270 results about "Priority scheduling" patented technology

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention provides a data processing priority scheduling method for high-speed satellite communication and related equipment, which can effectively improve the transmission performance of a high-speed satellite communication system so as to adapt to differentiated requirements of various communication scenes. The method comprises the following steps: acquiring cross-layer state information; calling a strategy matrix, wherein the strategy matrix is used for defining weight coefficients of different data types under different network states and channel qualities; calculating a target priority score for the to-be-transmitted data stream according to the cross-layer state information and the strategy matrix; sequencing the target priority scores from high to low to obtain a sequencing result; scheduling transmission queues of the data streams for transmission according to a sorting result, and allocating cache transmission queues for the data streams with high priority scores; when it is monitored that the priority score of the new data stream exceeds a preset emergency threshold value, the new data stream is inserted into a cache transmission queue of the transmission queue, and back pressure control is carried out on data streams of other priorities.

The invention discloses an edge computing node collaborative task unloading method for guaranteeing a low-delay service, and relates to the field of edge computing, each edge computing node generates a collaborative view comprising the edge computing node and a neighbor edge computing node through a local LSTM prediction model and federated learning, and the collaborative view comprises a predicted resource state and a predicted network state; according to the invention, the local LSTM prediction model and federated learning are combined to generate the collaborative view, so that accurate prediction and global information sharing of edge node resources and network states are realized, and the accuracy of decision making is improved; the tasks are analyzed into a dependency graph with key path marks, so that priority scheduling of the key tasks is ensured, and the overall task time delay is reduced; based on a weighted voting consensus mechanism of node credibility and resource adequacy, the efficiency and reliability of decision consensus among nodes are improved; the task unloading efficiency and the service quality of the low-delay service are effectively improved, and the stability and the reliability of the edge computing system are enhanced.

The invention discloses a thread group scheduling method and device for a GPU (Graphics Processing Unit), the GPU and equipment. The method comprises the following steps of: 1) receiving a scheduling request of a thread group; 2) task type priority scheduling; according to a thread group weight value set by a user, obtaining execution priorities of the vertex thread group and the fragment thread group in the current scheduling period; 3) instruction type priority scheduling; pre-analyzing to-be-executed instructions of the thread group, and determining a priority sequence of schedulable instruction types; and 4) priority scheduling of the thread groups: selecting the thread group with the highest priority for scheduling according to the specified task type priority and instruction type priority in combination with the thread group generation time. The invention provides a thread group three-level scheduling strategy so as to improve the instruction throughput rate and the key task response speed of the GPU under the complex load.

The invention relates to the technical field of water pump energy efficiency optimization, and discloses a water pump dynamic model calibration and efficiency optimization method. The method comprises the following steps: constructing a water pump dynamic characteristic model comprising a plurality of operation parameter nodes, an association relationship among the parameter nodes and characteristic information of each parameter node, and updating the model based on real-time operation data such as flow change, pressure fluctuation, rotating speed feedback and environment temperature; key influence parameters, low-efficiency parameter combinations and deviation nodes in the model are identified through a parameter sensitivity analysis algorithm; generating an initial calibration strategy set comprising a parameter compensation scheme, an operation condition adjustment scheme and a priority scheduling scheme based on the information; receiving a user configuration signal to determine a calibration operation type, and dynamically correcting the initial calibration strategy set according to the calibration operation type; the corrected strategy is executed, and the water pump dynamic characteristic model is updated based on execution result feedback. According to the method, collaborative implementation of model calibration and efficiency optimization in the operation process of the water pump is achieved.

