87 results about "Dynamic dispatch" patented technology

The invention relates to the technical field of PC (Personal Computer) terminal AI (Artificial Intelligence) computing, and discloses a method and a system for dynamically scheduling generative AI heterogeneous computing resources of a PC terminal. The system comprises a resource state acquisition module, a scheduling graph generation module, a resource fluctuation entropy analysis module and a scheduling decision engine module. The resource state acquisition module captures running state parameters of a GPU kernel, a CPU thread and a memory block in real time, and generates a resource state feature tensor through normalization processing; the scheduling atlas generation module analyzes and computes the node connection topology, extracts the correlation between the devices, and constructs a multi-dimensional scheduling atlas; the resource fluctuation entropy analysis module separates the load feature vectors, calculates the entropy of each calculation unit and generates a heterogeneous resource entropy matrix; and the scheduling decision engine module jointly analyzes the atlas and the matrix, identifies bottleneck node resource competition characteristics, generates a dynamic scheduling instruction set, adapts to generative AI task requirements, and ensures efficient and stable operation of the task.

The invention relates to a large language model and multi-agent collaborative power grid task self-matching and dynamic scheduling method and related equipment. The method comprises the following steps: analyzing and modeling task description information by using a large model to obtain initial structured task information, calibrating the initial structured task information based on a power grid task semantic graph to obtain standard structured task information, and generating a power grid task based on the standard structured task information; performing multi-dimensional vector modeling to obtain a resource object; calculating a matching degree between the power grid task and the resource object, and determining a candidate resource object of the power grid task based on the matching degree; enabling the task agent and the resource agent to perform cooperative scheduling, determining a target resource object of the power grid task, and generating task resource mapping information; and examining the task resource mapping information and sending orders. The task semantic analysis accuracy can be improved, the matching precision of resources and tasks can be improved, the scheduling self-adaption and optimization capability can be improved, and the compliance risk can be effectively avoided.

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.

The invention relates to the technical field of high-performance computing and artificial intelligence crossing, and discloses a method for automatically generating a simulation computing task on a supercomputing platform, which comprises the following steps of: receiving a task demand of a user; performing semantic understanding and parameter extraction on the input by using a parameter dynamic analysis engine to generate a simulation parameter table; based on the simulation parameter table and the input format specification of the target simulation software, an executable parallel computing task script is automatically generated through a self-adaptive script generator; according to the real-time resource state and the task requirement of the supercomputing platform, a dynamic scheduling algorithm is adopted to distribute the task script to the optimal computing node; in the task execution process, the running state is monitored in real time, and when abnormity is detected, a fault-tolerant and self-repairing mechanism is triggered; by automatically generating the task script, the user parameter configuration time is saved, and the overall scientific research efficiency is improved by more than 30%; natural language input and zero code configuration are supported, and common scientific researchers can quickly master the method.

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 relates to a Redis-based asynchronous task security scheduling method and system, and the method comprises the steps: creating an asynchronous task, and storing the asynchronous task to a task queue for scheduling; scheduling the asynchronous tasks through atomic operation executed by the script, preferentially acquiring the overtime asynchronous tasks from the task execution set, or popping up the asynchronous tasks to be executed from the task queue, and recording the acquired asynchronous tasks to the task execution set to track a scheduling state; prolonging the maximum execution time of the asynchronous task by updating the expiration time of the asynchronous task in the task execution set; and after the asynchronous task is completed or failed, deleting the asynchronous task from the task execution set. Compared with the prior art, the scheme has the advantages that the task scheduling stability in a high-concurrency scene is remarkably improved, the task loss risk is reduced, non-blocking access and dynamic scheduling are supported, the scheme is suitable for high-reliability systems such as financial systems and industrial control systems, the system performance is optimized, and the implementation complexity is simplified.

The invention discloses a multi-task allocation and scheduling method for a multi-core data stream processor. The method comprises the following steps: step 1, building a system architecture; 2, task collection and sparseness calculation; step 3, dynamic priority calculation; step 4, implementing a load prediction mechanism; step 5, task scheduling and distribution; and step 6, evaluating and optimizing the system. By introducing a dynamic priority adjustment mechanism, the system can respond to changes of task characteristics and kernel states in real time, the scheduling efficiency is remarkably improved, and the dynamic adaptability ensures that key tasks can preferentially obtain processing resources, so that the processing delay is reduced; through a load prediction mechanism, the load among the cores is effectively balanced, and the situation that some cores are overloaded and other cores are idle is avoided, so that the overall resource utilization rate of the system is improved, dynamic scheduling and load optimization are integrated, the system can keep efficient operation in a complex data stream processing scene, and the throughput and real-time response capability of data processing are improved.

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.

