330 results about "Parallel simulation" patented technology

The invention provides an EDA software implementation method and system for memory IP layout optimization, and relates to the technical field of memorizers, the method comprises the following steps: receiving a memory IP design parameter and an optimization target input by a user, and calling a matched basic unit module from a pre-established hierarchical memory IP library according to the design parameter; an initial memory IP layout scheme is generated based on the basic unit module, the initial memory IP layout scheme is rapidly evaluated by utilizing a neural network model, a plurality of optimized layout candidate schemes are generated by adopting a genetic algorithm, and each optimized layout candidate scheme comprises different basic unit arrangements, metal layer use strategies and buffer insertion positions; performing parallel simulation on the optimal layout candidate scheme, and selecting an optimal layout scheme; and performing post-simulation verification on the optimal layout scheme, wherein the post-simulation verification comprises static time sequence analysis, dynamic power consumption analysis and electromagnetic compatibility analysis.

The invention relates to the technical field of data processing, in particular to a data fusion intelligent equipment linkage management and control system based on a large model, and the system comprises a data collection module which outputs standardized data carrying privacy grading identifiers; the data processing module outputs an optimization algorithm identifier and a collaborative strategy identifier which are synchronously bound; the privacy algorithm fusion scheduling module generates atomization calculation graph data and strategy audit logs fusing privacy operation; the multiple optimization resource module executes calculation graph data and drives a parallel simulation environment rehearsal resource scheme, and the double-closed-loop feedback module is activated when an arbiter continuously detects that deviation exceeds the standard for three times; the safety instruction generation module fuses the output result and the audit log to generate a digital signature equipment control instruction; and the double-closed-loop feedback module updates knowledge base weight parameters through a data feedback loop, and controls the feedback loop to adjust a resource allocation threshold in a grading manner according to simulation confidence, so that dynamic balance between resource efficiency and privacy intensity is realized.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention provides a BIM-based engineering progress and cost dynamic management and control optimization method and system, and relates to the technical field of engineering management and control optimization, and the method comprises the steps: building a digital twin basic platform with a real-time sensing capability based on a BIM model and multi-source sensor data; a progress plan and cost reference data are imported, a progress-cost correlation model is established, a comprehensive deviation value is calculated, and multiple groups of optimization schemes including resource configuration, process organization and cost control parameters are automatically generated; parallel simulation is carried out on the digital twin platform, progress and cost prediction results are obtained, multi-target evaluation is carried out, and an optimal scheme is selected and issued to a site for execution; and continuously collecting actual execution data, comparing the actual execution data with a simulation result, and when a deviation exceeds a threshold value, updating a progress-cost correlation model in the digital twin platform by using the actual data, thereby realizing adaptive evolution and forming dynamic collaborative management and control of progress and cost. The accuracy and efficiency of progress and cost control can be effectively improved, and the project risk is reduced.

The invention discloses a cross-platform distributed synchronous simulation method adopting RoCEv2, and relates to the technical field of communication simulation, and the method comprises the following steps: configuring parameter information of a switch and nodes, and constructing a heterogeneous distributed network of a RoCEv2 enhanced network protocol; deploying a master node global logic time reference, calibrating a slave node clock, starting a sliding window to continuously monitor offset, dynamically detecting clock offset to trigger adjustment, optimizing a time step length in combination with an ECN mark, and dynamically compensating the clock offset of the heterogeneous platform; sequencing events according to logic timestamps to generate DAG topology, and preloading high-frequency events; event data are transmitted through cross-node RDMA, and the main node is sorted and scheduled according to a logic timestamp, so that cross-platform distributed parallel simulation and consistency verification are realized. The method can improve the processing efficiency and fault-tolerant capability of the communication simulation system on large-scale simulation, and can be widely applied to the technical field of communication simulation.