The invention provides a resource dynamic scheduling method and system, and the method comprises the steps: obtaining the real-time output, gradeability, start-stop records, health states and other operation information of various adjustable resources, and constructing an operation information sequence according to a unified time reference; performing quality verification and completion on the operation information to generate an operation information basic database; extracting a multi-dimensional capability index based on the operation information, and constructing a resource elastic portrait through normalization, time correlation modeling and feature fusion; generating a dynamic adjustable capacity index according to the elastic portrait, and reflecting the real-time adjustment capability of each resource along with the change of the running state; determining an output adjustment interval of the resources according to the capacity index, and forming a priority scheduling sequence; according to a power gap obtained by load prediction and wind and light output prediction, generating a resource combination scheme meeting an adjustment demand and issuing a scheduling instruction; and monitoring the actual output of the resources, updating the elastic portrait and the capacity index when the deviation exceeds the limit, and correcting the scheduling scheme in a rolling manner.

The invention provides an intelligent course arrangement method based on multi-constraint hierarchical optimization and dynamic backtracking, and is applied to the technical field of data processing. According to the method, basic data of setups, classes, courses and the like are firstly obtained, and a multi-dimensional constraint model is established through preprocessing and is quantified into a time slot state value; based on a two-level layering strategy and dynamic teacher priority scheduling, independent classes are processed firstly, then sub-classes are processed, conflicts are cleared, and a stage course arrangement result is generated. In combination with the weekly class hours of the courses, time is distributed through an intelligent hall connection and degradation strategy, conflicts are detected in real time, and a backtracking mechanism is triggered. And verifying the conflict-free persistent data, and generating an initial course arrangement scheme. And finally, according to indexes such as course arrangement success rate and the like and application feedback, a constraint model, a scheduling algorithm and a class connection strategy are optimized, a multi-dimensional coupled intelligent course arrangement optimization scheme is formed, and the course arrangement problem under complex constraints is efficiently solved.

The embodiment of the invention provides an adaptive priority scheduling method and device for large model training tasks, and effective planning of resources is realized through innovative design of a performance monitoring system, feature analysis and state prediction. A priority evaluation mechanism is constructed, and a reliable scheduling strategy is established in combination with multi-dimensional factors and decision making. Dynamic optimization is introduced, and scheduling adaptability is ensured by executing monitoring and parameter adjustment. According to the method, the defects of the traditional technology in the aspects of performance monitoring, priority evaluation, dynamic adjustment and the like are effectively overcome, and technical support is provided for large model training.

The invention discloses a model service API gateway flow and scheduling method, system and device and a medium, belongs to the technical field of API gateway technology, flow scheduling, cloud computing and artificial intelligence cross, and aims to solve the technical problem of how to realize accurate regulation and control of model service API gateway flow, improve the resource utilization rate and improve the service quality. According to the technical scheme, the method comprises the steps of collecting multi-dimensional data, wherein multi-dimensional flow and service data including API gateway real-time flow, model service states, historical logs and service labels are collected, and data cleaning is conducted on the multi-dimensional flow and service data to construct a standardized data set; intelligent prediction: receiving the standardized data set, and capturing a long-period rule, a short-period change and a service association rule through an attention mechanism by adopting a Transf ormer architecture large model; traffic scheduling: executing three strategies including a dynamic traffic limiting and shunting strategy, a service priority scheduling strategy and an API routing optimization strategy based on the prediction result and the real-time monitoring data; aPI gateway control is carried out; monitoring feedback; and carrying out heterogeneous adaptation.

The invention discloses a browser optimization method and system based on dynamic resource loading and intelligent caching, belongs to the technical field of browser optimization, and aims to solve the technical problems of how to improve browser resource loading efficiency, reduce bandwidth consumption and improve user experience. Comprising the steps of predicting a next page possibly accessed by a user or required resources through a lightweight learning model based on a historical browsing path of the user, and obtaining the resources required by the user; carrying out on-demand dynamic loading on resources required by the user; the loaded resources are cached through a multi-level caching strategy; comparing the resources of the local cache of the browser with the latest version of the server side based on the resource fingerprints, and if the resource versions are inconsistent, downloading the resources of the latest version from the server and updating the local cache of the browser; a priority scheduling and parallel loading strategy is adopted for resources of various levels; and dynamically adjusting a resource loading strategy and resource quality according to the current network state.

