352 results about "Task dependency" patented technology

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

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-UAV (unmanned aerial vehicle) task scheduling method and system with a dependency perception and feedback mechanism, and the method comprises the steps: enabling a commander to input a task demand in a voice or text form, inputting the task into a large language model based on a Python prompt template in combination with environment information and UAV capability configuration, and enabling the large language model to perform task scheduling; and completing subtask disassembly and dependency modeling of the natural language instruction. The method comprises the following steps: establishing a sub-task dependency graph, and determining a sequential relationship and execution logic between tasks; in the aspect of task scheduling, capability vector modeling is carried out on all online unmanned aerial vehicles, and an optimal unmanned aerial vehicle is selected or a multi-vehicle alliance is automatically constructed to execute a task based on a vector matching degree between task skill requirements and unmanned aerial vehicle capabilities. In the task execution process, task state information is collected in real time, and all feedback information is uploaded to the cloud control center for state judgment and abnormity recognition. When the system detects an abnormal condition, task reconstruction, alliance recombination and scheduling graph repair are automatically carried out, and closed-loop adjustment of the task is completed.

The invention relates to the technical field of electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The invention provides a task disassembly and multi-agent arrangement execution system and method based on a large language model, and belongs to the technical field of computers, and the system comprises an instruction analysis module, a DAG construction module, a scheduling execution module and a cache optimization module. Analysis is carried out according to the dependency relationship between the tasks, a task execution DAG is automatically constructed, and concurrent calling is carried out on the sub-modules without dependency; by caching an authentication result, a context reasoning result and the like, a universal module is executed in advance, the result is reused, and repeated calculation is reduced; the maintainability and the expandability of the system are improved through graph structure visualization and node element information injection; and performing context analysis and scheduling optimization in combination with the reasoning ability of the language model to realize an intelligent decision execution path.

The invention relates to a task execution method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of artificial intelligence chips. The processing efficiency of execution of the calculation task and the communication task on the artificial intelligence chip can be improved. The method comprises the steps of obtaining a task flow; in the same task flow, enabling a first processing unit of the artificial intelligence chip to execute a calculation task, and enabling a second processing unit of the artificial intelligence chip to execute a communication task; wherein the calculation task does not depend on the communication task; the communication task depends on a corresponding computing task, and the communication task does not depend on a non-corresponding computing task.

The invention discloses a dynamic production plan adjustment method in an industrial internet environment, and the method comprises the steps: constructing a task dependence graph according to the task attribute data of a production task; identifying a task state change event, and updating the task dependency graph in real time based on task nodes and edges influenced by the task state change event; a linear programming algorithm is adopted to obtain a task priority ranking result, and task priority ranking is dynamically adjusted; a distributed scheduling framework is adopted to map tasks to scheduling queues of all edge nodes, and a task distribution strategy is determined; and marking the abnormal task based on the task performance index in the task execution process, identifying the abnormal mode of the abnormal task, and optimizing the task allocation strategy based on the abnormal mode data of the abnormal task. The invention provides a dynamic, self-adaptive and efficient production plan adjustment method, which can comprehensively improve the global optimization capability, real-time performance and intelligent level of task scheduling in a dynamic production environment.

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

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 discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention relates to a digital twin platform GPU rendering resource dynamic scheduling method and system, and the method comprises the steps: S1, collecting a rendering task feature signal of a current frame in real time, and collecting a GPU multi-dimensional resource consumption signal; s2, based on the rendering task feature signal, the historical resource consumption signal and the scene dynamic change signal, generating a GPU resource demand prediction signal and a load fluctuation trend signal of a future frame; s3, predicting a signal, a task dependency relationship signal, a user priority signal and a real-time resource bottleneck type signal according to the GPU resource demand; s4, executing the dynamic resource allocation instruction signal; and S5, generating a parameter self-optimization signal for updating the prediction and scheduling logic of a subsequent frame according to the actual resource consumption signal of the current frame and the scheduling effect evaluation signal. According to the dynamic scheduling method and system for the GPU rendering resources of the digital twin platform, the problem that the GPU resource utilization rate is low and the real-time performance is difficult to consider at the same time under the dynamic load can be solved.

The invention relates to the technical field of task scheduling of an embedded system, and particularly discloses a real-time task scheduling optimization method, device and system for FreeRTOS, and the method comprises the steps: obtaining the current task load capacity in the FreeRTOS; if the current task load capacity in the FreeRTOS is greater than a preset load capacity threshold value, performing dynamic optimization adjustment on an execution priority sequence of tasks in the current task load capacity according to a dynamic optimization adjustment algorithm; performing dependency relationship checking on the dynamically optimized and adjusted task, and obtaining a task dependency relationship checking result; and determining a corresponding task priority processing mode according to the task dependency checking result. The real-time task scheduling optimization method for the FreeRTOS provided by the invention can realize real-time task scheduling so as to meet a multi-task and high-load application scene.