The invention discloses a coal mine power supply system multi-scene modeling method, system, device and medium, and belongs to the technical field of power system scene modeling, and the method comprises the steps: building a main circuit topology, and constructing a simulation model containing a user-defined module; configuring equipment parameters, and importing field survey data to construct a dynamic load matrix; setting a fault scene, and accurately simulating various power grid faults through a circuit breaker time sequence; parallel simulation is started, and bus and key node data are collected in the simulation process; performing multi-dimensional index analysis on the collected data, and extracting key indexes; a verification report is automatically generated, and evaluation is completed in combination with a manual checking mechanism; and visually outputting a simulation result. According to the method, real-time adaptation of load impedance characteristics is realized, historical load curves can be imported in batches, a dynamic load matrix can be generated, and time-varying characteristics of coal mine loads can be accurately reproduced. On the fault simulation level, circuit breaker opening and closing logic is adopted, superposition simulation of bilateral voltage loss and multi-source faults is supported, and the seamless switching performance of the energy storage system is verified.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention discloses a multi-parameter fusion pipe network overflow prediction system and method, relates to the technical field of pipe network overflow prediction, and aims at overflow risks caused by equipment faults, unstable communication and extreme weather in a combined drainage system. A system scheme integrating five steps of predictive maintenance, reliable real-time control, digital twinning check, layered emergency plan and cloud edge cooperation is provided; in the first step, equipment health is monitored and faults are predicted to ensure that key pump stations and gates are available; 2, generating a fault-tolerant scheduling instruction by adopting reliable optimization and multi-agent reinforcement learning; 3, pre-judging and correcting the deviation by utilizing digital twinning check before execution; 4, dealing with a single-point fault and communication paralysis by using a layered distributed emergency mechanism; 5, cross-regional parallel simulation and self-adaptive scheduling are realized through cloud edge collaboration and federated learning; overflow can be reduced, environmental pollution and waterlogging loss under the rainstorm condition can be reduced, and efficient collaborative management of the drainage system is achieved.

The invention discloses a photovoltaic digital twin operation and maintenance platform oriented to multi-source collaborative diagnosis, and particularly relates to the technical field of photovoltaic operation and maintenance, normalization, time-frequency feature extraction and adaptive filtering are performed on component health degree and environmental parameters through edge nodes, and a lightweight encrypted performance abstract is generated, and is subjected to differential compression and synchronized to a cloud end; the cloud executes security federal fusion and dynamic reputation weighting on the abstract, and outputs a robust global performance vector; the collaborative optimization engine calls reinforcement learning based on the vector and a historical strategy to generate an original power distribution strategy, and the original power distribution strategy is issued after being corrected through a projection gradient descent mapping function; an edge actuator loads an environment snapshot to perform parallel simulation verification and risk assessment in a digital twin environment, so that the problems of multi-source data isolation and privacy disclosure are effectively solved; a self-adaptive multi-resolution grid, heterogeneous acceleration parallel and distributed message scheduling are provided, and high efficiency and real-time performance of large-scale multi-physics coupling simulation are achieved.

The invention belongs to the technical field of electromagnetic transient simulation, and particularly discloses an electromagnetic transient simulation decoupling method and system based on small-step synthesis and a medium, and the method comprises the steps: obtaining a to-be-simulated power system model, extracting a topological structure, electrical parameters and control parameters, and setting a simulation step length; the method comprises the following steps: dividing a system into a plurality of subsystems, and inserting decoupling branches among the subsystems; constructing a discrete time state space model based on the decoupling branch, dividing a simulation step size into small steps, performing small-step modeling on the state space model, and establishing a small-step state updating relationship; synthesizing the plurality of small-step models into a synthetic state transition matrix and an input matrix under the corresponding simulation step size through an iteration mode; and decoupling simulation calculation between the subsystems and the decoupling branches is carried out based on the composite matrix, and parallel simulation of the multiple subsystems is completed. Compared with the existing artificial delay branch method, the method can effectively relieve the problem of unstable numerical value caused by improper step length selection and violent interface fluctuation.

The invention provides a multi-core parallel simulation method and device and electronic equipment, and relates to the technical field of computer simulation, and the method comprises the steps: determining all simulation entities in a simulation environment; all simulation entities in the simulation environment are respectively packaged into independently running entity processes, and a message manager module, an object manager module and a time manager module are respectively packaged into independently running management processes; running each entity process and the management process in parallel on respective pre-allocated operation cores, and monitoring each operation core; according to the resource use data and the task completion progress of the entity process and the management process on each operation core, determining an overloaded operation core; and dynamically adjusting the execution sequence and resource allocation of the entity process and the management process on the overloaded operation core. According to the invention, through real-time monitoring and dynamic adjustment, load balancing is effectively realized, and the situation that some cores are overloaded and other cores are idle is avoided.

