1672 results about "Utilization rate" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses an intelligent large-scale task scheduling method and system based on computing power host business characteristics, and belongs to the technical field of cloud computing and edge computing, and the method comprises the following steps: data acquisition and preprocessing of tasks and computing power host nodes; performing multi-dimensional quantitative analysis on the task business characteristics; dynamically monitoring the performance state of the computing power host node; constructing and optimizing a dynamic computing power matching model; task scheduling is optimized in an auxiliary mode based on an intelligent prediction mechanism; and finally distributing and executing the task. The problems that when an existing computing power host processes large-scale concurrent tasks, due to the fact that a scheduling strategy lacks accurate recognition and adaptation on different service characteristics, resources are wasted, tasks are stacked, and response is delayed are solved. The utilization rate of computing power resources can be improved, task response delay is reduced, and user experience and system overall performance are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent production scheduling method and system for a manufacturing system, and the method comprises the following steps: collecting an equipment state, a material neat rate and an order emergency degree in real time based on dynamic production data, and calculating an equipment availability coefficient, a material guarantee index and an order priority score through feature analysis; and forming a dynamic production feature set. The system constructs a production scheduling optimization model through work order-equipment matching degree calculation and process priority optimization, and performs multi-objective optimization solution by adopting an NSGA-III algorithm to realize the optimal combination of equipment utilization rate, order delivery rate and inventory balance. Meanwhile, an arbitration mechanism is introduced to coordinate process priority conflicts, and the scheme is verified through time, space and dynamic adaptability, so that feasibility and stability of production scheduling execution are ensured. According to the invention, the production scheduling efficiency of the manufacturing system can be improved, the equipment utilization rate can be improved, the order delivery delay rate can be reduced, and the adaptive ability to the change of the production environment can be enhanced.

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention relates to the technical field of digital factories, in particular to a full-process collaboration method and system for a digital factory. The method comprises the following steps: obtaining production decision data, and carrying out process parameter analysis according to the production decision data to obtain process parameter data; according to the process parameter data, building a factory resource agent through a preset digital factory model to obtain factory resource agent data; performing multi-agent negotiation according to the factory resource agent data to obtain digital workshop task allocation data; and carrying out industrial Internet of Things monitoring according to the digital workshop task allocation data to obtain industrial Internet of Things real-time data so as to carry out digital factory whole-process collaborative auxiliary operation. By constructing a task allocation mechanism based on semantic comprehension and multi-agent cooperation, intelligent matching and flexible scheduling among various resources in a digital factory are realized, and the task execution efficiency and the resource utilization rate of the system are effectively improved.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention discloses an AGV scheduling system optimization method based on dynamic priority adjustment and real-time load balance, and the method comprises the steps: dynamically monitoring the emergency degree and arrival time of cargos, enabling a system to adjust the task priority in real time, guaranteeing the priority processing of emergency cargos, and giving consideration to the scheduling efficiency of common cargos; information such as current positions, loads and electric quantity of the AGVs is collected, weighted load values are calculated, weight factors are dynamically adjusted according to task types, load balance is achieved, and the situation that part of the AGVs are overloaded and part of the AGVs are insufficient in load is avoided; and finally, scheduling optimization is carried out by comprehensively considering factors such as the adaptation degree between the task and the AGV, the path distance, the predicted completion time and the load balance, and the overall performance of the AGV system is fully exerted. The flexibility, efficiency and resource utilization rate of the AGV scheduling system are effectively improved, and the AGV scheduling system is suitable for various scenes such as logistics storage and production and manufacturing.

The invention relates to the technical field of modular assembly production lines, in particular to a modular flexible digital twin layout optimization method and system. Comprising the following steps: constructing multi-dimensional constraint modeling, constructing a multi-objective optimization model containing route length, equipment cost and human resource quantity, and generating an initial layout scheme based on three-dimensional geometric attributes and logistics paths of production line equipment; dynamic weight distribution is carried out, according to priority parameters input by a user, a route optimization weight # imgabs0 #, a cost optimization weight # imgabs1 # and a human resource weight # imgabs2 # are adjusted in real time, and a dynamic weight combination matrix is generated; digital twinborn simulation is carried out, the initial layout scheme is mapped to a digital twinborn platform, and the actual equipment utilization rate, the material carrying efficiency and the worker operation path are calculated through logistics path simulation; according to the scheme, dynamic balance multiple targets can be provided based on a simulation result, simulation and optimization real-time linkage can be realized, and a layout optimization technology of a multi-dimensional verifiable scheme is provided.

