862 results about "Execution time" patented technology

The invention discloses an intelligent resource scheduling method and system based on a dynamic data consanguinity atlas, and relates to the technical field of resource scheduling, the method comprises the following steps: collecting execution logs and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted consanguinity atlas; calculating a blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship map; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship map of the to-be-scheduled task, the real-time load state of the target node and the scheduling execution time delay; performing priority ranking on the to-be-scheduled tasks based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when detecting that the predicted load of the target node exceeds a threshold value and the weight ratio of the key consanguinity tasks borne by the target node exceeds a preset threshold value. Through a dynamic consanguinity map and an intelligent scheduling algorithm, high efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and task delay is reduced.

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 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 provides a GPU heterogeneous computing resource allocation method and system based on task scheduling, and the method comprises the steps: carrying out the feature analysis of a to-be-scheduled task, so as to extract the calculation intensity, video memory demands, inter-task data dependence and historical execution data of the to-be-scheduled task, and obtaining a task feature vector; constructing a performance model of heterogeneous GPU resources to predict execution efficiency and resource consumption of the GPU under the target task based on hardware performance parameters and running states of the GPU; and matching the to-be-scheduled task with the GPU resources according to the task feature vector and the performance model of the heterogeneous GPU resources. Through task feature analysis and GPU resource performance modeling, the adaptive relation between tasks and GPU resources can be deeply mined, to-be-scheduled tasks are accurately allocated to the most appropriate GPU, the resource utilization rate is greatly increased, the task execution sequence and resource allocation are dynamically planned, and the task execution time is effectively shortened.

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 invention discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively improved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

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 an NPU computing task scheduling method, device and equipment for a multi-mode SoC main control chip. The method comprises the following steps of: extracting task characteristic parameters of an NPU (Network Processing Unit) calculation task in a multimode SoC (System on Chip) main control chip, and establishing an initial scheduling time sequence table; computing resource partition division is carried out on the NPU computing unit, and a resource state matrix is established; an overlapping time sequence matrix is generated, and a Q-learning task optimization model is constructed; performing double-layer grouping sorting to generate a task allocation sequence; resource load calculation is carried out, a migration cost matrix is generated, task redistribution is executed, and a task redistribution result is obtained; and generating a global execution time sequence of the NPU calculation task, performing task scheduling on the NPU calculation task according to the global execution time sequence, and outputting task scheduling execution information. According to the method, the task execution delay and the resource utilization rate are balanced, the overall optimality of the scheduling scheme is ensured, and the response capability and the processing efficiency of the system to the burst task are improved.

The invention relates to the technical field of operating systems, in particular to a process scheduling method, system and equipment based on a heterogeneous multi-core processor, and the method comprises the following steps: monitoring and recording an operation behavior of a user in real time, and generating a log file of the user behavior; analyzing the log file of the user behavior by using a machine learning algorithm, and predicting the next operation behavior of the user; dynamically loading the application predicted to be used into a memory based on the prediction result; determining the optimal processor core of the task by comprehensively considering the execution time, energy consumption, dependency relationship and processor resource utilization rate of the task according to the prediction result in combination with the state of each processor core in the system and the current process scheduling strategy; and allocating different tasks to the corresponding optimal processor cores for execution. The overall performance of the system is improved, energy consumption is reduced, and optimization of energy efficiency is achieved.

The invention provides a timed task execution optimization method and system, and aims to solve the problems of inflexible static priority scheduling, unintelligent resource allocation and the like in the prior art. According to the method, a distributed timed task intelligent scheduling framework is constructed and comprises five core components including a task management console, a scheduling decision engine, a resource monitoring agent, a task execution cluster and metadata storage. Task basic information is obtained through task feature extraction, a dynamic priority score is calculated based on decision factors such as SLA urgency, resource matching degree and service weight, and task priority ranking is achieved. And performing intelligent task allocation by adopting a BestFit algorithm, and allocating the task to the optimal execution node. The system monitors the CPU utilization rate, the memory occupation and the IO waiting time of the nodes in real time, and when the CPU utilization rate exceeds 85%, an elastic resource allocation strategy is triggered. And when the task execution time exceeds the pre-estimated duration, performing task splitting and rescheduling based on a dynamic fragmentation algorithm.

The invention discloses a multi-queue scheduling method based on total and incremental reasoning overhead merging, which comprises the following steps of: firstly, calculating the total resource demand and memory overhead of a task according to the input length and the size of a predictor model in the process of performing total reasoning on a reasoning task; distributing tasks to queues with different priorities according to the total resource demand in combination with response time requirements and task importance; dynamically scheduling tasks according to a priority queue sequence, adjusting an execution sequence in combination with a system load, and preferentially executing tasks in a high-priority queue; distributing a time slice for each task, monitoring task execution time, reducing the priority of overtime tasks, moving the overtime tasks into a secondary priority queue, and triggering new task scheduling; and after preemptive scheduling, the key value cache of the latest scheduling task in the waiting queue is swapped from the accelerator memory to the CPU memory, and is swapped back to the accelerator memory before the task is re-executed.

