345 results about "Task completion" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to service scenes such as pension service, financial science and technology and medical health, and discloses a multi-agent cooperative task processing method, device and equipment and a medium, and the method comprises the steps: obtaining a task instruction, analyzing a core target, and decomposing the core target into a plurality of subtasks; obtaining environment information, dividing task areas, and generating a cooperation framework in combination with agent capability and area weight; real-time states of the agents are obtained, and the optimal agents are matched based on the cooperation framework to generate a task allocation table; a task distribution table is issued to control the intelligent agent to execute the task and upload execution information; monitoring an execution process, and performing dynamic adjustment and updating a task allocation table when detecting path conflicts or equipment faults; and after the subtask is completed, obtaining environment completion state data, and comparing the data with a preset standard model for acceptance. According to the method, efficient task decomposition and intelligent distribution are realized by fusing task semantics, environment information and intelligent agent capability, and the cooperation stability is improved by introducing a real-time state perception and self-adaptive mechanism.

The invention relates to a multi-agent scheduling method and system based on task intention matching. The method comprises the following steps that S1, tasks are received and analyzed; s2, intelligent agent matching candidates are obtained; s3, selecting an optimal agent by a scoring mechanism; s4, task assignment, execution monitoring and result acquisition; s5, performing reflection evaluation and strategy updating: dynamically updating an intelligent agent score, and adjusting a task label matching rule or other scheduling parameters so as to optimize a distribution decision of a subsequent task; and S6, result combination and output: when the task comprises a plurality of sub-tasks executed by a plurality of intelligent agents, the results are verified, sorted and combined. Compared with the prior art, the method has the advantages that the task target can be dynamically analyzed, the optimal agent can be intelligently matched to execute the task, and the scheduling strategy is continuously optimized through feedback after the task is completed, so that the task execution efficiency and effect of the multi-agent system are remarkably improved, and the accuracy, the adaptability and the intelligent level of task allocation are improved.

The invention provides a large model agent collaborative scheduling method and system oriented to complex tasks, relates to the technical field of artificial intelligence, and comprises the steps of task decomposition, feature extraction, agent matching, dynamic scoring, scheduling scheme generation and optimization, execution monitoring, exception handling and the like to realize efficient collaboration of large model agents. According to the method, accurate matching can be carried out according to task characteristics and intelligent agent capabilities, the task completion efficiency and quality are improved, meanwhile, the dynamic adjustment capability is achieved, abnormal conditions in the execution process are effectively handled, and the system robustness is enhanced.

The invention provides a multi-agent cooperation method, system and device and a storage medium, and relates to the technical field of multi-agent collaboration.The method comprises the steps that initial role allocation is conducted on multiple agents, one agent is an observer, the other agent is a coordinator, and the other agents are all executors; a strategy network based on deep reinforcement learning is introduced according to the running state of the multiple agents to dynamically adjust role allocation of the multiple agents, and a role allocation strategy is dynamically adjusted according to task completion rewards, role conflict punishment and resource conflict rewards; the coordinator constructs a task priority and a dependency relationship based on the task graph or the task dependency tree, and dynamically allocates tasks according to the state, the capability vector and the task adaptation degree of the executor; conflicts are recognized through resource contention detection, task overlapping detection and behavior conflict detection, and the conflicts are coordinated. According to the invention, multi-agent responsibilities are layered, and the task completion efficiency is improved through task allocation and conflict detection and coordination.