The invention provides a simulation scene construction and simulation method and system for a large-scale unmanned cluster, and relates to the technical field of unmanned system simulation. The method comprises the steps of modeling a target task based on execution logic for the target task of the large-scale unmanned cluster to obtain an execution structure; decoupling the target task into a plurality of weak coupling subsystems based on the execution structure, and constructing a parallel simulation environment for the target task; based on the execution function category of the target task, constructing a bus interface of a corresponding category; constructing an intelligent algorithm plug-in corresponding to the execution function category based on the bus interface of each category; integrating the intelligent algorithm plug-in into the execution structure of the target task to form an execution model of the target task; and deploying the execution model to a parallel simulation environment to obtain a simulation scene of the target task. The problems that in large-scale unmanned cluster complex task simulation, the calculated amount is large, real-time simulation cannot be achieved, and the simulation accuracy and effect of cross-platform complex tasks are poor are solved.

The invention discloses a passenger ship operation scheduling method and system based on big data analysis, and the method comprises the steps: building a port passenger ship operation digital twin model, synchronizing the ship positioning data, facility operation states and passenger dynamic distribution data of a port in real time through Internet of Things equipment, and generating a virtual simulation scene; calling a parallel simulation engine of the digital twin model, performing space-time conflict detection and resource load rehearsal on the candidate shift scheduling schemes, and screening initial scheduling schemes meeting conditions; boarding bridge distribution, waiting area capacity matching and ship berthing time sequence optimization are simulated in the digital twin model, and an operation scheduling plan is generated; and establishing a scheduling evaluation model, performing multi-target iterative optimization on the passenger retention risk, the facility vacancy rate and the carbon emission intensity of the operation scheduling plan according to a dynamic weight adjustment algorithm, and outputting an optimal operation scheduling plan. According to the method, the smoothness of the port operation process is improved, the boarding time of passengers and wait area resource waste are reduced, and the ship berthing efficiency is improved.

The invention relates to a multi-physics field coupling simulation method and equipment for industrial digital twinning. The method comprises the following steps: automatically dividing a plurality of heterogeneous physical sub-domains based on actual physical field data of industrial equipment, and defining a solving region and a boundary condition for each sub-domain; and then, a variable-driven region division mechanism is utilized to construct a parallel computing map of multiple physical domains, and efficient synchronization of inter-region coupling state variables and real-time interaction of boundary data are realized. In the parallel simulation process, calculation tasks and resources are dynamically and optimally distributed through a self-adaptive scheduling algorithm, bottleneck areas are effectively recognized and eliminated, and the overall simulation efficiency and stability are improved. According to the invention, co-evolution of a plurality of physical processes such as structure, heat, flow, magnetism, electricity and the like can be realized, and unified, flexible and extensible simulation support is provided for digital twinning, state monitoring, performance prediction, control optimization and other applications of a complex industrial system.

Disclosed in the embodiments of the present application are related methods and apparatuses for a power distribution network digital platform, and a device, a medium and a product. Among the related methods, a training method for a simulation model of a power distribution network comprises: acquiring real-time data or historical data of a power distribution network collected by sensors, instruments and devices; performing pre-processing operations on the real-time data or historical data of the power distribution network to obtain a training sample set, wherein the pre-processing operations comprise data cleaning, denoising and format conversion; on the basis of using actual data during an actual operation process of the power distribution network as labeling information, and on the basis of training samples, training a simulation model to be trained, so as to obtain a trained simulation model; on the basis of different device components in the power distribution network having different response speeds when a system operation state changes, determining a multi-rate parallel simulation technique for the power distribution network; and using the multi-rate parallel simulation technique for the power distribution network to adjust the simulation step size of the trained simulation model, so as to obtain a final optimized model.

The invention relates to the technical field of artificial intelligence and automatic driving, and discloses an automatic driving digital twin agent system based on reinforcement learning training, which comprises an agent library construction module, a simulation and configuration module, a distributed simulation execution module, a scheduling and learning module, a strategy training module and a simulation migration module. The method comprises the following steps: constructing a modular digital twin agent library, combining to generate an urban traffic simulation scene, executing large-scale parallel simulation on a distributed computing cluster, implementing dynamic task scheduling and course learning according to an agent learning progress, and training a reinforcement learning strategy and a value network based on simulation data. And verifying the strategy model and implementing migration deployment from simulation to reality. According to the method, the training efficiency can be improved, the diversity of simulation environments is enriched, and the sim-to-real migration difficulty is reduced, so that the robustness of the strategy model is improved.

