398 results about "Task dependency" patented technology

The invention relates to the technical field of information, in particular to a multi-task data analysis method and device and a storage medium, and the method comprises the steps: constructing a multi-dimensional task state feature vector as the input of a task loss prediction model by collecting a task execution progress, a resource use condition and an abnormal log index in a task operation process; constructing a task dependency graph and a resource conflict graph according to the dependency relationship between the tasks and the resource occupation condition, learning the relevance between the tasks and a resource competition mode through a graph neural network algorithm, and obtaining an influence factor and a propagation path of task loss; the performance of a task loss prediction model is continuously tracked, model parameters are dynamically updated through an incremental learning mechanism, the method adapts to changes of a multi-task environment, prediction accuracy and real-time performance are improved, and a reliable decision basis is provided for task scheduling and resource optimization.

The invention provides a multi-task monitoring and scheduling method and system, and relates to the technical field of task scheduling, and the method comprises the steps: receiving a task submission request, and constructing a task execution time prediction model based on a historical execution time sequence and a bidirectional gating recurrent neural network; monitoring a task execution process, and identifying an abnormal task by using a hierarchical Markov decision and a spectral clustering algorithm; for an abnormal task, constructing a task dependency graph based on the resource competition degree, calculating a critical path, determining an optimal calculation node, and performing re-execution; according to the method, the task execution time can be accurately predicted, abnormal tasks can be found in time, resource optimization scheduling is carried out, and the system operation efficiency is improved.

The present application discloses a task management method, system, device, and medium based on a graph structure, and relates to the field of computer technology. The method includes: decomposing a preset target task into at least two subtasks, creating a task node for each of the subtasks, and mapping the task node to a node set of a preset directed acyclic graph to obtain a task graph; updating the edge set of the task graph according to the dependency relationship between each of the task nodes to obtain a task dependency graph; topologically sorting all the task nodes in the task dependency graph, and determining the execution order corresponding to the independent task nodes and the dependent task nodes according to the topological sorting result; if there are multiple independent task nodes, executing the subtasks corresponding to the multiple independent task nodes in parallel, and executing all the dependent subtasks in sequence according to the execution order. The present application improves the processing efficiency of the task processing framework for complex tasks.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

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-UAV (unmanned aerial vehicle) task scheduling method and system with a dependency perception and feedback mechanism, and the method comprises the steps: enabling a commander to input a task demand in a voice or text form, inputting the task into a large language model based on a Python prompt template in combination with environment information and UAV capability configuration, and enabling the large language model to perform task scheduling; and completing subtask disassembly and dependency modeling of the natural language instruction. The method comprises the following steps: establishing a sub-task dependency graph, and determining a sequential relationship and execution logic between tasks; in the aspect of task scheduling, capability vector modeling is carried out on all online unmanned aerial vehicles, and an optimal unmanned aerial vehicle is selected or a multi-vehicle alliance is automatically constructed to execute a task based on a vector matching degree between task skill requirements and unmanned aerial vehicle capabilities. In the task execution process, task state information is collected in real time, and all feedback information is uploaded to the cloud control center for state judgment and abnormity recognition. When the system detects an abnormal condition, task reconstruction, alliance recombination and scheduling graph repair are automatically carried out, and closed-loop adjustment of the task is completed.

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a controllable content large model generation method and system based on multi-modal thinking chain reasoning, and the method comprises the steps: S1, improving the capability of multi-agent planning task dependence and execution sequence through employing a thinking chain method, enabling a complex task to be subdivided into a plurality of subtasks through the method, and enabling the subtasks to be subdivided into a plurality of subtasks; a resource transmission process between the sub-tasks is precisely planned and is executed in a chain mode; s2, complex tools are combined into sub-agents with different modal characteristics, so that the number of selectable sub-tasks during task planning is reduced, and the planning capability is improved; and S3, storing scene information of a previous dialogue by means of an external database, and when a new request is encountered, improving correlation and accuracy of multi-agent response by quoting historical interaction information and historical multi-modal resources which have context similarity with the current request, and realizing a more intelligent reaction mechanism.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