The invention discloses a multi-agent dynamic task allocation and collaborative path-finding system based on label-free distributed deep reinforcement learning. The system comprises the following steps: step 1, receiving state information and environment perception data of each agent in a multi-agent system based on distributed deep reinforcement learning; step 2, extracting feature representations of the environmental perception data and the intelligent agent state information, and performing multi-source heterogeneous information fusion through an attention mechanism to obtain state-task matching features; 3, transmitting the state-task matching characteristics to a multi-agent network in real time, realizing task allocation negotiation among agents by adopting a hierarchical scheduling and state exchange mechanism based on task priorities, dynamically detecting newly added task types, and performing incremental learning; the online updating iteration of the model is realized to assist the multi-agent optimization task allocation strategy and the path planning action; compared with the prior art, the method has the advantages that by applying the distributed deep reinforcement learning technology and a state exchange mechanism between intelligent agents, the system can quickly adapt to environment changes and task dynamics, and the resource utilization rate and the task completion efficiency are improved.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The invention belongs to the field of multi-mode sensing fusion, and relates to an intelligent multi-mode patrol sensing method, system and equipment and a storage medium, which are used for carrying out automatic patrol, real-time sensing and abnormity warning in a complex environment. Comprising the steps of natural language instruction analysis, task decomposition and distribution, intelligent agent scheduling and execution, multi-modal information collection and fusion, anomaly recognition and event response, result feedback and task closed loop, and through the integration of an OWL framework and a DeepSeek-R1 large model, a multi-modal data fusion technology and an NL-SLAM autonomous navigation algorithm, the multi-modal data fusion algorithm and the multi-modal data fusion technology are integrated. The problems that in the prior art, dynamic task understanding based on natural languages cannot be achieved, a sensing system lacks efficient multi-modal data fusion, so that the anomaly recognition precision is low, and complex tasks or emergencies are difficult to deal with are solved, various abnormal conditions in a complex environment are effectively dealt with, and the method is suitable for popularization and application. The stability and expansibility of task execution are improved, and the navigation success rate and the task completion rate of the system in an unstructured environment are enhanced.

The invention provides a multi-target production scheduling method and system based on reinforcement learning, and the method comprises the steps: carrying out the matching analysis based on a task demand list and an equipment capability baseline, and obtaining a scheduling constraint space containing a task execution dependency relationship and an equipment operation state boundary condition; performing structured feature extraction on the scheduling constraint space through a graph neural network, generating a production scheduling association graph containing node features and edge features, and calling a multi-target reinforcement learning strategy model to perform strategy iteration optimization on the production scheduling association graph, obtaining a scheduling strategy parameter set fusing the task completion timeliness and the resource utilization balance degree; and generating an equipment task time sequence allocation scheme. According to the invention, the overall effectiveness and reliability of production scheduling can be effectively improved.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

The invention provides an agent visual language navigation method and system based on task completion prediction. The method comprises the step of constructing a dual-drive structure composed of a self-adaptive mixed pooling mechanism and a task completion analysis module. Firstly, in the visual information processing process, a dynamic weight distribution strategy is adopted to carry out multi-scale adaptive mixed pooling on panoramic features, so that the fusion effect of local and global semantic information is optimized, and the retention capability and semantic integrity of navigation historical information in a dynamic topological map are improved. And then, inspired by a human navigation cognitive behavior mechanism, a task completion analysis module is designed and introduced, and the task execution progress is dynamically estimated based on the recognition condition of a key landmark in a navigation path, so that an intelligent agent is driven to preferentially select a key path node and invalid exploration is reduced. And finally, realizing efficient understanding and execution of the natural language instruction by the intelligent agent through a multi-round cyclic cross-modal reasoning and action prediction mechanism.

The invention discloses a man-machine cooperation task allocation method based on reinforcement learning, and the method comprises the steps: constructing a reinforcement learning model which comprises a task definition, a resource definition, a state space, an action set and a reward function, and carrying out the modeling of a cooperation relation among a task, a worker and a robot in man-machine cooperation through a heterogeneous graph neural network, and extracting high-order features of the state space. Based on the characteristics, a strategy network and a value network are trained through a near-end strategy optimization algorithm, and learning of an efficient task allocation strategy is achieved. The reward function combines the task completion duration and the resource utilization rate, and gives consideration to the production efficiency and resource balance. In the system operation process, workshop state data are collected in real time to dynamically update the state space, and the reinforcement learning model generates an optimal allocation action according to the latest state and issues an instruction to a worker or a robot for execution. The method has the advantages of high adaptability, high distribution efficiency, high scheduling intelligence degree and the like, and is suitable for task scheduling optimization in complex dynamic environments such as intelligent manufacturing and the like.