The invention relates to the technical field of automatic control, in particular to an automatic cutting fluid proportioning control system which comprises a flow control module, a delay compensation module, a dynamic adjusting module and a dynamic flow adjusting module. According to the invention, the flow and proportion of the cutting fluid are accurately adjusted, so that the requirements of different production stages are met, the use efficiency of the cutting fluid and the processing quality stability are improved, parameters such as the flow and the temperature are monitored in real time, the flow and the proportion are dynamically adjusted, instability caused by environmental fluctuation or equipment response lag is reduced, accurate supply of the cutting fluid is ensured, and the production efficiency is improved. Aiming at variable production requirements, environment changes are rapidly responded, a matching strategy is automatically optimized, machining errors and equipment abrasion caused by matching imbalance are effectively avoided, the improved control strategy improves the cutting fluid utilization rate, it is guaranteed that the machining quality and the equipment service life are synchronously prolonged, resource waste is reduced, meanwhile, the manual intervention requirement and the operation complexity are reduced, and the production efficiency is improved. And the overall production efficiency and the system stability are improved.

The present invention relates to the technical field of computing power resource scheduling, and in particular to a distributed computing power resource scheduling method and a system. The method comprises: step 1) querying a locally maintained mapping relationship between resource states of computing power resource nodes and forwarding nodes for a computing power resource service identifier in a computing power resource request message, so as to determine a group of candidate computing power resource nodes; step 2) on the basis of a resource demand preference field in the computing power resource request message, determining a corresponding computing power matching policy and, on the basis of the corresponding computing power matching policy and a computing power resource demand field and network resource demand field in the computing power resource request message, selecting from the group of candidate computing power resource nodes a computing power resource node meeting the resource demand of a user; and step 3) modifying the destination address of the computing power resource request message into the next hop forwarding address of the message corresponding to the selected computing power resource node. The present invention flexibly schedules computing power resources, and increases the overall utilization rate and scheduling success rate of computing power resources.

The invention relates to the technical field of resource scheduling, in particular to a cloud computing resource management system. In the cloud computing resource management process, the execution data of the computing tasks are analyzed, the task combinations with relevance are screened, and the computing path task set is established, so that the scheduling of the computing tasks is more intelligent, and the resource utilization rate is improved. And resource allocation is performed according to the matching degree of the calculation demand and the computing power pool, the adaptability of the calculation resources is dynamically adjusted, and accurate scheduling of the calculation task is realized. And in combination with the load state of the calculation node, the CPU utilization rate, the memory occupancy rate, the I / O load and other key indexes are monitored in real time, so that the resource allocation is more flexible, the calculation pressure of the high-load node is reduced, and the stability of the whole system is ensured. The load conditions of the computing power pools are evaluated, the task allocation strategy is adjusted, the computing tasks are reasonably distributed among the multiple computing power pools, the task migration and resource expansion strategy is optimized, and the balanced utilization rate of computing resources is improved.

The invention relates to the technical field of production resource management, and discloses a production resource management system based on an AI neural network. The system comprises a multi-source data acquisition module for acquiring multi-source heterogeneous data of a production environment; the multi-dimensional feature extraction module is used for extracting feature vectors of each data source; the heterogeneous data fusion module is used for generating a resource state semantic representation based on a cross-modal attention mechanism; the resource topological graph construction module is used for constructing a double-layer topological library; and the intelligent optimization decision module is used for generating a real-time resource scheduling strategy and a task allocation optimization scheme based on the result. In addition, the system also has the functions of anomaly detection, path re-planning, resource aging prediction and the like. The system can comprehensively process production data, optimize resource scheduling and task allocation, improve the resource utilization rate, shorten the task completion time, enhance the stability of the production process and the scientificity of equipment maintenance, and improve the production management level of enterprises.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

The invention provides a task scheduling method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of task scheduling. The method comprises the following steps: acquiring tasks, resources and user behavior data from a user side and a cloud server through an information collection module, and storing the tasks, the resources and the user behavior data into an information base; the task evaluation module analyzes the data and generates a task value score; the scheduling strategy generation module formulates a preliminary execution scheme in combination with a preset rule, and optimizes through a reinforcement learning algorithm to obtain a dynamic optimal scheduling decision; the task redundancy analysis module is used for identifying mergeable redundant tasks and generating a report for further optimizing a scheduling decision; the dynamic priority adjustment module adjusts task priorities and resource allocation according to the real-time monitoring data; and the scheduling execution module is responsible for performing specific execution and generating a state report. The multi-module collaborative design realizes the intellectualization and adaptivity of task scheduling, and improves the utilization rate of system resources.

