413 results about "Task completion" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to service scenes such as pension service, financial science and technology and medical health, and discloses a multi-agent cooperative task processing method, device and equipment and a medium, and the method comprises the steps: obtaining a task instruction, analyzing a core target, and decomposing the core target into a plurality of subtasks; obtaining environment information, dividing task areas, and generating a cooperation framework in combination with agent capability and area weight; real-time states of the agents are obtained, and the optimal agents are matched based on the cooperation framework to generate a task allocation table; a task distribution table is issued to control the intelligent agent to execute the task and upload execution information; monitoring an execution process, and performing dynamic adjustment and updating a task allocation table when detecting path conflicts or equipment faults; and after the subtask is completed, obtaining environment completion state data, and comparing the data with a preset standard model for acceptance. According to the method, efficient task decomposition and intelligent distribution are realized by fusing task semantics, environment information and intelligent agent capability, and the cooperation stability is improved by introducing a real-time state perception and self-adaptive mechanism.

The invention relates to a multi-agent scheduling method and system based on task intention matching. The method comprises the following steps that S1, tasks are received and analyzed; s2, intelligent agent matching candidates are obtained; s3, selecting an optimal agent by a scoring mechanism; s4, task assignment, execution monitoring and result acquisition; s5, performing reflection evaluation and strategy updating: dynamically updating an intelligent agent score, and adjusting a task label matching rule or other scheduling parameters so as to optimize a distribution decision of a subsequent task; and S6, result combination and output: when the task comprises a plurality of sub-tasks executed by a plurality of intelligent agents, the results are verified, sorted and combined. Compared with the prior art, the method has the advantages that the task target can be dynamically analyzed, the optimal agent can be intelligently matched to execute the task, and the scheduling strategy is continuously optimized through feedback after the task is completed, so that the task execution efficiency and effect of the multi-agent system are remarkably improved, and the accuracy, the adaptability and the intelligent level of task allocation are improved.

The invention provides a complex task decomposition and dynamic optimization method and device based on a large language model, and relates to the technical field of artificial intelligence. A complex task decomposition and dynamic optimization method based on a large language model comprises the following steps: based on a pre-trained large language model, decomposing a to-be-executed task into a plurality of sub-tasks, and generating a task instruction queue for executing the plurality of sub-tasks; sequentially executing the subtasks in the task instruction queue, and recording information in the execution process to obtain task execution data; generating execution state feedback information according to the task execution data and a preset normal range; and generating a task decomposition strategy adjustment instruction according to the execution state feedback information, wherein the task decomposition strategy adjustment instruction is used for adjusting the task instruction queue and / or the execution main body of the subtask. According to the technical scheme of the embodiment of the invention, the task process is adjusted in real time in the task execution process, the task processing adaptability is enhanced, the change of a complex environment can be flexibly coped with, and the task completion success rate and the overall efficiency are improved.

The invention provides a large model agent collaborative scheduling method and system oriented to complex tasks, relates to the technical field of artificial intelligence, and comprises the steps of task decomposition, feature extraction, agent matching, dynamic scoring, scheduling scheme generation and optimization, execution monitoring, exception handling and the like to realize efficient collaboration of large model agents. According to the method, accurate matching can be carried out according to task characteristics and intelligent agent capabilities, the task completion efficiency and quality are improved, meanwhile, the dynamic adjustment capability is achieved, abnormal conditions in the execution process are effectively handled, and the system robustness is enhanced.

