728 results about "Simulation training" patented technology

The invention relates to the technical field of robot training. The robot self-adaptive training method based on reinforcement learning comprises the steps that task sub-target information is generated through a high-level strategy network, the task sub-target information is input into a low-level execution network, an action control instruction is generated according to the task sub-target information, interaction feedback information is collected in the execution process, and the action control instruction is sent to a robot through a robot. Calculating a reward value according to the interaction feedback information, carrying out association processing on the reward value and the scene complexity parameter, executing a dynamic reward shaping operation, generating an adjusted reward signal, generating a strategy model optimized by meta-learning based on the adjusted reward signal, loading the strategy model in a simulation environment, and carrying out dynamic reward shaping. A target strategy model optimized through simulation training is generated, the target strategy model is loaded to the robot, and the robot is controlled to execute task operation in the actual interaction scene. The method has the effect of realizing adaptive task learning of the robot in a multi-interaction scene.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

The invention provides an automatic history fitting method for gas drive numerical reservoir simulation, which comprises the following steps of: 1, establishing a gas drive numerical simulation mathematical model, and correcting the gas drive numerical simulation mathematical model by using a gas drive PVT phase state fitting experiment; 2, solving the corrected gas drive numerical simulation mathematical model obtained in the step 1, performing parameter analysis, and screening main control factors influencing accumulated oil production; 3, generating a numerical simulation training sample set based on an orthogonal experiment, and obtaining the main control factor parameter initial value constraint screened out in the step 2 through a machine learning algorithm; step 4, establishing a multi-parameter fitting objective function for realizing historical fitting; and step 5, using the main control factor parameter initial value constraint obtained in the step 3, performing iterative calculation through a projection gradient method, fitting the target function established in the step 4, and terminating when a convergence condition is satisfied. According to the method provided by the invention, the constraint parameter values can be adaptively calculated, automatic historical fitting is completed, and the definition of the fitting parameter range does not depend on empirical and experimental determination any more.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

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.

The invention relates to the technical field of medical simulation training, in particular to an ultrasonic operation intelligent training method and device based on multiple modalities, and the method comprises the steps: obtaining real-time six-degree-of-freedom pose data of an ultrasonic probe held by an operator, and generating an ultrasonic image in real time through a first deep learning model in cooperation with scene parameters; wherein the first deep learning model is trained to learn and establish a continuous mapping relation from an ultrasonic probe pose space to an ultrasonic image space; performing section classification on the ultrasound image by using a second deep learning model, and determining deviation information for a non-standard section; and based on the real-time six-degree-of-freedom pose data and a division result of a preset standard section and an ultrasonic probe pose, generating visual guide information for guiding an operator to adjust the probe pose, and displaying the visual guide information. In this way, the problems that an existing virtual training image is discontinuous and guiding is inaccurate are solved, and meanwhile the training efficiency and the reality sense of ultrasonic operation are remarkably improved.

The invention relates to the technical field of occupational simulation training evaluation, and discloses a system for college student occupational scene simulation training evaluation. The system comprises a task analysis unit, a dynamic behavior modeling unit, a multi-mode interaction management unit, a state evolution tracking unit, a strategy adaptation unit and a comprehensive evaluation generation unit. The system receives the initial scene description and generates a structured task framework, and behavior parameters are configured for student roles; constructing a multi-modal interaction atlas by fusing aligned voice, action and sight line data in real time; identifying a key decision node, and dynamically tracking a team state evolution path; matching and adjusting strategies in a behavior rule base according to the evolution sequence, and outputting an adaptive intervention scheme; and finally generating a multi-dimensional structured evaluation report. According to the system, deep insight and dynamic and objective intelligent evaluation of complex interaction behaviors in the simulation training process are realized.