The invention relates to the technical field of methanol combustion optimization, in particular to a methanol fuel combustion multi-target collaborative optimization method and a self-adaptive energy consumption regulation and control system, and solves the problem of balance between model precision and real-time optimization efficiency. The method comprises the following steps: constructing a special digital twinborn model, integrating a parameterized chemical reaction dynamic sub-model, a calibration interface dynamically coupled with real-time in-cylinder pressure data and a flow field simulation module, and continuously and dynamically updating; parallel simulation control parameter combination is carried out by using the dynamic model, and multi-target performance including at least two items of heat efficiency, NOx emission and combustion stability is predicted; outputting an optimization parameter set through Pareto frontier analysis of a multi-objective optimization algorithm; and finally, combining the parameter set with the real-time state of the model to select a control parameter to adjust the operation of the equipment, thereby realizing multi-target collaborative optimization and self-adaptive energy consumption regulation and control. According to the method, the system working condition adaptability, the multi-target optimization fineness and the energy consumption regulation and control intelligent level are improved.

The invention discloses a heat exchange equipment intelligent fault diagnosis method and system based on digital twinning, relates to the technical field of digital twinning intelligent diagnosis, and is used for solving the problem that concurrent valve jamming and multi-equipment coupling faults are difficult to quickly and accurately position and safely dispose. Design configuration, maintenance history and real-time working condition data are written into a twin library through semantic mapping and unified time synchronization, and virtual-real synchronization is kept. Secondly, fusing the flow, the pressure difference, the temperature sequence and the color-infrared image, generating a health vector by means of a dimension reduction network, and performing real-time early warning; and after early warning occurs, a twin copy is cloned by using the combination of the abnormal equipment and the regulating valve, parallel simulation is performed on the jam disturbance of the injection valve, and a concurrent fault is locked through comprehensive similarity. Finally, a knowledge base is called to evaluate candidate disposal strategies, an optimal scheme is selected to be issued and executed, dynamic correction is monitored through residual errors, and a diagnosis-decision-execution closed loop is achieved. According to the method, the equipment reliability is improved, and the energy utilization rate is increased.

The invention relates to the technical field of simulation regression systems, and provides a parallel simulation regression method and system based on cluster servers. The method comprises the following steps: classifying simulation tasks according to task types, and determining a dependency relationship among a plurality of simulation tasks; generating a parallel simulation task list through a cluster server management tool according to the dependency relationship among the plurality of simulation tasks; and delivering the parallel simulation task list to a cluster server, and running simulation tasks by the cluster server to generate a simulation result. The parallel simulation regression system is conveniently constructed by utilizing the scheduling function of the cluster server, the dependency relationship of various types of tasks can be processed, the degree of parallelism of the simulation regression system is increased to the maximum extent, and the method has universality and can be widely applied to different scenes.

The invention relates to an asynchronous parallel simulation method, device and equipment based on time sequence association clustering. The method comprises the following steps: performing interest matching on interest expressions published and subscribed by simulation entities in a theme space to obtain simulation entity pairs with a message interaction relationship; constructing a time sequence dependency graph according to the message interaction relationship between the simulation entity pairs of each theme space; performing dynamic clustering on nodes of the time sequence dependency graph by adopting a density peak value method, constructing a plurality of groups according to a clustering result, combining simulation entities in each group into a group node, and constructing a group dependency relationship graph; and according to the sum of the minimum message timestamp and the weight of the group having the dependency relationship with the current group in the group dependency relationship graph, obtaining the security time boundary of the current group, according to the security time boundary of each group, constructing a directed acyclic graph, and according to the directed acyclic graph, carrying out asynchronous parallel scheduling. By adopting the method, computing resources can be fully utilized, and the simulation operation performance is improved.

