893 results about "Allocation method" patented technology

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

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

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention provides a server GPU computing power allocation method and system and a server, belongs to the field of server resource management, and solves the problems of low utilization rate and inflexible decision of traditional allocation resources. The method comprises the steps that a central agent constructs a directed weighted game diagram of tasks and a GPU cluster, and a global allocation strategy is solved based on Nash equilibrium; a local agent distributes tasks to a specific GPU through reinforcement learning, intelligent migration is achieved in combination with a dynamic threshold value and anomaly detection, and a strategy is iteratively optimized through federal learning. The system comprises a central agent, a local agent, a GPU cluster and an experience pool module, and a server carries the system execution method. According to the scheme, through double-layer agent cooperation and multi-algorithm fusion, the task cluster adaptation relation is quantified, the allocation strategy is dynamically adjusted, the resource utilization rate and the task processing efficiency are remarkably improved, and the adaptivity and reliability in a complex scene are enhanced.

The invention discloses an intelligent computing power cluster task allocation method and system based on cloud edge collaboration, and relates to the technical field of computing power task allocation, and the method comprises the steps: extracting a first task in to-be-allocated tasks, collecting and obtaining a first multi-dimensional feature parameter of the first task, and obtaining a first feature curve; performing clustering analysis on the to-be-allocated tasks to obtain a clustering result; judging whether distributed edge computing power in a cloud edge computing power cluster meets the first resource requirement or not; if yes, performing comparative analysis to obtain first fitness of the first clustering cluster; descending the computing power nodes to obtain a target computing power node; and performing task processing of the first clustering cluster. According to the method and the device, the technical problems of low task allocation efficiency and poor task and computing power node adaptability in the cloud-side collaborative environment in the prior art are solved, and the technical effects of realizing efficient allocation of the tasks in the intelligent computing power cluster in the cloud-side collaborative environment and improving the task processing adaptability and efficiency are achieved.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

The invention belongs to the technical field of charging pile power distribution, and discloses a charging pile power distribution method and system based on a depth-first algorithm, and the method comprises the steps: building a host power topology adjacency matrix as an initial adjacency matrix; obtaining a host fault state, and optimizing the initial adjacency matrix to obtain a real-time adjacency matrix; when a host fault state or charging gun power demand change is detected, each charging gun is taken as a starting point, the real-time adjacency matrix calculates each charging path and path efficiency through a depth-first algorithm, the charging paths with the path efficiency lower than a preset value are eliminated, weighting priorities of the remaining paths are calculated, and the charging paths are calculated according to the weighting priorities; selecting the charging path with the highest weighting priority as a target path; and performing cross verification on the target path and the parallel contactor path, if verification succeeds, outputting the target path, otherwise, outputting the path successfully verified last time, so that power distribution can be dynamically and flexibly adjusted according to the charging power demand changing in real time.

The invention relates to the technical field of equipment management, and discloses a multi-unmanned equipment task dynamic allocation method and system, and the method comprises the steps: collecting the initial state information of an unmanned equipment group and a to-be-allocated task set, analyzing the load balancing demands of equipment based on the initial state information, and extracting the task priority in the to-be-allocated task set, obtaining an initial task allocation scheme; monitoring the execution progress of the initial task allocation scheme to form execution progress data, and analyzing a task dependency relationship in the execution progress data to identify key task nodes; starting local negotiation between devices according to the priority of the key task node, processing device state information through a game coordination mechanism to form a coordination decision weight, and generating a dynamic adjustment instruction based on the weight; according to the method, the overall execution efficiency of the cooperative task of the multiple unmanned devices can be improved, meanwhile, reasonable configuration of device resources is achieved, the situation that the resources are idle or overloaded is avoided, and the stability and reliability of task execution are enhanced.

The embodiment of the invention provides an annotation task allocation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a plurality of annotation tasks to be distributed; determining task portraits of the plurality of to-be-distributed annotation tasks according to the plurality of to-be-distributed annotation tasks; obtaining capability portraits of a plurality of annotation personnel and capability portraits of a plurality of data annotation models, wherein the data annotation models are used for data annotation; according to the task portrait, the ability portraits of the plurality of annotation personnel and the ability portraits of the plurality of data annotation models, constructing an optimization model used for determining an allocation relationship among the to-be-allocated annotation task, the annotation personnel and the data annotation models; and solving the optimization model by using a solving algorithm to generate a target task allocation scheme.

