63 results about "Discrete event simulation" patented technology

The invention discloses a double-station robot sorting optimization method and system based on digital twinning and a terminal, double improvement of sorting efficiency and safety is achieved by constructing a digital twinning driven collaborative operation system, and the method comprises the steps that firstly, a laser radar and a polarization camera are used for forming a composite sensing unit; geometric morphology, material reflection characteristics and spatial pose data of a target object are synchronously obtained, and are input into a digital twin engine after time synchronization processing; an engine constructs a virtual sorting scene containing a material attribute database based on a physical rendering technology, sub-millimeter-level space registration is achieved through feature fusion of a binocular stereoscopic vision depth map and geometric parameters, high-precision digital twin stations are generated, a system plans a space-time constraint trajectory of a double-station robot in a virtual environment, and a target object is obtained. And an integrated discrete event simulation engine performs operation time sequence conflict prediction, and when a space overlapping risk is detected, an alternative path containing a dynamic obstacle avoidance strategy is generated through a trajectory re-planning algorithm.

The invention provides an intelligent control method and system for discrete event simulation of an aluminum template production line, and relates to the technical field of data processing.The method comprises the steps that a material transfer path instruction and a cutting process key point coordinate sequence are analyzed from a dynamic anti-interference scheduling strategy, the key point coordinate sequence is input into a curve driving algorithm, and a curve driving algorithm is obtained; a parameterized continuous cutting path curve is generated according to the aluminum template contour precision requirement and the cutting head dynamics constraint; the method comprises the following steps: performing equal-bow-height error discretization processing on a continuous cutting path curve to generate a discrete pose point set with a timestamp, superposing AGV dynamic constraints to generate a speed look-ahead control instruction set, splitting the instruction set into independent control units according to process sections, packaging the independent control units into a time trigger message through an industrial bus protocol, and sending the time trigger message to a server; and the simulation clock synchronization signal is injected into a physical execution unit. According to the invention, intelligent and accurate control of the aluminum template production process is realized, and the production efficiency is effectively improved.

The invention provides a JAX-based high-performance discrete event simulation method and system, and the method comprises the steps: firstly completing environment configuration and initialization through an immutable data structure, and generating an EnvParas object with fixed static parameters; secondly, processing event logic by adopting a pure function steepv, and realizing side-effect-free functional event processing and state iteration by combining JAX condition control primitive and lax.scan iterative loop; then, a batch processing task is generated through job packaging, and job-level parallel acceleration is achieved on multiple GPU / TPU devices by means of jax. Jit compiling and jax. Vmap vectorization; and finally, packaging a standardized AI interactive interface, supporting external algorithm calling and finishing result post-processing. Functional programming and JAX hardware acceleration are combined, the simulation speed is increased, meanwhile, the event dynamic accuracy is guaranteed, reinforcement learning large-scale sampling can be seamlessly supported, and the method is suitable for AI application scenes in the field of operational research.

The invention provides a process construction method and BIM-based carbon emission accounting and tracking method and system, and the method comprises the steps: constructing an extended BIM model fusing geology, process and carbon emission attributes, and building a construction carbon emission model through employing a mixed life cycle method; and mechanical energy consumption is simulated and predicted through discrete events, and model components are associated, so that dynamic tracking and visualization of the carbon footprint during the construction period are realized. According to the invention, accurate accounting, dynamic tracking and visual display of carbon emission in the construction period can be realized, a scientific basis is provided for carbon emission management in the construction process, and optimization of construction organization, reduction of carbon emission and improvement of environmental benefits and sustainability of construction projects are facilitated.

This invention belongs to the field of supply chain security assessment technology and discloses a supply chain security event disruption assessment method based on knowledge graphs. It constructs a supply chain knowledge graph containing supply and demand dependencies, BOM dependencies, logistics dependencies, and substitution relationships based on enterprise, factory, warehouse, logistics route, material, and order data. Events are extracted from security event texts and entities are linked to determine the initial affected nodes. The impact intensity and arrival time of downstream nodes are obtained through temporal propagation along the graph relationships. Buffer parameters such as inventory, in-transit status, and substitution switching are combined, and the recovery time distribution is obtained through neural survival analysis based on historical recovery records. In discrete event simulations, available and demand quantities are updated according to in-transit arrival, substitution effectiveness, demand adjustment, and supply recovery. When available quantity is less than demand quantity, a substantial disruption is determined, and the node-level disruption start time, duration, and impact range are output.

