84 results about "Single task" patented technology

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 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 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.

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 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 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.

The application discloses a cognitive risk stratification method based on a dual-task gait, which comprises the following steps: acquiring single-task and dual-task walking gait data of a target subject in a longitudinal tracking population cohort; calculating gait loss data of the dual task compared with the single task; performing prognosis correlation analysis in combination with cognitive function decline events in a preset time period, screening target features, determining an optimal cutoff value and a risk stratification determination standard; and completing cognitive risk grade division and outputting a result based on the standard. The method solves the problems of traditional cognitive evaluation, such as complicated operation and difficulty in large-scale application, realizes precise and non-invasive early screening of cognitive risk, and has important clinical application value.

The embodiment of the application provides a kind of algorithm warehouse management method and system based on service tool chain, belong to algorithm warehouse management technical field.The method is matched resource demand by task category identification, generates hardware resource available parameter sequence in combination with the dynamic resource occupation prediction of parallel task of execution terminal, the first screening action is based on the resource fitting degree calculation matching degree per time, substantially improve the resource adaptation accuracy of algorithm model and execution terminal, reduce resource waste, the second screening action is combined with the historical execution information of target service terminal and non-target service terminal, both guarantee the confidence of single task to meet standard, also through service terminal group confidence distribution adjustment execution terminal selection, ensure the overall execution stability of system.Based on the confidence ratio of service terminal group, the execution terminal selection range is dynamically adjusted, the intelligent management of system confidence and computing power reserve is realized, extreme imbalance is avoided, and the adjustability of long-term operation is guaranteed.

The invention discloses an order type task scheduling optimization method and system, and relates to the technical field of task scheduling and control optimization, and the method comprises the steps: constructing an order type task scheduling control model; by constructing a dual-objective function of total delay time minimization and overall execution efficiency maximization, global optimization of task resource allocation is achieved, it is clear that task execution time resources do not exceed the maximum processing duration, and the situation that due to excessive optimization, resources are occupied by a single task for a long time, and the task execution efficiency is improved is prevented. Two objective functions of total delay time and average processing efficiency are constructed, the total delay time objective function can reflect the influence of the task queuing length on the execution efficiency, a penalty term is introduced into a scheme violating constraints, the evaluation weight of the scheme is reduced, it is ensured that the generated scheduling scheme meets both the optimization objective and the actual constraint condition, and the scheduling efficiency is improved. The frontier solution set is generated through iterative solution, conversion of task planning from static rules to dynamic optimization is achieved, and an efficient and reliable solution is provided for an intelligent task management system.

The invention relates to the technical field of intelligent manufacturing, in particular to a vehicle repair store operation system and method using an intelligent station, and the system comprises a scheduling data processing module, a delay evaluation module, a scheduling adjustment module, a priority decision module, and a scheduling execution module. According to the method, quantitative evaluation and grading of the station delay state are achieved by calculating the handover time difference between the operations and comparing the handover time difference with the standard period, the chain reaction range possibly caused by single task delay can be recognized in a prospective mode, and then the adjustment requirement is focused on the affected follow-up tasks; unnecessary global disturbance on an overall plan is avoided, meanwhile, the emergency degrees of the tasks and the time proximity after adjustment are comprehensively evaluated, dynamic station time slot occupation weights are generated, all the tasks to be adjusted are globally sorted, the limitation of a traditional fixed sequence is broken through, and the task scheduling efficiency is improved. And finally, according to the dynamic priority of the task and the actual operation duration, automatically searching and matching an optimal idle station time slot.

The application discloses a kind of multi-task method and system based on multi-modal continuous learning, the method includes: obtaining multi-modal data and constructing multi-modal dataset;Multi-modal dataset is mapped to unified language model representation space, and unified semantic feature representation is obtained;Unified semantic feature representation is input into sparse mixed expert layer, and the previous expert is based on token level sparse activation by router, and forward calculation is carried out by self-attention and expert feedforward neural network;In continuous learning, expert activation frequency and average gradient norm are regularly counted, and low active cold experts or new experts are dynamically pruned, and sparse mixed expert layer is optimized;Decoupling low-rank adaptation mechanism is used, historical task direction is frozen, and only new task learning direction and amplitude are updated;Finally, the prediction result of classification, question answering or generation task is outputed.The application uses the above method, and breaks through the bottleneck of poor modality adaptability and single task of traditional multi-modal model.

Systems and methods described herein provide a computer-implemented approach for process simulations using multi-agent systems. Software agents are trained using historical process data associated with a process. Each software agent represents a single task in the process. Based on a candidate process model, the process is simulated to generate simulation results by executing a multi-agent system. The software agents operate autonomously within the multi-agent system based on trained behavior. The process is also executed using a runtime configuration that is based on the candidate process model. The 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 the simulation and the execution of the process are repeated using an adjusted multi-agent system and an adjusted runtime configuration, respectively. A deployment process model is generated based on the adjusted candidate process model.

The application provides a multi-role convention and exhibition scene-oriented intelligent reception scheduling and information collaboration method and system, aiming to solve the problems of information fragmentation, high dependence on manual scheduling and difficulty in multi-role collaboration in the prior art convention and exhibition reception. The application includes constructing a multi-role account and a resource pool; converting collected guest itinerary information and personalized needs into a structured form of a multi-tuple; intelligently scheduling based on the multi-tuple to generate a reception task scheme containing vehicle pick-up, accommodation and catering; visualizing the reception task scheme and differentially assigning it to corresponding executors and guests, supporting task state updating and rescheduling mechanism. The application not only schedules a single task, but also links the reception arrangement and state change of the three business boards of pick-up, check-in and catering, and realizes information synchronization of multiple roles.

The invention discloses an airspace task cooperative processing method and system based on multi-source target analysis, and belongs to the technical field of multi-source target analysis. Task execution information of task units is continuously collected in each data sampling period, a spatial position coordinate data sequence and a speed data sequence are constructed, and time weighted dynamic trajectory center parameters of a single task unit in a single data sampling period are calculated; calculating the dynamic space coupling degree of the adjacent task units in the data sampling period; calculating the task weight and the comprehensive coupling strength of a single task unit; acquiring a speed change rate of a single task unit, and calculating a task dynamic stability coefficient and a task hierarchy division threshold value; based on the task hierarchy division threshold value and the task dynamic stability coefficient, the stability of the task unit is analyzed, and cooperative processing is performed, so that an intelligent task processing system capable of adapting to airspace dynamic change in real time is realized, and the accuracy, robustness and overall operation efficiency of airspace task scheduling are remarkably improved.