58 results about "Dynamic dispatch" patented technology

The invention relates to the technical field of medical informatization and intelligent scheduling, and particularly discloses a ward nursing process prediction and dynamic scheduling method and system.The ward nursing process prediction and dynamic scheduling method comprises the steps that firstly, a digital twinborn simulation environment synchronized with a physical ward in real time is established, and a nursing process is simulated through a multi-agent network; carrying out multi-time sequence deduction based on the environment, generating a prospective scheduling instruction set, and issuing and executing the prospective scheduling instruction set; calculating a prediction mismatch degree index by collecting actual execution data and comparing the actual execution data with a plurality of dimensions of an expected state; when the index exceeds a dynamic threshold value, a meta control layer is triggered to carry out self-adaptive adjustment on simulation environment parameters, and the self-adaptive adjustment comprises reconstruction of an agent behavior logic weight or switching of a scene deduction mode; and deducing and generating an updated instruction set again based on the adjusted environment to form closed-loop optimization.

The invention provides a task analysis execution method and system based on a multi-modal large model, and belongs to the field of computers.The task analysis execution method comprises the steps that S1, multi-modal task data are received, a double-layer cross-modal fusion mechanism is adopted to conduct unified coding fusion on the multi-modal task data, and fusion features are obtained; s2, performing alignment processing on the fusion features based on a multi-modal embedding alignment mechanism to obtain alignment features, and performing structured task analysis based on the alignment features by using a pre-training language model to obtain task description; s3, decomposing the composite task into atomic tasks, and learning an optimal decomposition strategy by using an Actor-Critic network to form an atomic task set; and S4, generating a comprehensive adaptation score through a dynamic scheduling mechanism of collaborative awareness, and performing task distributed execution based on the comprehensive adaptation score. According to the method, the complex task is intelligently decomposed into the atomic task set capable of being executed in parallel for task distributed execution, and the task understanding accuracy and execution efficiency are improved.

The invention relates to a storage dynamic scheduling method and system based on digital twinning and deep learning and a storage medium, and the method comprises the following steps: constructing a digital twinning space synchronized with a physical storage through collecting operation tasks and storage global operation state data in real time; analyzing the data by using a pre-trained anomaly detection model, generating graded early warning information, inputting the graded early warning information into a dynamic scheduling model, and generating a scheduling instruction set subjected to simulation verification; and judging a high-risk area by executing feedback data of a scheduling instruction set, activating a virtual redundant device model in the digital twin space when a high risk is detected, generating a starting instruction, and scheduling a corresponding physical redundant device to access an operation system.

This application relates to the technical field of natural language processing, and provides a distributed pipeline-parallel LLM fine-tuning method for heterogeneous GPUs. A plurality of LoRA models are fine-tuned simultaneously based on a multi-job fine-tuning system; each LoRA model is partitioned into a plurality of parts distributed on a corresponding number of GPUs, and the GPUs are sorted. A job configuration module generates a plurality of jobs according to a user request, and divides each job into a plurality of training batches; a dynamic job scheduler generates a scheduling scheme based on a training batch sequence of each job and a dynamic scheduling strategy; and the scheduling scheme is sent to a multi-job training module on each corresponding GPU according to a positive sequence of the GPUs, to train all the LoRA models.

The invention discloses a steel production line dynamic scheduling system based on data integration analysis, which belongs to the technical field of steel production line plan scheduling, and is characterized in that a dynamic knowledge graph driven by integrated multiple models is constructed, and scattered and heterogeneous data acquired in real time is converged into a data stream; an initial scheduling scheme is produced based on original order data and production line resource data in the'data stream ', simulation deduction is performed based on digital twin driving, an optimized planned production list is obtained, production is executed with the planned production list as a reference, real-time production data and change order data in the real-time'data stream' are received, and the real-time production data and the change order data in the real-time'data stream 'are obtained. Process deviation and order change in the actual production process are dynamically responded, correlation root cause analysis and scheduling decision making are carried out on multiple disturbance factors based on model and map cooperation, dynamic scheduling based on priority and resource constraint is achieved, it is ensured that the actual production condition is consistent with a production plan after scheduling optimization, and production flexibility and adaptability are improved.

