3058 results about "Simulation system" patented technology

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention discloses a reservoir real-time scheduling simulation system based on a deep learning algorithm, and belongs to the technical field of intelligent water conservancy and artificial intelligence. Aiming at the problems of low prediction precision, poor multi-target coordination capability, weak coping uncertainty and the like of a traditional scheduling system, the system is designed to acquire hydrological, meteorological, water quality and engineering safety data through a multi-source data acquisition unit, and a multi-dimensional feature tensor is generated after preprocessing and fusion; the dispatching center server adopts an STGCN-LSTM mixed model to achieve high-precision prediction and uncertainty quantification of the water inflow process in the future 7-30 days, a reservoir hydrodynamic model and an MO-PPO algorithm are combined to complete multi-scene simulation and multi-target optimization decision, and an AF-DT mechanism dynamically adjusts the dispatching rule priority. According to the system, a sensing-decision-execution-feedback closed loop is constructed, the scheduling adaptive capacity and robustness are improved, the synergistic interaction of flood control, water supply, power generation and ecological protection is realized, and the system is suitable for real-time intelligent scheduling of large and medium reservoirs.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention relates to the technical field of simulation scheduling combining static scheduling and centralized scheduling, and discloses a simulation scheduling method and system combining static scheduling and centralized scheduling. According to the simulation scheduling method and system combining the static scheduling and the centralized scheduling, pre-allocation and resource planning of tasks are carried out through the static scheduling, and the execution efficiency of periodic tasks is ensured. In combination with centralized scheduling, resource allocation is adjusted in real time in the simulation operation process, the phenomena of resource idleness and overload are reduced to the maximum extent, and dynamic optimization of resources is achieved. The execution sequence of key tasks is locked in advance by static scheduling, and the time constraint of deterministic tasks is ensured. The centralized scheduling can perform global coordination on burst tasks, adjust task priorities in real time and ensure timely response of the burst tasks, so that the real-time response capability of the simulation system is improved.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention discloses an optimization analysis system for simulating an anesthesia process, relates to the technical field of anesthesia simulation optimization analysis, and is used for solving the problem of inaccurate anesthesia individualized simulation decision. By constructing an anesthesia simulation system fusing a pharmacokinetic and pharmacodynamic model and a physiological coupling model, dynamic process simulation under individual physiological states and operative characteristics is realized, state evolution trajectories of an induction period, a maintenance period and a resuscitation period can be accurately deduced, and by introducing deep learning calculation and stage continuity constraints, the accuracy of the state evolution trajectories of the induction period, the maintenance period and the resuscitation period is improved. The method comprises the following steps of: establishing a multi-path drug delivery and ventilation strategy, setting a risk-efficiency scoring function, completing state tracking and index evaluation of the whole process, generating a strategy scoring set with confidence information, further supporting an abnormal probability judgment and risk triggering mechanism by the system, and outputting key monitoring parameters and coping strategies, so as to improve the simulation accuracy and stability; therefore, closed-loop control of strategy optimization and risk early warning is realized, and the credibility of the anaesthesia simulation process is improved.

The invention discloses an ecological restoration effect evaluation and simulation system after polluted soil restoration, and relates to the technical field of soil restoration, and the system comprises a soil feature generation module which generates an ecological feature data set; the ecological restoration evaluation module is used for establishing a multi-dimensional evaluation model by using an analytic hierarchy process and a fuzzy comprehensive evaluation method based on the ecological characteristic data set, and generating an ecological restoration effect index; the dynamic simulation prediction module is used for constructing an ecological restoration dynamic simulation model and simulating the evolution trend of a soil ecological system under different environment conditions; and the restoration strategy optimization module is used for generating a targeted ecological restoration optimization scheme by combining a preset restoration strategy library according to the ecological restoration effect index and the dynamic simulation prediction result. According to the method, a closed-loop system integrating multi-source data acquisition and processing, multi-dimensional evaluation, dynamic simulation prediction and stepped strategy optimization is constructed, and accurate evaluation of the ecological restoration effect after contaminated soil restoration and scientific optimization of the restoration strategy are achieved.