The invention provides a multi-level heterogeneous computing power network task scheduling and resource allocation method and system. In a computing power network, each hierarchical network is connected with an intelligent agent for task scheduling and resource allocation. In the training process, the intelligent agent obtains local observation information of the computing power node in the hierarchical network. And the intelligent agent aggregates and converts local observation information according to the relation feature map to generate a feature matrix and an adjacent matrix. Local feature embedding is calculated by using a graph convolutional network, and feature aggregation is carried out in combination with information of a cooperative agent to obtain a local embedding state; and the intelligent agent executes a joint action according to the local embedding state, wherein the joint action comprises task scheduling and resource allocation. And according to an execution result, the agent obtains a reward value and state information and stores the reward value and the state information into an experience playback buffer area. And sampling data from a buffer area, and updating a value function and strategy parameters by taking optimization of calculation task completion time and resource use efficiency as a target. And after a preset training round, obtaining a trained model. The resource overhead of information interaction can be reduced, and the scheduling efficiency is improved.

The invention provides a multi-agent cooperation method, system and device and a storage medium, and relates to the technical field of multi-agent collaboration.The method comprises the steps that initial role allocation is conducted on multiple agents, one agent is an observer, the other agent is a coordinator, and the other agents are all executors; a strategy network based on deep reinforcement learning is introduced according to the running state of the multiple agents to dynamically adjust role allocation of the multiple agents, and a role allocation strategy is dynamically adjusted according to task completion rewards, role conflict punishment and resource conflict rewards; the coordinator constructs a task priority and a dependency relationship based on the task graph or the task dependency tree, and dynamically allocates tasks according to the state, the capability vector and the task adaptation degree of the executor; conflicts are recognized through resource contention detection, task overlapping detection and behavior conflict detection, and the conflicts are coordinated. According to the invention, multi-agent responsibilities are layered, and the task completion efficiency is improved through task allocation and conflict detection and coordination.

The invention discloses a multi-agent dynamic task allocation and collaborative path-finding system based on label-free distributed deep reinforcement learning. The system comprises the following steps: step 1, receiving state information and environment perception data of each agent in a multi-agent system based on distributed deep reinforcement learning; step 2, extracting feature representations of the environmental perception data and the intelligent agent state information, and performing multi-source heterogeneous information fusion through an attention mechanism to obtain state-task matching features; 3, transmitting the state-task matching characteristics to a multi-agent network in real time, realizing task allocation negotiation among agents by adopting a hierarchical scheduling and state exchange mechanism based on task priorities, dynamically detecting newly added task types, and performing incremental learning; the online updating iteration of the model is realized to assist the multi-agent optimization task allocation strategy and the path planning action; compared with the prior art, the method has the advantages that by applying the distributed deep reinforcement learning technology and a state exchange mechanism between intelligent agents, the system can quickly adapt to environment changes and task dynamics, and the resource utilization rate and the task completion efficiency are improved.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

The invention belongs to the field of multi-mode sensing fusion, and relates to an intelligent multi-mode patrol sensing method, system and equipment and a storage medium, which are used for carrying out automatic patrol, real-time sensing and abnormity warning in a complex environment. Comprising the steps of natural language instruction analysis, task decomposition and distribution, intelligent agent scheduling and execution, multi-modal information collection and fusion, anomaly recognition and event response, result feedback and task closed loop, and through the integration of an OWL framework and a DeepSeek-R1 large model, a multi-modal data fusion technology and an NL-SLAM autonomous navigation algorithm, the multi-modal data fusion algorithm and the multi-modal data fusion technology are integrated. The problems that in the prior art, dynamic task understanding based on natural languages cannot be achieved, a sensing system lacks efficient multi-modal data fusion, so that the anomaly recognition precision is low, and complex tasks or emergencies are difficult to deal with are solved, various abnormal conditions in a complex environment are effectively dealt with, and the method is suitable for popularization and application. The stability and expansibility of task execution are improved, and the navigation success rate and the task completion rate of the system in an unstructured environment are enhanced.

