252 results about "Parallel simulation" patented technology

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 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 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.

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 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 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 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 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.

The invention provides an edge calculation and AR collaborative intelligent first-aid method and system based on trauma grading, and relates to the technical field of trauma first-aid, and the method comprises the steps: collecting trauma field data through a multi-modal sensor, constructing a dynamic digital twinborn model, and calling a medical knowledge graph to carry out trauma grading evaluation. And generating multiple groups of emergency plans based on a biological plasticity calculation principle, performing parallel deduction to select an optimal plan, and continuously collecting, feeding back and optimizing the plan through the neural morphology adaptive sensing network. Accurate grading and intelligent decision making of the emergency treatment process are achieved, and the emergency treatment efficiency and accuracy are improved.

The invention discloses a high-interaction deception defense and attack tracing method based on an RASP technology, and the method specifically comprises the following steps: S1, RASP implantation and bait deployment, S2, attack detection and simulation triggering, S3, attack session redirection and high-interaction deception, S4, attack behavior monitoring and tracing data collection, and S5, tracing analysis and threat disposal. The invention relates to the technical field of network and information security. According to the high-interaction deception defense and attack tracing method based on the RASP technology, a deception mechanism is embedded into a real application / system in operation, and high fusion of a real business environment and a false trapping environment is achieved. Different from a traditional'main-auxiliary separation 'mode that a honeypot is placed in an independent network or a host, the method has the advantage that the application can be changed to generate a'trap' during running through the RASP. The seamless embedded spoofing makes attackers difficult to distinguish true and false: the attackers attack a part of a production system initially, and then are introduced into a parallel simulation space unconsciously.

The invention discloses a digital twinning-based intelligent simulation control system for a power system, and the system comprises a data processing module which is used for collecting and preprocessing the operation data of the power system; the storage mapping module is used for mapping the data to the virtual architecture and generating a twin snapshot sequence; the factorization module is used for executing graph embedding and node decomposition to generate embedding features; the twinning mapping module is used for constructing a twinning mapping network and realizing bidirectional fusion; the simulation modeling module is used for executing health evolution and power flow risk modeling on the GPU to generate a knowledge graph; the strategy solving module is used for carrying out constraint calculation on the knowledge graph and screening an optimal strategy; and the deduction regulation and control module is used for generating a causal path based on graph backtracking and completing closed-loop control. Real-time mapping, intelligent deduction and self-adaptive regulation and control of the power system are realized through digital twin modeling and GPU parallel simulation, and the scheduling precision, the response speed and the operation stability are remarkably improved.

The invention discloses a real machine deployment method and device for a quasi-direct-drive robot for migration from simulation to reality, and the method comprises the steps: S1, driving each joint motor through a PD controller on a real machine of the robot to follow a plurality of groups of reference tracks, and synchronously collecting the real motion track data of each joint; s2, in a parallel simulation environment, performing initialization based on a random frame in a real motion track, randomly endowing a group of motor kinetic parameters, then driving a simulation robot through a PD controller by using the same group of reference tracks, and generating corresponding simulation motion track data; s3, based on a trajectory error between the two types of trajectory data, iteratively updating motor kinetic parameters by using an optimization algorithm until the error converges, thereby obtaining a calibrated simulator; s4, training a control strategy network of the robot through reinforcement learning in the calibrated simulator; and S5, directly deploying the control strategy network obtained by training to a real machine for performance evaluation and security verification.

In order to solve the problems of high calculation complexity and low efficiency in hydraulic fracturing numerical simulation, the invention provides a method for parallel simulation of hydraulic fracturing crack propagation. A three-dimensional fracture grid is constructed by adopting a displacement discontinuity method (DDM) and a finite volume method (FVM), and stress and fluid pressure distribution in fractures are calculated. Crack expansion is driven by optimizing DDM stress coefficient matrix generation and combining FVM to calculate crack tip pressure. OpenMP multi-thread scheduling and Intel MKL and Eigen library optimization are adopted, LU or QR decomposition is dynamically selected according to the matrix scale, and therefore the calculation efficiency is improved. According to the method, by initializing a three-dimensional fracture grid, optimizing a stress matrix, constructing a fluid-solid coupling equation set, carrying out iterative solution, checking convergence and generating a three-dimensional fracture expansion model, efficient fracture expansion simulation is achieved, and an efficient and accurate numerical tool is provided for fracture design of unconventional oil and gas reservoirs.

