Robot formation and reuse method and system for aviation equipment cluster operation and maintenance scene

Abstract

The invention provides a robot formation and reuse method for an aviation equipment cluster operation and maintenance scene, and belongs to the technical field of industrial automation and intelligent robots, and the method comprises the steps: carrying out the evaluation of the health state of an equipment cluster, and analyzing the operation and maintenance demands of the equipment cluster operation and maintenance scene; evaluating an operation and maintenance resource state in the scene; generating a robot formation scheme in the current state; based on the robot formation scheme in each state, planning a path for distributing the robot to the vicinity of the operation and maintenance object; and after the operation and maintenance resources are distributed once, waiting for the end of the operation and maintenance process, and going to the next destination or returning to the robot warehouse according to the resource state and the task demand. According to the method, the operation and maintenance requirements of the equipment cluster full operation and maintenance scene are analyzed, the state of the operation and maintenance resources in the scene is evaluated, the robot formation scheme is generated based on the operation and maintenance requirements and the operation and maintenance resources, and the resource distribution path and the reuse method are planned, so that robot formation and reuse of the aviation equipment cluster operation and maintenance scene can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial automation and intelligent robots, and particularly relates to a method and system for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster. Background Art

[0002] In modern industrial production, as the core component of a production line, the stable operation of an equipment cluster is crucial for ensuring production efficiency and product quality. However, with the increase in the number of equipment and the extension of operation time, equipment fault and anomaly detection have become a heavy task. The traditional manual operation and maintenance method is not only inefficient but also difficult to ensure the timeliness and accuracy of operation and maintenance. Therefore, using intelligent robots for the operation and maintenance of an equipment cluster has become a trend.

[0003] However, most of the existing robot operation and maintenance methods are limited to the fault detection and repair of a single piece of equipment, lacking comprehensive consideration of the entire equipment cluster operation and maintenance scenario. In addition, the allocation and scheduling of robot resources often lack flexibility, resulting in low resource utilization rate and increased operation and maintenance costs. Therefore, an intelligent method that can comprehensively consider the operation and maintenance requirements of an equipment cluster, the status of robot resources, and the characteristics of the operation and maintenance scenario to achieve robot formation and reuse is needed. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method and system for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster, which can evaluate the health status of the equipment cluster, analyze the operation and maintenance requirements, evaluate the status of operation and maintenance resources, and based on this information, perform optimization and solution to generate a robot formation plan and path planning, thereby realizing the efficient utilization of robot resources and the automation of the operation and maintenance process.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster, comprising the following steps:

[0007] Step 1: By real-time monitoring of the operation data of the equipment cluster, using data analysis algorithms to evaluate the health status of the equipment, and analyzing the operation and maintenance requirements according to the health status evaluation results;

[0008] Step 2: Real-time obtain the number, task execution ability, battery life status, position information, etc. of the robots used for functions such as detection, repair, and transportation in the scenario, and classify the task execution ability of the robots to facilitate reasonable allocation during subsequent formation and path planning;

[0009] Step 3: Based on the parsed operation and maintenance requirements and the evaluated operation and maintenance resource status, construct a multi-objective optimization model that includes operation and maintenance requirements, robot resources, task execution capabilities, and battery status parameters, and use a combinatorial optimization algorithm to solve it to generate an optimal robot formation plan that meets the current operation and maintenance requirements and has the highest resource utilization rate;

[0010] Step 4: After completing an operation and maintenance task, according to the resource status of the robots and the new task requirements, use heuristic algorithms, dynamic programming algorithms, etc. to reuse and schedule the robots. For robots with insufficient battery life, arrange them to return to the warehouse for charging or maintenance; for idle robots, intelligently allocate them to the next operation and maintenance destination or perform other tasks to achieve efficient utilization of robot resources and automation of the operation and maintenance process;

[0011] Step 5: Collect the real-time data during the execution of the operation and maintenance tasks by the robots, and use machine learning algorithms to analyze the data, continuously optimize the algorithm models for operation and maintenance requirement parsing, resource evaluation, formation plan generation, path planning, and reuse and scheduling, and improve the intelligence level and operation and maintenance efficiency of the system.