The invention discloses an intelligent scheduling and path planning system and method for a mine carry-scraper, and belongs to the technical field of mining, and the system comprises an environment sensing module which is used for collecting the environment data of a mine operation area in real time, and the environment data comprise the equipment state, the road condition and the dynamic obstacle position; processing the collected data to generate environment state parameters; and the task scheduling module is used for allocating operation tasks according to the equipment state, the task target and the environment state parameters, and performing global optimization on task allocation to minimize task completion time and energy consumption. Generating a task allocation scheme based on the equipment state and the task target through a task scheduling module; the path planning module realizes optimal path planning through multi-device game optimization, and the global optimization control module coordinates the work of each module, optimizes global targets such as task completion time, energy consumption and conflicts, realizes deep fusion of scheduling and path planning, and improves the working efficiency, global optimality and robustness of mining devices.

The invention relates to the technical field of satellites, and discloses an artificial intelligence aerospace infrastructure agent system, which comprises a satellite agent, and the satellite agent comprises an energy management module, a calculation module, a task execution module and a communication module. The artificial intelligence aerospace infrastructure agent method comprises the following steps: acquiring real-time states of a plurality of satellite agents, performing task scheduling and resource allocation by using an optimal control theory, optimizing a task allocation strategy in combination with a game theory model, and optimizing a communication path according to a channel capacity theorem, so as to obtain an artificial intelligence space infrastructure agent; and the task sequence and the resource allocation strategy are adjusted in real time in the task execution process, so that efficient collaboration and task completion are ensured. According to the method, task scheduling is optimized by combining the optimal control theory and the game theory, a communication path is optimized by adopting the channel capacity theorem, the task execution sequence is adjusted in real time, adaptive resource allocation is performed, efficient cooperation between satellite agents is ensured, and the task execution efficiency, the resource utilization rate and the system stability are effectively improved.

The invention provides a multi-target production scheduling method and system based on reinforcement learning, and the method comprises the steps: carrying out the matching analysis based on a task demand list and an equipment capability baseline, and obtaining a scheduling constraint space containing a task execution dependency relationship and an equipment operation state boundary condition; performing structured feature extraction on the scheduling constraint space through a graph neural network, generating a production scheduling association graph containing node features and edge features, and calling a multi-target reinforcement learning strategy model to perform strategy iteration optimization on the production scheduling association graph, obtaining a scheduling strategy parameter set fusing the task completion timeliness and the resource utilization balance degree; and generating an equipment task time sequence allocation scheme. According to the invention, the overall effectiveness and reliability of production scheduling can be effectively improved.

The invention relates to the crossing field of the crowd sensing technology and the artificial intelligence technology, in particular to a crowd sensing task scheduling method and system based on multi-space modeling and fairness reinforcement learning, and the method comprises the steps: constructing a multi-layer space task structure of ground and air cooperation according to an obtained task; establishing a topological correlation model between tasks according to a space region division result, and determining a priority sequence of task scheduling through a critical path extraction mechanism; constructing a differentiated agent matching strategy, and performing reasonable configuration of a critical path task based on a priority sequence; performing dynamic allocation on non-critical path tasks by utilizing a fairness perception multi-target scheduling algorithm; according to the method, the search space is reduced through a space division and path screening mechanism, the task completion rate and the overall scheduling performance of the system are improved, and the method is suitable for efficient resource allocation requirements in a multi-space collaborative awareness task scene.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

The invention provides an agent visual language navigation method and system based on task completion prediction. The method comprises the step of constructing a dual-drive structure composed of a self-adaptive mixed pooling mechanism and a task completion analysis module. Firstly, in the visual information processing process, a dynamic weight distribution strategy is adopted to carry out multi-scale adaptive mixed pooling on panoramic features, so that the fusion effect of local and global semantic information is optimized, and the retention capability and semantic integrity of navigation historical information in a dynamic topological map are improved. And then, inspired by a human navigation cognitive behavior mechanism, a task completion analysis module is designed and introduced, and the task execution progress is dynamically estimated based on the recognition condition of a key landmark in a navigation path, so that an intelligent agent is driven to preferentially select a key path node and invalid exploration is reduced. And finally, realizing efficient understanding and execution of the natural language instruction by the intelligent agent through a multi-round cyclic cross-modal reasoning and action prediction mechanism.