The invention discloses an intelligent warehouse management and resource optimization method. The method comprises the steps of S1, multi-source data acquisition and fusion processing; s2, performing real-time analysis and dynamic prediction on the inventory state; s3, intelligently generating storage tasks and scheduling priorities; s4, collaborative path planning and resource optimization allocation are carried out; s5, task execution monitoring and real-time dynamic re-optimization are carried out; s6, performing task closed-loop analysis and strategy optimization; and S7, performing storage layout and resource configuration simulation optimization. According to the method, the storage data is collected in real time through the Internet of Things equipment and the sensor, intelligent collaborative optimization of the storage resources and the operation tasks is achieved in combination with the dynamic analysis model, the inventory state and the equipment operation condition can be monitored in real time, the storage tasks are automatically generated and adjusted, the equipment path and resource allocation are optimized, and the operation efficiency is improved. And the warehousing operation efficiency and the resource utilization rate are remarkably improved.

The invention belongs to the technical field of power system automation and energy management, and particularly discloses a standby power management system for power utilization information acquisition of a power grid and a control method. The system comprises a main power supply monitoring module, an energy reserve management module, a communication environment sensing module, an energy efficiency strategy decision core and a task priority scheduling module. The communication step length and the transmitting power are dynamically adjusted by capturing the states of a power grid, a battery and a channel in real time, and data scheduling is executed according to priorities. By the adoption of the technical scheme, fine distribution of electric energy is achieved, invalid standby power consumption is reduced, the life cycle of the collection terminal in the power-off state is prolonged, reliable reporting of key information under the extreme working condition is ensured, and data support is provided for research and judgment of power grid faults.

The invention relates to a priority-based high-visibility stream-by-stream data packet sampling method and system. Comprising the following steps: step 1, flow initiation and priority marking; step 2, flow establishment and controller decision making; step 3, rule issuing and strategy execution; and step 4, data forwarding, scheduling and sampling: a priority scheduling mechanism of a data plane and global optimization of a control plane are decoupled and coordinated. In a data plane, a terminal host marks a degradation priority for a data packet according to the number of bytes sent by a stream, and a switch performs strict queue scheduling and probability sampling based on the priority. In a control plane, a controller carries out collaborative modeling on the configuration of routing, sampling probability and degradation threshold as a nonlinear optimization problem with maximization of sampling utility as a target, and an efficient online primitive-dual interior point method is adopted for solving. According to the method, high visibility and load balancing are finally ensured, and meanwhile, the control and calculation overhead is remarkably reduced.

The invention belongs to the technical field of automotive electronics AUTOSAR, and discloses an AUTOSAR multi-task-oriented nested lock management and real-time priority scheduling control system, a detection and prevention module establishes a dynamic resource allocation diagram by taking tasks and locks as nodes, real-time traversal is performed in combination with a depth-first search algorithm, and a task-lock-task circulation path is accurately identified; when a potential deadlock is detected, preferentially releasing a low optimal lock occupied by a low-priority task through priority arbitration, and triggering an overtime release mechanism when the priorities are the same; a scene of accessing multiple layers of shared resources by multi-task nesting under an AutoSar framework is adapted, so that the system is ensured to stably run in a complex concurrent scene, and function failure caused by deadlock is avoided; the priority scheduling module establishes a scheduling rule in combination with dual priorities of tasks and locks, optimizes a nested priority inheritance mechanism, temporarily improves the priority when a low-optimal task occupies a lock required by a high-optimal task, and configures a preemption right for the high-optimal lock.

The invention discloses a medical record diagnosis auxiliary system for thyroid puncture pathology and clinical data association analysis, and relates to the technical field of medical diagnosis intelligent analysis. Comprising a regional risk sensing module, a self-adaptive partition compression module, a transmission priority scheduling module, a detail integrity verification module, a cross-domain feature fusion and detail enhancement module and a diagnosis result return and model optimization module, in the digital scanning process, a regional risk perception model based on a pathological diagnosis task is established, a high-sensitivity region is calibrated according to cell nucleus density distribution, cell nucleus morphological anomaly degree and chromatin particle change, and corresponding lossless fidelity demand data is generated. Pathology key detail fidelity transmission and multi-dimensional feature fusion are achieved under the condition that resources are limited, the early-stage tiny focus recognition rate is increased, misdiagnosis and missed diagnosis are reduced, and diagnosis accuracy and efficiency are continuously improved through closed-loop optimization.