The invention provides a dynamic scheduling method and system for a redundant executor based on a foreground theory and a Q learning evolutionary game method. The method comprises the following steps: constructing a strategy space, a profit structure and strategy evolution dynamics of the executor and a reward and punishment mechanism of an enhanced analyzer-regulator; according to the dynamic scheduling method, an executor simulates interaction under a tripartite game framework, iterative loop is carried out, and a strategy space of a mimicry defense system is output as a redundant executor after the loop is ended, the simulation interaction process comprises the following steps: the enhanced analyzer-regulator monitors and obtains objective income of each execution body in real time, calculates subjective perception utility of each execution body according to an income structure of the execution body, and then adjusts the subjective perception utility of the mimicry defense system according to a reward and punishment mechanism of the enhanced analyzer-regulator. And each execution body dynamically updates the strategy space of each execution body according to the adjusted subjective perception utility and strategy evolution.

The invention discloses a method and system for generating a software development scheme, and belongs to the technical field of software development, and the method comprises the steps: collecting system resource occupation data and thread operation data during multi-user concurrent operation, segmenting the system resource occupation data into resource fragments according to time dimensions, positioning resource competition points, and generating conflict mark data; constructing a thread execution graph based on the conflict mark data, identifying mutually exclusive resource points, constructing a double-layer distributed queue, setting a resource state monitor to dynamically allocate thread priorities, and generating scheduling processing codes; monitoring the running state of the scheduling processing code, and splitting the thread into a plurality of sub-threads for parallel execution when detecting that the thread is blocked; and collecting an execution result, recording the execution path with the shortest response time and the highest resource utilization rate as a scheduling template, and generating a software development scheme. According to the method, the utilization efficiency of system resources is improved through dynamic scheduling and parallel processing, and the thread blocking probability is reduced.

The application discloses a kind of based on performance perception's heterogeneous computing task dynamic scheduling system and method.The system is deployed in digital ecological platform complex system, through the collaborative work of performance knowledge base, real-time performance monitor, scheduling strategy re-evaluation intelligent contract and task allocator, the efficient dynamic scheduling of computing task is realized;Through computing task characteristic fingerprint analysis and AMCU adaptability score model, the computing task with matrix operation operation dominant and higher adaptability score is preferentially scheduled to AMCU computing node, and the efficiency and reliability of heterogeneous computing task scheduling are significantly improved.The application realizes the global performance optimization and trusted scheduling of various heterogeneous computing resources in platform by real-time sensing and dynamic scheduling.

The application provides a method for automatically decomposing and dynamically scheduling quantum tasks under a quantum super-fusion architecture. The method comprises: obtaining a task script corresponding to a quantum task, and performing parsing processing on the task script to obtain circuit information, the circuit information including quantum bit quantity and circuit gate depth information; generating a plurality of sub-task scripts and a sub-task execution chain according to the circuit information and the task script, the plurality of sub-task scripts including a compiled task script, a simulated task script and a post-processing task script; for any one sub-task script, determining a job scheduling script corresponding to the sub-task script according to the circuit information; associating and binding a plurality of job scheduling scripts according to the sub-task execution chain to generate a target execution script, and submitting the target execution script through a preset native interface, thereby improving the quantum task execution efficiency and resource utilization.

The invention discloses a thread pool dynamic scheduling method and system, and the method comprises the steps: dividing the time of a time period into a plurality of time blocks, merging the time blocks with the average time consumption exceeding a set first threshold value, and taking the merged time blocks as an estimated slow time period of a task A in a next time period; in response to an execution starting instruction of the task A, judging whether the current time is in an estimated slow time period of the task, if so, calculating comprehensive average time consumption based on average time consumption of the estimated slow time period and total time consumption of the task in the estimated slow time period, comparing the comprehensive average time consumption with a set second threshold value, and determining a thread pool allocated to the task; and if not, calculating the latest execution time consumption based on the average value of the latest set m times of execution time consumption of the task, comparing the latest execution time consumption with a set second threshold value, and determining the thread pool allocated to the task. According to the method, the thread pool distributed to the task A can be adjusted in advance, the system throughput can be improved, and the service stability is improved.