The invention discloses a high-pressure reaction kettle emergency discharge pre-arranged planning method, which comprises the following steps of: constructing a real-time monitoring network through a multi-physical quantity sensor, automatically generating a micro scene highly coupled with field disturbance by combining intelligent signal filtering, abnormal factor aggregation and parametric modeling, and realizing agile mapping of a historical optimal discharge strategy and adaptive parameter adjustment and optimization. The risk tolerance and the response rate are accurately evaluated by means of parallel simulation deduction; an actual discharge action is compared with simulation feedback, and if a significant deviation exists, a rapid reconstruction strategy is iterated to realize closed-loop safety management and control; according to the method, the accuracy, the self-adaptability and the safety of the release decision are improved, and the emergency prevention and control efficiency of the reaction kettle under the extreme working condition is improved.

The invention discloses a chain type hybrid deep reinforcement learning quadruped robot control system. The system comprises a sensing module, a data processing module, a chain type hybrid reinforcement learning model module, a feedback module and the like. The invention further discloses a control method of the chain type hybrid deep reinforcement learning quadruped robot control system, and the method comprises the steps: S1, collecting bionic motion data, robot state data and environment data, and training based on an attention mechanism space-time diagram neural network and antagonistic imitation learning to obtain a reference gait model containing motion prior; and S2, constructing a three-level parallel simulation environment, setting a progressive course target, training the progressive course target by adopting an improved PPO algorithm, and optimizing the gait stability, the movement efficiency and the environmental adaptability. By means of high-precision data acquisition with the sampling frequency larger than or equal to 240 Hz and antagonistic imitation learning, motion priori knowledge is introduced, the training period can be shortened by 50% or above, abnormal gaits are avoided, and the training process is controllable.

The invention belongs to the technical field of computers, particularly relates to an enterprise consultation service system based on big data, and aims to solve the problems of data islands, decision lagging and insufficient predictive ability in traditional enterprise consultation service. The system comprises a global data access module, a knowledge graph construction module, a dynamic simulation engine, an intelligent consultation generation module and an interactive feedback optimization module, a time sequence evolution knowledge network is constructed by fusing internal and external multi-source heterogeneous data, multi-scene parallel simulation and intelligent suggestion generation are realized, and closed-loop feedback learning is supported. According to the scheme, the accuracy, foresight and response efficiency of enterprise strategic decision can be improved, and large-scale concurrent service and real-time dynamic optimization are supported.

The invention discloses a real-time monitoring and early warning method and system for hoisting operation of a wind generating set, and relates to the technical field of wind generating set construction, the method comprises the following steps: establishing a digital twin model of a hoisting operation site, collecting state data in real time for dynamic coupling, and constructing a virtual monitoring network; performing short-term advanced parallel simulation in the network to obtain future state trajectory clusters under different potential interferences; extracting a key safety index time sequence from the trajectory cluster, and calculating a future instability risk probability; and when the risk probability exceeds an adaptive early warning threshold, generating multi-level early warning information and triggering active intervention. The technical problem that the instability risk is difficult to recognize and intervene in time due to the fact that the hoisting operation of the existing wind generating set lacks real-time prediction of the system state in a complex environment is solved, and the technical effects of real-time early warning and effective intervention in the hoisting process are achieved by constructing the digital twinborn monitoring network and carrying out risk propagation prediction.

The invention discloses a progressive event-driven synchronous control method, which comprises the following steps of: performing parallel simulation, simultaneously starting a plurality of simulation processes, respectively simulating each core particle in a multi-core particle system, and managing the synchronization among the simulation processes by a global manager; on-demand synchronization: when the core particles simulated by the simulation process are in inter-core-particle communication, the global manager synchronizes the related processes; the process is synchronized, and the global manager ensures that other cross-process communication events earlier than the request are simulated; if a simulation process does not reach the time point, the process initiating the request is suspended by the global manager until the request can be executed; and network simulation and time sequence simulation: when the request can be executed, the global manager starts the inter-chip network simulator and simulates the communication event. According to the method, the accuracy and the operation efficiency of multi-core particle parallel simulation are remarkably improved, and the method is suitable for function verification and performance analysis of large-scale complex multi-core chips.