The invention discloses an unmanned aerial vehicle autonomous positioning navigation method and device based on a cross-view azimuth regression network. The method comprises the steps of 1, constructing a cross-view-angle data set which is oriented to a cross-view-angle scene and contains azimuth information, 2, constructing an unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene, and 3, training and verifying the unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene by using a training set and a verification set. And 4, carrying out regression performance test on the unmanned aerial vehicle position heading regression model facing the cross-view scene by using the test set. The method only depends on a visual image, can reduce the dependence degree of unmanned aerial vehicle positioning navigation on a sensor, improves the survivability in an extreme environment, uses a cross-visual-angle remote sensing map to construct simulation training data and generate the absolute orientation regression capability, can eliminate the drift problem, and also has natural advantages in the aspect of migration from simulation to a real scene. The model is light in weight and is more suitable for unmanned aerial vehicle continuous positioning and directional navigation scenes with limited computing power resources.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention discloses a neurosurgical operation virtual simulation training system based on multi-modal data fusion, and relates to the technical field of operation simulation. The data acquisition and preprocessing module acquires data such as images and force feedback, and standardized storage is performed after filtering and registration; the multi-modal data fusion module performs weighted fusion on the data by using an attention algorithm to generate a three-dimensional model; the virtual operation scene construction module generates a high-fidelity environment through ray tracing and a physical engine; the surgical operation interaction simulation module realizes accurate interaction through positioning tracking and force feedback; the training evaluation and feedback module quantifies operation performance and provides guidance; and the system management and iteration module guarantees operation and supports data updating and optimization. According to the method, a high-reality scene is constructed through multi-modal fusion, interaction and multi-dimensional evaluation help skill improvement, dynamic difficulty and personalized paths are adaptive to different users, collaborative training and privacy data collaboration are supported, operation training standardization is promoted, and the risk of errors in an operation is reduced.

The invention relates to the technical field of intelligent teaching simulation training, and discloses a multi-mode interactive teaching simulation training system. According to the system, voice, action and visual data are obtained and fused to form a unified operation behavior flow, and the unified operation behavior flow is disassembled into task sequences according to knowledge points. And extracting an interaction quality feature vector from each task segment, inputting the interaction quality feature vector into an evaluation network constructed based on a knowledge graph, and obtaining a mastering state evaluation result through layer-by-layer mapping with a standard vector. And performing link type fusion on the results to generate a skill mastering degree total score and defect distribution map, and further adaptively generating a training intervention scheme comprising a knowledge point backtracking path, an interaction complexity step and an auxiliary strategy according to the map. According to the system, refined and structured evaluation of complex operation skills is realized, and accurate personalized training guidance can be dynamically provided.

The invention discloses a network attack path automatic generation and defense strategy optimization method, system and device and a medium, and relates to the technical field of network security, and the method comprises the steps: constructing a topology mapping model, obtaining a network architecture, an asset list and a dependency relationship, constructing a visual topology chart, building a vulnerability association analysis model, and combining a vulnerability scanning result. The method comprises the steps of obtaining a vulnerability knowledge graph, optimizing a path calculation process based on the vulnerability knowledge graph, establishing a path derivation model, generating an attack chain by applying a path derivation algorithm, optimizing defense logic, formulating an optimization step model, and establishing a real-time simulation feedback model based on the generated attack chain. And performing simulation implementation on the defense strategy through a multi-level simulation training scheme, and dynamically optimizing the defense strategy according to feedback data. According to the method, the crossing from passive protection to active prediction and from single-point defense to global optimization is realized, and the accuracy and adaptive capacity of network defense are remarkably improved.

The invention provides a breast endoscopic surgery simulation training system. The breast endoscopic surgery simulation training system comprises a hardware end and a software end, the hardware end comprises a simulation human body model, and the software end is provided with a 3D mesh model corresponding to the simulation human body model; the simulation surgical instrument comprises a simulation electrotome, a simulation endoscope, simulation grasping forceps and a simulation incision protection sleeve, and the software end is provided with an electrotome model, an endoscope model, a grasping forceps model and an incision protection sleeve model which correspond to one another; the pose detection device is used for detecting the current pose of the simulation surgical instrument in real time and sending the current pose to the software end, so that interaction between the simulation surgical instrument and the simulation human body model is mapped to interaction between the instrument model and the 3D mesh model; the software end further comprises a display screen, and the display screen is used for displaying interaction between the instrument model and the 3D mesh model in real time. According to the scheme, breast endoscopic surgery training, teaching and research which are closer to a real surgery scene can be provided, so that the safety and the operation precision in an actual breast endoscopic surgery are improved.