The invention provides an electric ship fire rescue scheme optimization method and system, and the method comprises the steps: operating an event queue in a discrete event simulation framework based on a rescue strength agent model and a fire environment model, simulating a fire development process and a multi-agent cooperative rescue process, and generating simulation state data in real time; based on the simulation state data, evaluating the comprehensive rescue efficiency of the rescue scheme in a plurality of preset rescue indexes; based on the comprehensive rescue efficiency, generating a rescue control instruction for optimizing a control strategy of at least one rescue strength agent, converting the rescue control instruction into at least one rescue control event, inserting the rescue control event into an event queue for execution, and performing closed-loop optimization on a multi-agent collaborative rescue process, according to the invention, a simulation-evaluation-regulation and control integrated dynamic closed-loop simulation framework is realized, and the scientificity and efficiency of rescue scheme optimization are improved.

The application provides a multi-vehicle mixed-flow production process reconstruction and dynamic derivation method based on a process network, and belongs to the technical field of intelligent manufacturing. A multi-path process network covering all feasible process paths is constructed to describe the correlation between different processes and the process order exchangeable relationship. A genetic algorithm is used to dynamically select a process path for different vehicles, so that process reconstruction optimization with the minimum maximum completion time of the whole batch of vehicles as the target is realized. A discrete event simulation is used to dynamically simulate the processing process of the vehicles in the process network, so that the vehicle occupation state and the station state information are recorded in real time. A process path dynamic derivation result is generated based on the timing state data, so that the dynamic expression and operation verification of the vehicle flow state and the station resource state evolution process are realized. The application can improve the flexibility of process path configuration and the predictability of the production process.

This invention relates to the fields of intelligent manufacturing and industrial internet technology, and discloses an intelligent scheduling method and system for the continuous production of automotive structural components across multiple processes. The method includes: acquiring equipment status data and material flow location data to obtain a current production state vector; obtaining a preliminary production schedule using a genetic algorithm based on the current production state vector; generating adjustment demand signals based on process time fluctuation indicators; obtaining an optimized production sequence using a simulated annealing algorithm based on the adjustment demand signals; embedding emergency order response demands based on production disturbance factors to obtain a final execution plan; loading the final execution plan into a preset discrete event simulation engine to generate virtual work trajectories, comparing and generating deviation correction parameters, and parsing to generate a production instruction sequence. This method can improve the dynamic adaptability and execution accuracy of the scheduling scheme.

The invention discloses an electronic equipment production line layout digital test method and device oriented to efficiency improvement, and belongs to the technical field of intelligent production line virtual test.The method comprises the steps that a multi-mode requirement input unit inputs the requirement of a user and converts the requirement into standardized interaction data; the intelligent analysis engine unit analyzes user requirements and outputs production line layout requirements, and the visual output unit displays a three-dimensional layout scheme to a user; the three-dimensional model storage and retrieval unit is used for storing a three-dimensional model, the topological relation constraint unit is used for setting a topological boundary for the three-dimensional model, and the historical scheme version management unit is used for storing a production line layout scheme for a user to check and manage; the production line layout debugging and testing module verifies the compliance of the three-dimensional production line layout through a rule engine, evaluates logistics efficiency through discrete event simulation, reinforces learning and optimizes a scheduling strategy, and realizes virtual-real linkage debugging by means of digital twinning. According to the invention, the problems of low communication efficiency and incomplete process constraint inspection of the traditional manual demand are solved.