The invention discloses a fluid pulsation accelerator architecture for reinforcement learning of a large language model, and belongs to the technical field of artificial intelligence hardware acceleration. The system aims at solving the problems that when a current universal processor executes an RLHF working load, an instruction driving normal form is not matched with data flow calculation, fixed parallel granularity cannot adapt to a dynamic load, and the efficiency is low due to the fact that a framework does not sense data statistical characteristics. The core of the system is a liquid systolic array calculation fabric capable of being dynamically reconstructed, execution is triggered through data flow, and kernel boundaries and instruction overhead are eliminated. According to the system, a global lookup table subsystem is fused, and nonlinear calculation is optimized through merging query and parallel lookup by using data distribution prior; and a dynamic scheduling unit is configured, and elastic parallelism is realized by adopting a resource allocation algorithm supporting work stealing. According to the method, the throughput and the energy efficiency of RLHF training and a large language model reasoning stage can be remarkably improved, and a new design normal form is provided for a next-generation AI special computing architecture.

This invention proposes a mobile swarm intelligence sensing task allocation method and system based on fuzzy clustering and dynamic scheduling. First, fuzzy clustering is used to mine the latent spatial structure of tasks, and tasks are classified into fuzzy clusters with membership attributes through soft partitioning. After obtaining the spatiotemporal distribution characteristics of tasks, a benefit matrix including spatiotemporal feasibility and time cost is constructed, and the Hungarian algorithm is used to achieve a globally optimal match between worker and task clusters. For the online execution phase, a dynamic scheduling mechanism including a primary task pool and a collaborative task pool is designed. Workers select tasks based on a hybrid priority index combining time urgency, spatial proximity, and cluster membership. This invention solves the problem of balancing global efficiency and local flexibility in large-scale task allocation through a hierarchical mechanism of "macro-partitioning-meso-matching-micro-scheduling," significantly improving the overall task completion rate while effectively reducing the system's time resource cost.

The application discloses a large model firewall fine-grained dynamic scheduling method and system. The application defines a unified abstract detection interface, each security module is used as an implementation class, and scheduling information is recorded through the static attribute of the interface; three types of execution resource pools, including a CPU thread pool, a GPU inference queue and an asynchronous I / O thread pool, are constructed; the security module is associated to the corresponding resource pool according to the scheduling information; a module performance probe is deployed, running indexes are collected after module execution, and the real-time load of each resource pool is calculated respectively; when the load of any resource pool exceeds a preset upper threshold, the security module in the pool is started to degrade according to the static attribute; and when the load of the resource pool is lower than a lower threshold and lasts for a preset length of time, the security module in the pool which has been degraded is tried to recover. The application realizes fine-grained scheduling of resource pool isolation and module-level perception, reduces the risk of false degradation, and improves the system throughput and recovery efficiency.

The invention belongs to the field of software system docking, and particularly relates to a docking execution method sequence scheduling method and system, and the method is applied to an adaptation layer between a collection system and a third-party system, and is executed by an execution scheduling module in the adaptation layer. According to the method, a configuration analysis result defining an execution sequence is obtained, and the sequence comprises a step identifier, a function module, a sequence and a configuration parameter; determining a current to-be-executed step according to the sequence and the scheduling state and loading configuration parameters of the current to-be-executed step; calling is initiated to the interface adaptation module or the data conversion module based on the parameters to execute operation; receiving an execution result and recording a state; and judging according to the result and an abnormal rule, if an abnormal condition is met, adjusting the execution sequence of the remaining steps according to the rule, otherwise, scheduling the next to-be-executed step according to the sequence until the sequence is completed. According to the invention, the configuration and dynamic scheduling of the docking process are realized, and the flexibility and maintainability of the system are improved.