The invention provides an elastic configuration and bidirectional collaborative adaptation system for application computing tasks, and relates to the technical field of computers. Equipment and network states are monitored in real time by applying an elastic configuration mechanism of a computing task, execution tasks and nodes in a task graph are dynamically adjusted, task response time is optimized, meanwhile, collaborative optimization is performed between an equipment end and a cloud end by applying a bidirectional collaborative adaptation algorithm of computing network resources, computing resources are reasonably distributed, and the computing efficiency is improved. The limitation of single optimization dimension in the prior art is overcome, in addition, the system adopts a self-adaptive learning mechanism, a task unloading strategy and a resource management scheme are continuously optimized by utilizing machine learning and a neural network model, the long-term performance is improved, and the technical scheme not only improves the dynamic response capability of the system, but also improves the reliability of the system. In addition, global optimization of resources is realized in a multi-user and multi-task environment, and task execution time and energy consumption are reduced.

The invention provides an SQL statement optimization suggestion generation method and device, a medium and electronic equipment, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring SQL log monitoring data, including an execution timestamp and a time consumption value of an SQL statement, dividing the data according to a preset time window, and constructing a to-be-processed data set; a median approximation algorithm is used for calculating the median of SQL execution time consumption, and then abnormal SQL statements in the running log are screened out. And for each abnormal SQL, reconstructing an execution context thereof, obtaining an abstract syntax tree and an execution plan, and substituting the abstract syntax tree and the execution plan into a preset cue word template to generate cue words. And finally, calling a large model to analyze the cue word so as to obtain an optimization suggestion for the abnormal SQL. By collecting and analyzing the SQL logs and combining the median algorithm and the large model technology, efficient SQL statement optimization is achieved.

The invention provides a task termination strategy generation method and system based on a dynamic programming algorithm, a storage medium and electronic equipment, and relates to the field of risk control. The method comprises the following steps: firstly, defining that the deterioration state of a system obeys a multi-stage continuous time Markov chain and random task duration; secondly, constructing a value function to obtain expected costs of tasks in all possible execution durations in each deterioration state; and finally, a reverse induction method is combined with a dynamic programming algorithm, forward recursion is carried out from the end point time of the longest task duration, so that an optimal task termination decision under each time step length is determined, and global optimization can be realized. According to the method, the uncertainty of the task execution time in an actual system can be reflected more truly, the dynamic change in the task execution process can be monitored in real time, and the task termination strategy is dynamically adjusted based on real-time data, so that a complex and changeable task environment can be flexibly coped, and the survival rate of the system is maximized.

The invention relates to a heterogeneous computing-based task scheduling method and device, a storage medium and equipment. The method comprises the following steps of: obtaining an inference task of a large language model; in the execution process of the reasoning task, at least based on the memory access intensity and the current reasoning stage, memory access intensive operators and calculation intensive operators in the reasoning task are recognized; allocating the memory access intensive operator to a first computing unit for executing a memory intensive task, and allocating a computing intensive operator to a second computing unit for executing a computing intensive task; obtaining estimated execution time of the two calculation units for executing the corresponding tasks and data transmission time between the two calculation units; according to the pre-estimated execution time and the data transmission time, the task starting moments of the first calculation unit and the second calculation unit are determined with the purpose of minimizing the overall execution delay of the reasoning task; and controlling the first calculation unit and the second calculation unit to asynchronously execute the corresponding tasks in parallel according to the task starting time.

The invention discloses a multi-interface interactive training simulation method and system, a medium and a program product, and relates to the technical field of simulation training, and the method comprises the steps: determining a target training subject according to the selection of a student, determining a standard training procedure, displaying a virtual operation panel corresponding to the target training subject on a first display screen, a working principle animation corresponding to the target training subject is displayed on a second display screen, and a real-time simulation state corresponding to the target training subject is displayed on a third display screen; acquiring operation data of a student on the virtual operation panel in real time, matching the operation data with an operation step sequence in a standard training procedure, generating an operation instruction sequence, driving a working principle animation of the second display screen and a real-time simulation state of the third display screen, and recording actual execution time and operation deviation data; and inputting to a preset scoring model to obtain a final operation score, and generating a training report. According to the invention, pertinence of the trainee in simulated driving training can be improved.

Techniques for facilitating efficient polling using machine learning are disclosed. A system uses historical data associated with execution of tasks to train a machine learning model to predict execution times. After receiving a request for execution of a task, the system provides a polling configuration to the requesting device that includes a polling frequency based on a prediction for when the task execution will be completed. This prediction is generated by the machine learning model.