The invention provides a production collaborative management method and system for a digital factory, and the method comprises the steps: calculating an affinity matrix between devices based on the physical positions, communication delays, device types and historical collaborative relationships of the devices, determining the collaborative weights of heterogeneous devices, and obtaining a device collaborative networking scheme; calculating a time sequence score according to the task completion quality, the energy consumption efficiency, the equipment stability and the collaborative adaptability of each equipment group, and performing dynamic weight adjustment in combination with environment monitoring data to obtain an equipment credit score; performing initial task allocation based on the equipment credit score, and performing continuous bias analysis and resource balancing by analyzing data in a task execution process to obtain a dynamic task allocation strategy; and finally, evaluating the collaborative effect of the equipment group according to the task allocation strategy, and dynamically optimizing the equipment group to obtain a collaborative evolution scheme. According to the method, the problem of unreasonable equipment resource allocation is effectively solved through a dynamic evaluation and balance mechanism.

A multi-user multi-exit DNN reasoning partitioning and task scheduling method includes the steps that trained multi-exit DNN models are deployed on each user device and an edge server, and in a time slot, tasks reach the user devices, meet Poisson distribution and are stored in task queues of the user devices; taking the strategy feasibility interval as a constraint, taking an exit point, a partition point and a scheduling decision of the task as optimization variables, and establishing an optimization problem P taking maximization of the total completion rate and the reasoning accuracy of task reasoning in a time slot as a target; decoupling the problem into a server scheduling optimization problem P1, establishing a graph model of a server task, and solving the problem P1 by using a double-greedy algorithm; and simplifying the problem into P2, establishing a Markov decision process, and solving an optimal strategy by using a multi-agent reinforcement learning algorithm. The method is suitable for multi-user equipment to carry out multi-outlet DNN reasoning partition, and the task completion rate and reasoning accuracy within a period of time can be effectively improved.

The invention relates to the technical field of intelligent management, in particular to a computer intelligent service management system and method based on network big data, and the system comprises a service state monitoring module, an operation state modeling module, a service optimization module, an execution scheme adjustment module and a service task distribution module. According to the invention, through data dynamic analysis of the resource consumption proportion, the task processing duration and the completion time, reliable support is provided for task allocation, the resource occupation relationship between tasks is disclosed, the rationality and efficiency improvement of resource allocation are ensured, the task allocation deviation and resource allocation are optimized, and the resource use is dynamically balanced; the service execution efficiency and accuracy are improved, resource waste and task delay are reduced by analyzing task delay and resource utilization changes, identifying execution bottlenecks and adjusting structures, the execution sequence is dynamically optimized in combination with task completion time and a resource allocation proportion, and the system operation efficiency and response capability are improved. And meanwhile, resource utilization, task scheduling and service quality are optimized.

The invention belongs to the technical field of AGVs, and provides an AGV transportation method and a robot, and the method comprises the steps: obtaining a task list and the initial position of each AGV; the task execution sequence is optimized through tabu search so as to minimize the time consumption of final task completion, the average completion time and the weighted cost of the number of pauses; according to the optimization sequence, constructing a space-time diagram of node representation time and space positions for each AGV; planning the path of each AGV on the space-time diagram by adopting a time enhancement algorithm, regarding the paths of other AGVs as time obstacles, and adopting a path cost function; in the path planning process, the AGVs are allowed to wait in situ to avoid collision, and the paths of the AGVs are updated in real time; performing Bezier curve smoothing on the planned discrete path for three times to generate a continuous and controllable driving track; and finally, each AGV is controlled to execute the task along the track. According to the invention, the problems of low task scheduling efficiency and frequent path conflicts of a plurality of AGVs in the prior art can be solved.