A crop growth modeling system includes a memory configured to store computer-readable instructions. The instructions cause to the system to use a digital twin component configured to create and manage a digital twin of a farm. The instructions cause to the system to use a data input component configured to receive data related to a defined set of land characteristics and environmental attributes for the farm. The instructions cause to the system to use a processing component configured to integrate the received data with the digital twin and to simulate at least one crop growth scenario based on the integrated data. The instructions cause to the system to use a prediction component configured to determine a predicted crop growth rate for the farm based on the simulations conducted by the processing component.

The invention relates to the technical field of data processing, in particular to a new energy uncertainty multi-type energy storage staged capacity optimization configuration method, which comprises the following steps: acquiring energy storage parameters, new energy output and user load data through edge equipment, and verifying photovoltaic conversion efficiency and lithium battery electrochemical behaviors through a physical model to generate a high-confidence data set; the federal cooperative system fuses distributed node data to output a health degree distribution diagram, and the memory network analyzes the temperature and the charge-discharge coupling effect to generate a dynamic compensation coefficient alpha; an optimization engine receives the alpha value and wind and light prediction, generates a staged scheme through a parallel algorithm, and inputs the staged scheme into a simulation system for verification; and the edge node executes the scheme and collects response data, and twin compares a simulation value with an actual deviation to update a collection strategy. The method eliminates data distortion through physical rule verification, corrects aging unit state deviation through a dynamic compensation mechanism, optimizes decision binding real-time health degree, and effectively defends charging and discharging analysis errors of a virtual power plant dispatching desk.

The invention relates to the technical field of mechanical property analysis, in particular to an inorganic mineral casting stress analysis method and system based on a virtual simulation system.The method comprises the following steps that grain voxel density difference is obtained, boundary points are screened, a boundary curve data set is generated, a defect appearance is formed through fitting, and a defect area grid model is constructed; and extracting a fatigue path node distribution diagram, screening thermal collaborative risk nodes, and drawing a concentrated stress area labeling result. According to the method, the density mutation feature is introduced as an identification basis when the grain structure boundary is obtained, boundary offset caused by traditional geometric approximation modeling is avoided, the closed curve network is established through boundary point space continuous connection, the refining degree of grain structure modeling is improved, and the modeling precision is improved. For a fatigue path calibration link, a continuous change relation of a node displacement trend is adopted to replace concentrated strain point acquisition, so that fatigue path identification is independent of single-point measurement, and the stress judgment integrity of a virtual simulation system under a multi-field condition is enhanced.

The invention relates to the technical field of coast engineering, in particular to an estuary sandy coast erosion and deposition simulation system based on the coupling effect of flood peak runoff, waves and tide, which comprises a training data generation module, an intelligent agent model construction module, a dynamic prediction engine module and a scene evaluation module. The training data generation module is used for outputting a hydrodynamic state field, a wave characteristic field, a bed surface shear stress field and a bed surface elevation variable quantity by utilizing a traditional numerical model to construct a training data set; the intelligent agent model building module is based on a deep learning network and introduces physical constraint loss function training to obtain an intelligent agent model; the dynamic prediction engine module loads the trained deep learning network to realize bed surface elevation variation prediction and update terrain boundary conditions; and the scene evaluation module executes erosion and deposition evolution simulation according to different hydrological boundary condition combinations and extracts terrain evolution data to quantitatively evaluate the coast stability and the channel deposition risk. The invention relates to the field of estuary dynamic landform simulation and coast engineering.