The invention discloses a parallel simulation method for guided wave focusing imaging of a piezoelectric array of a composite structure, belongs to the technical field of nondestructive testing, and aims at solving the problems that the sound field control precision of a current multi-array-element piezoelectric structure in a composite material plate is insufficient, and a traditional finite element method is large in calculation time consumption, low in convergence speed and high in reliability in the guided wave propagation simulation process. In order to solve the problem that effective convergence cannot be realized under the excitation condition of a high-frequency multi-channel array and even under the excitation condition of a high-frequency multi-channel array, the invention provides an efficient guided wave focusing simulation strategy combining spectral element high-order precision and GPU parallel computing, and the method comprises the following steps: establishing a system control equation considering a piezoelectric-structure coupling effect on the basis of a delay superposition principle; energy focusing and sidelobe suppression of a guided wave signal in a specific direction are realized by adjusting spatial arrangement and excitation time sequence of a piezoelectric array sensor, Gauss-Labatto-Legendre integral nodes are introduced to construct a high-order spectral element unit, unit-level parallel solution is realized under a CUDA architecture, and dynamic simulation capability and overall calculation efficiency of focusing field distribution are remarkably improved.

The present invention relates to a parallel simulation method, medium and device based on load balancing, which obtains several sub-models by splitting a complex system model, obtains performance index data of each computing node, allocates computing nodes corresponding to each sub-model according to the performance index data, determines the execution order and scheduling mode of the sub-models according to the priority scheduling strategy, dynamic scheduling strategy and resource conflict avoidance strategy, and controls the computing nodes corresponding to each sub-model to perform simulation operations according to the execution order and scheduling mode of the sub-models to obtain simulation results. The method solves the problem that the existing technology cannot improve the simulation speed and resource utilization while ensuring the accuracy of the simulation results.

The invention relates to the technical field of chip simulation, in particular to a voltage logic simulation design method and system for a passive internet-of-things tag chip. The method comprises the following steps: performing energy characteristic measurement on the tag chip, and calculating voltage fluctuation and logic gate triggering probability to obtain energy logic state mapping data; state transition probability distribution mapping is carried out according to the energy logic state mapping data, and a transition probability distribution tensor is constructed; collecting measurement environment parameters in the energy characteristic measurement process, and performing dynamic environment interference compensation on the transition probability distribution tensor to generate an interference compensation distribution tensor; carrying out simulation precision demand distribution based on the interference compensation distribution tensor, and constructing a layered simulation configuration file; and performing multi-precision parallel simulation execution based on the layered simulation configuration file, and generating a probabilistic verification report. Through precise and efficient logic simulation, the reliability of chip design is improved, and the fault probability is reduced.

The invention relates to the technical field of electric power simulation, and discloses an electric power system data parallel simulation processing method based on distributed modeling. The method comprises the following steps: acquiring voltage, frequency and power parameters of a new energy inverter and an energy storage converter, and generating a state switching feature vector through a fuzzy C-means algorithm; determining and calculating a load abrupt change subsystem according to the load index overrun, and calculating the electrical coupling strength with an adjacent subsystem; allocating reserved computing resources for the mutation subsystem through a particle swarm optimization algorithm to form a dynamic resource allocation table; and adjusting the data synchronization period of the mutation subsystem to an extended time window, and continuing parallel simulation after the adjacent subsystems wait for boundary data. According to the method, the problems that in an existing power system data parallel simulation processing method based on distributed modeling, the calculation load abrupt change and the data synchronization failure caused by strong nonlinear component state switching cannot be processed are solved.

The invention belongs to the field of computational lithography, and particularly discloses a lithography model calibration method based on a proxy model assisted hybrid genetic algorithm, which comprises the following steps: determining to-be-optimized parameters of a lithography model, and generating an initial population according to the to-be-optimized parameters; performing parallel simulation on the current population to obtain a photoetching model simulation result of the current population; performing parallel evaluation on the current population to obtain photoetching model evaluation indexes and fitness distribution of the current population; screening excellent individuals in the current population to construct an agent model, and performing optimal prediction to obtain a first type of individuals; screening excellent individuals in the current population and carrying out crossover and variation to obtain a second type of individuals; and combining the first type of individuals and the second type of individuals, carrying out parallel simulation and evaluation on the next-generation population, and carrying out loop iteration updating until a preset termination condition is met, so as to obtain a photoetching model parameter set of the optimal individual. According to the invention, the calibration process of the photoetching model can be simplified, and the automation, precision efficiency and global search capability are improved.