The invention discloses a resource scheduling method and system based on multi-dimensional load prediction, relates to the technical field of cloud computing and intelligent scheduling, and is used for solving the problems of efficient prediction and adaptive scheduling under multi-dimensional resource load fluctuation. The method comprises the following steps: collecting and preprocessing historical resource use data to construct a multi-dimensional time sequence sample; secondly, predicting a future load, and inhibiting an abnormal fluctuation influence in combination with a sudden change detection mechanism; thirdly, according to task request features and a prediction load result, task priority is evaluated, a preliminary scheduling scheme is generated, and reasonable resource allocation is achieved; finally, key feedback indexes in the scheduling execution process are collected, a scheduling quality scoring system is constructed, and a scheduling strategy is dynamically adjusted and optimized through a multi-objective optimization algorithm. The method has the characteristics of high prediction precision, fast scheduling response, strong adaptability and the like, can effectively improve the resource utilization rate and the task completion efficiency, and reduces the scheduling delay and default risk.

The invention discloses a man-machine cooperation task allocation method based on reinforcement learning, and the method comprises the steps: constructing a reinforcement learning model which comprises a task definition, a resource definition, a state space, an action set and a reward function, and carrying out the modeling of a cooperation relation among a task, a worker and a robot in man-machine cooperation through a heterogeneous graph neural network, and extracting high-order features of the state space. Based on the characteristics, a strategy network and a value network are trained through a near-end strategy optimization algorithm, and learning of an efficient task allocation strategy is achieved. The reward function combines the task completion duration and the resource utilization rate, and gives consideration to the production efficiency and resource balance. In the system operation process, workshop state data are collected in real time to dynamically update the state space, and the reinforcement learning model generates an optimal allocation action according to the latest state and issues an instruction to a worker or a robot for execution. The method has the advantages of high adaptability, high distribution efficiency, high scheduling intelligence degree and the like, and is suitable for task scheduling optimization in complex dynamic environments such as intelligent manufacturing and the like.

The invention belongs to the technical field of vehicle-mounted edge computing (VEC), and particularly relates to an unloading and resource allocation method for a DAG task in a vehicle-mounted edge computing environment. The method comprises the following steps: firstly, constructing a VEC unloading system in a two-way lane scene, and collecting task and equipment information and establishing an optimization model in combination with a vehicle moving model, a communication model and a calculation model; and secondly, aiming at a task in a directed acyclic graph (DAG), a task priority scheduling method based on a DAG topological structure is designed, so that a task scheduling strategy is optimized. On the basis, the problem is modeled as a Markov decision process by taking minimization of task completion time delay and system energy consumption as optimization objectives. The invention relates to the field of resource allocation, in particular to a task-dependent computing unloading and resource allocation algorithm based on a depth deterministic policy gradient (DDPG), and further provides a task-dependent computing unloading and resource allocation algorithm based on the depth deterministic policy gradient (DDPG) so as to solve the optimization problem.

The invention discloses a vehicle task unloading system and method based on a deep Q network, and relates to the technical field of Internet of Vehicles mobile edge computing, and the method comprises the steps that a client vehicle receives a locally generated computing task, obtains basic parameters of the task according to the current hardware configuration and task requirements, carries out task unloading decision making through a deep Q network model, and carries out task unloading; an optimal task unloading strategy is obtained; when the task is unloaded to the edge node, the edge node dynamically adjusts the allocation of computing resources according to the computing resource allocation condition of the existing task, and executes concurrent task scheduling; then, the client vehicle updates the weight of the deep Q network model according to the task completion condition, and trains and updates the global model; therefore, by the adoption of the vehicle task unloading system and method based on the deep Q network, the task unloading strategy can be dynamically adjusted and the task unloading decision can be optimized according to the actual conditions of the client vehicle and the edge computing node in the face of the complex task unloading environment.