The application discloses a heterogeneous computing resource dynamic scheduling method and system of a multi-modal end-side model, relates to the technical field of heterogeneous computing resource management, and comprises the following steps: obtaining an operator sequence output by a multi-modal end-side model, and identifying data exchange points among the operator sequence based on execution characteristics; identifying execution pause positions based on the data exchange points, obtaining an execution pause set, interval dividing the operator sequence, extracting an intermediate execution state of each interval connection in a preset visual task after the interval dividing, and obtaining a visual task structure diagram; traversing the visual task structure diagram, identifying a cut-in interval position, obtaining a cut-in sequence, embedding an execution section of a preset language task, obtaining an interleaved execution sequence, and coordinately controlling the tasks, so that the problem that the execution section of the language task cannot be timely inserted into the visual task due to the continuity and non-interruptibility of GPU operator execution, resulting in insufficient utilization of heterogeneous computing resources and task coordination lag is solved.

The invention discloses a resource scheduling and load balancing method and device for an MCP tool and a storage medium. The resource scheduling and load balancing method comprises the steps of obtaining resource state data of multiple MCP tool server nodes; obtaining task request data of an MCP tool calling task; generating a resource allocation scheme based on the resource state data, the task request data and a dynamic scheduling algorithm; and executing and dynamically adjusting the resource allocation scheme in real time. According to the method and the device, the technical problem of low resource allocation efficiency and execution efficiency during MCP tool calling in the prior art can be solved, and the resource allocation efficiency and the overall execution efficiency can be improved. The method can be widely applied to the technical field of artificial intelligence infrastructure optimization.

This disclosure relates to a dynamic scheduling method, apparatus, device, and storage medium based on CiM. The method includes: extracting modality information and task information of an input task; dynamically determining an allocation and migration scheme for the input task from a reconfigurable mapping table based on the modality information and the task information; the reconfigurable mapping table integrating allocation and migration schemes for CiM levels corresponding to different multimodal task scenarios; allocating and migrating the input task to a target CiM level according to the allocation and migration scheme; and executing the input task in the target CiM level. This addresses the limitations of traditional fixed-mapping hardware architectures, further improves the flexibility and throughput efficiency of hierarchical CiM architectures in multimodal inference scenarios, and promotes the deep application of multimodal technology in more complex scenarios.

The invention provides a mobile crowd sensing task allocation method and system based on fuzzy clustering and dynamic scheduling. The method comprises the following steps: firstly, mining a potential space structure of a task by using fuzzy clustering, and classifying the task into fuzzy clustering with membership attribute through soft division; after spatio-temporal distribution characteristics of tasks are obtained, a benefit matrix containing spatio-temporal feasibility and time cost is constructed, and global optimal matching between workers and task clusters is realized by adopting a Hungary algorithm. For an online execution stage, a dynamic scheduling mechanism comprising a primary task pool and a cooperative task pool is designed, and a worker screens tasks according to a mixed priority index combining time urgency, spatial proximity and clustering membership. Through a hierarchical mechanism of'macroscopic division-mesoscopic matching-microscopic scheduling ', the problem that global efficiency and local flexibility in large-scale task allocation are difficult to consider at the same time is solved, and the time resource cost of the system is effectively reduced while the overall task completion rate is remarkably improved.

This invention discloses a dynamic priority-based reconciliation task scheduling and management method and system, belonging to the field of task scheduling and management technology. The method includes: aggregating multiple original reconciliation tasks according to task dependency topology; setting multiple basic priorities for the original reconciliation tasks; outputting multiple dynamic priorities; performing hierarchical correction and connection of a hierarchical task tree to obtain a target correction task tree; segmenting the target correction task tree; performing temporal scheduling analysis on Q task execution containers to output a flexible round-robin container scheduling chain; and performing dynamic scheduling of reconciliation. This invention solves the technical problems in existing technologies, such as the lack of dynamic priority consideration in reconciliation task scheduling, low task execution efficiency, and unreasonable resource allocation, leading to long reconciliation times and serious resource waste. It achieves dynamic and efficient priority-based scheduling of reconciliation tasks, improving task execution efficiency and optimizing resource allocation.