The application discloses a shooting simulation training method and system based on trajectory prediction and position feedback, and relates to the technical field of shooting training. The method comprises the following steps: acquiring the trajectory parameters of a bullet, the state information of a firearm, and the position information of a moving target; predicting the position relationship between the bullet and the moving target within a preset time in the future based on the state information of the firearm, the trajectory parameters, and the position information of the moving target; judging whether the hit condition is met according to the position relationship; if the hit condition is met, sending a hit instruction to the signal receiving module of the moving target, and the moving target performing a hit action in response to the hit instruction. The application replaces the virtual rendering scene by introducing the spatial posture, motion information of a real firearm and the trajectory parameters of a bullet for trajectory prediction, so that the trajectory law and the hit determination are closer to the physical reality, the dependence on VR / AR equipment can be reduced, the gap between simulation training and live firing is greatly reduced, and the training effect is effectively improved.

The invention discloses a self-supervised tomographic SAR (Synthetic Aperture Radar) reconstruction method based on orthogonal subspace decomposition. According to the method, a traditional single original space is reconstructed and expanded into a Range-Null-Raw triple space collaborative learning framework, an R2R and N2N principle-based self-supervised loss function is constructed by using an orthogonal subspace decomposition characteristic of a linear measurement operator, direct mapping learning from noise measurement data to a high-quality reconstruction result is realized, and the real-time performance of a real-time reconstruction system is greatly improved, so that the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance of the real-time performance is improved. Observable components are learned through Range space, unobservable components are learned through Null space, a cross constraint strategy of global consistency is ensured through Raw space, and the adaptability of the model to real measurement conditions is enhanced. The performance gap between simulation training and actual deployment in deep learning TomoSAR reconstruction is effectively solved, and a new solution is provided for reliable application of an unsupervised deep reconstruction network in a real TomoSAR imaging task.

The invention relates to a visual physical simulation training system for power distribution equipment. The system comprises a scene construction module, a state simulation module, an instruction processing module, an interaction response module and a visual rendering module. The scene construction module constructs a simulation power distribution operation environment according to training scene configuration information input by a user, the state simulation module simulates and generates operation state data containing telemetering and telesignalling data based on the environment, and the instruction processing module verifies a user operation instruction in combination with the data and generates a simulation control instruction. The interactive response module updates the simulation environment state according to the instruction, and the visual rendering module renders the running state data to generate a training interface for displaying the dynamic change of the network. By adopting the system, the problems of fixed practical training scene, static state simulation, no operation feedback and poor visualization in the prior art can be solved, and the practical operation training effect of power distribution automation personnel is improved.

The invention relates to the technical field of intelligent teaching, and provides an engineering integrated intelligent teaching system for rail transit intelligent operation and maintenance. In the system, a teaching data center module aggregates and processes teaching data; the teaching twinborn body module constructs a three-layer twinborn model and generates a multi-dimensional teaching task; the engineering and learning integrated teaching engine module automatically arranges an optimal teaching activity sequence through reinforcement learning according to student ability portraits and teaching tasks; the element universe scene construction module constructs an immersive simulation training scene according to the sequence; the ability evolution evaluation module analyzes trainee training data and generates an ability increment graph; and the teaching feedback optimization module feeds back the atlas to the task generator to drive a new round of teaching. According to the invention, teaching dynamic generation, intelligent arrangement, accurate evaluation and closed-loop optimization in the field of rail transit intelligent operation and maintenance are realized, and the problems of insufficient intelligence, mismatching of teaching strategies, mismatching of engineering and single teaching quality evaluation in rail transit teaching are solved.

The invention provides a robot skill collaborative optimization method and system based on digital twinning and continuous learning. The method comprises the following steps: acquiring robot data by adopting a sensor and an internet of things technology; constructing a high-precision digital twinborn model, and synchronizing the high-precision digital twinborn model; robot data are input into a coevolution scheduling module for intelligent scheduling and fusion of learning tasks and experience, and a global skill strategy is obtained; carrying out reinforcement learning on the synchronous high-precision digital twinborn model by adopting an improved near-end strategy optimization algorithm based on a global skill strategy to obtain a robot prediction skill; carrying out online adaptive learning on the prediction skill of the device person by adopting a strategy deviation regular term model irrelevant element learning algorithm; an evaluation and migration module is adopted to evaluate the learned robot skills, and the evaluated skills are migrated; storing the migrated skills and experiences in a continuous learning knowledge base to form a group knowledge system; large-scale simulation training is carried out on the cloud through the digital twin technology, and the training cost and the safety risk of the entity robot are remarkably reduced.