The invention belongs to the technical field of vehicle-mounted edge computing (VEC), and particularly relates to an unloading and resource allocation method for a DAG task in a vehicle-mounted edge computing environment. The method comprises the following steps: firstly, constructing a VEC unloading system in a two-way lane scene, and collecting task and equipment information and establishing an optimization model in combination with a vehicle moving model, a communication model and a calculation model; and secondly, aiming at a task in a directed acyclic graph (DAG), a task priority scheduling method based on a DAG topological structure is designed, so that a task scheduling strategy is optimized. On the basis, the problem is modeled as a Markov decision process by taking minimization of task completion time delay and system energy consumption as optimization objectives. The invention relates to the field of resource allocation, in particular to a task-dependent computing unloading and resource allocation algorithm based on a depth deterministic policy gradient (DDPG), and further provides a task-dependent computing unloading and resource allocation algorithm based on the depth deterministic policy gradient (DDPG) so as to solve the optimization problem.

The invention discloses a multi-agent cooperation system construction method, a medium and equipment, and the method comprises the steps: carrying out the task modeling of a target business scene, and constructing a task dependence graph; initial cooperation strength weights are set for the atomic tasks with the cooperation relationship, and multi-stage cooperation training courses from easy to difficult are generated based on the cooperation complexity of the atomic tasks; the intelligent agent is controlled to execute a task in a training course, interaction behavior data is collected, the overall task completion efficiency is calculated, the cooperation weight is dynamically updated, and interaction data and the updated weight are input into a reinforcement learning model to iteratively optimize a cooperation strategy; and finally, solidifying the converged cooperation strategy into the constructed multi-agent cooperation system. According to the invention, through the combination of course learning and reinforcement learning, the cooperation efficiency and robustness of the system in a complex business scene can be significantly improved.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention discloses an industrial control software public test resource scheduling method based on a self-adaptive genetic algorithm. The method comprises the steps of task scheduling modeling, resource constraint condition definition, task dependency relationship construction and dynamic scheduling strategy making. By introducing a self-adaptive mechanism, the crossover rate and the mutation rate in the genetic algorithm are adjusted to adapt to the search requirements of different stages, so that the optimization efficiency and the diversity of solutions are improved. According to the method, a public test resource scheduling optimization process specially aiming at industrial control software characteristics is designed, and the complex scheduling problems of multiple tasks, multiple constraints and limited resources can be effectively solved. Compared with a traditional static scheduling strategy, the method has the advantages that the test period is remarkably shortened, the resource utilization rate and the task completion rate are increased, the method is particularly suitable for industrial control software public test scenes with large-scale and multi-skill requirements and complex task dependence, and the method has good application prospects and popularization value.

The invention discloses an artificial intelligence (AI) digital human interaction evaluation method and device. Comprising the following steps: step 1, defining evaluation indexes, wherein the evaluation indexes comprise task completion evaluation, task complexity evaluation, independent capability evaluation, attention cost evaluation, free time evaluation and lever multiple evaluation; by defining multiple evaluation indexes such as task completion, task complexity, independence capability, attention cost, free time and lever multiple, the performance of the AI digital human in different dimensions can be comprehensively and systematically evaluated, developers are helped to comprehensively understand the interaction capability of the AI digital human, and through collection and interaction analysis of user interaction data, the interaction capability of the AI digital human can be comprehensively and systematically evaluated. Problems and defects existing in the process of the AI digital human are recognized, optimization is carried out according to the evaluation result, and the interaction experience of the user can be obviously improved.