This invention discloses a robust optimization-based dynamic scheduling method for remanufacturing workshops, comprising a pre-scheduling stage and a dynamic scheduling stage. In the pre-scheduling stage, a discrete scenario set is used to describe the arrival and processing times of jobs in the remanufacturing workshop. A robust optimization objective function is established based on minimizing the completion time of the pre-scheduling scheme and the difference in completion time under different scenarios. A biogeographical optimization algorithm is used to output the pre-scheduling scheme. In the dynamic scheduling stage, if a disturbance event occurs, the pre-scheduling scheme is used as the input scheme, and a job right-shifting and rescheduling strategy is executed to obtain the dynamic scheduling scheme. If the dynamic scheduling scheme is equal to or does not dominate the input scheme, the dynamic scheduling scheme is output; otherwise, a dynamic scheduling objective function is established by minimizing efficiency and robustness indices, and a biogeographical optimization algorithm is used to output the dynamic scheduling scheme. This invention considers the problems of multiple uncertainties and the impact of disturbance events, resulting in a better scheduling scheme.

The invention discloses an account checking task scheduling management method and system based on a dynamic priority, and relates to the technical field of task scheduling management, and the method comprises the steps: aggregating a plurality of original account checking tasks according to a task dependence topology; setting a plurality of basic priorities of the plurality of original account checking tasks; outputting a plurality of dynamic priorities; performing hierarchical correction connection of the hierarchical task tree to obtain a target correction task tree; segmenting the target correction task tree; analyzing time sequence scheduling of the Q task execution containers, and outputting an elastic rotation container scheduling chain; and performing dynamic scheduling and account checking. The technical problems that in the prior art, account checking task scheduling lacks dynamic priority consideration, task execution efficiency is low, resource allocation is unreasonable, account checking work consumes long time, and resource waste is serious are solved, dynamic and efficient scheduling of the account checking tasks based on the priority is achieved, the task execution efficiency is improved, and user experience is improved. And the technical effect of resource allocation is optimized.

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.

The invention discloses a Transform-based elastic computing architecture multi-task dynamic scheduling method, which comprises the following steps of: S1, inputting an original feature Bi of a to-be-scheduled task Ai into a Transform encoder to obtain a high-dimensional feature Bi'of the to-be-scheduled task Ai; wherein the original feature Bi comprises a task type, a task data scale and task predicted execution time; inputting a resource state feature time sequence Dj = {dj, t-M [delta] t, dj, t-(M-1) [delta] t,..., dj, t-m [delta] t,..., dj.t-[delta] t, dj, t} of a computing node Cj in the elastic computing architecture into a time sequence prediction model based on Transform to obtain predicted resource state features dj, t + [delta] t of the computing node Cj; wherein the resource state features comprise a CPU utilization rate, a memory usage amount and a disk I / O rate; and obtaining an optimal computing node of the to-be-scheduled task Ai based on the priority of the to-be-scheduled task Ai, the high-dimensional feature Bi'and the predicted resource state feature dj, t + delta t. According to the method, multi-task dynamic scheduling can be efficiently and accurately carried out.

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 invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-agent dynamic scheduling and inference chain synthesis method, which comprises the following steps that: step 1, a dynamic scheduler monitors the calculation load and task queue state of each agent in real time: step 2, an inference chain synthesis engine decomposes a target task into sub-task chains of which the dependency relationship can be dynamically recombined; and step 3, dynamically allocating the sub-tasks to idle agents based on the dependency relationship of the sub-task chain and the load state of the agents. According to the method, a dynamic scheduler monitors an intelligent agent load and a task queue state in real time; the inference chain synthesis engine decomposes the target task into sub-task chains which can be dynamically recombined; sub-tasks are dynamically allocated based on the dependency relationship and the load state; through load monitoring and task recombination, resource elastic telescoping is realized, burst task insertion is supported, and the method is suitable for the field needing real-time response. And meanwhile, physical scheduling and a logical reasoning chain are fused, so that the reasoning accuracy is greatly improved in a complex task.

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 application discloses a loop scheduling method in the form of a dynamic task chain, relates to the technical field of dynamic task scheduling of a heterogeneous multi-core computing system, and is suitable for dynamic task scheduling of a heterogeneous multi-core computing system based on a coarse-grained reconfigurable architecture and oriented to dense computing applications. The loop body task is regarded as a whole, and a unified priority is given. In the first round of executing the loop task, the loop body task is sequentially scheduled and the loop task scheduling result is recorded to generate a dynamic task chain. In the loop after the first round, the dynamic task chain is used for scheduling. In the subsequent loop execution process, only the input and output of the dynamic task chain are scheduled, and secondary scheduling in the loop body is avoided. The loop scheduling method in the technical scheme of the application is specially used for scheduling in the second round and the subsequent rounds of the loop body task, solves the defect that the original dynamic scheduling method does not support loop task scheduling, and effectively avoids scheduling bottlenecks caused by repeated scheduling of the same structure task.

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.