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

The invention provides a cross-platform database heterogeneous migration and fault-tolerant control method and system, and relates to the field of database migration. Analyzing the access logs to generate a popularity list with weights, and calculating priorities in a classified manner; establishing a task dependence topological graph and arranging a migration task; converting data according to rules and transmitting the data in a fragmented manner; and the change information is synchronized through the distributed message queue, and the consistency is verified. According to the method, efficient migration among heterogeneous databases can be realized, the downtime of the system is shortened, the data consistency is ensured, and the migration efficiency is improved.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

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 provides an intelligent task scheduling and optimizing method and system, a medium and equipment, and the method comprises the steps: analyzing task metadata through a database storage process, generating a task dependency graph according to an obtained task dependency matrix, and converting the task dependency graph into a visual interaction interface; identifying a key link with longest time consumption and a bottleneck task on the key link through the created time prediction model, and generating a multi-dimensional optimization suggestion based on an identification result; automatically adjusting a task scheduling script according to the multi-dimensional optimization suggestion and generating a standardized configuration file, dynamically allocating resources according to the standardized configuration file, and monitoring an execution state of a task through a fault-tolerant mechanism; and performing intelligent early warning according to the task log data collected in real time and the system performance index, and generating a multi-dimensional analysis report. The system is an intelligent scheduling system integrating dependency analysis, link optimization and execution monitoring, and automatic analysis and optimization of a complex task network are achieved.

The invention relates to the technical field of intelligent unmanned systems, in particular to a task allocation method of a sea area cross-medium unmanned system based on a graph attention network and deep reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph and calculating task priority; s2, screening and selecting schedulable tasks; s3, intelligent agent distribution and state updating; s4, dynamic reordering and cycle control are carried out; according to the method, a space-time coupling dynamic graph structure is constructed, task attributes and agent states are coded into node features, a four-layer graph attention network is designed to extract task priority distribution, and a self-adaptive scheduling decision is realized in combination with a near-end strategy optimization algorithm; a task dependence verification and resource availability check mechanism is established, and approximate optimal agent allocation is realized; a multi-dimensional reward function is included, training stability is guaranteed in combination with an experience playback mechanism and a gradient clipping technology, and task allocation efficiency of a multi-agent system in a complex environment is remarkably improved.

The invention discloses a method and a device for optimizing dynamic task scheduling resources of a distributed system. The method comprises the following steps of: monitoring a task dependency relationship and hardware resource state data in real time; a dependency release amount R representing the downstream task activation capability is generated based on the dependency relationship; generating an index A representing the resource utilization efficiency based on the resource state; a scheduling strategy is dynamically selected according to the load state: when the load is low, the task with the maximum R is preferentially allocated to improve the CPU parallelism degree, when the load is stable, allocation is carried out according to R and A weighted values to balance resource utilization, and when the load is high, the task with the maximum A is preferentially allocated to reduce the memory pressure; and finally driving task execution and updating a resource state. By dynamically sensing the task topological relation and the resource state, adaptive scheduling under load fluctuation is realized, the collaborative utilization efficiency of CPU and memory resources is effectively improved, and the problem of performance bottleneck of a traditional scheme under complex dependence and high-concurrency scenes is solved.

The invention relates to the technical field of energy-saving control, in particular to a computer energy-saving state control method and system, and the method comprises the following steps: constructing a task graph model based on a computer task dependency relationship, calculating a dependency intensity value between tasks, recognizing a task resource scheduling sequence, and analyzing the association degree between the tasks; tasks with high dependency are preferentially distributed to shared computing resources, and a task relevance scheduling scheme is generated. In the invention, through dynamic scheduling based on the task load and the dependency relationship, the system can accurately adjust the allocation and the power state of the computing resources under different loads, improve the resource use efficiency when the load is high, monitor the power consumption and the temperature change of the computing unit in real time, and dynamically adjust the working mode and the frequency of the computing resources; excessive energy consumption and heat accumulation are effectively avoided, the system can adjust the power state according to the actual demand of a task, efficient energy use of the system is kept, unnecessary resource consumption and temperature fluctuation are reduced, and the overall energy efficiency is improved.