The invention discloses an intelligent simulation system and method based on a power distribution digital system, and relates to the technical field of power distribution network intelligent simulation. The event triggering module is used for constructing a composite triggering condition and obtaining an operation event based on the power distribution network data and the composite triggering condition; the event classification module is used for constructing an event template library and obtaining a target event type based on the running event and the event template library; the event-modeling demand mapping module is used for constructing an association rule base and obtaining a scene structure body and a modeling task package based on the association rule base and the target event type; and the modeling module is used for constructing a simulation model based on the modeling task package and the scene structure body, and obtaining an execution result based on the simulation model, so that the problem that the intelligence degree of event perception and response of the current dynamic modeling system of the power distribution network is insufficient is solved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention discloses a vehicle-mounted electrical control unit (ECU) debugging simulation system and method based on digital twinning. The system is composed of a data aggregation and time reference module, a consistency evaluation module, a synchronization and correction module, a semantic communication and scheduling module, an arbitration and security debugging module, and a cross-layer adaptive degradation and execution and iteration module. Channel-level time delay is estimated through a unified time reference, a consistency difference spectrum containing amplitude, time sequence, event and derivative components is constructed, confidence is given, time delay compensation and event-level alignment are calculated, and deterministic and priority scheduling is implemented according to semantic benefits; actions such as parameter recharge, message injection, task rhythm perturbation and environmental stimulation are generated and limited under the constraint of a security invariant set, control right soft handover and gating are realized based on confidence, and grading and degrading are performed when the confidence is low or the constraint is triggered; millisecond-level bidirectional collaborative debugging of twin and physical ECUs is realized, and consistency convergence, safety and bandwidth time arrival are improved.

The invention is applicable to the technical field of coal bed gas development and geological sequestration, and provides a THMC multi-field coupling simulation method and system for a process of displacing CH4 by coal bed CO2, and the method comprises the following steps: collecting multi-source parameter information of a target coal bed; according to the multi-source parameter information, constructing a coal seam structure model comprising a matrix and a crack system; constructing a control equation system based on the coal seam structure model, and introducing a multi-field coupling mechanism to obtain a fully-coupled mathematical model; performing numerical solution on the fully-coupled mathematical model to obtain a spatio-temporal evolution result of the key field variables; and according to the spatio-temporal evolution result of the key field variable, evaluating the index of the target coal seam. According to the invention, a heat-force-gas-chemical multi-physical field full-coupling simulation system is constructed, so that the CH4 recovery rate and the CO2 storage stability can be accurately predicted. The method is suitable for gas injection-storage coupling optimization of a multi-coal-rank and low-permeability coal seam, and has high precision, high adaptability and remarkable engineering guidance value.

The invention relates to the technical field of artificial intelligence and virtual reality, in particular to a large model scene drilling system based on AI-VR. The system comprises a student portrait construction module, a dynamic scene generation module, a multi-source data evaluation module and a holographic guide interaction module. According to the invention, the student portrait construction module collects multi-dimensional information and constructs a detailed portrait through clustering analysis, the dynamic scene generation module generates an initial scene model, and the multi-source data evaluation module constructs an operation step dependence graph, so that the operation condition in the learning process of the student is comprehensively and accurately evaluated; the holographic guidance interaction module uses a holographic projection technology to accurately guide students to operate correctly, and meanwhile, the AI large model dynamically adjusts scene difficulty and complexity according to real-time performance of the students, so that individual requirements of the students in different learning stages are met, error sources and influences are deeply analyzed, and a quantitative and scientific basis is provided for teaching evaluation and improved learning of the students.

The invention discloses a reinforcement learning calculation simulation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence computing, and the method comprises the steps: inputting the determined current model parameter configuration, current hardware configuration and current working load into a target simulation system to obtain a plurality of parallel grouping combinations, determining a target simulation system according to the current hardware configuration, determining an effective parallel packet combination from the plurality of parallel packet combinations based on a preset Monte Carlo method, inputting the effective parallel packet combination into a simulator of a preset neural network model, and performing delay time calculation according to the effective parallel packet combination through the simulator to obtain a delay time sequence; and the combination corresponding to the shortest delay time is used as a target parallel grouping combination, so that the technical problems of mismatching of simulation scenes, insufficient precision and lack of effective support for heterogeneous clusters are solved, reliable performance prediction and optimal parallel strategy suggestions are provided through high-precision performance modeling and automatic exploration, and the method is suitable for large-scale popularization and application. Therefore, the resource consumption of large-scale GRPO training is reduced.

