480 results about "Task analysis" patented technology

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention relates to the technical field of patrol path optimization, and particularly discloses an unmanned aerial vehicle patrol path optimization method and system based on a mobile unmanned aerial vehicle nest, and the method comprises the steps: carrying out the dynamic environment perception and multi-source data fusion, generating a global environment situation map, carrying out the task intelligent analysis and dynamic priority sorting, and obtaining a task distribution matrix; the method comprises the steps of performing multi-target hierarchical path planning, outputting a dynamic flight path set with a timestamp, constructing a real-time obstacle avoidance and emergency self-healing system, obtaining a safety correction path set, performing mobile nest collaborative scheduling and energy optimization, outputting a nest mobile instruction set, triggering global environment situation map updating, obtaining an unmanned aerial vehicle execution log, and performing knowledge evolution. And end-to-end technical iteration is completed. According to the method, the problems that the route planning of the traditional patrol path planning unmanned aerial vehicle cannot achieve refinement and is not high in applicability under complex terrain conditions are solved.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

The invention discloses a task processing 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: responding to a to-be-executed task input by a user, inputting the to-be-executed task and a preset cue word into a large language model, and obtaining a task analysis result output by the large language model; distributing each sub-task corresponding to the to-be-executed task to a corresponding service agent based on the task analysis result; a technical agent corresponding to the subtask is scheduled through the service agent to execute the subtask, and subtask execution results are summarized; and determining a task execution result corresponding to the to-be-executed task based on the sub-task execution result. Compared with the mode that an existing automatic robot can only execute the set task according to the fixed processing flow, the method has the advantages that the to-be-executed task can be fully understood, the to-be-executed task can be split, the to-be-executed task is executed in a graded mode through the corresponding service agent scheduling technology agent, human intervention can be reduced, and the user satisfaction degree can be improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-agent cooperative communication and real-time resource optimization management method and system based on large model driving, and the method comprises the following steps: semantic understanding and task analysis, communication protocol and mechanism design, resource demand prediction and allocation scheduling, information sharing and interaction, and feedback and evaluation mechanism. The method has the beneficial effects that efficient reliability allocation and feedback error correction are realized through a multi-agent reinforcement learning framework by utilizing powerful reasoning and generalization capabilities of a large model, so that the communication and cooperation capabilities among multiple agents are remarkably improved, and meanwhile, the method is combined with a real-time resource optimization management technology, and through reasonable resource allocation and scheduling, the real-time resource optimization management efficiency is improved. And maximum utilization of resources is realized.

The invention provides a multi-target collaborative optimization scheduling system based on artificial intelligence, relates to the technical field of intelligent scheduling, and realizes automatic task classification, task association relationship modeling, resource optimization distribution and dynamic adjustment of a scheduling strategy through task analysis, target association analysis, scheduling strategy generation, real-time monitoring and a dynamic scheduling module. The system utilizes natural language processing, machine learning, graph neural network, reinforcement learning and other technologies to extract features from task information, analyze association between tasks and generate a scheduling strategy according to resource requirements, a real-time monitoring module tracks a task execution state, a dynamic scheduling module evaluates deviation influence through a causal inference model, and a task scheduling strategy is generated. The system is suitable for the electronic equipment, the instructions are stored through the storage medium, and efficient task scheduling and management are achieved.

The invention discloses a mechanical arm operation method based on a multi-mode large visual language model, which comprises the following steps of: firstly, acquiring image data of a mechanical arm operation scene through a visual sensor, and acquiring a user instruction in combination with a voice recognition module or a text input module; and inputting the natural language instruction and the visual scene data into a multi-modal large visual language model for multi-modal task analysis. The model identifies the category, position and attribute of a target object in a scene, and generates a specific operation sequence in combination with an input instruction. And then, according to the operation sequence generated through analysis, the system directly drives the mechanical arm to execute corresponding tasks including specific actions such as grabbing, moving and placing of the target object, and in the task execution process, the mechanical arm sequentially completes the actions according to the operation sequence generated in advance. The problems that in the prior art, a mechanical arm is inaccurate in complex task instruction analysis, poor in operation flexibility and adaptability and poor in real-time response capacity in a dynamic environment are effectively solved.