The invention discloses a task scheduling method and system based on an electric power big data model. The method comprises the following steps: S1, collecting and preprocessing electric power system data by utilizing the electric power big data model, calculating a data integrity index, and integrating high-integrity data; s2, on the basis of the integrated data, analyzing features of a task to be scheduled, calculating average execution time of the task and an expected value of resource consumption, and determining an execution difficulty coefficient of the task; s3, evaluating a system resource condition, calculating a resource utilization rate and a residual amount, and allocating resources according to a task execution difficulty coefficient; s4, optimizing the execution sequence and time arrangement of the tasks in combination with the task characteristics and the resource allocation condition, and formulating a task scheduling scheme; s5, executing the task, monitoring the task progress and the resource use condition, calculating the task execution progress deviation and the resource consumption change rate, and adjusting the scheduling scheme according to the monitoring result; according to the method, the reasonability, accuracy and overall operation efficiency of task scheduling in the power system can be effectively improved.

The invention discloses a use case testing method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the steps that multiple to-be-tested use cases are acquired; determining a comprehensive weight of each to-be-tested case according to the priority information, the function modification information, the historical test fault information and the execution duration corresponding to each to-be-tested case; according to the comprehensive weight corresponding to each to-be-tested case, determining a sorting sequence of the to-be-tested cases; and grouping the to-be-tested cases according to the sorting sequence, and distributing each group of to-be-tested cases to test environments with different test efficiencies for testing. According to the application, the comprehensive weight of each to-be-tested case during testing is determined according to the priority information, the function modification information, the historical test fault information and the execution duration of different to-be-tested cases, and the to-be-tested cases are distributed to test environments with different test efficiencies for testing based on the comprehensive weight, so that the test period is shortened, and the test efficiency is improved. The test efficiency is improved.

The invention discloses a micro-service asynchronous exporting method. The method comprises the following steps that a user initiates an exporting request through a front end; the business service receives an export request initiated by the front end and assembles the query statement and the export template parameters to the export request according to the business; the business service forwards the processed export request to an asynchronous export service; after receiving an export request of the business service, the asynchronous export service creates an export task and returns a success state to the business service; the asynchronous export service evaluates the complexity, the dependency relationship and the execution time of the task according to the data volume and the system performance of the exported task, selects a task splitting strategy, splits the task into a plurality of sub-tasks and puts the sub-tasks into a task pool; the task pool triggers scheduling, parallel execution of subtasks is achieved through multiple threads or multiple processes, data are read in batches, and the data are written into temporary files. Through asynchronous processing, the user does not need to wait for export completion, and the user experience is improved.

The invention discloses a cross-domain task scheduling strategy determination method and device, equipment and a storage medium. The method comprises the steps of obtaining a first calculation task and a first cluster parameter; and based on the initial scheduling strategy, simulating task allocation processing between clusters according to the first cluster parameter and the first calculation task, and determining a fitness function required by adopting the initial scheduling strategy. And for each task stage, evolutionary operation is performed on the task scheduling strategy corresponding to the minimum fitness function based on a genetic algorithm, the evolutionary task scheduling strategy of each task stage is obtained, the fitness function is determined, and the evolutionary operation of the task scheduling strategy is circularly executed. And when the condition is met, determining that the circulation is ended, and determining a target scheduling strategy according to the task scheduling strategy of the minimum fitness function of each task stage. According to the technical scheme, the optimal task load can be accurately allocated to each cluster, the task execution time can be effectively shortened, and the task execution efficiency is further improved.

The invention relates to the technical field of man-machine interaction, and provides an interactive task control method and device and a storage medium, an operation process of an interactive task comprises an execution process of a first sub-task and a second sub-task, the first sub-task can be controlled to be executed by operation of a user, and the second sub-task is controlled to be executed by an intelligent agent; the method comprises the steps of obtaining first subtask evaluation data of a first subtask in task execution time when an interactive task is in a running state; determining a preset deviation corresponding to the task execution time according to reference evaluation data corresponding to the task execution time and the first sub-task evaluation data; and according to the preset deviation, adjusting the preset interaction parameter of the intelligent agent so as to improve the difficulty adjustment accuracy of the interactive task.

The invention discloses a cloud PLC runtime task dynamic reconstruction system and method, and relates to the technical field of automation control, the system comprises a function library used for storing function block types and function block instances; the task trigger is used for managing the execution time of the task executor; the task executor is used for calling the corresponding function block instance from the function library; the configuration file analysis unit is used for receiving and analyzing the parameter information of the new function block type to obtain the new function block type, and receiving and analyzing the task process description file to construct task process information; the function block management unit is used for storing the new function block type into a function library and constructing a corresponding function block instance based on the function block type; and the task construction unit is used for modifying the task flow information and configuring the execution time. The technical problems that in the prior art, the system reconstruction resource loss is large, and reconstruction can only be carried out in the existing function block range during operation are solved.