The invention relates to a medical training analog simulation system based on big data analysis, in particular to the field of medical training analog simulation, the medical training analog simulation system provides an efficient, accurate and dynamically adaptive training environment through cooperative work of a plurality of modules; the data processing module ensures high quality of input data, the construction module improves the generalization ability of the model through transfer learning and multi-task learning, the dynamic optimization module improves the model prediction precision through self-supervised learning and learning rate adjustment, and the real-time feedback module monitors student operation and provides personalized tutoring; the whole system combines high-quality data, intelligent training and real-time feedback, provides accurate and personalized support for medical education and practical training, and improves operation skills and coping ability of students.

Techniques for generating simulations to evaluate a performance of an object controller. The controller may be configured to control one or more functionalities of a simulated object (e.g., a “smart object”) in a simulation, such as make the smart object operate like a human in a given scenario. A simulation system may generate one or more simulations based on log data captured by a vehicle operating in a physical environment. The simulation system may cause the object controller to control at least one smart object in the simulation, the smart object being representative of an actual object detected in the physical environment. The simulation system may compare a smart object trajectory to an actual object trajectory of the object to determine a performance metric associated with the object controller. Based on a determination that the performance metric satisfies a threshold, the simulation system may validate the object controller.

The invention discloses a BNCT dose calculation method, which comprises the following steps: S1, constructing a voxel model, initializing one or more particles in a simulation system, and respectively acquiring the initial position, energy and movement direction of each particle; s2, adaptively selecting a Monte Carlo particle tracking algorithm and a flux counting mode based on the region where the particles are located and the energy of the particles; s3, repeating the step S2 until the particles leave the simulation system or are killed when meeting a truncation condition; and S4, cumulatively calculating dose distribution. Through the BNCT dose calculation method, the Monte Carlo particle transport simulation time is greatly shortened, the BNCT dose calculation efficiency is optimized, and the method is suitable for rapid and high-precision radiation dose simulation under a BNCT complex tissue structure.

The application provides a large-span bridge substructure vehicle-fire wind force real-time hybrid test platform and implementation method, the platform comprises a physical loading and transmission system, a T-shaped wind-fire coupling simulation system, a modularized prying integrated system, an intelligent control and calculation system and a multi-dimensional high-precision monitoring system. The method realizes real-time pre-test of the boundary displacement / force of the test piece through the construction of a numerical substructure fast calculation model based on LSTM and converts the boundary displacement / force into a loading instruction to drive the through core jack to realize thermal force coupling interactive loading; the T-shaped wind-fire coupling simulation system reproduces the real vehicle fire scene under the influence of the environmental wind. The test platform supports independent loading of any component of the cable system, can execute open space self-defined fire test and closed space standard fire test, and accurately analyzes the cable failure mode, critical temperature threshold and damage evolution law under fire through the multi-dimensional high-precision monitoring system, thereby providing a high-fidelity test platform and analysis method for fire resistance design of large-span bridges.

A hardware emulator and an emulation system including the hardware emulator are provided. The hardware emulator includes an artificial neural network-based reconstruction model configured to reconstruct dynamics of a dynamical system based on input data and a memristor-based circuit configured to emulate state space representation of the dynamical system based on the reconstruction model.

Provided are a thermal-hydrological-mechanical-chemical coupling exploitation experimental device for a deep oil and gas reservoir, and a simulation system. The device includes a loading bin, a sample holding bin, a true triaxial pressure loading mechanism arranged on the loading bin, a temperature control system arranged on the true triaxial pressure loading mechanism, and a monitoring system having a detection element arranged in the rock sample and the sample holding bin. A sample entry port is formed in one side surface of the loading bin. The sample holding bin has one end slidably arranged in the sample entry port, can be sealed and connected with the sample entry port, and is configured for placing and fixing a rock sample. Multiple through holes are uniformly formed on an outer end cover of the sample holding bin and are configured for detecting and adding water, gas, and oil.

The invention belongs to the technical field of analog simulation, and particularly discloses an intelligent agent-based dynamic confrontation simulation system, which comprises a data acquisition module, an intelligent analysis module, a decision generation module, an intelligent agent behavior self-adaption module, a training evaluation module and a multi-mode man-machine interaction module, physiological, action, voice and eye movement data of trainees are collected in real time through a multi-modal sensor, and a tactical intention is recognized and a dynamic three-dimensional battlefield situation thermodynamic diagram is generated in combination with virtual battlefield environment parameters; an agent coping strategy is generated based on reinforcement learning and a decision tree, and an agent is driven to carry out real-time confrontation; and performing multi-dimensional quantitative evaluation on the whole training process through a training evaluation module, and performing closed-loop optimization on an agent decision and strategy library based on an evaluation result. The method supports various natural interaction modes such as voice, gestures and eye movement, remarkably improves the fidelity, intelligence and training efficiency of simulation training, and is suitable for the field of military training and tactical drilling.