The invention discloses a cargo distribution method and device and a computer program product. The method comprises the following steps: determining a target cargo hold of a passenger plane loaded with a to-be-distributed cargo set, and obtaining cargo characteristics of each cargo and cargo hold characteristics of the target cargo hold; obtaining a plurality of cargo loading constraint conditions, and constructing a search space of a heuristic algorithm based on the plurality of cargo loading constraint conditions, the cargo hold features and the cargo features; generating cue words based on the cargo loading constraint conditions, the cargo hold features and the cargo features, and inputting the cue words and the search space into a target model to obtain a reduced search space; randomly extracting N initial cargo distribution schemes from the reduced search space, and screening out a target cargo distribution scheme from the N initial cargo distribution schemes based on a target evaluation function; and distributing the cargo to the target cargo hold based on the target cargo distribution scheme. According to the invention, the problem of low efficiency of distributing the goods to the passenger plane cargo hold in the prior art is solved.

The invention provides an intelligent terminal task allocation method based on AI edge decision and a related device, and the method comprises the steps: obtaining environment data and task attribute data in a target power operation region, obtaining target environment data and m pieces of task attribute data, transmitting the target environment data to n intelligent terminals, calculating according to the target environment data through n intelligent terminals to obtain n pieces of first calculation performance data, returning the n pieces of first calculation performance data to the electronic equipment, and determining a first adaptation degree matrix of tasks and intelligent terminal performance according to the n pieces of first calculation performance data and the m pieces of task attribute data, determining a target task allocation strategy according to a preset constraint condition and the first adaptation degree matrix; and controlling the n intelligent terminals to execute the edge decision according to the target task allocation strategy. In an electric power operation scene, the accuracy and robustness of task allocation for distributed terminal decision making can be improved.

The invention provides a distributed countering unmanned aerial vehicle group cooperative expelling path dynamic allocation method and system. The method comprises the following steps: firstly, acquiring a real-time motion track and a formation topological structure of an invading unmanned aerial vehicle group, then identifying a cooperative flight mode of the invading unmanned aerial vehicle group as a dynamic confrontation game behavior, then activating a countering unit combination adaptive to a current threat, and then integrating the countering unit combination into a dynamic available expelling resource. And according to the real-time motion trail and the dynamic available expelling resources, a multi-target allocation strategy is synchronously generated among the distributed computing nodes through a consensus protocol, and finally, a collaborative expelling action is executed according to the multi-target allocation strategy. According to the technical scheme provided by the invention, the real-time response capability and combat reliability of the system to the non-cooperative unmanned aerial vehicle group are improved, and cooperative combat and adaptive confrontation of multiple countering units in a complex environment are also realized.

The invention discloses an intelligent task allocation method and related equipment. The method comprises the steps of receiving multi-format task description information, extracting multi-dimensional attributes to generate a task vector, constructing an executive vector containing multiple capability dimensions, matching an optimal main executive through a multi-layer perception model, and performing real-time monitoring and adjustment. According to the scheme, the existing defects are specifically solved: multi-format information is analyzed to extract multi-dimensional attributes, so that a system comprehensively understands the context of a task, and the understanding insufficiency of a traditional method is avoided; constructing a multi-dimensional executive capacity vector and combining a scoring model to realize accurate matching of a task and an executive, and solving the problem of unreasonable distribution caused by asymmetry of man-machine capacity; the execution state is monitored in real time, redistribution is triggered, a dynamic feedback closed loop is formed, and the defect that the dynamic performance of a labor division mechanism is insufficient is overcome, so that the task execution flexibility and reliability are improved, resource waste and decision errors are reduced, and the execution efficiency is improved.

The invention discloses an OpenHarmony multi-queue scheduler intelligent distribution method based on multi-dimensional load feature perception, and relates to an OpenHarmony multi-queue scheduler intelligent distribution method. The problems that the CPU utilization rate is low, the average response delay is high, and resource scheduling self-adaptive adjustment and optimization cannot be achieved are solved. The method comprises the following steps: step 1, deploying a data acquisition module; step 2, feature preprocessing and coding; step 3, task load classification; step 4, executing dynamic optimization of scheduler parameters according to a prediction result; and step 5, performing performance feedback and online model updating. The invention belongs to the technical field of operating system resource management and artificial intelligence.