The invention relates to the technical field of sequence control, in particular to a sequence control test system based on the Internet of Things, which comprises a task decomposition module for analyzing equipment load, decomposing tasks according to equipment operation capability, acquiring task requirements and equipment parameters, judging operation conditions of equipment stations, refining task dependency relationships and performing task scheduling on the basis of a system task sequence; and associating the tasks with the equipment parameters to generate an equipment task sequence table. According to the method, efficient coordination of multiple devices under different loads is ensured by adopting a distributed coordination algorithm, task scheduling is more flexible and efficient by analyzing task dependency and state changes of the devices, optimizing the task execution sequence and arranging the execution time and stations of the devices, and the task scheduling efficiency is improved by judging synchronous execution conditions and obtaining time nodes. The synchronous execution sequence of the tasks is coordinated, repeated occupation or idling of equipment resources is avoided, and the stability and task processing capacity of a sequence control system are improved.

The present invention provides a multi-project scheduling optimization algorithm and system based on resource constraints. By loading test tasks, personnel information and equipment status, a task dependency graph is constructed to determine the execution order. Through the priority calculation module, each task is assigned a priority according to the deadline, resource requirements and task urgency, ensuring that high-priority tasks are executed in a timely manner. The scheduling execution module uses the shortest job first (SJF) or backtracking algorithm to optimize task scheduling. In addition, the real-time monitoring module monitors the task progress and resource status, and the conflict detection and processing module can adjust the personnel and task allocation in real time to cope with resource contention. Finally, the evaluation and optimization module generates an evaluation report after the project is completed, records the resource utilization efficiency, and provides improvement suggestions for subsequent projects. The present invention effectively improves the efficiency of multi-project management in a resource-constrained environment, and is suitable for scientific research, engineering and other multi-task collaborative fields.

The invention relates to the technical field of electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The invention provides a task disassembly and multi-agent arrangement execution system and method based on a large language model, and belongs to the technical field of computers, and the system comprises an instruction analysis module, a DAG construction module, a scheduling execution module and a cache optimization module. Analysis is carried out according to the dependency relationship between the tasks, a task execution DAG is automatically constructed, and concurrent calling is carried out on the sub-modules without dependency; by caching an authentication result, a context reasoning result and the like, a universal module is executed in advance, the result is reused, and repeated calculation is reduced; the maintainability and the expandability of the system are improved through graph structure visualization and node element information injection; and performing context analysis and scheduling optimization in combination with the reasoning ability of the language model to realize an intelligent decision execution path.

The invention relates to a task execution method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of artificial intelligence chips. The processing efficiency of execution of the calculation task and the communication task on the artificial intelligence chip can be improved. The method comprises the steps of obtaining a task flow; in the same task flow, enabling a first processing unit of the artificial intelligence chip to execute a calculation task, and enabling a second processing unit of the artificial intelligence chip to execute a communication task; wherein the calculation task does not depend on the communication task; the communication task depends on a corresponding computing task, and the communication task does not depend on a non-corresponding computing task.

The invention discloses a dynamic production plan adjustment method in an industrial internet environment, and the method comprises the steps: constructing a task dependence graph according to the task attribute data of a production task; identifying a task state change event, and updating the task dependency graph in real time based on task nodes and edges influenced by the task state change event; a linear programming algorithm is adopted to obtain a task priority ranking result, and task priority ranking is dynamically adjusted; a distributed scheduling framework is adopted to map tasks to scheduling queues of all edge nodes, and a task distribution strategy is determined; and marking the abnormal task based on the task performance index in the task execution process, identifying the abnormal mode of the abnormal task, and optimizing the task allocation strategy based on the abnormal mode data of the abnormal task. The invention provides a dynamic, self-adaptive and efficient production plan adjustment method, which can comprehensively improve the global optimization capability, real-time performance and intelligent level of task scheduling in a dynamic production environment.

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention relates to a multi-task processing method, system and equipment, and the method comprises the steps: generating a topological sequence of task execution according to a task dependency relationship; based on historical execution data, obtaining predicted execution time of each task, the historical execution data including task execution time, task associated input and system load; distributing a priority for each task according to a task dependency relationship, a task type and the predicted execution time, and determining a task execution sequence; and based on the task dependency relationship, the priority and the predicted execution time, generating a resource allocation and scheduling strategy, the resource allocation and scheduling strategy comprising a task and thread binding strategy and a thread and CPU binding strategy. According to the method, the software operation frame rate can be improved, the waiting time between tasks can be shortened, the method can adapt to the change of different task loads, and the overall performance and stability are improved.