The invention discloses a group voice message playing method and system based on priority scheduling, and relates to the field of group voice message processing.The method comprises the steps that when it is detected that the number of unbroadcasted voices exceeds a threshold value, the system automatically enters a simplified broadcasting mode; key metadata such as a sender identity, a message label and message sending time are extracted from the voice message; key entities in voice content are recognized through semantic analysis, and content value indexes of a semantic level are constructed in combination with semantic relevancy of a user at a receiving end. Then, the multi-dimensional metadata and semantic correlation are used as input, the priority score of each voice message is calculated, and a structured priority message queue is generated based on the priority scores; when a user initiates a playing request, the system sequentially broadcasts high-value voices according to a priority order, and quick presentation of key contents is realized. Therefore, the probability of delayed playing of the high-value voice is reduced, the information acquisition efficiency of the user is improved, and efficient scheduling of the group voice message is realized.

The application discloses a computer scheduling method for multitask operation environment state, and particularly relates to the technical field of computer operating system; the application generates a priority scheduling coefficient by quantitatively calculating a resource adaptation coefficient, a urgency coefficient and a constraint reflection coefficient, and then combining a weighting logic, so as to ensure that high-urgency tasks are preferentially executed to reduce postponement, avoid system overload or overheating through resource adaptation and thermal constraint control, and reserve reasonable heat production space for high-priority tasks, and balance of task execution efficiency, resource utilization and hardware safety is realized.

The invention discloses a packaging bonding process basket level process time accurate control method and a related device, and the method comprises the steps: carrying out the one-to-one mapping of basket physical slot positions and virtual slot positions, and setting the basket level process time, the batch level process time and the single chip level process time; obtaining the time sequence correlation information of the wafer in each virtual slot, and dynamically calculating the processing sequence of each basket under the process time of the basket level, the batch level and the single wafer level based on a multi-factor priority scheduling algorithm according to the time sequence correlation information of the wafer in each virtual slot; and in the process of machining according to the machining sequence of the lifting baskets, the machining sequence of the lifting baskets, the batches and the single pieces is dynamically adjusted. According to the invention, the processing sequence of lifting baskets, batches and single wafers is dynamically adjusted, so that the continuity and consistency of the wafers in the production process can be better ensured. By considering the time sequence association information, the wafer quality difference caused by an improper processing sequence can be avoided, and the overall product quality is improved.

The invention discloses a computer fault rapid positioning method, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: firstly, obtaining global monitoring data of a target computer, carrying out the hierarchical topology modeling of the target computer, and constructing a candidate monitoring point set and node data according to a hierarchical topology model; secondly, performing optimization positioning calculation on the monitoring point set and the node data by using an RCO algorithm to obtain a monitoring point subset; constructing a hierarchical fault positioning model based on the hierarchical topology model and the monitoring point subset, and performing backtracking hierarchical positioning on global monitoring data to obtain the fault probability of each component of the computer; and finally, constructing a diagnosis scheduler, issuing the diagnosis scheduler to each monitoring point in a distributed manner, and generating a fault priority scheduling queue in a closed-loop manner, thereby remarkably improving the positioning fault-tolerant rate in a complex fault scene, and reducing the resource occupancy rate.

The present application relates to the technical field of product warehouse dynamic cleaning, and particularly relates to a product warehouse customized dynamic cleaning implementation method and system.The implementation method comprises the following steps: a business layer is responsible for scene logic implementation, and has a system type, an environment and a time range set by a user;S2: setting a cleaning rule, adopting a time priority scheduling algorithm, arranging a cleaning task execution order according to a task execution priority and a current product warehouse load state;S3: an execution layer is responsible for screening out products needing to be cleaned in a logic layer, and performing product cleaning by calling a system level command.The present application realizes accurate product warehouse cleaning, maximizes storage space release, reduces operation and maintenance costs, and improves product warehouse storage efficiency and service stability on the premise of guaranteeing core product safety.

