680 results about "Cooperative work" patented technology

The invention discloses an industrial digital factory cooperative work method and a cloud platform, and the method comprises the steps: collecting factory multi-mode discrete source data, carrying out the cross-domain feature integration through a dynamic tensor fusion algorithm, and generating a global consistent factory situation fusion tensor; inputting the factory situation fusion tensor into a resource constrained collaborative decision engine, constructing a multi-device collaborative task optimization scheme based on a dynamic resource game model, and outputting a non-competitive multi-node collaborative scheduling instruction; performing abnormal situation recovery processing on the multi-node cooperative scheduling instruction, and outputting a continuous production control flow with enhanced stability; and inputting the continuous production control flow into a distributed edge collaborative architecture, dynamically configuring computing power resources based on an intelligent contract of a trusted execution environment, and generating a globally convergent factory collaborative control strategy set. By utilizing the embodiment of the invention, a high-robustness and self-adaptive cooperative working method can be provided for the industrial digital factory, the production efficiency is improved, and the energy consumption is reduced.

The invention discloses a construction elevator intelligent dynamic scheduling system and method based on multi-modal deep learning, and relates to the technical field of elevator scheduling management, the system comprises a multi-modal data acquisition module, a multi-modal deep learning processing module, a hierarchical decision module, an adaptive learning module, a hybrid reinforcement learning scheduling module, and a conflict prediction and solution module; time sequence data, image data and text requirements are processed at the same time through a multi-mode deep learning architecture, a three-layer neural network decision architecture is adopted to realize full-process intelligent decision from strategy to execution, and a learning strategy and decision weight are automatically adjusted according to a project progress stage in combination with an adaptive learning system. The optimal scheduling scheme can be automatically generated according to information such as material types, quantity and latest completion time submitted by a team, the use efficiency of the construction elevator is effectively improved, resource waste and scheduling conflicts are reduced, and the method is suitable for scenes such as building construction elevator scheduling and a multi-elevator cooperative working environment.

The invention discloses a heterogeneous equipment intelligent management system and method based on an artificial intelligence technology, and relates to the technical field of computers, and the system comprises a heterogeneous equipment intelligent management platform: a heterogeneous equipment layer is provided with multiple types of equipment; the equipment access layer supports a plurality of communication protocols; the device adaptation layer is used for converting communication protocols and data formats of different devices into model context protocols, and abstracting the devices by using a heterogeneous device unified description language; the resource management layer is responsible for monitoring the resource use condition of each device in real time and carrying out dynamic resource allocation according to a preset strategy; the task scheduling layer is used for intelligently distributing tasks according to task requirements and equipment capabilities and realizing cooperative work among equipment; and the user interface layer is used for providing a user interface, so that a user can conveniently configure, monitor and manage the equipment. According to the invention, heterogeneous equipment can be effectively managed, the compatibility and efficiency of equipment management are improved, the management cost is reduced, and intelligent control is realized.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses a contract generation system and method based on multiple agents, and the system comprises a user interaction module which is used for receiving a contract demand inputted by a user and displaying a generation result; the knowledge base module is used for storing and retrieving legal terms, industry specifications and contract templates in a distributed manner; the agent cluster module comprises a demand analysis agent, a law expert agent, an industry expert agent, a risk assessment agent, a language optimization agent and a coordination control agent, the agents interact through a standardized communication protocol, and a coordination strategy is dynamically adjusted based on contract complexity calculated in real time; and full-process automation of contract generation is realized. Through cooperative work of multiple agents, the efficiency and quality of contract generation are improved, the law compliance and industry suitability of the contract are ensured, the risk in the contract generation process is reduced, and the user experience is improved.

The invention relates to the technical field of large language models, in particular to a large language model multi-agent cooperative working method, which comprises the following steps: S1, designing a plurality of agents to simulate social division and cooperation, so that each agent executes different professional abilities, the plurality of agents including a planning agent, a tool agent and a reflection agent; s2, when task input is obtained, the planning agent filters retrieved tools, and a complex task is decomposed into subtasks easy to understand in a code and annotation mode; s3, performing targeted tool enhancement on the filtered tool set by the tool agent, and executing specific operation planned by the planning agent to obtain a subtask result; s4, evaluating a subtask result by the reflection agent, and planning the task from different angles by combining static planning and dynamic planning; and S5, the planning agent feeds back a summary result according to execution of the subtasks.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention provides a multi-tenant-based centralized authentication and authorization method, system, device and medium, and belongs to the technical field of security and identity management, and the system comprises a unified portal layer, an authentication layer, an authorization layer, a strategy center and an audit monitoring layer. The unified portal layer receives a user request, identifies and injects a tenant identifier, and executes WAF rule verification, JWT signature verification and Token blacklist check; the authentication layer performs user or client credential verification on different accessed identity sources, generates and issues a JWT, and monitors the life cycle of a token; the authorization layer extracts an authority statement in the JWT and a locally cached strategy snapshot; the strategy center provides centralized storage, version management and visual editing of multi-tenant strategies; through cooperative work of each layer, accurate identification, unified authentication, centralized strategy management and comprehensive audit monitoring of tenants are realized. And the security, the expandability and the management efficiency of the system are effectively improved.

