110 results about "Single task" patented technology

The invention belongs to the technical field of computers, and discloses a computing power dynamic segmentation method and system, computer equipment and a storage medium, and the method comprises the steps: collecting system historical load data and a task execution mode, and predicting a future resource use condition by adopting a machine learning algorithm model; monitoring the overall load data of the system; searching available computing resources in the system; if the overall load data of the system exceeds an overall load threshold value, the resource demand of a single task exceeds a resource tension threshold value and / or the future resource use condition exceeds the computing power of the system, triggering a computing power segmentation decision; matching the compatibility between the available computing resources and resource parameters of a to-be-executed task, and obtaining a compatibility result; according to the compatibility result, the available computing resources and resource parameters of the to-be-executed task, a segmentation strategy is formulated; and redistributing the available computing resources according to the segmentation strategy. The technical problems of low resource utilization rate and long waiting time existing in computing power segmentation can be solved.

The invention provides a safety measure generation method and system based on a power distribution network multi-task parallel work ticket, and the method comprises the steps: receiving a to-be-executed work ticket containing a plurality of parallel tasks, and extracting the task information of each task; obtaining a plurality of task groups and task group associated equipment sets corresponding to the task groups; obtaining a task group minimum operation sequence set; obtaining a task group topology boundary equipment set, and performing topology search by using a topology boundary equipment set subset to obtain a safety measure set of each task group; and based on the minimum range maintenance decision model, optimizing the safety measure set of each task group to obtain a final multi-task parallel safety measure set. According to the scheme, the tasks which are electrically connected adjacently are combined into one task group, so that repeated operation on the same equipment can be avoided, unnecessary workload is reduced, and the safety measure generation efficiency of the safety measure generation method based on the multi-task parallel work ticket of the power distribution network is improved.

The invention discloses a task scheduling method which comprises the following steps: receiving a task, scheduling the task or a task fragment of the task to a first execution node in a distributed cluster, and when the first execution node executes the task or the task fragment, scheduling the task or the task fragment of the task to the distributed cluster; and receiving a fragment adjustment request sent by the first execution node according to the load of the task or the task fragment on the first execution node, and according to the fragment adjustment request, adding the task fragment of the task on a second execution node in the distributed cluster, or reducing the task fragment of the task in the first execution node. According to the method, self-adaptive adjustment of the task or the task fragment in the running state can be realized, and resource use is adjusted by dynamically adjusting the task fragment of the task, so that the overall cluster resource achieves load balance, and the risk of task failure or node downtime caused by the fact that an execution node is completely occupied by a single task or the task fragment is avoided.

The invention discloses a distributed layout information reading method and system, a storage medium and electronic device.The distributed layout information reading method comprises the steps that a task configuration file is read through a master control node, the task configuration file comprises layout information configuration, and the layout information configuration comprises a layout processing area and a single task processing area; the main control node is used for dividing the layout processing area according to the single-task processing area to obtain a plurality of sub-task areas, and each sub-task area corresponds to one sub-task; adopting the master control node to respectively distribute the plurality of subtasks to the corresponding slave nodes according to the distribution strategy; the slave node sends a layout reading request corresponding to the subtask to a target database based on a load balancing mechanism; returning layout information corresponding to the layout reading request to the slave node through the target database; and performing corresponding task processing on the layout information through the slave node, and returning a processing result to the master control node. The layout information reading efficiency can be improved.

The invention discloses a Presto task scheduling method and a Presto task scheduling device. According to the method, after a task request is received, tasks are allocated to matched clusters according to user information and task information in the request, then task types are further analyzed, execution modes of the tasks are determined, whether the tasks belong to task types needing to exclusively share resources or not is judged, and if the tasks belong to resource exclusive tasks, the tasks are allocated to the clusters. Tasks are allocated to reserved single tasks and special resource nodes, and when the tasks exclusively share working node resources, no other types of tasks are executed on the nodes, so that the tasks exclusively share the resources on specified cluster nodes, physical isolation of the working nodes in a cluster is realized, the performance problem caused by resource contention is avoided, and the service life of the cluster is prolonged. Therefore, the method is suitable for a complex query environment shared by multiple users, efficient execution of the important tasks can be ensured even when a large number of tasks are processed, and the overall reliability and stability of the system are improved.