The invention discloses a multi-agent cooperation system construction method, a medium and equipment, and the method comprises the steps: carrying out the task modeling of a target business scene, and constructing a task dependence graph; initial cooperation strength weights are set for the atomic tasks with the cooperation relationship, and multi-stage cooperation training courses from easy to difficult are generated based on the cooperation complexity of the atomic tasks; the intelligent agent is controlled to execute a task in a training course, interaction behavior data is collected, the overall task completion efficiency is calculated, the cooperation weight is dynamically updated, and interaction data and the updated weight are input into a reinforcement learning model to iteratively optimize a cooperation strategy; and finally, solidifying the converged cooperation strategy into the constructed multi-agent cooperation system. According to the invention, through the combination of course learning and reinforcement learning, the cooperation efficiency and robustness of the system in a complex business scene can be significantly improved.

The invention discloses a task scheduling method for an intelligent fire-fighting management service platform, and relates to the technical field of fire-fighting informationization, and the method comprises the steps: collecting environment data and position coordinates, constructing a support vector machine (SVM) model, carrying out the task grade division of a monitoring region, and forming a task priority list; extracting a historical inspection record, calculating a deviation value between task completion duration and standard duration in the historical inspection record as an efficiency coefficient, and performing grade grouping on firefighters according to the efficiency coefficient; the position coordinates are converted into task distance parameters, the task matching degree is calculated in combination with the efficiency coefficient, a two-way selection queue is established, and firefighters are matched for the monitoring area according to a task priority list; and recording a task execution track of the firefighter through a positioning module, recording execution data of the task to a historical inspection record, and updating an efficiency coefficient to form a task scheduling mechanism. Through multi-dimensional data analysis and a dynamic evaluation mechanism, the task scheduling efficiency of the fire-fighting management platform is improved.

The invention provides a task planning method for an intelligent world model with a body based on visual reasoning, and the method combines the language processing capability of a large language model and an advanced visual perception technology, so that an intelligent agent can pre-reasone an environment state and simulate possible actions through the world model under the condition of obtaining a small amount of environment visual information; therefore, effective decision making and rapid adaptation to constantly changing environment states are realized in a complex environment, and the task execution efficiency and the task completion success rate of the intelligent agent in a complex scene are remarkably improved.

The invention relates to the technical field of intelligent teaching, in particular to a teacher console task scheduling and monitoring method based on data analysis, and the method comprises the steps: collecting task execution data, including participation degree, task completion progress and response time, of students, and combining machine learning models such as an adaptive enhancement algorithm, a long-short-term memory network and a random forest; according to the method and the system, personalized task scheduling of students is realized, a real-time data monitoring and dynamic adjustment strategy is adopted, optimal configuration of task difficulty and time length is generated according to student performance data, task risks are identified through a support vector machine model, and multi-level early warning and intervention suggestions are provided. Performing long-term trend analysis and grouping on historical data by using a K-Means clustering algorithm, and supporting a personalized optimization scheme of task allocation; according to the invention, the problem that the task allocation is not matched with the actual learning state of the student in the prior art is effectively solved, the flexibility of task scheduling and the learning effect of the student are improved, and a more accurate and efficient teaching management tool is provided for a teacher.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention discloses an industrial control software public test resource scheduling method based on a self-adaptive genetic algorithm. The method comprises the steps of task scheduling modeling, resource constraint condition definition, task dependency relationship construction and dynamic scheduling strategy making. By introducing a self-adaptive mechanism, the crossover rate and the mutation rate in the genetic algorithm are adjusted to adapt to the search requirements of different stages, so that the optimization efficiency and the diversity of solutions are improved. According to the method, a public test resource scheduling optimization process specially aiming at industrial control software characteristics is designed, and the complex scheduling problems of multiple tasks, multiple constraints and limited resources can be effectively solved. Compared with a traditional static scheduling strategy, the method has the advantages that the test period is remarkably shortened, the resource utilization rate and the task completion rate are increased, the method is particularly suitable for industrial control software public test scenes with large-scale and multi-skill requirements and complex task dependence, and the method has good application prospects and popularization value.