The invention provides a multi-transaction deadlock detection and processing method in resource-constrained equipment, which comprises the following steps of: in a transaction execution process, constructing a resource waiting graph which is used for recording a waiting relationship among transactions so as to reflect the occupation condition of shared resources by the transactions and a dependency relationship among the transactions; before a transaction is subjected to read-write operation each time, whether execution of the current transaction can cause a deadlock risk or not is judged in real time by detecting a dependency relationship between the current transaction and a target resource and combining a resource waiting graph; and when a deadlock risk is detected, deadlock avoidance strategies of priority processing, transaction rollback, transaction retry and exception processing are adopted to avoid deadlock. The deadlock problem occurring in the transaction parallel processing process can be effectively avoided or solved, and meanwhile the response efficiency of the application and the utilization rate of the storage space are guaranteed.

The invention discloses a distributed data storage management system and method, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining an access log, metadata and content features of each node; data object attributes are judged, cold data are marked, and cold data distribution nodes are positioned; the method comprises the following steps: dividing cold data into permanent, periodic and sudden basic access modes through first-layer classification; generating a composite classification label through second-layer classification; matching a predefined strategy matrix according to a classification result, dynamically selecting a compression algorithm and a storage medium, and allocating bandwidth; operation auditing, compliance verification and exception handling are realized through the smart contract; and collecting strategy execution indexes in real time, and adjusting classification model parameters and resource scheduling weights. Through an intelligent cold data judgment and classification mechanism, cold data is accurately identified and managed, occupation of storage resources is reduced, and the overall utilization rate of the storage resources is improved.

The invention discloses a building engineering full life cycle resource intelligent scheduling system and method, and belongs to the technical field of building management, and the method specifically comprises the steps: collecting dynamic physical parameters of a component installation process in a construction stage; constructing a spatio-temporal index engine, associating the dynamic physical parameters to corresponding component nodes of the BIM model, and generating a spatio-temporal binding data unit; in an engineering operation and maintenance stage, monitoring operation state data of the component, and when an abnormal state is recognized, extracting current space coordinates of the abnormal component and activating a reverse traceability channel; taking the space coordinates of the abnormal components as retrieval anchor points, matching the space-time binding data units with the same space coordinates, and obtaining a dynamic physical parameter sequence in a construction stage; comparing and mapping the dynamic physical parameter sequence and the abnormal state, generating an operation and maintenance decision instruction set with a construction defect positioning identifier, and inputting a resource scheduling sequence; according to the method, the utilization rate of operation and maintenance decisions on construction stage data is remarkably improved.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention provides a multi-factory collaborative production management method, and the method comprises the steps: obtaining market demand information, and real-time equipment operation state data, real-time material inventory information, historical sales parameters, production demand data and production process evaluation information of each factory; an industrial knowledge graph is constructed for the production demand data and the production process evaluation information based on a large language model, the market demand information and the historical sales data are analyzed to obtain production configuration data, and a dynamic production demand model is generated; according to the real-time equipment operation state data and the real-time material inventory information, a basic capacity allocation scheme is determined and corrected, and a collaborative production scheduling instruction set is obtained for collaborative production management; according to the method, the unified industrial knowledge graph is constructed through the large language model, the data utilization rate is improved, the production scheduling scheme is generated based on the real-time dynamic production demand model, the order delivery punctuality rate is improved, the traceability relation chain fully covered in the production process is constructed, the comprehensive utilization of equipment is improved, and the logistics scheduling cost is reduced.