The invention relates to the technical field of computer storage, and particularly provides a large model training storage resource dynamic allocation method, device and system, and the method comprises the steps: collecting a workload index and a storage system state index of a large model training task; inputting the real-time monitoring data into a convolutional neural network (CNN) model, and outputting a feature identifier of a current training stage; inputting the real-time data and the stage identifier into a long short-term memory network LSTM model, and predicting future bandwidth, IOPS and storage space requirements; and generating a resource allocation scheme through multi-objective optimization according to a prediction result in combination with a system state, and executing load allocation, data layering and bandwidth reservation operations. According to the method, training stage perception and resource demand prediction are realized through cooperation of the CNN and the LSTM, and dynamic allocation and advanced scheduling of storage resources are realized, so that the resource utilization rate, the training efficiency and the system stability are improved.

The invention provides a container position distribution method based on a large model driven intelligent agent network. The container position distribution method based on the large model driven intelligent agent network comprises the following steps: step 101, carrying out multi-view hybrid coding on a container by adopting a double-flow attention mechanism to obtain overall feature representation of the container; step 102, adopting an autoregression decoder to generate a candidate container set according to the overall feature representation of the container and the current slot position; step 103, based on the candidate container set, using a deterministic physical model to carry out constraint check and index calculation; and 104, carrying out structured evaluation on the container by adopting the large model, and carrying out intelligent sorting. By the adoption of the method, the accurate and reasonable loading sequence and position of the containers can be ensured, and the loading efficiency of the containers is greatly improved.

The invention relates to the field of manufacturing and production process management, in particular to a multi-laser SLM layer vector data multi-source collaborative dynamic allocation method. The method comprises the following steps: processing a three-dimensional model according to a production order containing commercial constraints to generate a hierarchical process topology set carrying process rules; fusing the static attribute and the real-time process state of the topology set to generate a dynamic collaborative matching matrix, and dynamically scheduling multiple laser production resources according to the dynamic collaborative matching matrix; the real-time process state is compared with a preset space-time process potential field graph, a correction strategy is called, and closed-loop management of the manufacturing task is achieved. According to the method, the dynamic decision model is constructed, so that the defect that a static distribution strategy cannot cope with dynamic working conditions in the background technology is overcome, intelligent optimal configuration of production resources under the condition that order business constraints are met is realized, and the quality, efficiency and cost effectiveness of complex component manufacturing are improved.

The embodiment of the present invention discloses a method, device, equipment, medium and program product for edge collaborative computing power allocation. It includes: obtaining the business logistics information of each terminal node in the current business cycle; inputting the business logistics information of each terminal node into the pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; determining the real-time computing power demand according to the real-time terminal node and the real-time computing power prediction value, and dividing the total computing power resources into the real-time business container area and the non-real-time business container area according to the real-time computing power demand; determining the non-real-time access node for each time slot corresponding to the next business cycle according to the non-real-time computing power resources of the non-real-time business container area and the node weight of each non-real-time terminal node, and allocating non-real-time computing power resources to each non-real-time access node in the non-real-time business container area. This enables the computing power resources of the edge node to be fully utilized, distributed services to be flexibly deployed, and improves the processing capability of the power Internet of Things network for services.

The invention discloses an optical network dynamic resource allocation method fused with an AI large model, which relates to the technical field of real-time scheduling of network resources, and comprises the following steps: constructing a service prediction model based on terminal behavior characteristics, calculating bandwidth sensitivity Bs and burst probability Pt, generating priority weight Wp, and improving burst service scheduling response; the T-CONT allocation rate and period are adjusted in a self-adaptive mode through a resource allocation efficiency function Ef and a buffer area demand index Bh, and priority scheduling of key services is guaranteed; the scheduling pressure is judged by introducing double thresholds of a signaling overhead index So and a network congestion degree Oc, AI reestimation and resource reallocation are triggered when abnormity occurs, and meanwhile, a bandwidth recovery parameter Br is recorded to optimize a subsequent strategy, so that minute-level intelligent scheduling and resource utilization maximization are realized.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.

The invention discloses an execution time aware computing power network heterogeneous GPU cluster deep learning task node allocation method and system. The method comprises the following steps: firstly, based on a deep learning task, extracting and preprocessing task features and available node features; secondly, a sampler equally divides new tasks without historical data to available nodes, and each node performs mixed sampling on the tasks until all the tasks estimate execution time data; taking execution time data as a training set, taking the task features and the node features as a test set, and using a regression decision tree model to predict the execution time of the task on each node; performing task allocation on each node by using a cost search algorithm and a short job total JCT priority strategy; and finally, periodically monitoring node resources released in the cluster to obtain an optimal node allocation result. According to the method, task delay and total task JCT are remarkably reduced, cluster node resource changes are monitored in real time, and the resource utilization rate is increased.