The invention discloses a multi-heterogeneous edge node-oriented task allocation method and device, equipment and a medium. The method comprises the following steps of: decomposing a single task into a plurality of sub-tasks; when a plurality of equipment nodes are grouped, constructing a variance of execution time of each group of equipment nodes based on a preset grouping number, and constructing a target function by using the variance and the throughput of all grouped equipment nodes by taking the execution time balance of all the subtasks executed by each grouped equipment node and the maximum throughput of all the grouped equipment nodes as a target; and respectively setting constraint conditions for each device node and each subtask, determining groups of the plurality of device nodes and the corresponding subtasks by utilizing the target function and the constraint conditions, and scheduling the corresponding subtasks for each device node. According to the method and the device, when the sub-tasks are scheduled for each device node, each group of device nodes can simultaneously process all the sub-tasks in parallel, and the execution time is kept balanced.

The invention discloses a data acquisition method and system, and the method comprises the steps: obtaining an acquisition task submitted by a user based on an API interface, and carrying out the task analysis of the acquisition task, so as to split the task into a plurality of acquisition subtasks; determining a corresponding task template based on the type of the acquisition sub-tasks, and distributing the plurality of acquisition sub-tasks to different acquisition message queues based on the determined task template; monitoring each acquisition message queue in real time by utilizing an acquisition program, acquiring an acquisition sub-task in each acquisition message queue by utilizing at least one thread, executing the acquisition sub-task by multiple threads to acquire original data, and writing the acquired original data into a summary queue; and consuming the original data by using a consumption program, analyzing the original data into structured format data according to a preset rule, and storing the structured format data into a database. According to the method, a single task is decomposed into a plurality of subtasks and concurrent execution is realized, so that the data acquisition efficiency is greatly improved.

The invention discloses a communication computing parallel optimization method, a multiprocessor system, a medium and a program product, and the method comprises the steps: configuring a first computing core and a second computing core on a single task flow for a general matrix multiplication subtask allocated to a single processor; wherein the first calculation core is used for executing a general matrix multiplication subtask, and the second calculation core is used for executing a set communication task; the set communication task comprises a full accumulation operator or a protocol dispersion operator; then, starting scheduling is conducted on the first calculation core and the second calculation core according to a preset dependency mechanism, so that the general matrix multiplication subtask and the set communication task are executed asynchronously in an overlapped mode; according to the method, the problem of poor reusability of the original Kernel caused by intrusive modification of the GEMM or re-implementation of the Kernel can be effectively avoided through parallel optimization of GEMM calculation and ensemble communication operation, and the performance overhead of the processor is reduced.

The application relates to a multi-round scientific research workflow evaluation system and method for a multi-modal intelligent agent, and belongs to the technical field of large language models and multi-modal large models. The application comprises a multi-round task generation module, a multi-modal information processing module, and the like. The multi-round task generation module constructs a multi-round task scene closer to a real scientific research process. The multi-modal information processing module enables the measured intelligent agent to complete paper active retrieval, multi-modal evidence extraction alignment, cross-paper evidence integration, and round reuse of evidence. The protection points of the application are not limited to a single task sample or a single scoring index, but are focused on a multi-round evaluation benchmark constructed around a real scientific research workflow, a process-level evaluation protocol matched with the benchmark, and an overall implementation scheme supporting landing execution and fine-grained diagnosis of the benchmark.