The invention relates to the technical field of power system communication, and particularly discloses an optimization method for an operation and maintenance strategy of a power communication network, which performs space-time alignment and feature reconstruction on multi-source heterogeneous telemetry data of the power communication network by using a sliding window and a graph neural network, and constructs a state tensor reflecting network physical and logic panorama. Generating a candidate strategy set through a deep reinforcement learning action network, and mapping the candidate strategy set to a digital twin environment synchronized with the current network in real time for parallel simulation rehearsal; a dynamic scoring mechanism including network pressure sensing and marginal utility analysis is introduced, fusing is carried out on a high-risk strategy in combination with a logarithmic barrier function, and an optimal strategy giving consideration to performance and safety is selected from a candidate set. And finally, a final execution instruction is generated through verification of a power service hard rule filter, the executed real network is fed back and stored in an experience playback buffer area to update model parameters, and the problems that a traditional static strategy cannot dynamically adapt to the network pressure and the direct trial and error risk is high are solved.

The invention provides an automatic driving situation awareness and decision-making method and an automatic driving situation awareness and decision-making system. According to the method, standardized scene data is generated through multi-source data fusion, a self-adaptive digital twinning environment is constructed, semantic reasoning is performed by using a knowledge graph to generate a situation tag, a decision evaluation matrix is generated based on multi-hypothesis parallel simulation, an optimal action is selected in combination with user preference, and finally system self-evolution is realized through closed-loop learning optimization. Therefore, the security and robustness of the system in a complex scene are improved. The method can solve the technical problems that prediction is not stable due to lack of semantic situation understanding in the existing automatic driving technology, and behaviors are conservative and low in efficiency due to rigid decoupling of prediction and planning.

The invention relates to the technical field of simulation, and particularly discloses a self-adaptive communication method and a heterogeneous interconnection multi-core distributed simulation experiment system node communication architecture, and the method comprises the steps: S1, detecting the state of a simulation entity, and judging the event triggering opportunity; the simulation entity comprises a semi-physical simulation entity and a parallel simulation entity; s2, when event communication is triggered, determining simulation entities which communicate with each other; and S3, performing data transmission between the simulation entities which are determined to communicate with each other, and encoding and decoding the data. According to the method, the distributed simulation requirement of the large-scale simulation entity is met, an optimal communication mechanism and a semi-physical model distributed simulation strategy oriented to large-scale simulation entity scheduling management are constructed, and the virtual-real interconnection and cluster control semi-physical simulation capability of a simulation experiment platform is further improved.

The invention belongs to the technical field of power system simulation, and particularly relates to a sub-grid parallel simulation method and simulation system for a large-scale power grid, and the method comprises the steps: obtaining the power grid topological data and simulation type of a target power grid; determining the power grid scale of the target power grid based on the power grid topological data and the simulation type; dividing the target power grid into a plurality of sub-grids based on the power grid scale; based on the power grid scale, the number of the subnets and the performance indexes of the FPGAs, the number of the FPGAs is determined, and the number of the FPGAs is larger than or equal to the number of the subnets; based on the FPGAs, the simulation tasks of all the subnets are distributed, so that all the FPGAs bear the simulation tasks of one subnet, and simulation results corresponding to all the subnets are obtained through parallel simulation; and obtaining simulation results corresponding to the subnets and fusing the simulation results to obtain a target simulation result of the target power grid. According to the technical scheme provided by the invention, the precision and real-time performance of large-scale power grid simulation are improved.

The invention discloses a stake car wind resistance dynamic optimization method and system based on airflow simulation, and belongs to the field of automobile pneumatic optimization. A stake car three-dimensional structure model is constructed, and spatial distribution parameters and surface roughness parameters of a stake component are extracted; establishing a multi-working-condition external flow field simulation model, and obtaining a unit area wind resistance coefficient distribution diagram; constructing a wind resistance sensitivity matrix, and identifying a high-sensitivity component; performing disturbance modeling on the high-sensitivity component, generating a local deformation configuration sample and establishing a simulation model set; parallel simulation calculation is carried out, and wind resistance coefficient and aerodynamic stress data are extracted; based on a multi-objective optimization algorithm, determining an optimal disturbance configuration; outputting a structure adjustment suggestion for guiding dynamic design optimization; according to the method, collaborative optimization of wind resistance reduction and structure safety can be achieved, and the pneumatic performance and the energy-saving level of the whole stake car are improved.