A system and method for automatically generating immersive training scenarios for workers in an industrial environment for hazardous events in a computer simulated environment is provided. The method includes acquiring, by processing unit, information pertaining to operation of assets and related anomalies and hazards in industrial environment for which training scenarios are to be simulated, loading simulation sequence of industrial environment including assets operating at ideal behavior, wherein simulation sequence executes a first set of tasks on virtual assets within ideal behavior, defining one or morse sequence of events that lead to hazardous event in industrial environment based on acquired information, determining second set of tasks to be executed in loaded simulation sequence, simulating second set of tasks in loaded simulation sequence, and generating immersive training scenario in computer simulated environment based on execution of the first set of tasks and the second set of tasks in loaded simulation sequence.

The invention discloses an in-library sorting operation system simulation optimization method, equipment and a medium. The method comprises the following steps: step S01, constructing a simulation model of an in-library sorting operation system; s02, sorting task simulation training is carried out in the constructed simulation model based on a deep reinforcement learning model of a double-target network, and state parameters of all sorting stations in the simulation training process are obtained; s03, according to the state parameters, obtained in the step S02, of all the sorting stations, identifying nodes needing to be optimized in the production line structure of the simulation model, and according to the identified nodes needing to be optimized, constructing a shunting path to obtain an optimization scheme of the production line structure; and step S04, performing optimization reconstruction on the topological structure of the simulation model according to the obtained optimization scheme, and generating an optimized simulation model. According to the method, the sorting performance can be systematically improved, the sorting accuracy and efficiency are balanced, and meanwhile it is ensured that the optimization effect is continuous and stable.

The invention provides a control method and system for interaction between emergency simulation training and a real respirator, and the method comprises the steps: collecting control parameters of the respirator, and carrying out the preprocessing of the control parameters, and obtaining an equipment parameter vector; calculating a physiological response state based on the device parameter vector; the physiological response state comprises alveolar pressure and oxygenation estimation; based on the physiological response state, performing quantitative judgment on the disease stage of the patient to obtain a current disease stage, and executing a corresponding evolution instruction set; a medical specification operation reference table is constructed, a target interval is extracted from the current illness state stage and the evolution instruction set, the matching degree of the target interval and a reference interval is calculated, and the reference interval is extracted from the medical specification operation reference table; determining whether to continue evolution, enter relieving or mark training is completed based on the matching degree; the breathing gas of the patient is simulated and restored through the gas compressor and the gas mixing device and is conveyed to the real breathing machine.

The invention relates to the technical field of virtual reality simulation training, in particular to a shooting simulation training method and system based on virtual reality, and the method comprises the steps that a virtual training space is constructed, and dynamic light and shadow and terrain disturbance variables are injected into a visual environment model; loading a physical attribute template by the trajectory simulation engine; and loading a standard attitude sequence into the instrument action library. In training, the system captures real-time posture, instrument operation and environment interaction data of a trainee, inputs the data into a trajectory simulation engine to generate a trajectory prediction data set containing a speed curve and a deflection angle, compares the trajectory prediction data set with a standard posture frame by frame, and generates an action analysis report. According to the method, the authenticity and adaptability of training are enhanced by simulating continuously changing environmental physical interference; through full-process trajectory simulation and action traceability, accurate evaluation and deviation correction guidance of shooting operation details are realized.

The invention discloses a digital twin platform of an aircraft flight control system, a data processing method of the digital twin platform and a medium, and relates to the technical field of aircraft simulation training, and the platform comprises a parameter setting unit which is used for setting aircraft basic information and current flight state parameters; the control signal acquisition unit is used for acquiring an electric signal generated when a user operates the control device; the calculation unit is used for calculating the initial control surface aerodynamic force according to the control surface aerodynamic force calculation model, calculating the control surface deflection angle according to the control surface aerodynamic-control device force transmission model, and recalculating the control surface aerodynamic force according to the control surface aerodynamic force calculation model; and the force sensing feedback unit is used for calculating joystick feedback force in the current aircraft flight state and the current control state according to the control surface aerodynamic force-control device force transmission model, and feeding back generated corresponding control resistance to the control device. The platform not only saves the economic cost and occupied area for establishing an entity iron bird test bench, but also can test the performance of a flight control system of an airplane in different flight states.