The invention relates to the technical field of automation control, and discloses a PLC single task to multi-thread task realization method and system, and the method comprises the steps: obtaining PLC task arrangement information which comprises at least one PLC task and a corresponding program organization unit POU; based on the PLC task arrangement information, creating a real-time operating system (RTOS) thread corresponding to each PLC task in the RTOS, and distributing a corresponding stack space for each RTOS thread; when the RTOS task scheduler executes task switching, calling a preset task switching hook function to carry out stack pressing storage on the additional context of the PLC program unit corresponding to the currently running RTOS thread, and carrying out elastic stack recovery on the additional context of the PLC program unit corresponding to the to-be-running RTOS thread, according to the method for transforming the PLC single task into the RTOS-based multi-thread task, the practical requirement that multi-thread task transformation needs to be carried out due to the fact that the real-time performance of a single task mode of a traditional PLC manufacturer is limited is met.

The present application belongs to the field of equipment operation control, and particularly relates to an equipment operation energy consumption control method and system. For the single task software mode of the equipment, a timer of periodic interruption determined according to the required operation period of the equipment and the required load rate of the equipment is set, and the timer count value is read after each time the function module is operated in series to recalculate the main frequency of the next equipment operation to make the energy consumption relatively low and ensure the task to be completed. For the multi-task software mode of the equipment, the self-balancing task priority is set to the lowest, and the operation time of the self-balancing task is used to judge the equipment in the multi-task mode to recalculate the main frequency of the next equipment operation to make the energy consumption relatively low and ensure the task to be completed. Thus, in the single task software mode or the multi-task software mode of the equipment, the equipment energy consumption can be dynamically adjusted, the corresponding task is completed with a relatively low main frequency on the premise of ensuring the normal operation of the equipment, and a relatively low equipment operation energy consumption is achieved.

The present invention provides a general description language data system for directed acyclic graph automatic task flow, including: Step definition layer, Workflow definition layer and Template definition layer; The Step definition layer is the description of a single task, for the input and output declarations of each docker image or other executor, comprises name, type, file and parameters. The Workflow definition layer is a workflow composed of one or more Steps, the dependency topology of these Steps needs to be defined, and shared parameters can also be defined. The Template definition layer is based on a Workflow definition layer. The Template definition layer pre-sets the parameters, and supplies the descriptions, checkers or data source definitions of the parameters. The data center of the present invention is used with the task execution tool, and a programming language needs to be used to implement the corresponding tool.

Systems and methods described herein provide a computer-implemented method for process simulation using a multi-agent system. A software agent is trained using historical process data associated with a process. Each software agent represents a single task in the process. Based on the candidate process model, the process is simulated by executing the multi-agent system to generate simulation results. The software agent operates autonomously within the multi-agent system based on the trained behavior. The process is also performed using a runtime configuration based on the candidate process model. Execution of the process generates runtime results for comparison with the simulation results. In some examples, the candidate process model is adjusted based on the comparison, and simulation and execution of the process are repeated using the adjusted multi-agent system and the adjusted runtime configuration, respectively. A deployment process model is generated based on the adjusted candidate process model.

The application discloses a kind of based on multi-task learning's light path transmission quality prediction method, network reliability and prediction model accuracy are considered simultaneously, a kind of multi-task loss function is designed, and model is trained using the way of joint training, further reduce the error and design allowance of model, improve the accuracy of model, while guaranteeing network reliability, realize network capacity promotion.Compared with the traditional QoT estimation model based on single task, the scheme proposed in the patent can improve the accuracy while ensuring the reliability of the model, thereby achieving the purpose of improving the allocation efficiency of optical path modulation format and improving the capacity of optical network.

The invention discloses a computing resource allocation method and system for heterogeneous GPU hybrid scheduling, and belongs to the technical field of computer equipment.The method is characterized by comprising the steps that allocation types corresponding to tasks to be allocated are identified, and the allocation types comprise single task allocation, group task allocation and emergency task allocation; if the allocation type is grouped task allocation, acquiring the priority of each task in the to-be-allocated task group; the first task is allocated to a GPU node according to the priority, so that the GPU node generates an affinity label of the first task and the unallocated task, and the affinity label responds to the identity label of the unallocated task; according to the method, the affinity labels are obtained through the response mechanism, the tasks are distributed according to the affinity labels, and compared with the method that the affinity relations of the undistributed tasks are obtained by traversing a query table and are distributed, the method has the advantages that the distribution time is shortened, and the scheduling efficiency is improved.