The invention relates to the technical field of task scheduling, and discloses a distributed task scheduling method, device and equipment for OpenHarmony Internet of Things equipment, and the method comprises the steps: carrying out the hardware parameter collection and equipment classification of the Internet of Things equipment in an OpenHarmony distributed network, and obtaining an equipment capability data set; performing task analysis and dependency analysis on the application program to obtain a task directed acyclic graph; carrying out calculation and resource allocation prediction on the equipment load to obtain an equipment resource reservation table; constructing a nonlinear optimization model, and solving to obtain an initial task allocation scheme; performing two-stage distributed reinforcement learning optimization to obtain a target task scheduling scheme; the target task scheduling scheme is deployed to the Internet of Things equipment, data collection and performance evaluation are carried out, and a performance evaluation data set is obtained.The efficient communication and cooperative computing between the equipment are achieved based on the distributed soft bus, the overhead of task migration and data transmission is reduced, and the overall performance of the system is improved.

The invention relates to a multi-agent cooperation strategy generation method, device and equipment and a medium, and the method comprises the steps: separating acoustic spectrum features and text semantic features of conference voice through environment perception processing, solving a cross-modal information conflict problem, and generating an accurate semantic understanding result; identifying the essence of the problem based on task analysis, associating the responsibility field, and constructing a classifiable problem point set; calling an agent capability library to dynamically match problem requirements, and generating a candidate agent list; quantifying a problem influence range and a decision time limit through weighted emergency scores, and generating a priority-sorted agent sequence; screening and confirming a core problem point and a primary agent; and finally, generating an executable cooperation scheme through multi-agent collaborative optimization. According to the method, the problems of incomplete feature extraction, task allocation delay and resource conflict in the prior art are solved, and the operability and decision-making efficiency of a cooperation strategy are remarkably improved.

The invention discloses a GPU resource scheduling method and system based on dynamic weight calculation, and relates to the technical field of algorithm services, and the method comprises the steps: carrying out the pre-operation of an algorithm according to the initial weight of the algorithm and the algorithm flow of analysis task design; obtaining algorithm computing power resource data, and pre-evaluating algorithm computing power resources; according to the algorithm computing power resource data, the pre-evaluated algorithm computing power resource and algorithm task analysis, setting a priority sequence and a dynamic weight for executing an algorithm process, computing the algorithm computing power resource and generating a dynamic scheduling scheme; verifying and adjusting the computing power resource of the algorithm; and loading a full data operation algorithm, executing an algorithm process, and monitoring an execution process of the algorithm process. According to the method, under the condition that servers and GPUs are limited, more users can execute analysis tasks as much as possible, the situation of task failure caused by queuing waiting or resource competition does not occur, and efficient and stable operation of an algorithm platform is ensured.

The invention provides a multi-round automatic machine learning agent system based on reinforcement learning optimization. Comprising a task analysis module used for generating an initial prompt for an MLE agent to call; the MLE agent module is used for generating an executable code; the code executor is used for generating an execution result; the evaluator is used for outputting a normalized value of each index and a code correctness identifier; the reward construction module is used for generating a reward value; the reinforcement learning optimizer is used for calculating group average return and candidate advantages and updating strategy parameters of the MLE intelligent agent module based on the candidate advantages; and the multi-round interaction control module is used for feeding back the execution result of the previous round and the reward value to the MLE agent module in the multi-round interaction process, and controlling code generation of the next round until a preset termination condition is met. According to the invention, strategy adaptive evolution, reinforcement learning optimization of fine-grained credit distribution and multi-round closed-loop automatic process improvement can be realized.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention relates to the technical field of artificial intelligence, and discloses an agent rapid construction method and system based on enhanced retrieval, and the method comprises the steps: constructing a multi-dimensional task analysis system, retrieval task characteristics are automatically represented through characteristics such as data distribution, query complexity and semantic requirements; executing a progressive architecture evolution algorithm, and automatically searching an optimal architecture meeting multi-dimensional balance of retrieval precision, calculation complexity and memory occupation based on the task characteristic vector; constructing a hierarchical knowledge distillation transfer chain, and realizing efficient knowledge migration from a large model to a small model through feature matching and attention guidance; implementing mixing precision quantification and structured pruning on different hierarchies based on sensitivity analysis, and adapting to a target deployment environment; according to the method, the model volume is reduced, the reasoning speed is increased, the energy consumption is reduced, and the enhanced retrieval agent can efficiently operate on resource-constrained equipment.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The embodiment of the invention provides an interaction method for upper limb cooperative control of a humanoid robot. The interaction method comprises the following steps: performing task analysis on a received voice request; if the target object needing to be executed exists, target object recognition processing is carried out according to the task analysis result; based on target identification information and target pose information output by identification processing and a task analysis result, performing task planning processing by utilizing a large language model according to a double-arm division cooperation principle, and generating a task sequence and a character instruction corresponding to each task; sequentially executing the following operations on each target task in the task sequence: based on the target pose information and the target identification information, controlling upper limbs of the humanoid robot to execute an operation action corresponding to the target task on the target object; therefore, on the basis of the large language model, the visual module and the voice module are matched, so that a user can communicate with the robot more visually; and the operation capability of the upper limbs of the humanoid robot for executing complex tasks can be improved.