The application provides a pre-treatment-integration-based offshore wind power discrete event simulation constraint condition integration simplification method, system, device and medium, and is oriented to complex offshore wind power construction simulation. Before simulation starts, possible influencing factors in the simulation process are processed, and are integrated and simplified into a simulation time axis, so that the simulation time axis can be flexibly called in the simulation process. The method provided by the application comprises the following steps: establishing basic simulation content, obtaining limiting conditions affecting construction, establishing an influencing factor model, pre-processing various influencing factors, integrating and generating a process executable time axis, and calling the process executable time axis. On the premise of ensuring simulation efficiency, various factors affecting offshore wind power construction are reasonably analyzed and judged, and the method is suitable for a realistic construction environment and has flexibility and higher confidence.

The application discloses a micro-grid building simulation training system based on a block chain, and relates to the technical fields of block chain application and power system simulation training. The system comprises a request analysis module, a simulation kernel module, a data encapsulation module and a consensus writing module. The request analysis module analyzes initial configuration parameters of a micro-grid topology structure contained in a simulation training request initiated by a student terminal. The simulation kernel module generates a virtual micro-grid component instance set based on the parameters and calls a discrete event simulation kernel pre-deployed in a block chain smart contract. The data encapsulation module encapsulates electrical attribute parameters of each component instance into a to-be-verified state data block. The consensus writing module performs hash collision verification on the to-be-verified state data block through a block chain consensus node, packs and writes data blocks that pass the verification into a private data channel of a block chain ledger. The step driving module drives the discrete event simulation kernel to step and update running state parameters of each component instance according to the written data blocks and a preset time axis.

The invention discloses a multi-core-oriented high-performance parallel discrete event simulation propulsion method, and relates to the technical field of combat simulation and deduction, and the method comprises the steps: constructing a parallel simulation engine, supporting parallel simulation operation through a time management service and an event management service, and determining parallel discrete event simulation logic based on the parallel simulation engine. Determining a processing flow of a logic process execution event; based on a parallel simulation engine, multi-core-oriented parallel simulation is executed, and the multi-core-oriented parallel simulation comprises a basic service, a parallel discrete event simulation service and an optimization expansion service. According to the method, discrete event simulation can be efficiently carried out in a multi-core processor environment, the simulation speed is improved, the sequence and consistency of event processing are ensured, meanwhile, load balancing is achieved, and overload of a single core is avoided.

The invention discloses a production line scheduling optimization method based on real-time data feedback, and relates to the technical field of production scheduling, and the method comprises the steps: collecting multi-source heterogeneous data of a production line in real time, carrying out the time sequence alignment and difference normalization, and generating a deviation state vector; based on the scheduling feedback intensity index, constructing a lightweight deep reasoning model, performing dynamic mapping, generating a corresponding scheduling intervention level, performing multi-dimensional disturbance influence modeling on the deviation state vector through a graph neural network, and constructing a disturbance causal map; on the basis of the scheduling intervention level and the disturbance causal atlas, key nodes and propagation paths are identified, rule reasoning is carried out in combination with a scheduling rule, and multiple scheduling adjustment actions are generated; and performing multi-stream coupling disturbance cost prediction on the plurality of scheduling adjustment actions through discrete event simulation, and selecting a candidate scheduling adjustment action with the minimum disturbance cost and the optimal comprehensive benefit as a production line scheduling optimization scheme. According to the method, high-robustness and self-adaptive closed-loop scheduling optimization is achieved.

The invention provides an engineering equipment intelligent scheduling system and scheduling method, and belongs to the technical field of engineering equipment intelligent control. The method comprises the steps that a multi-source sensing terminal reads and uploads standardized engineering equipment data to an edge node through a heterogeneous adaptation module; the cloud end constructs a three-dimensional terrain and a digital twinborn scene by using the unmanned aerial vehicle point cloud, and virtual-real calibration is completed through an iterative nearest point algorithm; the intelligent task distribution system decomposes a task into atomization operation units, and an optimal scheduling scheme is screened by adopting an improved genetic algorithm in combination with discrete event simulation; the edge decision node performs conflict resolution and energy consumption optimization cooperative control based on the four-dimensional space-time grid; and when an abnormality is detected, triggering an emergency response mechanism using a DLite algorithm to re-plan the path. According to the invention, the problems of difficult compatibility, scheduling stiffness and multi-machine conflict of heterogeneous equipment are solved, and real-time accurate scheduling and safe closed-loop control of the engineering equipment cluster are realized.