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

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 discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention relates to a digital twin platform GPU rendering resource dynamic scheduling method and system, and the method comprises the steps: S1, collecting a rendering task feature signal of a current frame in real time, and collecting a GPU multi-dimensional resource consumption signal; s2, based on the rendering task feature signal, the historical resource consumption signal and the scene dynamic change signal, generating a GPU resource demand prediction signal and a load fluctuation trend signal of a future frame; s3, predicting a signal, a task dependency relationship signal, a user priority signal and a real-time resource bottleneck type signal according to the GPU resource demand; s4, executing the dynamic resource allocation instruction signal; and S5, generating a parameter self-optimization signal for updating the prediction and scheduling logic of a subsequent frame according to the actual resource consumption signal of the current frame and the scheduling effect evaluation signal. According to the dynamic scheduling method and system for the GPU rendering resources of the digital twin platform, the problem that the GPU resource utilization rate is low and the real-time performance is difficult to consider at the same time under the dynamic load can be solved.

The invention relates to the technical field of task scheduling of an embedded system, and particularly discloses a real-time task scheduling optimization method, device and system for FreeRTOS, and the method comprises the steps: obtaining the current task load capacity in the FreeRTOS; if the current task load capacity in the FreeRTOS is greater than a preset load capacity threshold value, performing dynamic optimization adjustment on an execution priority sequence of tasks in the current task load capacity according to a dynamic optimization adjustment algorithm; performing dependency relationship checking on the dynamically optimized and adjusted task, and obtaining a task dependency relationship checking result; and determining a corresponding task priority processing mode according to the task dependency checking result. The real-time task scheduling optimization method for the FreeRTOS provided by the invention can realize real-time task scheduling so as to meet a multi-task and high-load application scene.

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

The invention discloses a near-end strategy optimization method for heterogeneous supercomputing cluster job flow dynamic scheduling, which comprises the following steps of: abstracting a directed graph job flow scheduling problem in a heterogeneous supercomputing cluster, determining a scheduling target and an evaluation index, and constructing a reinforcement learning framework; and adopting a near-end strategy to optimize and realize heterogeneous supercomputing cluster job flow dynamic scheduling. According to the method, a hierarchical scheduling method is provided, processing of a scheduler on state information is reduced through a sparse scheduling decision, efficient task scheduling is achieved, task dependency relationship characteristics are extracted through a GCN and serve as a part of the state information, and communication overhead between tasks is reduced by introducing resource inheritance and an intra-task parallel mechanism; and the optimal scheduling strategy is trained and learned by using the improved PPO method, and the trained scheduling strategy can make an optimal scheduling action decision according to the dynamic change task and the state information of the computing resources.

The invention provides a cross-platform database heterogeneous migration and fault-tolerant control method and system, and relates to the field of database migration. Analyzing the access logs to generate a popularity list with weights, and calculating priorities in a classified manner; establishing a task dependence topological graph and arranging a migration task; converting data according to rules and transmitting the data in a fragmented manner; and the change information is synchronized through the distributed message queue, and the consistency is verified. According to the method, efficient migration among heterogeneous databases can be realized, the downtime of the system is shortened, the data consistency is ensured, and the migration efficiency is improved.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The embodiment of the invention provides a project execution path planning method and device based on multi-path resource detection and multi-strategy decision, and an electronic product, and the method comprises the steps: analyzing a description file of a target project, so as to recognize function modules defined in the target project from the description file, and matching a target task of each function module; generating a task dependency path tree for the target task, and determining a minimum resource demand when the target project is executed; allocating task initialization configuration for each target task based on the minimum resource demand so as to determine a task execution path; adjusting the task initial configuration and / or the task execution path based on a set plan execution limiting condition until the task initial configuration is matched with a plurality of set planning strategy configurations; and generating a project execution path plan based on the resource configuration and the task execution path during matching.

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.