[0012] The present invention also provides a robot formation and reuse system for an aircraft equipment cluster operation and maintenance scenario, including the following modules:

[0013] Equipment cluster health status evaluation and operation and maintenance requirement parsing module, which is used to evaluate the health status of the equipment by using data analysis algorithms through real-time monitoring of the operation data of the equipment cluster, and parse the operation and maintenance requirements according to the health status evaluation results;

[0014] Operation and maintenance resource status evaluation module, which is used to obtain in real time the number, task execution capabilities, battery status, location information, etc. of the robots used for functions such as detection, repair, and transportation in the scenario, and classify the task execution capabilities of the robots to facilitate reasonable allocation during subsequent formation and path planning;

[0015] Robot formation plan generation module, based on the parsed operation and maintenance requirements and the evaluated operation and maintenance resource status, constructs a multi-objective optimization model that includes operation and maintenance requirements, robot resources, task execution capabilities, and battery status parameters, and uses a combinatorial optimization algorithm to solve it to generate an optimal robot formation plan that meets the current operation and maintenance requirements and has the highest resource utilization rate;

[0016] Robot reuse and scheduling module, after completing an operation and maintenance task, according to the resource status of the robots and the new task requirements, uses heuristic algorithms, dynamic programming algorithms, etc. to reuse and schedule the robots. For robots with insufficient battery life, arrange them to return to the warehouse for charging or maintenance; for idle robots, intelligently allocate them to the next operation and maintenance destination or perform other tasks to achieve efficient utilization of robot resources and automation of the operation and maintenance process;

[0017] The feedback and optimization module collects real-time data during the operation and maintenance tasks executed by the robot, analyzes the data using machine learning algorithms, and continuously optimizes the algorithm models for operation and maintenance requirement analysis, resource evaluation, formation plan generation, path planning, and reuse scheduling, improving the intelligence level and operation and maintenance efficiency of the system.

[0018] Beneficial effects:

[0019] By analyzing the operation and maintenance requirements of the full operation and maintenance scenarios of the equipment cluster, evaluating the status of operation and maintenance resources within the scenarios, generating a robot formation plan based on the operation and maintenance requirements and resources, and planning the resource delivery path and reuse method, the present invention can achieve the formation and reuse of robots in the operation and maintenance scenarios of the aviation equipment cluster. By comprehensively considering the operation and maintenance requirements of the equipment cluster and the status of robot resources, the present invention realizes the intelligence and automation of robot formation and reuse, improves the operation and maintenance efficiency and resource utilization rate, and reduces the operation and maintenance cost. Brief description of the drawings

[0020] Figure 1 is a flowchart of a method for robot formation and reuse in the operation and maintenance scenarios of an aviation equipment cluster according to the present invention.

[0021] Figure 2 is a module diagram of a robot formation and reuse system in the operation and maintenance scenarios of an aviation equipment cluster provided by an embodiment of the present invention.

[0022] Among them, the reference numerals are: equipment cluster health status evaluation and operation and maintenance requirement analysis module 201, operation and maintenance resource status evaluation module 202, robot formation plan generation module 203, robot reuse and scheduling module 204, feedback and optimization module 205. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0024] As Figure 1 shown, a method for robot formation and reuse in the operation and maintenance scenarios of an aviation equipment cluster according to an embodiment of the present invention includes the following steps:

[0025] Step 1: Conduct the health status assessment of the equipment cluster and analyze the operation and maintenance requirements. This is used to evaluate the health status of the equipment by real-time monitoring of the operation data of the equipment cluster, and analyze the operation and maintenance requirements based on the health status assessment results. The operation and maintenance requirements include, but are not limited to, equipment anomaly detection, fault repair, preventive maintenance, etc.;

[0026] Step 2: Evaluate the status of operation and maintenance resources. This is used to obtain in real time the number, task execution ability, battery life status, location information, etc. of the robots for functions such as detection, repair, and transportation in the scenario, and classify the task execution ability of the robots, so as to make reasonable allocation during subsequent formation and path planning;

[0027] Step 3: Based on the analyzed operation and maintenance requirements and the evaluated status of operation and maintenance resources, construct a multi-objective optimization model including parameters such as operation and maintenance requirements, robot resources, task execution ability, battery life status, etc., and use one or more combined optimization algorithms such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm, etc. for solution to generate the optimal robot formation plan that meets the current operation and maintenance requirements and has the highest resource utilization rate;

[0028] Step 4: Reuse and schedule the robots. After completing an operation and maintenance task, according to the resource status of the robots (including remaining battery power, task completion degree, maintenance status, etc.) and new task requirements, use heuristic algorithms, dynamic programming algorithms, etc. to reuse and schedule the robots. For robots with insufficient battery life, arrange them to return to the warehouse for charging or maintenance; for idle robots, intelligently allocate them to the next operation and maintenance destination or perform other tasks according to factors such as task priority, distance, and robot ability, so as to achieve the efficient utilization of robot resources and the automation of the operation and maintenance process;

[0029] Step 5: Conduct feedback and optimization. Collect the real-time data during the robots' execution of operation and maintenance tasks, including task completion degree, resource consumption, path efficiency, etc., and use machine learning algorithms to analyze the data, and continuously optimize the algorithm models for operation and maintenance requirement analysis, resource evaluation, formation plan generation, path planning, and reuse and scheduling, so as to improve the intelligence level and operation and maintenance efficiency of the system.