The invention discloses a talkback method and system based on label door lock cooperation, and the method comprises the steps: broadcasting door lock binding information: obtaining a broadcasting result of the door lock binding information according to the fact that a door lock A detects that a Bluetooth label Tag-DID carried by an owner a leaves a communication range; neighbor door lock state detection: sending an empty query request according to a call button of a Bluetooth tag pressed by an owner a, and obtaining a response result of an idle door lock; door lock selection and call establishment: selecting a door lock ID displayed on a display screen of a door lock Z according to the owner a, extracting the cached IP by the door lock Z and initiating a call request to the door lock A by the door lock Z to obtain an establishment result of call connection between the door locks; and voice communication and switching management: realizing voice communication between the door lock Z and the door lock A according to the established call connection, and obtaining a cross-door-lock voice communication switching result. By utilizing the embodiment of the invention, the cooperative work capability between the door locks can be improved, and the communication experience of a user in an intelligent home environment is optimized.

The invention provides a method, a device and a product for realizing intelligent scheduling and cooperative work of multiple unmanned aerial vehicles. The method comprises the following steps: step S1, receiving a task to be processed in real time and acquiring a real-time operation state of each unmanned aerial vehicle in an unmanned aerial vehicle group; s2, performing intelligent dynamic allocation on the tasks by using a pre-established task value scoring model, an unmanned aerial vehicle task adaptation degree model and a global multi-task dynamic allocation optimization model; the global multi-task dynamic allocation optimization model is a mixed integer programming model established by taking maximization of the total task income as a target under a predetermined constraint condition; and S3, after task allocation is completed, a pre-constructed three-dimensional grid airway model G (V, E) is used to plan an airway for each unmanned aerial vehicle executing the task. According to the technical scheme, the mixed integer programming algorithm and the particle swarm optimization are combined, and the global task allocation optimization model is constructed, so that task allocation can be efficiently solved, and various actual constraints can be met.

The invention discloses a multi-device cooperative line loss anomaly cooperative detection method and system, and belongs to the technical field of electric power, and the method comprises the steps: collecting original time series data of multiple dimensions, carrying out the preprocessing, generating a dynamic space-time diagram based on the time series data to reflect the cooperative work characteristics of devices, extracting the cooperative feature vectors of the devices, and carrying out the fault detection. And meanwhile, self-optimization is carried out, a system strategy is adjusted, and a result is finally output to the equipment. According to the scheme, the problem that an existing method cannot adapt to dynamic changes of power equipment in real time is solved, and the adaptive capacity of the system to the complexity of a power network is enhanced through self-adaptive optimization of an intelligent algorithm.

The invention relates to the technical field of underground structure management, in particular to an underground structure full life cycle management method and system based on fusion sensing data. According to the technical scheme, the system comprises a sensing layer, a data transmission layer, a data processing and analysis layer and a decision support and application layer. Through cooperation of the distributed optical fiber sensor and the multiple sensors, all-around and multi-parameter monitoring of the underground structure is achieved, the monitoring accuracy and reliability are improved, historical data and operation rules of the structure are automatically learned through a data driving prediction model, different geological environments and load conditions are adapted, a scientific basis is provided for maintenance decision making, and meanwhile, the system has good application prospects. The self-adjusting capability is realized; monitoring system application and data accumulation and utilization in each stage are considered, seamless connection and cooperative work are achieved, full-life-cycle integrated management is achieved, the overall performance of the structure is improved, the service life of the structure is prolonged, the maintenance cost is reduced, and sustainable development is promoted.

