12results about How to "Improve collaboration" patented technology

The utility model discloses a novel fire rescue cabinet, and relates to the technical field of fire fighting equipment, and the scheme is that the novel fire rescue cabinet comprises a fire fighting cabinet body, and a cabinet door mounting opening is formed in the fire fighting cabinet body; the two guide rails are arranged oppositely, and the two guide rails are assembled on the two opposite sides of the cabinet door mounting opening respectively; the cabinet door assembly comprises a first folding plate, a second folding plate and two oppositely-arranged sliding blocks, and one end of the first folding plate is rotationally connected with the top ends of the two guide rails; the two sliding blocks are assembled in the two guide rails in a sliding mode correspondingly, and the sliding blocks can slide in the extending direction of the guide rails in a reciprocating mode. One end of the second folding plate is rotationally connected with the first folding plate, and the other end of the second folding plate is rotationally connected with the two sliding blocks respectively; by means of the unique vertical opening and closing design, the surrounding space of the cabinet body is prevented from being occupied after the cabinet door is opened, a lifesaving channel is kept smooth all the time, and the taking efficiency of fire fighting equipment is greatly improved.

This utility model belongs to the field of collaborative robot loading and unloading technology, and discloses a collaborative robot dual-machine tool automated loading and unloading device, including a grinding machine and a lathe. An integrated cabinet is arranged between the grinding machine and the lathe. The integrated cabinet includes a control cabinet, a robotic arm, and a mounting cabinet. The control cabinet and the robotic arm are fixedly installed on the upper end of the mounting cabinet. The control cabinet is located on one side of the robotic arm. A control system is set in the mounting cabinet for controlling the loading and unloading of the robotic arm. The control system controls the robotic arm to automatically complete the loading and unloading operations, quickly and accurately placing the workpiece into the designated position and removing it in a timely manner after processing, shortening the loading and unloading time and reducing downtime. The robotic arm is precisely controlled by the control system to ensure the positional accuracy during the loading and unloading process, avoiding processing quality problems caused by human operation errors.

This invention provides a method and system for testing and optimizing joint logistics equipment based on digital twin modeling, belonging to the field of digital twin technology. The method includes: collecting multi-source heterogeneous data of the target joint logistics equipment; performing simulation modeling to generate a target twin model; conducting usage correlation analysis to obtain multiple sets of correlated twin models; obtaining a set of operating environment conditions; conducting simulated operation and collecting operating performance datasets for performance deviation analysis to locate the first set of performance bottlenecks; conducting collaborative operation tests on the target twin model and multiple sets of correlated twin models, and identifying performance deviations based on the operating process data to locate the second set of performance bottlenecks; and optimizing the equipment design of the target joint logistics equipment based on the bottleneck integration analysis results. This invention solves the technical problems of existing technologies that rely on limited experimental data, and where test data is often limited to specific environments and lacks comprehensive data coverage across multiple dimensions and scenarios, resulting in poor testing efficiency and accuracy.

The application discloses a kind of off-site computing power dynamic collaborative optimization methods based on multi-agent reinforcement learning, including the following steps: constructing resource topology diagram;Based on resource topology diagram, get node-level state feature vector and system-level state feature vector;Multi-agent environment is constructed;Local observation, global information and agent action set are input into improved CTDE model, output policy network parameters and value network parameters and construct training batch;Based on training batch, get converged policy network parameters and converged value network parameters;Get execution result;Obtain dynamically updated policy network parameters and dynamically updated value network parameters, realize the dynamic collaborative optimization of task acceptance, resource allocation, task migration, replica start-stop and bandwidth matching.

The present application relates to the technical field of system engineering, and relates to a method for supporting conversion of an automobile motor system architecture model to a simulation model. A domain meta-model library for constructing the automobile motor system architecture model and a template library of Simulink models are acquired. According to a construction requirement of the automobile motor system architecture model, a target automobile motor system architecture model is constructed. Each template corresponding to the target automobile motor system architecture model is determined from the template library as a template set of the target simulation model. According to the attributes of each meta-model contained in the target automobile motor system architecture model, each template in the template set is filled in to generate a target simulation model code according to each filled template, and a target Simulink simulation model is generated according to the target simulation model code. By constructing a mapping relationship between the domain meta-model library and the Simulink template library, automatic conversion of the automobile motor system architecture model to the simulation model is realized.

This application discloses an external data repair method, apparatus, device, and storage medium for data lakes, relating to the fields of financial technology and data engineering technology. The method includes: acquiring scheduling information and reference relationship information of external data in the data lake; determining corresponding downstream protection link information based on the scheduling and reference relationship information; monitoring corresponding data entry start operations and abnormal failure operations based on the downstream protection link information to determine the data entry start operation information and abnormal failure operation information; identifying emergency handling tasks corresponding to the affected downstream operation links based on the data entry start operation information and abnormal failure operation information to determine emergency handling task information; and controlling the system based on the emergency handling task information to update or repair the data in the data lake. This application significantly improves response efficiency and ensures data lake security by accurately identifying affected downstream operation links and automatically generating emergency task control data repair.