The invention provides a multi-agent-based task cooperative execution method and device, and the method comprises the steps: carrying out the calculation based on the task type of a target task and the current system state of a multi-agent system, and obtaining an index weight; determining a task allocation scheme of each subtask based on the index weight and the agent bidding information of each subtask; and determining an execution strategy of each sub-task based on the task attribute of each sub-task, the task allocation scheme and the environment perception data of the multi-agent system. According to the method and device provided by the invention, the index weight is calculated according to the task type of the target task and / or the current system state of the multi-agent system; the task allocation scheme of each subtask is determined based on the index weight and the bidding information of each subtask corresponding to the plurality of agents, so that dynamic task allocation adapting to environment change is realized, the task allocation rationality is improved, and the task execution efficiency and task completion quality of the system are improved.

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 construction management system and method based on multi-source data analysis, and relates to the technical field of building construction informatization and intelligent management and control, and the method comprises the steps: capturing a front path task completion signal and a subsequent task starting instruction; obtaining preposed task completion time, a dynamic condition type and a threshold parameter; executing time logic verification based on the dynamic condition type, and judging a construction dynamic context ready state; bIM design coordinates, field positioning coordinates and space tolerance parameters of the construction machinery are obtained, and space logic verification is executed by calculating space deviation; when both the time verification and the space verification pass, subsequent task state conversion is allowed, and otherwise, blocking is carried out; and finally generating a structured report containing all verification parameters and results.

The invention discloses a task scheduling method based on dynamic weight, and belongs to the technical field of workflow engines and distributed computing. The method comprises the steps of establishing a to-be-allocated task set; calculating a dynamic priority weight; establishing a task queue; sequentially processing the tasks according to a task queue sequence; establishing a calculation node set of all available processing tasks, and screening out an optimal calculation node corresponding to a to-be-processed task in the task queue; judging whether the to-be-processed task is partitioned or not; establishing a task preemption condition until the to-be-processed task is processed; and repeating the steps until all tasks are processed. The method can be applied to cloud computing, intelligent manufacturing and distributed system task scheduling scenes, and the resource utilization rate and task completion timeliness are remarkably improved.

A DNN reasoning dynamic partitioning and task scheduling method based on GA-DQN collaboration comprises the steps that a trained multi-outlet DNN model is deployed for each device in a multi-user and multi-server scene to conduct task reasoning, and in a time slot, tasks reach user devices, meet Poisson distribution and are stored in task queues of the user devices; establishing an optimization problem P aiming at maximizing the total completion rate and reasoning accuracy of task reasoning in a time slot by taking a strategy feasibility interval as a constraint and taking a partition point of the task, advanced exit selection, server selection and a scheduling decision as optimization variables; and establishing a Markov decision process, and solving a problem P by using a GA-DQN collaborative optimization strategy. The method is suitable for dynamic DNN reasoning partitioning and scheduling of multiple users and multiple servers, and the task completion rate and the reasoning accuracy within a period of time can be effectively increased.

The embodiment of the invention provides a context information management method and device based on hierarchical memory, equipment and a medium, and the method comprises the steps: analyzing a task request to generate a sub-task node sequence containing a target description and acceptance function, and arranging a plurality of agents to cooperatively execute a task, and after the audit is passed, a complete execution context is stored in a long-term memory, and a structured abstract is generated and stored in a short-term memory, so that the problem that the context management mechanism of the existing multi-agent system is single is effectively solved, stable maintenance of cross-session cognitive continuity is realized, and the efficiency of the multi-agent system is improved. The method improves the execution reliability of a complex long-process task, optimizes the information storage and retrieval efficiency through hierarchical memory, reduces the risk of semantic deviation accumulation and amplification, clarifies the task target and acceptance standard of each stage, enhances the multi-agent cooperation consistency, and improves the efficiency of task execution. And the execution efficiency and the task completion quality of the intelligent agent system in a complex application scene are comprehensively improved.