The invention discloses a task load balanced distribution system and method based on a node resource state, and relates to the technical field of distributed computing, and the method comprises the steps: collecting and associating multi-dimensional node resource data and dependency relationship data between tasks; establishing a virtual synaptic connection for each pair of tasks with data interaction, and constructing a task-dependent neural network model; the node resource state is monitored in real time, and the synaptic connection strength is adjusted; dynamically generating an optimal task allocation strategy; when the node resource saturation is continuously too high or the task execution efficiency is obviously reduced, starting a task migration process; and in the migration process, a synaptic cache region is created in the intermediate node, interaction data is cached, a dependency task node mapping relation is updated, data is synchronized, and the collaboration efficiency is verified. According to the method, CPU and GPU resource states and task dependency are considered at the same time, accurate matching and cooperative scheduling are achieved, overload or idling of a single resource is avoided, and the resource utilization rate is increased.

The invention discloses a priority scheduling method and system for an on-orbit data processing task. The method comprises the following steps: constructing a DAG model, decomposing a macroscopic task into subtasks, defining a subtask dependency relationship, and endowing a subtask node with a priority attribute; setting or dynamically adjusting the priorities of the sub-tasks according to task service types, processing delay requirements or external instructions; maintaining a schedulable queue containing subtasks which have completed precursor tasks and meet scheduling conditions; calculating task scheduling priority indexes based on the queue, sorting, and selecting optimal calculation resource allocation; the priority is dynamically adjusted according to task waiting time or an external instruction during execution, and hunger of low-priority tasks is avoided; the multiple processing units execute the high-priority tasks in parallel according to needs; and after the task is completed, updating a subsequent node dependency state, and adding a new schedulable task cycle. According to the method, high-priority task resource allocation can be guaranteed preferentially, low-priority task hunger is avoided, and the overall throughput rate of the system and the self-adaptive capacity to on-orbit diversified tasks are remarkably improved.

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.

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 invention relates to the technical field of task execution, and provides a workflow parallel computing execution method and system, terminal equipment and a storage medium. The workflow parallel computing execution method comprises the steps that a to-be-executed task is obtained, and after the to-be-executed task is subjected to logic analysis, the to-be-executed task is obtained; dividing a to-be-executed task into a plurality of sub-tasks according to the task type, constructing a task dependency graph, and making an execution model according to the characteristics of the sub-tasks; according to the resource states and the task priorities of the sub-tasks, resource allocation is carried out on the sub-tasks, and an execution sequence is set for the sub-tasks based on the dependency relationship in the task dependency graph; according to the execution mode and the execution sequence, the subtasks enter a parallel execution state, and an execution result of each subtask is obtained; and according to the original logic and data dependence of the to-be-executed task, summarizing, sorting and combining the execution results to obtain a summarized result, and verifying the summarized result to obtain a final execution result.

The invention relates to the technical field of computers, discloses a heterogeneous computing low-delay communication method and system, and aims to solve the problem of high delay caused by high communication protocol overhead, lack of dynamic scheduling collaboration, memory migration redundancy and non-uniform cross-node communication abstraction in existing heterogeneous computing. The method comprises the following steps: receiving a task scheduling request and analyzing a task dependency graph; tasks are dynamically allocated based on node loads and link states; rDMA, NVLink or PCIe straight-through protocols are adaptively selected according to node types to establish communication channels; zero-copy data exchange is realized through a shared memory mapping buffer area; hardware timestamps are utilized to synchronize feedback delays with PTP to optimize scheduling. The system comprises a heterogeneous computing node cluster, a unified communication scheduling controller, a low-delay communication protocol stack, a shared memory mapping buffer area and a communication delay sensing task distributor. According to the scheme, the communication delay is remarkably reduced, and the throughput and the task execution efficiency are improved.

The invention discloses a distributed job scheduling method and device, a storage medium and computer equipment, relates to the technical field of distributed task scheduling, is suitable for the field of financial and medical services, and mainly aims at solving the problem that an existing distributed job scheduling system lacks task dependence management and dynamic scheduling capability. Comprising the following steps: performing task modeling processing on each to-be-executed task by adopting a binary tree structure to obtain a task binary tree corresponding to each to-be-executed task; performing serialization processing on the task binary tree by adopting a job arrangement engine to obtain a corresponding task sequence; issuing the task sequence to an event flow bus; after the event flow bus is started, tasks to be executed are allocated to different distributed actuators for execution based on the central control scheduler; and the distributed executor returns an execution result to the event flow bus in real time, so that the event flow bus triggers the downstream task after the upstream task is completed until the task sequence is completely executed.