The invention provides a scheduling control method, a scheduling control device, electronic equipment and a storage medium, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: generating a plurality of groups of operation basic sequences in a full arrangement form based on Monte Carlo random sampling; aiming at each group of operation basic sequences, generating an initial operation sequence of each crane corresponding to the group of operation basic sequences, and replacing tail end tasks of each initial operation sequence; performing discrete event simulation on each operation sequence after replacement by taking a rigid demand period of a power generation side as a step length, and performing screening by taking single-period consumption meeting the power generation side in the whole process as a constraint condition to obtain a candidate operation sequence meeting the constraint condition; and selecting a sequence meeting a preset optimization index from the candidate operation sequences as a final scheduling sequence, and controlling each crane to operate based on the final scheduling sequence. According to the method and the device, the final scheduling sequence is generated to coordinate the operation of the multiple cranes, so that the congestion is avoided, and the working efficiency is improved.

The application belongs to the technical field of parallel discrete event simulation and network system simulation, and discloses an intelligent hybrid parallel synchronization method for large-scale distributed simulation, which collects key operation indexes such as neighborhood timestamp lower bound, next event time, round-trip delay quantile value, cross-partition event output rate and virtual time jitter, evaluates system load and communication state in real time, judges the best time to enter optimism or rollback conservation based on the collected key operation indexes, prevents frequent switching by using hysteresis mechanism and minimum residence time, sets time-bounded gates at the mode switching boundary, only allows events within the safe range to propagate, temporarily stores events exceeding the range and sends them in batches when the conditions are met, and thus guarantees causal consistency. The application takes into account real-time performance and robustness, shortens the global empty waiting time, facilitates real-time online acceleration and stable submission in a simulation platform and a message passing interface, and has both performance and interpretability.

The invention provides an outfield test safety early warning method and system based on simulation extrapolation, and relates to the technical field of virtual simulation, and the outfield test safety early warning method based on simulation extrapolation comprises the steps: obtaining model parameters and application scene parameters of to-be-tested equipment, and creating an initial simulation model; according to the motion performance parameters and hierarchical driving strategies of the to-be-tested equipment in various application scenes, in combination with discrete event simulation and dynamics principles, determining a state transition matrix of the motion state of the to-be-tested equipment changing along with time, and correcting the initial simulation model by using the state transition matrix to obtain a state transition matrix of the motion state of the to-be-tested equipment; obtaining a target prediction model; the motion performance parameters comprise a speed parameter, an acceleration parameter, a temperature parameter, a pressure parameter and a displacement parameter; and fusing multi-source detection data acquired in real time to obtain multi-source fusion data, and inputting the multi-source fusion data into the target prediction model to obtain a predicted motion trajectory of the to-be-detected device.

The present application relates to the technical field of ship operation, and provides a container loading optimization method based on real-time scheduling and discrete event simulation. The method establishes a loading scene model containing multi-platform loading demand, container characteristic parameters and resource parameter configuration, adopts a double-layer coding scheme of loading sequence priority sequence and loading mode identification generated by a genetic algorithm, dynamically advances a time pointer to handle loading events such as vertical operation and hoisting operation by using discrete event simulation, solves resource contention problems in combination with loading priority, takes the total loading time as a fitness index, iteratively optimizes through selection, crossover and mutation evolution operations, and finally outputs a loading sequence and mode combination scheme that minimizes the loading time. The present application effectively coordinates loading resources, reduces operation waiting time, and improves overall loading efficiency in a multi-ship parallel operation scene. Meanwhile, the combination of the genetic algorithm and the discrete event simulation enhances the optimization capability and adaptability for complex loading processes.