The application provides a node energy consumption optimization method, device, equipment, medium and product of a supercomputing cluster. The method comprises: obtaining resource occupation data corresponding to a task flow; predicting unit task energy consumption when the task flow is deployed on each computing node in the supercomputing cluster based on hardware configuration information and the resource occupation data of each computing node; determining a target computing node from the plurality of computing nodes according to the unit task energy consumption corresponding to each computing node, and distributing the task flow to the target computing node for execution; and adjusting physical operating parameters of the target computing node according to task load changes during task execution, so as to reduce overall operating energy consumption and achieve node energy consumption optimization. The two-stage collaborative control of the scheduling stage and the execution stage can realize fine energy consumption control for tasks, and reduce single task execution energy consumption and overall operating energy consumption of the supercomputing cluster.

The embodiment of the invention provides display equipment. In the embodiment of the invention, the display equipment receives a selection instruction of a multi-window template under the condition that the currently played media asset is a screen projection media asset picture, and supports the screen projection media asset which is played previously to be continuously played based on the screen projection instance by multiplexing the screen projection instance in a single task mode in a task stack. And supporting screen projection media resources indicated by the screen projection instruction sent by the user later based on the screen projection instance. And the multi-window layout in the multi-window playing mode is adjusted based on the screen projection instance, so that mutual shielding among the multiple windows is avoided. On the basis of reuse of the screen projection instance, the problem of playing interruption of previous screen projection media resources caused by screen projection signal conflicts in a multi-window mode is effectively avoided.

The invention discloses a method and a system for realizing intelligent control of a tool body based on a multi-agent architecture, and belongs to the technical field of artificial intelligence and robots. The method aims to solve the problems of single task processing capability, poor robustness and difficulty in expansion of the existing single-body intelligent model. The core thought of the method is to introduce a multi-agent collaboration architecture, decouple'high-level task planning 'and'bottom-level skill execution', and decompose complex control tasks to specialized agents with different functions. The method comprises the following steps: receiving a high-order task instruction; decomposing the high-order task instruction into a plurality of subtasks by a planning agent; the planning agent assigns at least one corresponding execution agent to the subtask from a set comprising a plurality of specialized execution agents according to the attribute of each subtask; and the assigned execution agent cooperatively executes the allocated sub-task so as to complete the high-order task. According to the invention, the modularization, the expandability and the task execution robustness of the intelligent system are obviously improved, and the method is suitable for various intelligent scenes such as industrial manufacturing and home service.

The invention relates to a space crowdsourcing task allocation method based on trajectory prediction, and the method comprises the steps: constructing an MMLSTM model based on a meta path and a Mogrimer LSTM module in combination with end-to-end training, and enabling the MMLSTM model to be used for predicting a user trajectory; and according to the predicted user track, adopting a dynamic strategy of combining grouping insertion and single task insertion to distribute the published new task to the corresponding user on line. Compared with the prior art, on one hand, by introducing the space-time diagram and the multi-type meta path, the complex space-time interaction relationship between the users can be explicitly and structurally modeled, and by combining with the Mogrifier LSTM, the time sequence evolution can be dynamically captured, so that the prediction of the future trajectory of the user is more accurate; and on the other hand, a dynamic strategy of combining grouping insertion and single task insertion is adopted, geographically adjacent tasks are packaged and allocated, and an access sequence is optimized, so that the total detour overhead when a user executes a plurality of tasks can be remarkably reduced.

