimization, the robot's autonomy and execution efficiency are improved. If a serious fault, emergency or safety hazard is detected, the alarm mechanism is triggered and the emergency response process is initiated, such as safe shutdown, remote control, etc., to ensure the safety of the robot and the surrounding environment.

[0150] The real-time monitoring and control system can comprehensively monitor, dynamically adjust and optimize the status of the robot during operation, ensuring that the robot can perform tasks efficiently and safely, and taking timely measures to deal with failures or abnormalities. The implementation of these steps relies on the combination of multiple technologies, including data acquisition, sensor networks, control algorithms, artificial intelligence, etc.

[0151] Embodiment 3: A robot ground station intelligent monitoring system described in this embodiment includes a set partitioning module, a coefficient calculation module, and a task allocation and scheduling module;

[0152] Set partitioning module: Based on the management platform, all robot information in the ground station is obtained, and the strategy space of each robot is defined (the strategy space includes task selection, path planning, energy management, resource allocation, etc.). All robots with interaction are classified into cooperative sets, and all robots without interaction are classified into non-cooperative sets. The set partitioning results are sent to the coefficient calculation module and the task allocation and scheduling module.

[0153] Coefficient calculation module: After the robot selects the strategy space, it generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates an optimal coefficient for each strategy by combining the target index and the utility index. The optimal coefficient is sent to the task allocation and scheduling module;

[0154] Task allocation and scheduling module: In the cooperative set, the robot calculates the total benefit of cooperation based on the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy based on the optimization coefficient, and schedules tasks based on the selected optimal strategy, and conducts real-time monitoring and control during the operation of all robots.

[0155] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0156] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0157] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0158] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A robot ground station intelligent monitoring method, characterized in that: The monitoring method comprises the following steps: The monitoring system obtains all robot information in the ground station based on the management platform and defines the strategy space of each robot, classifying all robots with interaction into the cooperative set and all robots without interaction into the non-cooperative set; After the robot selects the strategy space, it generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates an optimization coefficient for each strategy by combining the target index and the utility index; In the cooperative set, the robot calculates the total benefit of cooperation based on the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy based on the optimization coefficient, and schedules tasks based on the selected optimal strategy, and conducts real-time monitoring and control during the operation of all robots.

2. A robot ground station intelligent monitoring method according to claim 1, characterized in that: Combining the target index and the utility index to generate the optimization coefficient for each strategy includes the following steps: After obtaining the target index and utility index of the strategy, the optimization coefficient of the strategy is calculated. The expression is: , where select is the optimization coefficient, is the target index, is the utility index, , are the adjustment coefficients of the target index and utility index respectively, and , Both are greater than 0.

3. A robot ground station intelligent monitoring method according to claim 2, characterized in that: After the robot selects the strategy space, it generates a target index for each strategy through the objective function, including the following steps: Obtain the historical target data of the strategy, which includes performance factors and resource factors. Normalize the performance factors and resource factors so that their value ranges are mapped to [0,1]. Obtain the normalized value of the performance factor and the normalized value of the resource factor. Subtract the normalized value of the resource factor from the normalized value of the performance factor to obtain the target index.

4. A robot ground station intelligent monitoring method according to claim 3, characterized in that: Using the utility function to generate a utility index for each strategy includes the following steps: The historical utility data of the strategy is obtained. The historical utility data includes the completion factor and the conflict factor. The completion factor and the conflict factor are normalized so that their value ranges are mapped to [0, 1]. The normalized value of the completion factor and the normalized value of the conflict factor are obtained. The normalized value of the factor and the normalized value of the conflict factor are summed to obtain the utility index.

5. The method for intelligent monitoring of a robot ground station according to claim 2, characterized in that: In the cooperation set, the robot calculates the total cooperation benefit according to the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method, including the following steps: In the cooperation set, the optimization coefficient and operation cost of the strategy are obtained. After normalizing the operation cost, the optimization coefficient is divided by the normalized value of the operation cost to obtain the total cooperation benefit. After calculating the total cooperation benefit, the average distribution method is adopted to evenly distribute the total cooperation benefit to each robot in the cooperation set. The expression is: , where For the robot's distribution income, is the number of robots in the cooperative set, For the total benefit of cooperation; The robots share resources and coordinate task execution in cooperation according to the optimized distribution benefits, and adjust the execution strategy according to the priority of the task.