The application provides a scheduling decision method and device for concrete arch dam pouring engineering, and the method comprises the following steps: constructing an arch dam pouring progress simulation model through discrete event simulation and an agent-based model, pre-training the expert trajectory dataset by using behavior cloning, performing reinforcement learning on the behavior cloning pre-training model with reward remodeling to generate an intelligent scheduling decision model; perceiving the state variables required by the progress simulation model, initializing and running the progress simulation model; generating and executing the cable crane pouring scheduling decision result according to the state variables through the intelligent scheduling decision model. The two-stage training method fusing expert experience and reinforcement learning enables the model to autonomously learn and optimize long-term construction benefits, improves the robustness and scheduling efficiency of the model, and through offline training and online application of the model, a closed-loop intelligent decision process from perception, simulation, decision to control is formed, and reliable support is provided for the automation and intelligent management of arch dam pouring construction.

The invention provides a flight dynamic deduction method and device, computer equipment and a storage medium, and belongs to the field of airport planning, and the method comprises the steps: obtaining flight plan data, airport resource capacity data and airport flow limit data in the next 24 hours, and constructing a parameter set of three simulation scenes of normal operation, large-area flight cancellation and airport closing; based on the parameter set, a discrete event simulation framework is adopted, and the whole-process state transition of each flight is pushed and deduced according to time through the operation logic of three subjects of collaborative air traffic control, an airport and an airline company; flight operation data in the deduction process are recorded, and flight operation indexes and a visual chart are generated. According to the deduction process, the problem of multi-agent cooperation in a complex operation environment can be effectively solved, the operation efficiency is improved, meanwhile, the emergency disposal capacity is enhanced, and the dynamic deduction characteristic can help managers to quickly recognize potential conflicts and optimize a resource allocation scheme.

The application discloses an electromechanical construction management system and method based on an AI algorithm, relates to the technical field of electromechanical construction management, and is based on historical project data, analyzes installation sequence constraints between electromechanical modules, and constructs a directed graph containing strong dependency relationships; analyzes the construction proficiency of each engineer on different electromechanical modules, and forms a personnel skill matrix; uses the directed graph to simulate task progress and discrete events, outputs a comprehensive bottleneck electromechanical module list and corresponding construction period delay risk value indicators; compares and simulates by virtually replacing engineers with different construction proficiency indicators, and screens implicit bottleneck electromechanical modules; in new project construction, analyzes construction period execution deviation indicators, generates an early warning signal and sends the early warning signal to an administrator when the construction period execution deviation indicators continuously exceed a threshold value; and after the project ends, updates the personnel skill matrix by using actual installation data. The reliability and analysis accuracy of the model are improved.

A system and method for large-scale internet health forecasting and internet noise analysis. The system and method feature the ability to scan for, ingest and process, and then use various data stores for capturing entity data, their relationships, and actions associated with them. This data forms the basis for cyber enrichment service databases which can used to provide information responsive to user submitted queries as well as to produce large-scale (e.g., Internet scale) simulation models using statistical models, generative ML models, massively multiplayer online gaming simulation systems, or full discrete event simulation engines or some combination. User submitted queries can be ran against the raw data or against simulations to provide simulation results that can be used improve cybersecurity for an organization against actual observed behaviors or simulations.

The invention discloses a mixed flow assembly line collaborative optimization method based on deep reinforcement learning, and the method achieves the systematic collaborative optimization of three key problems through constructing a double-layer multi-following optimization model of upper-layer line balance and lower-layer buffer area configuration and production sorting. The upper layer uses a genetic algorithm to optimize a line balance scheme, and the lower layer uses knowledge to enhance deep reinforcement learning to optimize buffer configuration, and solves a production order in combination with the genetic algorithm. According to the method, a discrete event simulation system is integrated to realize high-fidelity performance evaluation, and finally a high-quality and robust joint optimization scheme is output.

In view of the problem that existing discrete event simulation tools cannot perform differential simulation analysis on the production state and performance index of specific batches when simulating the process of aircraft assembly work, the application provides a method for analyzing the completion time of aircraft assembly batches based on discrete event simulation. Based on the traditional discrete event simulation framework, the application proposes a method of marking simulation work-in-process elements with corresponding labels by taking aircraft batch information as simulation input, and proposes a simulation model execution mechanism, a promotion framework and a completion time solving method around the batch label. The application can provide control personnel with reliable analysis results of the estimated completion time of each batch on the assembly line, thereby supporting the perception of the assembly state of each batch on site.