The present application relates to the technical field of power system automation control, in particular to a fish-light complementary reactive power compensation control method and device; the method comprises the following steps: through real-time acquisition of photovoltaic output, fishery load operation intensity and water surface state and other multi-source information, after time sequence alignment and feature extraction, a unified fish-light complementary operation state index is constructed by fusion; short-term change trend analysis and evolution mode classification are carried out, potential reactive power demand in the future period is predicted, and a smooth and continuous short-term demand index is generated; according to the high and low levels of the demand index, a variety of resources are dynamically matched, a hierarchical priority scheduling and multi-resource parallel compensation strategy is formed; in the compensation execution process, output and system stability indexes are monitored in real time, and dynamic correction is carried out through synchronization deviation and stability deviation; at the same time, trend learning is carried out combined with long-term historical data, and the prediction model and resource allocation strategy are continuously optimized. The present application effectively improves the operation stability and energy efficiency of the fish-light complementary system.

The invention discloses an order type task planning method and system for real-time tasks, and relates to the field of task planning. According to two task planning modes of active request scheduling and forced priority scheduling, a task unit autonomous request model, a control center minimum cost distribution model and a multi-stage optimization model are constructed, and an improved genetic algorithm is adopted to perform task resource distribution and time sequence optimization. The advantages of the planning mode in the aspects of task completion timeliness, resource consumption, task success rate and response capability are quantitatively compared, optimization is carried out by combining a secondary autonomous request and a distribution mechanism with a forced priority strategy, the efficient planning and scheduling requirements of real-time tasks can be effectively met, the solving efficiency is remarkably improved through the improved genetic algorithm, and the real-time task scheduling efficiency is improved. And a forced priority mechanism is adopted to optimize secondary autonomous request and distribution, so that the comprehensive cost of the system can be further reduced on the basis of guaranteeing the task execution effect, and the generalization ability of the model under different task scenes and resource conditions is verified at the same time.

The invention discloses a distributed data processing method and system for an intelligent factory, and relates to the technical field of communication data processing. Determining target equipment according to the original data; based on the centrality index of the target equipment in the factory equipment topological graph, calculating the centrality index to obtain a topological criticality score; analyzing the original data based on a preset rule base to obtain an event severity score; multiplying the topology criticality score by the event severity score to obtain a final priority; inserting the original data into a priority scheduling queue according to the final priority; the storage nodes with the resource loads lower than a preset health threshold value are summarized into a current available node pool; and taking out the target data with the highest final priority from the head of the priority scheduling queue, selecting a storage node with the lowest resource load from the current available node pool as a target storage node, and preferentially distributing the target data to the target storage node for processing. By implementing the technical scheme, priority processing of high-value data is ensured.

This invention provides a distributed event processing method and an electronic device, applicable to the field of distributed technology. The distributed event processing method includes: receiving an event processing request at the request receiving entry point of a distributed system; searching for the priority and mutually exclusive execution type corresponding to the event type from a preset event registration database based on the identifier representing the event type in the event processing request; inserting the event object, encapsulated from the event processing request, into a priority scheduling queue based on the priority and the timestamp of the event processing request; after extracting the event object for execution from the priority scheduling queue, performing conflict detection on the mutually exclusive execution type corresponding to the event type based on a global mapping table maintained in the distributed system; if no conflict is detected, invoking the business processor corresponding to the event type to execute the event object, and updating the current execution count of the event type in the global mapping table based on the execution status.