The invention relates to a multi-modal agent collaborative orchestration method and system, and relates to the technical field of agent collaborative orchestration, and the method comprises the steps: a generative AI orchestration layer analyzes a multi-modal task description after obtaining the multi-modal task description submitted by a client, obtains a task analysis result, queries an adaptive agent based on the task analysis result, and carries out agent collaborative orchestration; the method comprises the steps that an execution layer obtains an intelligent agent matching result, constructs a multi-modal DAG according to the intelligent agent matching result, carries out modal-to-path optimization on the multi-modal DAG to obtain an optimal path planning result, generates a workflow scheme according to the optimal path planning result and sends the workflow scheme to a client side, and the execution layer executes the workflow scheme after receiving a workflow execution request submitted by the client side. And executing the workflow execution request, generating an execution result, and sending the execution result to the client. According to the method, the dynamic requirement of the multi-modal task can be met, cross-modal resource allocation is optimized, and finally the processing efficiency of the multi-modal task is improved.

The invention provides an allocation method and system for improving the GPU resource utilization rate, a medium and a program product, and relates to the technical field of data processing. The method comprises the following steps of: constructing a hybrid resource pool which comprises a divided resource pool divided according to set computing power and video memory granularity and a whole spare resource pool retaining a complete GPU resource unit; then, a to-be-processed task is obtained from an interface end, and the computing power scale and continuity demand data of the to-be-processed task are determined through a task analysis model obtained based on multi-factor machine learning training; finally, according to the data, computing power resources are distributed from the hybrid resource pool according to a preset method, so that diversified task requirements are met, efficient and flexible distribution and utilization of GPU resources are achieved, and the overall efficiency is improved.

A three-dimensional point cloud task analysis method fusing local features and global context information belongs to the field of three-dimensional point cloud task analysis, and comprises the following steps: step 1, constructing a point cloud data set: step 1.1), defining the data set; step 1.2), preprocessing the point cloud data set; step 2, point cloud data set sampling: down-sampling the point cloud number of the original point cloud data set to a fixed value through a farthest point sampling FPS mode; step 3, grouping point cloud data sets; 4, extracting local features; step 5, neighborhood feature propagation; step 6, constructing a space shape position code; step 7, constructing channel attention; step 8, local feature enhancement; step 9, global context feature extraction; and step 10, performing three-dimensional point cloud classification and segmentation tasks. According to the invention, the precision of the three-dimensional point cloud task is effectively improved.

The invention relates to the technical field of digital twinning, in particular to a digital twinning task scheduling system and a digital twinning task scheduling method oriented to multi-machine cooperation, which realize intelligent mapping between resources and tasks by introducing a topological resource space model. Comprising a topological resource space builder, a task analysis and modeling device, a task resource mapping decision-making device, a global monitoring and optimizing unit and a data fusion processor, the topological resource space builder builds a topological space model of a resource and task mapping relation, and the task analysis and modeling device extracts feature information and a dependency relation of a digital twin task; a task resource mapping decision maker generates a task scheduling decision based on a topological space model and feature information, a global monitoring and optimizing unit dynamically adjusts connectivity parameters of the topological space model according to execution information, a scheduling strategy is optimized, the resource utilization rate is increased, and a data fusion processor merges execution result data and improves the task scheduling efficiency. And through multi-dimensional task feature analysis and intelligent decomposition combination, complex tasks are flexibly dealt with, and the execution efficiency is improved.