The invention relates to the field of task processing, and particularly discloses a task allocation method and system for a heterogeneous computing architecture, a terminal and a medium, and the method comprises the steps: receiving a to-be-allocated task queue, and extracting a target attribute parameter of each to-be-allocated task; obtaining a resource utilization state parameter and an energy consumption state parameter of each processing unit; according to the target attribute parameters of the to-be-allocated tasks and the resource utilization state parameters of the processing unit, obtaining predicted execution duration of each to-be-allocated task through a pre-constructed predicted execution duration model; calculating predicted energy consumption of the to-be-allocated task executed on the processing unit according to the predicted execution duration and the energy consumption state parameter; according to the predicted execution duration and the predicted energy consumption, through a pre-constructed task allocation cost model, obtaining a predicted execution cost of the to-be-allocated task executed in each processing unit; and distributing the to-be-distributed task to the processing unit with the lowest predicted execution cost. The resource utilization rate of the processing unit is effectively improved.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The invention relates to the technical field of multi-modal task processing, in particular to a multi-modal task allocation and evaluation method combining a genetic algorithm and agent optimization. According to the method, behavior events on a multi-modal platform serve as modal subtasks to be packaged into a standardized triple, and the standardized triple is mapped into a task graph structure; then defining a gene coding rule of a task scheduling scheme, iterating a population by adopting a genetic algorithm, and obtaining an optimized scheduling scheme of each task; and the agents arranged on the execution nodes carry out task refined distribution. The invention provides a multi-modal task allocation method combining a genetic algorithm and intelligent agent optimization. The system can accurately identify an implicit logic relationship between tasks by constructing a task graph structure and introducing a time / semantic / modal three-dimensional dependency modeling rule; a global scheduling and modal agent execution mechanism driven by a genetic algorithm is adopted, and cooperation of generation of an upper-layer scheduling scheme and refined execution of local tasks of a lower-layer node is achieved. The method shows excellent resource load balancing capability in a heterogeneous computing environment, and has good expansibility and convergence performance.

The invention relates to the technical field of computers, in particular to an AGV task allocation method and system based on multi-objective optimization, and the method comprises the steps: obtaining a task demand set and a state information set of AGV equipment in a current operation scene, and the task demand set comprises a plurality of task units; performing feature extraction processing on the task demand set and the state information set to obtain task feature description of each task unit and capability feature description of each AGV device; performing joint optimization matching processing according to the task feature description and the capability feature description to generate a matching association relationship set of the task and the AGV equipment, wherein the matching association relationship set comprises a corresponding relationship between the task unit and the AGV equipment and a task execution sequence relationship; and generating a task allocation scheme based on the matching association relationship set, wherein the task allocation scheme comprises a to-be-executed task list of each AGV device and execution time sequence arrangement information of each task. Therefore, task requirements and equipment states can be accurately grasped, and efficient and matched innovative task distribution processing is realized.

The invention relates to the field of computer hardware architecture, in particular to a parallel processor dynamic resource allocation method, system and device and a storage medium, and the method comprises the steps: monitoring inter-node communication indexes and in-node parallel processor index data in real time; calculating an optimal connection path through an optimization algorithm which comprehensively considers the communication cost between nodes and the load balancing condition of a parallel processor; dynamically adjusting the configuration of a switch matrix according to the optimal connection path so as to switch the connection topology between the nodes; inputting the monitoring data of the continuous time window into the trained LSTM prediction model, and predicting a load prediction value in a future set time period; and according to the task priority, the resource state of the parallel processor and the load prediction value, dynamically adjusting the resource allocation of the parallel processor, and generating a resource allocation table. The communication cost between the nodes is reduced, the performance bottleneck caused by unsmooth communication is avoided, the load balancing of the parallel processor is realized, and the resource utilization rate is improved.

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.

The invention relates to the technical field of inspection, in particular to an agent inspection task allocation method and device and an inspection system. The agent inspection task allocation method comprises the steps of obtaining an inspection task, performing task splitting on the inspection task to obtain sub-task information of a plurality of sub-tasks, sending the sub-task information to a plurality of second devices, so that the plurality of second devices generate first bidding information for a target sub-task according to the sub-task information, and allocating the first bidding information to the target sub-task according to the first bidding information. And obtaining first bidding information sent by the plurality of second devices, determining a target device from the plurality of second devices according to the first bidding information, and sending a first task execution instruction of the target sub-task to the target device, so that the target device executes the target sub-task according to the first task execution instruction. By splitting the complex inspection task and selecting the most suitable equipment to execute each split sub-task, not only can the task deadlock be avoided, but also the inspection task time delay can be reduced.