6. A robot ground station intelligent monitoring method according to claim 2, characterized in that: In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy according to the optimization coefficient, including the following steps: The condition of Nash equilibrium is that for any robot i, given the choice of other robots' strategies, robot i cannot improve its utility by changing its own strategy, that is: , where Represents a robot The optimal coefficient under the corresponding strategy, Represents a robot The strategy of choice, Represents a robot Alternative strategies, That is, the strategies chosen by other robots; Through the greedy algorithm, find the optimal strategy combination for each robot when the strategies of other robots are fixed, and obtain the Nash equilibrium strategy; After solving the Nash equilibrium, each robot will select the optimal strategy based on the optimization coefficient of its own strategy. After sorting all strategies from large to small according to the optimization coefficient, the strategy ranked first will be selected as the optimal strategy. After the robot determines the optimal strategy, it will perform the task according to the optimal strategy.

7. A robot ground station intelligent monitoring method according to claim 4, characterized in that: The calculation logic of the completion factor is as follows: obtain the actual task completion time of the robot after the strategy is selected, divide the actual task completion time by the scheduled task completion time to obtain the task completion efficiency, obtain the number of tasks completed by the robot after the strategy is selected, divide the number of tasks completed by the monitoring time to obtain the task completion rate, and add the task completion rate to the task completion efficiency to obtain the completion factor; The calculation logic of the conflict factor is as follows: after obtaining the number of path conflicts with other robots and the number of shared resource conflicts during the robot's task execution after the strategy is selected, the number of robot path conflicts plus the number of shared resource conflicts are added to obtain the conflict amplitude, and the conflict amplitude is divided by the total number of conflicts to obtain the conflict influence coefficient. After obtaining the actual position and expected position of each monitoring point on the robot's moving path, the navigation path deviation at the monitoring point is calculated. After obtaining the navigation path deviation at all monitoring points, the navigation deviation coefficient is calculated. The expression is: , where NDI is the navigation deviation coefficient, For the The navigation path deviation at each monitoring point is is the number of monitoring points on the moving path, and the conflict factor is obtained by summing the conflict influence coefficient and the navigation deviation coefficient.

8. The method for intelligent monitoring of a robot ground station according to claim 3, characterized in that: The calculation expression of the performance factor is: , where is the performance factor, The number of nodes covered by the strategy’s tasks, is the number of all nodes in the ground station; The calculation expression of the resource factor is: , where is the resource factor, Indicates the cumulative time period, Indicates the robot at time The power consumption rate at the moment, Indicates the robot at time The occupancy rate of shared tools at each moment.

9. A robot ground station intelligent monitoring method according to claim 7, characterized in that: The calculation expression of the navigation path deviation is: after obtaining the actual position and expected position of each monitoring point on the moving path of the robot, the navigation path deviation at the monitoring point is calculated, and the expression is: , where For the The navigation path deviation at each monitoring point is , is the actual location, , is the expected position.

10. A robot ground station intelligent monitoring system, used to implement the monitoring method according to any one of claims 1 to 9, characterized in that: It includes set partitioning module, coefficient calculation module, task allocation and scheduling module; Set division module: Based on the management platform, all robot information in the ground station is obtained, and the strategy space of each robot is defined. All robots with interaction are classified into cooperative sets, and all robots without interaction are classified into non-cooperative sets. Coefficient calculation module: After the robot selects the strategy space, it generates a target index for each strategy through the objective function, generates a utility index for each strategy using the utility function, and generates an optimization coefficient for each strategy by combining the target index and the utility index; Task allocation and scheduling module: In the cooperative set, the robot calculates the total benefit of cooperation based on the optimization coefficient, and shares resources after optimizing the benefit distribution through the allocation method. In the non-cooperative set, after solving the Nash equilibrium configuration strategy, the robot selects the optimal strategy based on the optimization coefficient, and schedules tasks based on the selected optimal strategy, and conducts real-time monitoring and control during the operation of all robots.

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