The invention relates to the technical field of sharing management, in particular to an instrument sharing management method and system based on a cloud platform, and the method comprises the following steps: obtaining a task generation structure sequence, generating and publishing screening equipment, constructing a channel list generation partition table, and binding a resource output management record. According to the method, the experimental task text content is automatically extracted to perform refined analysis on the behavior structure relationship, the cooperative relationship between the devices is accurately split according to the operation sequence and the behavior difference, and the device response capability and the resource occupancy characteristic are quantitatively identified through the multi-dimensional index; the conflict relation is executed to jointly judge by difference analysis and resource intersection, a priority execution path is constructed in the equipment operation sorting and channel scheduling process according to a response level, and task blocks are accurately divided by jointly deducing an execution proportion and a starting interval in operation time interval arrangement; and automatic closed-loop linkage management of task analysis, equipment identification, conflict detection, priority ordering, block division, resource allocation and sharing registration is realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a GPU (Graphics Processing Unit) and NPU (Network Processing Unit) resource scheduling method for high-performance computing, which comprises the following steps of: waiting for a scheduling optimization module to call acquired resource condition data of GPU and NPU hardware accelerators; the task analysis module analyzes a to-be-executed AI calculation task, obtains task feature data and transmits the task feature data to the scheduling optimization module; the scheduling optimization module calculates an optimal task allocation scheme and transmits the optimal task allocation scheme to the dynamic scheduling module; the dynamic scheduling module monitors task execution conditions and resource changes in real time, dynamically adjusts a task allocation scheme according to feedback information, and feeds back the task allocation scheme to the scheduling optimization module; meanwhile, the dynamic scheduling module feeds back the change information of the resource condition data and the task execution condition to the resource monitoring module and the task analysis module respectively; according to the method, dynamic scheduling is carried out on heterogeneous hardware accelerators such as the GPU and the NPU, respective advantages are fully exerted, and the overall calculation performance is improved.

The invention belongs to the field of unmanned aerial vehicle control, and particularly relates to a complex task-oriented unmanned aerial vehicle modular integrated control platform, which comprises an acquisition module for acquiring and processing information such as a real-time flight state of an unmanned aerial vehicle group, a complex task text, meteorological data and the like; the task analysis module decomposes a complex task into a single subtask set and an incidence matrix, and evaluates the task complexity for resource allocation; the atmospheric analysis module is used for inverting a real-time flight association adjustment graph based on the corrected meteorological data, flight states and the like in combination with a target optimization algorithm and an optimization function taking resource quantity minimization and task completion probability maximization as targets; the record analysis module generates a performance transfer matrix by using a hidden Markov algorithm; and the flight control module responds to the adjustment graph to correct the flight state, and ensures that the task completion probability meets a preset safety threshold. According to the platform, efficient cooperative control and resource optimization allocation of the unmanned aerial vehicle group under complex tasks are realized through modular integration.

The invention discloses an intelligent sand excavation supervision system based on multi-source data fusion, and relates to the technical field of machine learning, and the system collects target river reach data in real time through a multi-source sensing network module; the spatial-temporal feature fusion module generates a dynamic state fingerprint matrix; the adaptive baseline monitoring module establishes and dynamically updates a normal state baseline under multiple conditions, and triggers an abnormal disturbance alarm by calculating a mahalanobis distance between a real-time fingerprint and the baseline and combining collaborative deviation verification of acoustics, turbidity and water flow characteristics, and the multi-task analysis module adopts a parallel neural network architecture, so that a multi-task analysis result is obtained. The illegal operation type probability, the strength estimation value and the environment disturbance level are synchronously output; the three-dimensional visualization early warning module generates an early warning interface based on the analysis result; the dynamic knowledge management module and the self-adaptive optimization module are used for improving the analysis accuracy and continuously optimizing the system performance by using historical experience; the method has the advantages that abnormal disturbance events such as illegal sand excavation and the like can be accurately and intelligently supervised in real time, and powerful capability is provided.

The invention relates to the technical field of computing power-energy consumption optimization, in particular to an edge AI reasoning computing power-power consumption cooperative system, which comprises a task decomposition module for decomposing a complete reasoning task into n independent sub-tasks, n being greater than or equal to 1; the task analysis module is used for extracting feature parameters of the subtasks and generating task feature vectors; and the computing power prediction module inputs the obtained sub-task feature vectors and different computing unit type labels into a pre-constructed task computing power demand prediction model, and predicts the computing power demands of the sub-tasks on different computing units. In the scheme construction and task allocation stage, the system carries out candidate calculation unit screening and combination generation based on a multi-scheme calculation power energy consumption matrix, calculates the total energy consumption and time delay matching degree of each combination, and selects an execution scheme with the lowest energy consumption under the real-time condition from the total energy consumption and time delay matching degree. The mechanism enables each subtask to be flexibly migrated between different types of computing units.