The application relates to a 3D dispensing and mounting detection integrated system based on a dual-arm multi-vision cooperative robot, and belongs to the field of intelligent robots; a sensor tooling table is horizontally arranged on the upper surface of a workbench; a sensor detection table is arranged on one side of the sensor tooling table; a detection robot arm position corresponds to the sensor tooling table; a shock absorber tooling table and a chip placement position are adjacently arranged on the upper surface of the workbench; a dispensing robot arm is installed on one side of the shock absorber tooling table; a dispensing device is installed at the top end of the dispensing robot arm; a multi-vision visual assembly and a height sensor position correspond to the dispensing robot arm; a glue cleaning device is installed on one side of the shock absorber tooling table; the application realizes accurate positioning of parts and accurate detection of dispensing effect through the visual assembly, can effectively improve the production speed of products, and guarantees the qualified quality of products; moreover, the system guarantees stable taking and placing of the parts through the mechanical clamps driven by the robot and the air cylinder, realizes steady execution of the overall process, and improves the flexibility and cooperation of assembly.

The invention provides a multi-agent near-end strategy optimization method and system based on embedded strategy representation. The method comprises the following steps: inputting local observation data of agents into a variational auto-encoder (VAE) for encoding to obtain potential strategy codes; inputting the potential strategy code into a contrast learning framework to obtain a feature vector, and splicing the potential strategy code and the feature vector to obtain discriminative strategy representation; the method comprises the following steps: modeling a multi-agent system into an undirected graph, and inputting the undirected graph into a pre-trained GAT-Transform fusion model to obtain fusion features; and inputting the local observation data and the discriminative strategy representation into an Actor network of an EPR-MAPPO algorithm, inputting the global state data, the local observation data and the fusion features into a Critic network of the EPR-MAPPO algorithm, and outputting action probability distribution of the intelligent agent. According to the method, the cooperation efficiency among the agents in the multi-agent system can be improved, a better strategy can be more quickly converged by adopting the improved algorithm, the training time and resource consumption are reduced, and the overall performance and adaptability of the system are improved.

The application relates to the technical field of agent cooperation optimization, and discloses a multi-agent cooperation optimization method and system based on multi-terminal training access, which comprises the following steps: constructing an operation intention vector according to multi-modal training operation data from different terminals; mapping the operation intention vector to a pre-constructed task graph to predict a first computing resource; setting a coupling relationship between each agent and a task in different task roles, calculating the value of an agent combination executing a task sequence, and obtaining a first resource allocation strategy; simulating a task execution process of the agent combination executing the task sequence, optimizing the first resource allocation strategy, and obtaining a second resource allocation strategy; and allocating corresponding computing resources to each agent according to the second resource allocation strategy, and scheduling each agent to execute each task in the task sequence. The application can improve resource utilization, improve agent cooperation performance, and thus improve the quality and efficiency of multi-terminal training.

This invention discloses a multi-robot on-orbit maintenance task allocation method based on Q-learning, comprising the following steps: Step 1, rasterizing the simulated on-orbit maintenance task space into a three-dimensional array model; Step 2, initializing the positions of the end effectors of each robot arm and the positions of multiple target points for maintenance tasks; Step 3, based on the Q-learning algorithm, obtaining the ideal paths for each robot arm that start simultaneously, achieve the optimal time to complete all tasks, and maximize the cooperation among the robots; Step 4, obtaining the task allocation and execution order of each robot arm based on its ideal path. This invention effectively solves the problem of unclear task allocation in multi-robot, multi-target scenarios, improves the cooperation between robots, reduces task completion time, and thus leverages the advantages of multiple robots.

This invention discloses a hierarchical dynamic scheduling method based on the policy optimization DDQN algorithm for solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines. First, the objective function and constraints of the scheduling problem are determined. Then, by introducing a self-attention mechanism, a hierarchical structure based on DDQN is proposed, constructing two agents: a batching agent and a scheduling agent, to solve the batching and scheduling subproblems respectively. Furthermore, to address the multi-stage batch processing and reentrant scheduling characteristics of the problem, a Markov decision process considering the characteristics of these two agents is designed, including state, action, and reward. Further, an action selection strategy based on masking combined with an ε-greedy strategy and a soft-start target network update strategy are proposed to improve efficiency and generalization ability. This invention demonstrates significant effectiveness in solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines.

The invention relates to the technical field of multi-model collaboration, and discloses a task process automatic generation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining multi-modal input information; extracting semantic elements of the multi-modal input information, inputting the semantic elements into a preset story template, and injecting domain knowledge into the story template to output a story framework; performing structured processing and compliance and integrity verification on the story framework; inputting the structured user story which is verified to be qualified into a project management system to generate a final user story; and based on the final user story, updating the knowledge base to form an executable automatic task process. Through structural processing, knowledge enhancement and compliance integrity verification, in combination with an automatic access program and object storage-based knowledge base support, the problems of low efficiency and insufficient automation of traditional manual writing are solved, the compliance and speciality of user stories are guaranteed, and the convenient development requirements in the financial and medical fields are met.