[0030] As Figure 2 shown, the embodiment of the present invention also provides a robot formation and reuse system for the operation and maintenance scenario of an aviation equipment cluster, including an equipment cluster health status assessment and operation and maintenance requirement analysis module 201, an operation and maintenance resource status assessment module 202, a robot formation plan generation module 203, a robot reuse and scheduling module 204, a feedback and optimization module 205, and a user interaction interface. Information interaction is carried out between each module through a data interface to form a closed-loop operation and maintenance management system.

[0031] The equipment cluster health status evaluation and operation and maintenance requirement analysis module 201 monitors the operation data of the equipment cluster in real time, including sensor signals such as vibration, temperature, and pressure, as well as the operation status and fault records of the equipment. Using data analysis algorithms, such as time series analysis, clustering analysis, association rule mining, etc., to evaluate the equipment health status, identify fault patterns, and predict future fault trends. Combining the equipment operation environment and working conditions, to classify and prioritize the operation and maintenance requirements in a refined manner, ensuring that critical equipment and important tasks are given priority. When the equipment health status is lower than the preset threshold, it automatically triggers the operation and maintenance requirement analysis process, generates an operation and maintenance task list, and pushes it to the operation and maintenance resource status evaluation module.

[0032] The operation and maintenance resource status evaluation module 202 obtains information such as the number, type, location, battery status, and task execution ability of the robots in the scene in real time. Based on the historical fault data and current operation status of the robots, predict the possible fault types and times of the robots, and arrange maintenance tasks in advance. Introduce a robot ability evaluation model, comprehensively consider performance indicators such as the speed, accuracy, and load capacity of the robots, as well as the impact of environmental factors on the performance of the robots, to provide a resource evaluation basis for robot formation and path planning. According to the changes in operation and maintenance requirements and the adjustment of resource status, dynamically update the robot status information to ensure the accuracy and timeliness of the resource evaluation results.

[0033] The robot formation plan generation module 203 constructs a multi-objective optimization model based on the operation and maintenance task list generated by the operation and maintenance requirement analysis module and the robot resource information provided by the resource evaluation module. Use combined optimization algorithms such as genetic algorithms, particle swarm algorithms, simulated annealing algorithms, etc., to solve the optimal robot formation plan. According to the generated robot formation plan, select a suitable path planning algorithm or algorithm combination to achieve the optimization and intelligence of path planning. Consider the evaluation and optimization of the robot team collaboration ability, and use methods such as multi-agent system theory and game theory to study the collaboration mechanism, conflict resolution strategy, and task allocation algorithm among robots. According to the changes in operation and maintenance requirements and the adjustment of resource status, dynamically adjust the scale and structure of the robot formation to ensure the effectiveness and adaptability of the formation plan. Introduce a risk management and emergency response mechanism to predict and evaluate possible operation and maintenance risks, and formulate corresponding emergency response plans. According to the generated robot formation plan, use algorithms, Dijkstra algorithm, Floyd-Warshall algorithm, RRT algorithm and other path planning algorithms to plan the optimal path from the current position of the robot to near the operation and maintenance object. Considering the dynamic changes in the scene, such as personnel flow, equipment movement, etc., the path is adjusted and optimized in real time.

[0034] After completing an operation and maintenance task, the robot reuse and scheduling module 204 reuses and schedules the robot according to the resource status of the robot (including remaining battery power, task completion degree, maintenance status, etc.) and new task requirements, using heuristic algorithms, dynamic programming algorithms, etc. Considering the energy consumption and cost factors of the robot, during the reuse and scheduling process, optimize the task allocation and path planning of the robot to reduce the operation and maintenance cost and improve the economic benefit. According to the changes in operation and maintenance requirements and the adjustment of resource status, dynamically adjust the quantity and type of robots to ensure the full utilization of robot resources and efficient operation and maintenance.