The invention discloses a low-to-low multi-satellite observation task planning and designing method, and the method comprises the following specific steps: 1) determining target constellation orbit deployment related parameters in a low-to-low multi-satellite observation task system, the target constellation orbit deployment related parameters specifically comprising a target constellation orbit height H < Tar >, a target constellation orbit inclination angle i < Tar >, a target constellation orbit height i < Tar >, a target constellation orbit inclination angle i < Tar > and a target constellation orbit inclination angle i < Tar >; the target constellation orbital plane number PTar and the target constellation same orbital plane satellite number NSar are calculated; 2) determining the orbit height of the monitoring satellite by referring to the target constellation orbit height distribution characteristics; 3) determining the number of orbital planes needing to be deployed by the monitoring satellite system and the number of monitoring satellites on each plane by combining target constellation orbit deployment related parameters and task time requirements; 4) analyzing task requirements according to given specific tasks, and proposing a task planning strategy of a single satellite to multiple satellites; and 5) completing multi-target simulation analysis of multiple satellites, and giving a low-to-low multi-satellite observation task result. According to the method, two technologies of maneuvering orbital transfer and natural intersection are combined, the problem of narrow imaging window of a single-to-single target different-plane grazing autonomous task planning algorithm in actual engineering use is successfully solved, and low-consumption and high-efficiency multi-target observation is finally realized.

The invention relates to an energy storage power station control strategy updating method based on a cloud-side cooperation technology, and the method comprises the steps: starting to call a data processing program when an edge calculation gateway collects data or receives alarm information or a task processing request actively transmitted by an energy storage power station; an edge computing gateway data processing program judges the type of a currently executed task, if the currently executed task is alarm information, an alarm information analysis processing program is executed, if the currently executed task is a computing task actively requested by the energy storage power station, a computing task analysis processing program is executed, and if the currently executed task is a high-complexity computing task, a cloud server is requested to assist computing; the two-way updating method of the control strategy is designed, both the edge control gateway and the cloud server can actively initiate strategy updating, and compared with an edge-side or cloud-side-based one-way strategy updating method in the prior art, the method provided by the invention has more applicability.

The invention relates to the technical field of large model data processing, in particular to an agent automatic configuration method and system based on a large model, a knowledge base and a tool, and provides the following scheme: obtaining training data, inputting a natural language into an inference model to generate a query task set, and after optimization, inputting the query task set into an Embedding model to generate a preliminary retrieval result set; the optimization result is input into a reordering model to generate a final retrieval result; and when the training conditions are met, packaging the inference model, the Embedding model and the reordering model into an intelligent agent. The system consists of an inference model configuration module, a retrieval model configuration module and a reordering model configuration module, and cooperatively realizes task analysis, semantic retrieval and result optimization. The method improves the retrieval accuracy and efficiency, and is suitable for knowledge base question answering and complex data processing scenes.

The invention is suitable for the technical field of robots, and provides a task management method and system, a robot and a computer program product, and the method comprises the steps: obtaining task information of a to-be-executed task; the task information is input into the large model for task analysis, and a conflict prediction result is obtained; starting an adaptive algorithm to generate a conflict solution according to a conflict prediction result and a robot state; adjusting a task execution plan according to the conflict solution, executing the task according to the adjusted task execution plan, utilizing a large model to deeply analyze the task, predicting a possible conflict task, and dynamically generating an optimal conflict solution of the conflict task through an adaptive algorithm. The task is executed based on the optimized task execution sequence and the resource allocation scheme in the conflict solution, so that the occurrence of task conflicts can be effectively reduced, the task execution efficiency is improved, the resource waste and execution delay caused by the task conflicts are reduced, and the reliability and stability of a task scheduling system are improved.

The invention relates to the field of industrial intellectualization, and provides a fault prediction and prevention method and system in an intellectualized equipment transformation technology, and the method comprises the steps: obtaining real-time data of intellectualized equipment, carrying out the filtering processing, screening an abnormal entropy value, extracting an abnormal parameter set, constructing a fault prediction frame based on the abnormal entropy value, and carrying out the fault prediction. The method comprises the following steps: predicting a fault probability, determining a residual operation life and calculating an equipment operation and maintenance period, then evaluating an operation state to obtain a state evaluation parameter, querying a fault type and analyzing occurrence time, constructing a preventive maintenance strategy in combination with a current state, generating a maintenance task list and querying a maintenance task, and analyzing a prevention target in the maintenance task. And identifying prevention obstruction factors, and finally generating an operation report based on the monitored operation feedback data. According to the invention, the operation stability of intelligent equipment can be improved.