The invention discloses an electromagnetic environment simulation method based on armored vehicle trunking communication, which comprises the following steps of: constructing a structured behavior library containing a plurality of attack behaviors and defense strategies, and defining a cross-layer state vector penetrating through a physical layer, a network layer and a task layer; in the simulation process, an attack and defense agent driven by multi-agent reinforcement learning performs game decision according to a payment function associated with task success and failure, and selects actions in a behavior library; an attack and defense event is executed through a discrete event simulation engine, and an electromagnetic environment and cross-layer state conduction including antenna attitude influence are accurately evolved; after simulation is finished, a causal inference algorithm is applied to analyze mass time series data, a causal relation graph is constructed, and automatic root diagnosis of task failures is achieved. The method aims at solving the problems that traditional simulation cannot simulate an intelligent game, has hierarchical splitting and lacks deep diagnosis ability, and meanwhile, high-fidelity intelligent game simulation and causal mechanism insight can be achieved.

A semiconductor processing system includes a plurality of processing modules in which events for a wafer occur and a processor configured to receive system data related to the event and the plurality of processing modules and perform discrete event simulation of an order of occurrence of the events. The processor is configured to select at least one event that may occur after an event on the wafer ends as an event candidate using the system data, determine one of the event candidates as a selected event, store the selected event in an event queue, sequentially proceed with the selected event stored in the event queue, and delete the selected event whose progress has ended from the event queue. When all the selected events stored in the event queue are deleted, the processor is configured to terminate the discrete event simulation and output simulation result data.

The invention belongs to the technical field of parallel discrete event simulation and network system simulation, and discloses an intelligent hybrid parallel synchronization method oriented to large-scale distributed simulation. The method comprises the following steps of: evaluating the load and communication state of a system in real time, acquiring key operation indexes, judging to enter an optimistic or conservative optimal opportunity, and preventing frequent switching by utilizing a hysteretic mechanism and minimum residence time, so as to achieve the aim of improving the communication performance of the system, such as a neighborhood timestamp lower bound, next event time, round-trip delay quantile, cross-partition event output rate, virtual time jitter and the like. A time bounded gate is arranged at a mode switching boundary, only events in a safe range are allowed to spread, events exceeding the range are temporarily stored and sent in batches after conditions are met, and therefore causal consistency is guaranteed. According to the method, the real-time performance and the robustness are both considered, the global null waiting time is shortened, real-time online acceleration and stable submission are conveniently realized in a simulation platform and a message passing interface, and the method has both performance and interpretability.

The application discloses a kind of multi-station assembly workshop intelligent scheduling methods of fusing proactive evaluation, material processing data, warehouse data, distribution system information and assembly progress etc. in combination with production line management and control system, the material of workshop is complete and the proactive evaluation of assembly progress is carried out;Based on discrete event simulation, simulation workshop simulates the running condition of real workshop, initializes simulation environment using proactive evaluation result and workshop related process data etc., designs multi-agent negotiation model to generate scheduling scheme and resource demand list;For abnormal events occurring in production process, a response plan combining artificial and agent simulation is developed.The application can to some extent avoid the problem of frequent changes in plan caused by material shortage and other disturbance events, and can quickly generate the globally optimal workshop scheduling result.

The application is a hospital operation process optimization and abnormality detection system and method, belonging to the technical field of hospital informationization and operation management. The technical problem to be solved is that the existing hospital operation management has the problems of serious data island, static process modeling, lagging abnormality discovery, lack of data support for optimization and insufficient real-time monitoring capability, resulting in invisible process execution, untimely problem handling and difficult quantification of optimization effect. The technical solution points are: non-intrusive collection of full operation logs of each business system of the hospital to build a unified process event model; multi-level primary key association is adopted to form a complete process event sequence, and an improved Alpha++ algorithm is used to automatically discover the actual business process model; multi-dimensional identification of process bottlenecks is adopted, and an algorithm combining cosine similarity and edit distance is used to detect abnormalities in real time and give graded early warnings; case-based reasoning and discrete event simulation are used to generate implementable optimization suggestions, forming a closed-loop operation management system.