The invention discloses a path planning method for collaborative operation of multiple ship washing robots, which belongs to the technical field of robot path planning, and comprises the following steps: initializing parameters, and collecting real-time data acquired by sensors carried on ship washing robots; making a robot group priority strategy; rasterizing the surface of the ship body by adopting a grid contour method; an improved # imgabs0 # algorithm is adopted to generate an initial path of the ship washing robot group; adding a constraint tree of a binary tree structure, and performing path planning; and when there is no obstacle on a front and back node connecting line of a certain node, deleting redundant nodes of the path, only retaining starting and ending points and inflection points, then deleting redundant inflection points, extracting key nodes as intermediate target points, and outputting an optimal path scheme. According to the technical scheme, the cooperative work efficiency of the multiple ship brushing robots can be improved, and the technical problems of dynamic obstacle avoidance response lag, low multi-machine cooperation efficiency and the like existing in traditional path planning in a complex curved surface environment are solved.

The invention relates to the technical field of post return support, in particular to a cancer surviver post return support system, which comprises a health state evaluation module, a post matching adjustment module, a working duration optimization module, a task load balancing module and a cooperative work scheduling module. According to the invention, through fusion analysis of health data and physiological parameters and evaluation standards of individual rehabilitation stages, post matching adjustment can accurately adapt to key ability indexes based on actual health states, so that adverse effects of a fixed post distribution mode on rehabilitation of survivors are avoided; daytime high-tolerance, moderate-load and low-tolerance time periods are divided according to the physical ability fluctuation trend, so that the task arrangement sequence can be matched with the real-time states of survivors, the situation that high-load tasks are concentrated in the low-tolerance time period is avoided, the work fitness is optimized, matching is conducted based on the post competency indexes of the members in the same group, the team task allocation strategy is optimized, and the task allocation efficiency is improved. And survivors can complete the adaptive task within the adaptive working duration.

The invention relates to the technical field of underwater robot cooperative control, and provides an elastic consistency control method and system of a multi-underwater robot system. The elastic consistency control method for the multi-AUV system comprises the following steps: discretizing and linearizing the multi-AUV system, introducing a performance index to describe the influence of communication topology on the dynamic behavior of the system, and calculating the elastic consistency of the multi-AUV system on the basis of a centralized optimal control method of a local decoupling system and a robust stability theory. And designing an elastic consistency controller and obtaining controller gain by solving a local algebraic Riccati equation so as to realize consistency control of multiple AUV system states. According to a sparse mode matrix spectrum of a distributed controller, a system stability condition is given, and efficient cooperation and stable operation of a multi-AUV system in a complex underwater environment are ensured. According to the method, the calculation and communication complexity is remarkably reduced, the method is suitable for a multi-AUV cooperative working environment, and a new technical solution is provided for cooperative control of the underwater unmanned system.

The invention discloses a supply chain management-oriented intelligent freight platform, which integrates artificial intelligence, big data analysis and Internet of Things technologies, and aims at optimizing the transportation efficiency, reducing the cost and improving the intelligent level of supply chain management. Route planning and freight demand prediction are carried out through an intelligent algorithm, and optimal route selection of cargo distribution and optimal configuration of vehicle resources are realized; meanwhile, transportation data are collected and analyzed by using a big data technology, and business insight and supply chain optimization management decision support are provided. And due to the introduction of the Internet of Things technology, real-time freight monitoring and vehicle tracking are realized, and the transparency and safety of transportation are enhanced. Besides, the platform also has high compatibility and expandability, and can be seamlessly connected with existing ERP, WMS and other systems of an enterprise to realize data sharing and cooperative work. According to the invention, the efficiency and response speed of supply chain management are significantly improved, and significant competitive advantages are brought to enterprises.

The invention discloses a multi-mechanical-arm flexible production line scheduling method and system based on a visual language model, and belongs to the technical field of production line scheduling. The method comprises the steps of obtaining a task target image and a task description text, and converting the task target image and the task description text into a task state joint embedding vector through a visual language model; the task urgency degree, the semantic matching degree between the task state joint embedding vector and the mechanical arm state embedding vector and the future state of the mechanical arm are introduced into a space-time weighted cost function, task allocation is conducted through the space-time weighted cost function, and a mechanical arm task is determined and executed; and the task completion state of the mechanical arm is determined, and the task of the mechanical arm is dynamically adjusted with the minimization of the space-time weighting cost and the feedback cost as the target. The multi-mechanical-arm cooperative work efficiency, the task scheduling precision and the production takt time stability can be improved in the dynamic production environment, and the problem that an existing multi-mechanical-arm flexible production line faces double bottlenecks of efficiency and reliability is solved.

The invention discloses a data modeling-based home decoration field information processing system, method and device, and a medium, and the system comprises a BIM model generation module which is used for restoring a field environment by using a point cloud technology, and generating a BIM model; the design module is used for carrying out home decoration design on the BIM model and generating a home decoration design scheme; the cost estimation module is used for estimating the cost of the home decoration design scheme; the permission management module is used for managing permissions of all parties of home decoration project participants; the central server is used for synchronizing data in the cooperative work process of all parties in real time; and the archive management module is used for constructing a home decoration project full-life-cycle data archive. Compared with a traditional decoration mode, digital, intelligent and fine management in the field of home decoration is achieved. Through continuous iteration and optimization of the data model, the transparency and accuracy of project management can be significantly improved.