The application relates to the technical field of network slices, in particular to a message priority scheduling test system for high-density V2X communication, which comprises a V2X slice congestion state monitoring module, which is used for monitoring the C-V2X message arrival rate and the physical buffer occupancy rate of each network slice, calculating a virtual queue length value, and aggregating the virtual queue length value of all network slices with corresponding slice identifiers. In the application, the C-V2X message arrival rate and the physical buffer occupancy rate of each network slice are synchronously observed and numerically integrated, a slice congestion state representation system with time sensitivity and dynamic adaptability is established, the identification capability for network operation pressure is improved, and through coupling modeling of the message life cycle and the slice bearing state, a potential hazard degree index for a single message is constructed, so that the scheduling system has stronger perceptual granularity and task level discrimination capability.

The application discloses a kind of intelligent fusion terminal heterogeneous protocol conversion method and system, the method includes the following steps: first, the three-layer system architecture of perception layer, protocol analysis layer and intelligent conversion layer is constructed, realizes from data acquisition, semantic understanding to the whole process of intelligent conversion.Through the construction of uniform protocol description model based on JSON Schema, centralized metadata registration and version management are carried out on protocol template, the standardization of heterogeneous protocol is realized;Intelligent identification of protocol type and analysis field is realized by using convolutional neural network, and mapping rules consistent with semantics are automatically generated by combining knowledge graph and rule engine;Model pruning and quantization technology are used to optimize deep learning model, and priority scheduling and cache mechanism are designed.The application effectively solves the interoperability problem caused by the heterogeneous communication protocol of power equipment, and improves the protocol conversion efficiency and system expansibility of intelligent fusion terminal.

The invention discloses an unmanned aerial vehicle airspace situation awareness fusion system and method. The method comprises the following steps: dividing an airspace into grids by an edge computing node, judging situations of different risk levels of red, yellow and green, and generating lightweight situation data; then, according to the type and the communication capability of the unmanned aerial vehicle, adaptively selecting a link, and sending the data to the unmanned aerial vehicle by adopting a priority scheduling mechanism; after the unmanned aerial vehicle receives the external situation data, the external situation data and real-time sensing data collected by a sensor of the unmanned aerial vehicle are fused, a final airspace environment analysis decision is generated through a dynamic weight rule, and flight is controlled according to the final airspace environment analysis decision. According to the method, the problems of limited sensing range and lack of global situation of the unmanned aerial vehicle are solved, and through edge-end cooperation, the early warning range is expanded, the accuracy and robustness of decision making are improved, and the flight safety of the unmanned aerial vehicle in a complex airspace is enhanced.

This invention relates to the field of data synchronization technology, specifically to a method, device, and storage medium for efficient data synchronization in an office system. The invention collects several changed fields and their attribute data, time-series data, and concurrent operation data of the current business process within the current synchronization cycle, as well as the data of the current business process itself. Based on the changed field attribute data and concurrent operation data, the business weight of each changed field in the current business process is obtained. Atomic synchronization units are constructed by analyzing the closeness of business linkage between each changed field and other changed fields. The exclusivity and overlap rate of atomic synchronization units containing the same changed fields are analyzed to select conflict-free synchronization units. Based on the numerical distribution of the changed field business weights within the conflict-free synchronization units and the timeliness characteristics of the time-series data, the priority scheduling coefficient of the conflict-free synchronization units is obtained. Combined with the basic risk characteristics in the changed field attribute data, this guides the orderly execution of the final update synchronization.

The invention discloses a dynamic task priority scheduling method and system for flexible production, and belongs to the technical field of machine learning and intelligent manufacturing. The method comprises the following steps: collecting equipment state and load data in real time; constructing a weight adjustable optimization objective function of the production efficiency, the resource utilization rate and the delivery date satisfaction rate; generating an initial task priority sequence through a clustering algorithm based on the equipment abnormal characteristics, the task urgency degree and the load data; the priority weight is dynamically adjusted by simulating the environment temperature change; when an equipment fault occurs, the task is redistributed to the standby equipment; and calculating a resource scheduling scheme by adopting a particle swarm optimization algorithm and taking a task priority as a particle position. According to the invention, through multi-dimensional dynamic weight adjustment and real-time fault response, the resource utilization rate of the production line, the order delivery efficiency and the flexible response capability are effectively improved.