The application provides a kind of based on self-supervised contrast learning's aspect-level sentiment analysis model training method, system, storage medium and electronic equipment, it is related to natural language processing technical field.Compared with complex downstream structure, the framework only contains pooling operation and fully connected dimension reduction operation, realizes with less additional parameter fine-tuning BERT model.Different from single task fine-tuning mode, the framework takes self-supervised contrast learning as auxiliary task, aspect-level sentiment analysis task as main task, constructs the way of joint fine-tuning to fine-tune pre-training BERT model.Thereby, with the simple downstream structure, more accurate mining of each aspect sentiment in review text is realized.In addition, by means of the characteristics of maximizing similar entity distance and minimizing non-similar entity distance, the BERT model fine-tuned as downstream task can generate more representative features, and effectively assist the aspect-level sentiment analysis task to judge the sentiment conveyed by aspect word entity.

The application provides a fault-tolerant scheduling method and device for an OODAflow operating system, which comprises the following steps: receiving an intelligent computing task and splitting the intelligent computing task into single tasks, encapsulating the single tasks and an environment health degree monitoring process into self-contained containers, and waiting for a computing device to schedule; deploying the single tasks encapsulated into the self-contained containers to corresponding computing nodes; in the process of executing the tasks on the computing nodes, the environment health degree monitoring process monitors the health condition of a computing device to which the computing node belongs in real time, if the health condition has a fault, the computing node exits the task and saves a checkpoint file; if there is no fault, the execution result of the task is written back to a shared storage area. The fault-tolerant scheduling system provided by the application realizes a fault-tolerant mechanism and efficient fault-tolerant scheduling for the OODAflow operating system task, and guarantees the real-time performance and reliability of the OODAflow operating system in processing the computing task.

The invention discloses a fine-grained and high-quality privacy protection data aggregation method. A trust mechanism, a cloud service platform, a fog node, a task requester and a task executor are included. The trust mechanism is responsible for completing generation, distribution and management of a secret key and generation of a pseudonym of a task executor; the cloud service platform is responsible for receiving tasks issued by a task requester, uniformly collecting data and distributing the tasks and information; the fog node is responsible for encrypting privacy information of task executors, distributing selection tasks of an adaptive task executor group and collecting and uploading sensing data; the task requester is responsible for making a task request to the cloud service platform and receiving data collected by a task; the task executor is responsible for executing the task and submitting sensing data to the fog node; according to the method, high-quality data aggregation can be realized, and meanwhile, the location privacy and the data privacy of the user are protected. And the contribution of each user to a single task is considered, so that fine-grained personalized rewards are realized.

The invention provides an interface test method and system for multi-task software. The interface test method comprises the following steps: constructing a message structure analysis library corresponding to a task message structure of a tested end at a test end; establishing communication connection between the tested end and the test end; the test end sends a to-be-checked task message name to the tested end; when the tested end detects that the message name of the task message transmitted between the task software is consistent with the task message name sent by the test end, the task message is sent to the test end; the test end receives the task message sent by the tested end, and decodes the task message based on the message structure analysis library; and the test end checks the decoded task message. By constructing the message structure analysis library corresponding to the task message structure of the tested end, the input and output logic of the single task software in the multi-task software can be tested very flexibly, and the test strength is expanded.

The embodiment of the application belongs to the technical field of laser processing, and relates to an intelligent processing method, comprising the following steps: prestoring at least two processing modes; acquiring a task cycle size and a single task maximum size; calculating a processing width according to the acquired task cycle size and single task maximum size, and selecting one processing mode from the prestored processing modes according to the processing width; moving a marking device to a starting position of the marking device and moving a cutting device to a starting position of the cutting device according to the selected processing mode; controlling the marking device to mark according to the selected processing mode, and controlling the cutting device to cut according to the selected processing mode. The application further provides an intelligent processing device, a computer device and a storage medium. The method can adapt to batch processing of different tasks, has strong adaptability, does not need human participation and equipment replacement, and saves the cost of producing products of different sizes.