The utility model discloses rectangular ring assembling equipment which comprises a tensioning claw, a clamping claw, a claw driving device and an adjusting module. The paw driving device comprises a tensioning driving block and a clamping driving block, and the tensioning driving block and the clamping driving block drive the tensioning paw and the clamping paw to open and close in the horizontal direction based on the same central point; the moving paths of the tensioning claw and the clamping claw are not intersected; the clamping driving block can further drive the clamping claw to ascend and descend in the vertical direction. The rectangular ring is grabbed and compressed through cooperative work of the tensioning claw and the clamping claw; the adjusting module drives the assembling module to convey the compressed rectangular ring to a mounting area in the workpiece, and then the clamping claw relieves grabbing and compression of the rectangular ring, so that the rectangular ring automatically resets to complete mounting; and therefore, the equipment integrates the functions of grabbing, compressing, transporting and installing the rectangular ring, the manufacturing cost and the maintenance difficulty of the equipment are reduced, the problem of cooperation between mechanisms is avoided to the greatest extent, and the reliability and the stability are improved.

The invention discloses an intelligent construction digital delivery method and system based on master-slave multiple chains, and relates to the technical field of engineering construction management, and the method comprises the steps: carrying out the unique identification code coding of an engineering construction file, generating a file identifier, and constructing a multi-participant co-construction platform; performing sharing and encryption management on file data among participants based on a master-slave multi-chain consensus mechanism; and managing business process data through the cooperative work of the main chain and the side chain, and automatically judging the authority of the access request of the user. According to the intelligent construction digital delivery method based on the master-slave multiple chains, the identification and management efficiency of the file is enhanced by generating the unique engineering construction file identification code, the file sharing efficiency is improved through point-to-point butt joint and data sharing of the engineering file among multiple parties, and the intelligent construction digital delivery efficiency is improved through a master-slave multiple chain intelligent contract mechanism. The safety and the management efficiency of the project file are improved, and better effects are achieved in the aspects of the management efficiency, the safety and the sharing efficiency.

The invention provides an AGV task scheduling method and system based on a graph neural network, and the method comprises the following steps: 1, receiving a task request, distributing a task to an AGV according to the task request, and continuously collecting the data of the AGV; 2, cleaning and standardizing the collected data; 3, calling in a graph neural network model, inputting the cleaned and standardized data into the graph neural network model, and performing real-time prediction and AGV task scheduling and execution adjustment through the model; 4, the AGV executes the task and updates the data in real time, and the graph neural network model continues to conduct task scheduling and execution adjustment according to the data updated in real time till the task request is completed. According to the invention, cooperative work of a plurality of AGVs can be realized, and conflicts are avoided, so that the overall working efficiency is improved, and scheduling management of cooperative tasks of the plurality of AGVs is optimized.

The invention discloses a cooperative operation method of a smart home system, and relates to the technical field of home cooperation, and the method comprises the steps: enabling a plurality of smart homes to be connected into the smart home system through Wi-Fi, controlling the smart homes to carry out the cooperative operation through a set rule, enabling the smart homes to learn the behavior habits of a user through a machine learning algorithm, and enabling the smart homes to carry out the cooperative operation. The method has the advantages that through cooperative operation of the equipment, a user does not need to manually operate multiple pieces of equipment to meet the requirement of one scene, for example, in a home returning mode, after the user opens a home door, equipment such as an intelligent door lock, an intelligent lamp, an intelligent air conditioner and an intelligent curtain automatically and cooperatively work, light is automatically turned on, the air conditioner is adjusted to the comfortable temperature, and the curtain is pulled open; a warm and comfortable environment is created for the user, the time and energy of opening the equipment one by one by the user are saved, and the smart home system can cooperate according to the habits and preferences of the user.