The application relates to a warehouse dynamic scheduling method and system based on digital twinning and deep learning and a storage medium, the method comprising the following steps: collecting job tasks and warehouse global operation state data in real time, constructing a digital twinning space synchronized with a physical warehouse; analyzing the data by using a pre-trained anomaly detection model, generating hierarchical early warning information and inputting the dynamic scheduling model to generate a scheduling instruction set verified by simulation; judging a high-risk area by feeding back data by executing the scheduling instruction set, activating a virtual redundant equipment model in the digital twinning space when the high risk is detected, generating an enabling instruction and scheduling a corresponding physical redundant equipment to access a job system.

The application discloses a task dynamic scheduling method and device based on a time-sensitive rule engine, a storage medium and computer equipment, and the method comprises the following steps: determining time priority data of a plurality of to-be-processed tasks through a time-sensitive rule engine; determining a plurality of candidate processors, constructing a cost matrix containing task allocation costs of allocating each to-be-processed task to each candidate processor, and constructing a priority matrix corresponding to the cost matrix according to each task allocation cost in the cost matrix; reducing the cost matrix to update the cost matrix, performing initial task matching on the to-be-processed tasks based on the cost matrix; finding an augmented path for an unmatched task according to the priority matrix, calculating a task allocation cost adjustment amount based on the augmented path, adjusting each task allocation cost in the cost matrix according to the allocation cost adjustment amount, continuing to find an augmented path in the next round, and obtaining a feasible path; and determining the processor of the unmatched task according to the feasible path.

This invention belongs to the field of distributed large language model inference scheduling technology, and relates to a multi-model dynamic scheduling method for high-concurrency dialogue requests. The method includes: collecting the network card hard interrupt timestamp sequence at the streaming response bus to establish a streaming output time interval vector; extracting the differential fluctuation envelope of the streaming output time interval vector and comparing it with a reference transmission delay line to separate the channel congestion index; calculating the real-time routing suppression coefficient based on the queue backlog length and the channel congestion index and reconstructing the cost-aware routing matrix; compressing the sampling window when concurrent data traffic is overloaded, and sending a redirection signal to shift the concurrent data stream when the real-time routing suppression coefficient exceeds the limit. This invention eliminates the need for active probing, decouples the calculation of congestion and network jitter in situ, effectively eliminates long-tail response latency, and improves throughput.

The application relates to the technical field of resource scheduling, in particular to a dynamic scheduling method and system based on real-time orders, which comprises the following steps: obtaining target task text in a real-time task flow and resource state information matched with the target task text; performing feature extraction on the target task text to obtain multiple scheduling features of each task entry of the target task text; calculating a scheduling priority score of each task entry according to the scheduling features of each task entry; and classifying each task entry according to the scheduling priority score of each task entry to obtain an entry category of each task entry; wherein the entry category comprises an emergency category and a routine category. The application can realize real-time acquisition of a task flow and resource state information and dynamic scheduling according to the information, so that the system can respond to changes in time, and the flexibility and adaptability of scheduling are improved.

The invention relates to the technical field of script processing, and provides a script dynamic scheduling and interrupt processing method, which comprises the following steps: in a parent script execution process, when a child script calling instruction is detected, creating an independent child script execution environment, and isolating the child script execution environment from a parent script execution environment; the child script execution environment is started to execute the child scripts in parallel, and meanwhile the parent script continues to execute subsequent instructions; an interrupt event is monitored in real time, when the interrupt event is detected, execution of the current sub-script is suspended, an execution context is stored, and the execution context comprises an instruction pointer, locked resources and non-persistent intermediate data; selecting a processing strategy according to the interrupt event type; and after the interrupt processing is completed, recovering the execution of the child script based on the stored execution context, or terminating the child script and notifying the parent script to update the global state.