The invention relates to the technical field of data processing, in particular to a digital twinning-based comprehensive energy efficiency analysis and judgment system for a high-speed rail station building, and the system comprises a multi-dimensional perception acquisition module which outputs an environment tensor, an equipment vector and a people flow matrix which are synchronous in time; the dynamic coupling modeling module generates an equipment coupling characteristic impedance coefficient matrix; the optimization algorithm knowledge base module dynamically activates a multi-target genetic algorithm, a particle swarm optimization algorithm and a model prediction control or reinforcement learning algorithm according to the passenger flow growth rate and temperature rise rate characteristic indexes, and outputs an optimization parameter set to the four-dimensional parallel simulation module; the simulation module generates an equipment state parameter combination and an energy efficiency index; the energy efficiency collaborative evaluation module generates a global energy efficiency evaluation report through Pareto frontier analysis and Markov decision; the energy efficiency report generation module outputs a visual chart and equipment maintenance suggestions, and the energy efficiency and state double-closed-loop module triggers model correction based on pipe network pressure difference, temperature uniformity and energy efficiency ratio deviation.

The invention relates to the technical field of electromagnetic simulation, and discloses an optical storage system fine-grained decoupling simulation solving method, system and device and a medium. According to the method, electromagnetic transient simulation modeling is carried out on a plurality of elements in an optical storage system, and an electromagnetic transient simulation model of the optical storage system is constructed; according to the method, an electromagnetic transient simulation model of the optical storage system is layered, fine-grained decoupling network division is carried out after layering, and multi-thread parallel simulation solving is carried out on the electromagnetic transient simulation decoupling model of the optical storage system based on a multi-thread CPU, so that flexible network division is realized, the calculated amount of the large-scale optical storage system is reduced, and the calculation efficiency of the large-scale optical storage system is improved. And by establishing a conflict-free parallel simulation scheme and a simulation process of the large-scale optical storage system, the simulation efficiency is improved on the premise of ensuring the simulation precision, and the method is suitable for solving the simulated large-scale optical storage system.

The invention discloses an atomic scale MD-KMC parallel simulation unified modeling method and system. The method comprises the following steps: establishing an atomic scale unified architecture; carrying out molecular dynamics simulation based on the atomic scale unified architecture; and dynamics Monte Carlo simulation based on the atomic scale unified architecture. According to the method, decoupling between the MD software development module and the KMC software development module can be facilitated; the effect of optimizing one part and improving multiple parts is achieved, MD simulation and KMC simulation are supported, and meanwhile the large-scale simulation calculation level is achieved.

The invention discloses a parallel antenna topology optimization design method and system adopting multiple transfer functions. According to the method, on the basis of a traditional machine learning assisted antenna topological pixel optimization design algorithm architecture, in the optimization stage of each iteration, a parallel BPSO algorithm based on multiple transfer functions is adopted, a plurality of mutually independent BPSO algorithms are called in parallel, and on the basis of the same machine learning agent model, the same objective function is optimized; different algorithms are distinguished as different transfer functions and whether a known optimal solution is added into an initial population of the algorithm. According to the method, a plurality of high-quality candidate solutions for parallel simulation verification can be obtained in each iteration, and compared with a traditional machine learning assisted optimization antenna topological pixel optimization design method, the time required for optimization is greatly shortened, and the optimization result is greatly improved within the same time. The method can be applied to the field of topological optimization of different types of antennas.

The invention discloses an automatic configuration method and system for a power supply loop, and relates to the technical field of intelligent power distribution. After a plug device is rotationally locked, an identity is read in a near field, and corresponding voltage and power templates are called; inputting the power template and bus real-time current into an edge algorithm to obtain residual capacity and generate an initial power distribution matrix; when the residual capacity is higher than a safety threshold value, a phase sequence, a breaking value and overcurrent delay are issued to the intelligent circuit breaker, and zero-voltage window spark-free closing is realized; during operation, load change is predicted, time dislocation and power coupling parameters are calculated, a peak regulation coefficient is obtained through fuzzy reasoning, and a non-critical load migration stabilizing peak value is triggered; and finally, synchronizing the identity, the power distribution matrix and peak regulation parameters to the digital twin model, and realizing self-learning, energy efficiency improvement and system stability improvement by means of a parallel simulation optimization rule and second-level multiplexing during similar access.