The invention provides an interaction method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining an interaction task, a first interaction control of a first GUI associated with the interaction task, an executable first action list, and a first picture displaying the first GUI; and calling an intelligent agent integrated with a backtracking mechanism, determining an execution action under at least one time step according to the interaction task, the first picture, the first interaction control and the first action list, and executing the execution action so as to complete the interaction task. Therefore, the task completion quality can be improved, repeated operation or wrong operation caused by the fact that the action deviates from the task target is avoided, the task execution process is smoother, unnecessary resource waste is reduced, and then the task completion efficiency is improved.

A task management system detects condition(s) are satisfied with respect to task record(s), and triggers action(s) at least in part by substituting information from the task record(s) into placeholder(s) of user-modifiable template(s) to prompt large language model(s) for generating narrative(s) about how the condition(s) were satisfied. The triggered action(s) retrieve information from identified task record(s), substitute value(s) from the record(s) into the placeholder(s) of the user-modifiable template(s), and trigger(s) call(s) to large language model(s) to generate task completion narrative(s) or other narrative(s) for the task record(s). The narrative(s) may be stored in narrative field(s) of the record(s) for use in displaying the narrative(s) on a user interface, email notification, or other message.

The invention provides a double-arm robot control method and a double-arm robot, and the method comprises the steps: obtaining a task request; determining environment information and an action set corresponding to the task request according to the task request; generating a target task dependency graph corresponding to the task request according to the environment information, the action set and a large language model; and according to the target task dependency graph, motion planning and motion execution are carried out on two mechanical arms in the double-arm robot in parallel, so that the double-arm robot is controlled to execute the task request. The task execution process of the double-arm robot can be optimized, and the double arms of the double-arm robot can execute actions in parallel in a dynamic scheduling mode, so that the double-arm utilization rate of the double-arm robot is maximized on the premise of ensuring successful execution of the task request, the task completion time is shortened, and meanwhile, the task execution efficiency is improved. Through the mode of generating the target task dependency graph, a large amount of data is prevented from being used for model training and iteration, and the training cost is reduced.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention discloses a distributed computing task scheduling security guarantee method and system based on dynamic trust evaluation, and the method comprises the steps: collecting and preprocessing the operation state data of each computing node in real time, and dynamically calculating and updating the trust degree of each node according to the operation state data; according to the credibility and in combination with node resource loads, the tasks are only allocated to nodes with the credibility not lower than a preset threshold value and sufficient resources to be executed; the node state is continuously monitored in the task execution process, when a node fault is detected or the node trust degree is lower than the threshold value, task rescheduling is triggered, and the affected task is migrated to the remaining nodes with high trust degree and sufficient resources to continue to be executed; the key data of task allocation, node selection and task completion state are written into the block chain by using the characteristics of decentralization, non-tampering and encryption protection of the block chain so as to ensure the transparency and data security of the task execution process.

The invention discloses a message-driven enterprise multi-service system centralized to-do processing method and system, and the method comprises the steps: S1, obtaining resource investment and task processing data, and obtaining a resource consumption and task state cluster through clustering; s2, using a regression algorithm to analyze correlation between the two, and determining a resource utilization rate change trend; s3, when the threshold value is exceeded, calculating a task completion rate statistical characteristic to obtain a resource optimization index; s4, constructing an interactive model by using a decision tree in combination with historical data, and judging an input strategy adjustment point; s5, rendering visual resource distribution; s6, abnormal tasks are screened, and an optimization area is determined; and S7, updating an instrument panel to obtain a decision view. Through data clustering, correlation analysis and dynamic adjustment, the resource and task matching degree is improved, and decision scientificity and efficiency are enhanced.

The invention provides a complex task multi-agent collaborative disassembling method and system based on a business process, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a graph through analyzing a business process document, matching a complex task with a process node, identifying a closed loop dependence path, and selecting an agent based on a constraint logic formula. And establishing a causal tracing chain to process abnormal conditions, and performing semantic alignment to ensure data transmission between subtasks. According to the method, the automation degree and the execution efficiency of multi-agent cooperative processing of complex tasks are improved, and the fault-tolerant capability of the system and the reliability of task completion are enhanced.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.