The invention discloses a multi-priority hybrid task dynamic scheduling strategy in a heterogeneous environment. According to the principle, a directed acyclic graph is constructed according to the mutual dependency relationship between sub-tasks of hard real-time and soft real-time tasks after decomposition, a single directed acyclic graph heterogeneous system model is established, related definitions in the model are determined, and the heterogeneous system model is established by introducing the concept of urgent density and surplus value density and performing detailed definition and related certification on the concept of urgent density and surplus value density. According to the method, the urgent density and surplus value density priority queues are constructed based on the multi-priority tasks, a multi-priority mixed task dynamic scheduling strategy under the heterogeneous environment is provided, the real-time performance of multi-priority task scheduling is guaranteed, the mixed tasks are efficiently processed, and meanwhile the bumping phenomenon of the scheduling strategy in the preemption process is avoided. According to the method, the execution efficiency of the scheduling strategy in the real-time embedded heterogeneous environment is improved, and meanwhile, the hybrid task can be better scheduled under the high system load, so that the existence and uniqueness of the interaction behavior of the real-time embedded industrial control system are ensured, and the certainty of system resource allocation and real-time task scheduling is more accurately expressed.

The invention provides a task arrangement and dynamic scheduling method oriented to multi-machine concurrency, and relates to the technical field of workflow engines and intelligent arrangement, and the method comprises the steps: receiving a to-be-executed task; writing the to-be-executed task into a task waiting queue; refreshing a task running state of a to-be-executed task in each task node; determining whether the available capacity in the task machine is greater than 0, and if yes, determining whether a task node in an idle state exists in the task machine; if yes, determining whether a to-be-executed task exists in a task waiting queue corresponding to the task machine; if yes, writing the to-be-executed task in the task waiting queue into the task node; determining a task output result based on the to-be-executed task; the problems that at present, multiple computers often depend on manual writing and maintenance of multiple sets of submission scripts, when a master control program abnormally exits, jobs and states are difficult to recover consistently, and resource idling, repeated submission or result loss are easily caused are solved.

This disclosure provides a task scheduling method, apparatus, and electronic device for a Completely Fair Scheduler (CFS), relating to the fields of artificial intelligence and cloud computing technologies, and particularly to operating system scheduling technology. The implementation scheme includes: determining the waiting time of ready tasks in the processor's run queue; updating the processor's latency information based on the waiting time of a ready task leaving the run queue when the ready task leaves; comparing the maximum waiting time of ready tasks in the run queue with a latency threshold, wherein the latency threshold is based on the processor's latency information; and scheduling the ready task corresponding to the maximum waiting time in response to the maximum waiting time exceeding the latency threshold. Using embodiments of this disclosure, the latency threshold for prioritizing task scheduling is dynamically determined by utilizing the processor's latency, thus achieving dynamic scheduling based on system operating conditions on top of CFS.

This invention belongs to the field of distributed systems and artificial intelligence technology, and relates to a multi-agent task scheduling method and system based on a distributed architecture, applicable to heterogeneous task collaboration and dynamic scheduling in scenarios such as game theory and adversarial competition. The method includes: dividing the original task into sub-task sets through multi-dimensional adaptive granularity; evaluating the capabilities of the agents, and scheduling each sub-task in the sub-task set to multiple agents for execution based on the agents' capabilities; and aggregating the sub-task execution results of each agent to obtain the final task execution result. This invention can solve the problems of single point of failure, poor scalability, insufficient resource awareness, and task conflicts existing in traditional centralized scheduling systems. By constructing a "decentralized + self-organizing + collaborative decision-making" agent network, and utilizing a lightweight consensus mechanism and dynamic task splitting strategy, it achieves distributed, dynamic, and adaptive task scheduling among multiple agents.