[0035] The feedback and optimization module 205 collects real-time data during the execution of the operation and maintenance task by the robot, including task completion degree, resource consumption, path efficiency, etc. Analyze the collected data using machine learning algorithms, and continuously optimize the algorithm models for operation and maintenance requirement analysis, resource evaluation, formation plan generation, path planning, and reuse and scheduling. Establish an evaluation system for the execution effect of the operation and maintenance task to quantitatively evaluate the execution effect of the robot operation and maintenance task. Continuously optimize and improve the system according to the evaluation results and the changes in operation and maintenance requirements.

[0036] The user interface designs a user-friendly interface and operation process, facilitating the operation and maintenance personnel to quickly get started and use this method efficiently. Provide real-time monitoring and visual display of information such as the health status of the equipment cluster, the progress of operation and maintenance tasks, and the resource status of the robot. Support the operation and maintenance personnel to perform operations such as task scheduling, robot configuration, and path planning through the interface. Provide the function of querying historical data and generating reports, facilitating the operation and maintenance personnel to conduct analysis and decision-making.

[0037] In summary, the present invention provides a method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster, which is comprehensive, intelligent, and flexible. Through steps such as real-time monitoring of the equipment health status, evaluating the operation and maintenance resource status, generating the optimal robot formation plan, planning the optimal path, reusing and scheduling the robot resources, and feedback and optimizing the system performance, the efficient, automated, and intelligent management of the operation and maintenance tasks of the equipment cluster is realized. This method not only improves the operation and maintenance efficiency and resource utilization rate, reduces the operation and maintenance cost, but also provides a strong guarantee for the long-term stable operation of the equipment cluster.

Claims

1. A method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster, characterized in that It includes the following steps: Step 1: By real-time monitoring the operation data of the equipment cluster, using data analysis algorithms to evaluate the health status of the equipment, and parsing out the operation and maintenance requirements according to the health status evaluation results; Step 2: Real-time obtain the number, task execution ability, battery life status, location information, etc. of the robots for functions such as detection, repair, and transportation in the scene, and classify the task execution ability of the robots to facilitate reasonable allocation during subsequent formation and path planning; Step 3: Based on the parsed operation and maintenance requirements and the evaluated operation and maintenance resource status, construct a multi-objective optimization model including operation and maintenance requirements, robot resources, task execution ability, and battery life status parameters, and use combinatorial optimization algorithms for solution to generate the optimal robot formation plan that meets the current operation and maintenance requirements and has the highest resource utilization rate; Step 4: After completing an operation and maintenance task, according to the resource status of the robots and new task requirements, use heuristic algorithms, dynamic programming algorithms, etc. to reuse and schedule the robots. For robots with insufficient battery life, arrange them to return to the warehouse for charging or maintenance; for idle robots, intelligently allocate them to the next operation and maintenance destination or perform other tasks to achieve efficient utilization of robot resources and automation of the operation and maintenance process; Step 5: Collect the real-time data of the robots during the execution of the operation and maintenance tasks, and use machine learning algorithms to analyze the data, continuously optimize the algorithm models for operation and maintenance requirement parsing, resource evaluation, formation plan generation, path planning, and reuse and scheduling, and improve the intelligence level and operation and maintenance efficiency of the system.

2. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said Step 1 further includes: Deep mining and analysis of the equipment historical operation and maintenance data, using techniques such as time series analysis, clustering analysis, and association rule mining to identify equipment failure modes and predict future failure trends, providing forward-looking decision-making support for robot formation and reuse.

3. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein, The said Step 1 further includes: Combined with the equipment operation environment and working conditions, conduct refined classification and priority ranking of the operation and maintenance requirements to ensure that key equipment and important tasks are given priority.

4. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said Step 1 further includes: Realize real-time monitoring and early warning of the equipment health status. When the equipment health status is lower than the preset threshold, automatically trigger the operation and maintenance requirement parsing process to ensure the timeliness and accuracy of the operation and maintenance response; Formulate robot failure early warning and maintenance plans. Based on the robot historical failure data and the current operation status, predict the possible failure types and times of the robots, and arrange maintenance tasks in advance to ensure the continuous availability and efficient operation and maintenance of the robots.

5. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said Step 2 includes: Introduce a robot ability evaluation model, comprehensively consider the performance indicators such as the speed, accuracy, and load capacity of the robots, as well as the impact of environmental factors on the robot performance, to provide a more accurate resource evaluation basis for robot formation and path planning; realize dynamic monitoring and management of robot resources, and real-time update the robot status information to ensure the accuracy and timeliness of the resource evaluation results.

6. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said Step 3 includes: For the evaluation and optimization of the collaborative ability of a robot team, the multi-agent system theory or game theory is adopted to study the collaboration mechanism, conflict resolution strategy and task allocation algorithm among robots, so as to realize the ability of multiple robots to cooperate to complete complex operation and maintenance tasks; considering the flexibility and scalability of the robot formation, according to the changes in operation and maintenance requirements and the adjustment of resource status, dynamically adjust the scale and structure of the robot formation to ensure the effectiveness and adaptability of the formation plan; introduce a risk management and emergency response mechanism to predict and evaluate possible operation and maintenance risks, and formulate corresponding emergency response plans to ensure the safety and reliability of operation and maintenance tasks.

7. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said step 4 includes: Enhance and optimize the obstacle avoidance ability of the robot. Adopt deep learning or reinforcement learning to train the robot to recognize and understand obstacle information in complex scenarios, and improve the robot's autonomous obstacle avoidance ability and path planning efficiency; consider the dynamic changes of the scenario, and adjust and optimize the path in real time to ensure the safe and efficient operation of the robot in complex scenarios; introduce the combination and fusion technology of path planning algorithms, and select appropriate path planning algorithms or algorithm combinations according to the scenario characteristics and operation and maintenance requirements to achieve the optimization and intelligence of path planning.

8. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, characterized in that, The said step 5 includes: Dynamically adjust and optimize the allocation of robot resources. According to the changes in operation and maintenance requirements and the adjustment of resource status, dynamically adjust the quantity and type of robots to ensure the full utilization and efficient operation and maintenance of robot resources; consider the energy consumption and cost factors of the robot, and optimize the task allocation and path planning of the robot during the reuse and scheduling process to reduce the operation and maintenance cost and improve the economic benefit.

9. The method for robot formation and reuse in the operation and maintenance scenario of an aviation equipment cluster according to claim 1, wherein The said method further includes: Visualize and monitor the execution process of the robot operation and maintenance task. Adopt virtual reality or augmented reality technology to realize the remote visualization and real-time monitoring of the operation and maintenance task, so that the operation and maintenance personnel can understand the task progress and resource status in real time and perform necessary intervention and adjustment; Establish an evaluation system for the execution effect of the operation and maintenance task, and quantitatively evaluate the execution effect of the robot operation and maintenance task to provide data support for subsequent operation and maintenance optimization; Provide a user-friendly interaction interface and operation process to facilitate the operation and maintenance personnel to quickly get started and use the method efficiently, and improve the convenience and intelligence level of the operation and maintenance work.

10. A robot formation and reuse system for the operation and maintenance scenario of an aviation equipment cluster, characterized in that, It includes the following modules: An equipment cluster health status evaluation and operation and maintenance requirement analysis module, which is used to evaluate the health status of the equipment by real-time monitoring the operation data of the equipment cluster and using data analysis algorithms, and analyze the operation and maintenance requirements according to the health status evaluation results; An operation and maintenance resource status evaluation module, which is used to obtain in real time the quantity, task execution ability, battery life status, position information, etc. of the robots used for functions such as detection, repair, and transportation in the scenario, and classify the task execution ability of the robots to facilitate reasonable allocation during subsequent formation and path planning; The robot formation plan generation module constructs a multi-objective optimization model including operation and maintenance requirements, robot resources, task execution capabilities, and battery status parameters based on the parsed operation and maintenance requirements and the evaluated operation and maintenance resource status, and uses a combinatorial optimization algorithm to solve it, generating an optimal robot formation plan that meets the current operation and maintenance requirements and has the highest resource utilization rate; The robot reuse and scheduling module, after completing an operation and maintenance task, uses heuristic algorithms, dynamic programming algorithms, etc. to reuse and schedule the robots according to the resource status of the robots and the new task requirements. For robots with insufficient battery life, arrange them to return to the warehouse for charging or maintenance; for idle robots, intelligently allocate them to the next operation and maintenance destination or perform other tasks, realizing the efficient utilization of robot resources and the automation of the operation and maintenance process; The feedback and optimization module collects real-time data during the execution of operation and maintenance tasks by the robots, analyzes the data using machine learning algorithms, and continuously optimizes the algorithm models for operation and maintenance requirement parsing, resource evaluation, formation plan generation, path planning, and reuse and scheduling, improving the intelligence level and operation and maintenance efficiency of the system.

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