The invention relates to the technical field of packaging, in particular to an intelligent metering method for remnant base paper rolls of a corrugated paper board assembly line. The intelligent metering method for the remnant base paper rolls of the corrugated paper board assembly line comprises steps as follows: a plurality of base paper feeding positions are arranged in the conveying direction of the assembly line at intervals, a base paper feeding frame is arranged in each base paper feeding position, and two base paper feeding rolls located on two sides of each base paper feeding frame are arranged on the base paper feeding frame; each base paper feeding roll is provided with lap recording sensors and incremental encoders, the lap recording sensors are used for monitoring the number of paper feeding laps of base paper clamped on the corresponding base paper feeding roll, the incremental encoders are used for monitoring the length of each lap after the base paper on the base paper feeding roll passes by one lap, and the lap recording sensors and the incremental encoders are connected with a controller. Intelligent metering can be realized, the operation cost of an enterprise is reduced, labor is saved, and the utilization rate of enterprise resources is increased.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention relates to a calculation-storage stream joint scheduling optimization method and system based on DDQN and a heuristic strategy, and belongs to the technical field of distributed calculation and intelligent scheduling. The calculation-storage stream joint scheduling optimization method comprises the following steps: (1) defining a calculation stream and a storage stream; (2) workflow scheduling and execution: when the workflow is triggered, a scheduler evaluates the executable states of the tasks one by one according to the dependency relationship between the tasks and a scheduling strategy, and submits the executable tasks to a task executor; the task executor is responsible for executing the tasks on the working nodes and ensuring that the tasks are completed according to a preset sequence and priority; (3) collecting a DAG structure, queue information and an operation number; (4) task operation time and resource use prediction based on historical operation data; (5) scheduling optimization based on DDQN and a heuristic strategy; and (6) acquiring and storing scheduling information. The task execution efficiency and the resource utilization rate of the system are remarkably improved.

The invention relates to the technical field of cloud computing, and discloses an enterprise resource planning data efficient processing method based on cloud computing, and the method comprises the steps: collecting system data of finance, supply chain, production management and the like through a multi-source interface module, and carrying out the preprocessing through a hierarchical fusion framework, thereby obtaining a standardized data set; the method comprises the following steps of: selecting a multi-target scheduling model, inputting the multi-target scheduling model into a task decomposition model, generating task decomposition parameters based on a block processing mechanism and a service association rule, constructing the multi-target scheduling model aiming at the shortest processing duration and the minimum resource occupation, and outputting an optimal scheduling scheme by adopting a dynamic allocation algorithm. A hierarchical execution control model comprising a strategy layer, a coordination layer and an execution layer is established, the strategy layer performs global task planning, the coordination layer performs local resource adjustment, and the execution layer tracks tasks based on a load balancing algorithm and outputs a processing control instruction to realize efficient processing. The method improves the data processing efficiency and the resource utilization rate, and provides support for enterprise decision making.

The invention discloses a warehouse-in and warehouse-out path planning system and method applied to stored goods, and relates to the technical field of intelligent storage. The method comprises the steps of establishing a three-dimensional space structure of a warehouse, generating a menu to be stored and matching various storage schemes, carrying out importance degree assignment and priority ranking on goods, calculating the residual space and the total weight of the warehouse, judging and optimizing the space and the weight, and planning and optimizing warehouse-in and warehouse-out paths. Through the mathematical planning method, the total cost is minimized, and the space utilization rate and the access efficiency are maximized. And meanwhile, the operation condition in the warehouse is monitored in real time, the in-out path and the in-out path are dynamically adjusted, and congestion and conflicts are avoided. In addition, a high-definition camera and a sensor are further installed, and safety management in the warehouse is ensured. According to the invention, intelligent warehouse-in and warehouse-out path planning of the stored goods is realized, the warehouse management efficiency and the space utilization rate are improved, and safe storage and efficient warehouse-in and warehouse-out of the goods are ensured.

The invention discloses a production line operation monitoring system based on a digital twin technology, and the system comprises a data collection module which is used for collecting the data of each task in a production line; the scheduling unit construction module is used for mapping each task instance in the virtual task relationship into a cat-shaped scheduling unit and initializing a role field based on a preset threshold value; the scheduling game engine module is used for executing a combat cat algorithm on the game model and generating a scheduling scheme set comprising a plurality of candidate scheduling schemes; the simulation evaluation module is used for executing simulation evaluation on each candidate scheduling scheme in the digital twin environment to obtain a corresponding task completion rate, a resource utilization rate and a system load balance degree; and the strategy management module is used for sorting the candidate scheduling schemes according to the simulation evaluation result and screening the current optimal scheduling scheme. According to the invention, intelligent production task scheduling, real-time response and strategy adaptive optimization are realized.