The application relates to a serial intelligent scheduling method in a multitask producer environment, and belongs to the technical field of embedded special equipment. The method learns the design idea of a state machine processing mode, and utilizes the state machine to process each single task. In order to save the context of the multitask, a general state machine is designed in structure and logic, but can be customized according to the specific interactive process of each type of task and the data required in the middle. The method can realize intelligent task scheduling of multitask parallel in a serial (single thread) mode in the multitask producer environment.

The invention provides a MapReduce task execution method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the current load information corresponding to each current partition, and the current load information comprises the current data size and the current load condition; under the condition that it is determined that multiple target low-load partitions exist according to the current load information corresponding to each current partition, the multiple target low-load partitions are combined, and a combined partition is obtained; and executing processing of the data in the merged partition through a target task to obtain a processed result. According to the method and the device, a plurality of target low-load partitions are combined into the combined partition, and processing of the data in the combined partition is executed through one target task, so that the purpose of improving the load of a single task can be achieved, and the technical effect of improving the resource utilization efficiency is achieved; therefore, the problems of poor static partition performance and low resource utilization efficiency of the MapReduce framework in the prior art are solved.

The invention relates to a reservoir water resource management technology, discloses a reservoir water temperature machine learning prediction method considering reservoir area vertical-discharged water temperature linkage, and solves the problems that numerical simulation in an existing reservoir water temperature prediction method is difficult to adapt to a dynamic boundary; and a machine learning single-task model does not consider the cascade relationship between the vertical water temperature and the discharged water temperature of the reservoir area, so that the generalization ability is insufficient, and the prediction efficiency is low. According to the scheme, firstly, monitoring data related to the water temperature of the reservoir are collected and preprocessed, and a data set is obtained; then, constructing an LSTM model architecture of'shared feature extraction + dual-task output '; then training the dual-task LSTM model by using the data set; in practical application, historical time sequence data of the preprocessed water temperature correlation indexes are input, and vertical water temperature data and discharged water temperature data in a future time period are obtained based on the trained double-task LSTM model.

A system and method for a using a genetic algorithm with an extra bit in the genome to obtain a best-fit of resource combinations for a particular task to be scheduled. The system can generate a genome from a plurality of jobs and then add an extra bit that can be used to find the best resource combination in the event that multiple resource combinations are equally suitable as an initial fit.

The invention discloses a CTC simulation practical training emergency disposal process task issuing method and system, and relates to the technical field of railway dispatching simulation practical training, and the method comprises the following steps: S1, a teacher imports a scheduling table of a dispatching station into a CTC simulation practical training system for visual interface display; s2, based on visual interface display, the teacher formulates a reasonable emergency disposal process task for the student and sets task basic information; s3, the teacher stores, modifies, exports and deletes the emergency disposal process task; s4, issuing an emergency disposal process task corresponding to the trainee, and enabling and completing the task by the trainee; and S5, carrying out training task training condition statistics and query. According to the invention, tasks can be distributed in a personalized manner, the training time and training tasks of trainees are reasonably arranged, and the flexibility of training task configuration and the objectivity of evaluation are improved. The problems that a traditional system is single in task issuing mode, generally, only the same task content can be set for all trainees, and personalized training requirements cannot be met are solved.

The application provides a multi-robot dynamic task allocation planning method and related device, and relates to the technical field of robot control. The application carries out robot work zoning on all task points in the target cooperative work scene by taking the actual spatial position and actual residual energy of all idle robots in the target cooperative work scene at the current time as the basis, so that the actual residual energy of any idle robot in the robot zoning result of a single task point meets the task execution energy consumption requirement of the robot near the task point, to realize the robot adaptive dynamic zoning effect considering the global robot real-time state information in the dynamic and variable scene. Then, the application allocates the to-be-executed task associated with each task point to the multiple robots in the robot zoning result of the task point, to realize the efficient and reasonable multi-robot dynamic task coordination allocation effect in the dynamic and variable scene, and avoid the task allocation conflict phenomenon.