Embodiments of the present application provide a method, apparatus, electronic device, and storage medium for managing an automated process. The method includes: obtaining time constraint information between two adjacent nodes in the automated process, where the time constraint information is used to constrain the execution time when performing a transfer operation on a target between two adjacent nodes; determining the execution order of the minimum sub-operations between two adjacent nodes according to the connection relationship between the two adjacent nodes in the automated process; determining the total time for performing the transfer operation between two adjacent nodes at least according to the execution order and the preset execution time of each minimum sub-operation; and verifying whether the time constraint information is reasonable according to the total time. This solution can ensure the effectiveness of the target in the automated process, thereby ensuring the accurate and smooth progress of the automated process.

The embodiment of the invention provides a hardware verification method and device, electronic equipment and a storage medium, and relates to the technical field of hardware verification. The method comprises the following steps: dividing a time slice into an execution time slice of a software test tool and an execution time slice of a hardware simulator; writing simulation input data into the hardware simulator through a data interface of the hardware simulator after all events which can be executed at the current moment in the software test tool are executed in the execution time slice of the software test tool; converting the time slice into an execution time slice of a hardware simulator, calling a control interface of the hardware simulator in a software environment, and controlling the hardware simulator to operate so as to perform hardware verification; and converting the time slice into an execution time slice of a software test tool, and obtaining a hardware verification result through a data interface of the hardware simulator. Event manager data of software and hardware are synchronized through the data interface, and hardware verification in a software environment by using a software test tool is supported.

The invention provides a timed task execution optimization method and system. The method comprises the following steps: forming a timed task execution optimization system architecture; determining a corresponding calculation factor to obtain a dynamic priority score; predicting the to-be-predicted task data by using the task prediction model to obtain a task execution time prediction value; analyzing the to-be-executed task queue, calculating the average waiting time, determining a multiple factor according to the task priority and the system load state, and identifying the task with the task execution time prediction value larger than the dynamic threshold value in the to-be-executed task queue as a long task; performing automatic fragmentation processing on the long task, generating a fragmentation result, determining a scheduling sequence based on the dynamic priority score and the task execution time prediction value, and generating a scheduling decision; and performing resource availability evaluation on the resource use data to obtain a node resource evaluation result, and allocating execution resources for the selected task. According to the method, the problems of efficiency and reliability of the timed task execution system in a high-load scene are solved.

The invention relates to a memory access adaptive self-attention mechanism for a transformer model. A method may include estimating a first execution time for selecting a number k of dominant data elements from an initial self-attention input matrix of a transformer model to generate a sparse self-attention input matrix; estimating a second execution time for performing a self-attention operation of the transformer model based on the sparse self-attention input matrix; estimating a third execution time for performing a self-attention operation based on the initial self-attention input matrix; and performing a self-attention operation based on the first execution time, the second execution time, and the third execution time.

The invention relates to the technical field of data processing, in particular to a data mining system based on artificial intelligence. According to the method, a finer computing resource regulation and control mechanism is constructed through links of inter-task correlation analysis, load task priority adjustment, computing cost evaluation, abnormal task identification, resource use trend analysis and the like. A topological structure is constructed for the relevance between the tasks through a graph neural network, and fuzzy matching is performed in combination with the computing resource sharing degree, so that automatic reasoning of the task dependency relationship is realized, and the intelligence of scheduling optimization is improved. The task priority adjustment adopts a mode of combining a CPU utilization rate and a calculation time proportion, load tasks are dynamically marked, a scheduling sequence is optimized in combination with a task time overlapping degree, and calculation resource conflicts are effectively reduced. According to calculation resource consumption analysis, task calculation load influence coefficients are calculated from multiple dimensions of task execution time, data reading amount and I / O operation times, and the reasonability of calculation resource allocation is improved.

The invention provides a model training method. The method comprises the steps of collecting information of a first model; and making a re-calculation strategy according to the communication time of the first model in the communication stage and the execution time required by the plurality of operators in the first model. The information of the first model comprises execution time respectively required by a plurality of operators in the first model, the recalculation strategy comprises at least one recalculation operator in the plurality of operators and opportunity for executing a recalculation process by the at least one recalculation operator, the recalculation operator is an operator used for executing the recalculation process in the plurality of operators, and the opportunity for executing the recalculation process by the at least one recalculation operator in the plurality of operators is the opportunity for executing the recalculation process by the at least one recalculation operator. The opportunity at which the at least one recalculation operator performs the recalculation process includes performing the recalculation process in parallel with the communication phase of the first model. The recalculation process is parallel to the communication process of the model, so that the model training time is shortened, and the throughput of model training is improved.