A simulation system may comprise a headset and an equipment. A wireless communication interface of the headset may receive position information from the equipment. The headset may detect a passively-instrumented object represented in a captured image. The headset may detect a wearable device coupled to the passively-instrumented object. The headset may determine position information for the passively-instrumented object. The headset may present an image of virtual equipment corresponding to the equipment in accordance with the position information from the equipment and the position information detected for the passively-instrumented object.

The invention belongs to the technical field of analog simulation, and particularly relates to an intelligent simulation system with an autonomous decision-making capability, which comprises simulation management used for task scene initialization, system parameter configuration and simulation process control and operation. The display system is used for 2D / 3D visual display and playback of simulation results; the model implementation system is connected with the simulation management and display system and is used for implementing a target model and a flight mode model according to configuration parameters; the intelligent target generation system is used for automatically generating a maneuvering combination strategy, a task load control strategy and a radar control strategy according to the simulation situation information generated by the model implementation system, and feeding back the maneuvering combination strategy, the task load control strategy and the radar control strategy to the model implementation system to drive simulation deduction; wherein the system can execute a plurality of simulation modes including large sample space random task simulation, task simulation without autonomous capability level and task simulation with certain autonomous capability level according to simulation types set by the simulation management and display system.

The invention discloses a social group simulation method and device based on multi-agent driving. The method comprises the following steps: performing user attribute sampling processing on a real e-commerce user data set, and constructing agent basic attributes based on user attributes in combination with a large language model to obtain agent basic attribute features; performing construction processing on the agent basic behaviors based on a preset memory mechanism in combination with the agent basic attribute features; according to the agent basic attribute features and the agent basic behavior features, performing construction processing on agent interaction behaviors in combination with a large language model; performing relation network construction processing based on a small-world network model on the plurality of agents; and performing simulation processing based on execution agent behaviors on the agent social group model to obtain a multi-user behavior simulation result. By constructing a multi-agent simulation system in an e-commerce scene and combining the multi-agent simulation system with a large language model, social group simulation in a complex e-commerce interaction scene is realized, and the accuracy of social group simulation in the e-commerce scene is improved.

The invention provides experimental equipment and method for simulating filling and sealing of carbon dioxide in a goaf, and the experimental equipment comprises a simulation system, a slurry injection system, a gas injection system, a data acquisition and processing module, a recording, analyzing and displaying module and a data acquisition system. A simulated overlying strata of the simulation system naturally collapses to form a porous medium space model, slurry and carbon dioxide are injected into the porous medium space model through the slurry injection system and the gas injection system, and data acquisition is conducted on the curing process of the carbon dioxide through the data acquisition system; and the data is transmitted to the recording, analyzing and displaying module through the data collecting and processing module. According to the invention, the influence of carbon dioxide on the slurry solidification behavior and the solidification reaction penetration depth can be comprehensively researched, and a scientific basis is provided for optimizing material selection, proportion design and injection process parameters in practical engineering.

The invention discloses a vehicle driving simulation test method and system based on digital twinning, belongs to the technical field of vehicle driving, and realizes real interaction of a driving behavior model and a control algorithm in a virtual scene by constructing a digital twinning body highly corresponding to an actual vehicle and a driving environment. Time stamps and coordinate information of all the modules are collected, time differences and space residual errors are extracted, a space-time semantic consistency index model is constructed, and the loading process is intelligently judged and optimized; when inconsistency is detected, adjustment is performed through clock synchronization and coordinate recalibration, a closed-loop optimization mechanism is formed, and high collaboration of the model and the environment is ensured, so that the accuracy, the stability and the credibility of the simulation system are improved, and the method is suitable for complex and changeable automatic driving test scenes.