The invention relates to the technical field of positioning devices, in particular to an efficient positioning device special for accurate positioning of a flange, which comprises a positioning frame and further comprises a pipe clamping mechanism used for being connected with a to-be-welded pipeline and monitoring the connection tightness with the to-be-welded pipeline in real time, a swing frame rotatably mounted on the positioning frame, a movable fastening frame mounted on the swing frame, and a clamping mechanism mounted on the swing frame and used for clamping the to-be-welded pipeline. A rotatable rotating frame is installed on the fastening frame, an expanding and supporting mechanism is installed on the rotating frame, and two position-adjustable clamps and two position-adjustable transmission clamps are connected to the expanding and supporting mechanism. Multiple clamping modes are adopted for cooperative work, the problem that an existing clamping mode is single and unreliable is solved, the inner supporting block, the pressing block and the inner clamping face on the clamp can select to conduct clamping from the inner wall or the outer wall according to the flange type, the clamp is suitable for multiple flange types such as flat welding, butt welding, socket and sleeve loosening, and clamping equipment does not need to be frequently replaced.

The invention discloses a ZIGBEE-based Wi-Fi module dormancy method, which comprises the following steps of: intelligently controlling dormancy and awakening of a Wi-Fi module through cooperative work of a ZIGBEE monitoring module, a Wi-Fi state control module, a data caching module, a dormancy mode switching module, an awakening control module, a self-adaptive parameter adjusting module and a fault monitoring module; and the power consumption management and cruising ability of the system is improved.

The invention discloses an electroencephalogram voice annotation calibration and scheduling system and method for individual differences. The system comprises an electroencephalogram signal collecting and preprocessing module, a voice instruction receiving and recognizing module, a rapid individual calibration module, a dynamic task scheduling module, an electroencephalogram feature online decoding and annotation module and an annotation storage and feedback module. Through cooperative work of the two core modules, namely the rapid individual calibration module and the dynamic task scheduling module, the problems that decoding performance is reduced during cross-subject and cross-task switching caused by individual differences, and multi-task labeling efficiency is low due to task scheduling strategy stiffness are solved accurately. Data persistent storage, model online updating and user interaction feedback are completed through storage, optimization and feedback links, periodic retraining is carried out by utilizing daily successful labeling data through a model continuous learning link, and finally, a zero-threshold individual rapid adaptation and high-efficiency multi-task collaborative labeling target is achieved.

The invention discloses a customer service question and answer processing method and device based on a multi-Agent large model, equipment and a medium, and the method comprises the steps: obtaining the dialogue data of a customer service and a user, carrying out the preprocessing of the dialogue data, and generating a preprocessed dialogue text; performing semantic analysis on the dialogue text, identifying user questions and customer service answers, and generating question and answer pairs containing question solving states; judging whether the customer service answer perfectly solves the user question or not, and generating a question-answer pair set marked as perfectly solved; classifying and sorting the question and answer pair set marked as the perfect solution so as to conclude similar question and answer pairs and extract structured knowledge entries; storing similar question and answer pairs into a professional knowledge base based on the structured knowledge entries, and updating the professional knowledge base; and in response to the received actual user service question, providing a corresponding answer to the customer service based on the professional knowledge base. Through cooperative work of multiple Agents, the answering efficiency and accuracy of the customer service staff to the user questions are improved.

The invention discloses a multi-agent collaborative production optimization method and system based on an industrial large model, and relates to the technical field of production control, and the method comprises the steps: connecting an industrial knowledge base platform, carrying out the platform retrieval and interpretation reconstruction of a target production scene, and determining a production line interaction chain; aiming at a production line interaction chain and a driving agent construction interface, calling and splicing optimization of a code fragment library are carried out, lightweight construction of agents is carried out, multi-agent integration is carried out by introducing an intelligent interaction contract, and an industrial large model is established; and deploying the industrial large model in an industrial production system, establishing interaction with production line equipment, triggering the industrial large model along with the start of a target production scene, and performing automatic production management of the production line equipment through multi-agent cooperative control driven by an intelligent interaction contract. The technical problem that in the prior art, an industrial production system lacks intelligent and multi-dimensional collaborative optimization is solved, and the technical effects of improving the production efficiency, the resource utilization rate and collaborative work of production line equipment are achieved.

The invention discloses a multi-robot cooperative work method based on a contrast learning role representation reinforcement learning algorithm, and belongs to the field of multi-agent reinforcement learning. Comprising the following steps: each robot obtains observation information of a current environment, and performs time sequence modeling, role modeling, spatial modeling, action selection, award value obtaining and state value generation respectively; in the process, observation information of each robot, global state information of the current environment, a reward value, role representation and the like are stored in an experience playback pool, the steps are repeated, and finally model optimization is completed by using data stored in the experience playback pool. The intelligent agent behavior homogenization problem and the reputation distribution problem in the multi-robot cooperative work process are solved, cooperative contribution between the robots can be effectively quantified, and therefore the robots are stimulated to work cooperatively better in future tasks, the robots preferentially select behaviors beneficial to the whole system, and the overall operation efficiency is improved.