The invention discloses an intelligent ship lock system based on digital twinning, which belongs to the technical field of ship locks and comprises a data acquisition module, a data processing module, a model construction module, a simulation training module, an analysis and prediction module and a fault operation and maintenance module. The intelligent ship lock management and control system solves the problem that an existing ship lock system cannot monitor, manage, control and operate and maintain the ship lock in real time, so that the intelligent ship lock management and control effect is poor, ship lock operation scheduling automation, infrastructure maintenance management intelligence and navigation service information transparency can be achieved, the intelligent and integrated management level of the ship lock is effectively improved, and the intelligent ship lock management and control effect is improved. The ship lockage intelligent safety management and control is achieved, ship lock intelligent gear shifting and intelligent lockage guiding are achieved in the whole ship lockage process, the lockage safety level and the passing efficiency are comprehensively improved, the scientific decision making and comprehensive display ability is provided for a management department, and the intelligent ship lock management and control effect can be improved.

According to the power grid regulation and control and relay protection semi-physical collaborative simulation training method and system, through power grid-substation joint simulation and integrated fault driving, full-link fault simulation and response from the power grid level to the device level are achieved, and collaborative training of scheduling and operation and maintenance personnel is supported; multi-mode relay protection practical training equipment is constructed, a real device, a simulation device, three-dimensional digital protection and regulation alarm signal simulation are fused, functions of device practical operation, logic unvarnished transmission, abnormal injection, operation tracing and the like are supported through different training carriers, and different levels of training requirements are met; a dynamic power flow long-period time sequence simulation module is introduced, dynamic following of power grid loads and power generation plans is achieved, the development and recovery process of faults in a real power grid environment is simulated, the authenticity, the collaboration and the systematicness of power grid fault training are remarkably improved, and the training efficiency is improved. And a key technical support is provided for the cultivation of cross-professional co-processing capability under the background of a novel power system.

The invention relates to the technical field of orthopedics teaching simulation, in particular to an orthopedics drilling technique teaching simulation device which comprises a supporting seat, an operation table arranged above the supporting seat and a model arranged at the top of the operation table, a sleeve is fixedly arranged outside a rotating shaft in a sleeving mode, and a plurality of clamping grooves which are evenly distributed are formed in the outer portion of the sleeve. An angle fixing mechanism and a reset mechanism are arranged in the top end of the supporting seat, and an operation mechanism is arranged at the bottom of the supporting seat. The simulation device has the advantages that the rotating shaft can be unlocked by stepping on any pedal, and then the operation table can be adjusted at any angle. Therefore, when the medical students carry out drilling technique training in different directions, the medical students do not need to change the positions and rotate the operation table to adjust the angle of the top model, it is ensured that the medical students can completely watch the pictures of the display screen in the simulation training process, and drilling technique training of the medical students is prevented from being affected.

The invention relates to the technical field of intelligent transportation, in particular to a state space modeling method for ship ice region navigation decision model training, which comprises the following steps of: constructing a sea ice dynamic model, and estimating an unobservable sea ice state by using unscented Kalman filtering to obtain a hidden variable vector; inputting the hidden variable vector, the observable state information and the external force field code into a Transform encoder, and outputting future sea ice center position distribution; constructing a confidence collision avoidance domain of the time index according to future sea ice center position distribution, wherein the confidence collision domain is updated in a rolling manner along with time; taking observable state information, hidden variable vectors and transformation embedding of a confidence collision avoidance domain as reinforcement learning states, and adopting near-end strategy optimization to train a sea ice dynamic model; and obtaining a ship ice region navigation decision model based on a joint termination criterion of the verification set collision rate, the arrival rate and the strategy divergence. The method improves the strategy convergence stability and the ice region safety passing rate, and is suitable for autonomous navigation and simulation training of polar regions and high-latitude sea areas.

The invention relates to the technical field of simulation training application integration frameworks based on micro-service architecture, and discloses a simulation training application integration framework method and system based on micro-service architecture. According to the simulation training application integration framework method and system based on the micro-service architecture provided by the invention, a simulation training process is divided into a preparation stage, an execution stage and an evaluation stage, and independent data storage spaces and communication rules are respectively configured, so that the isolation and controllability of task processing of each stage are remarkably improved; and inter-stage data interference is avoided. A staged resource use monitoring mechanism is introduced, a dynamic capacity expansion and contraction strategy based on service request changes, the CPU utilization rate and the memory occupancy rate is achieved, the system resource utilization rate is effectively improved, and the operation stability is guaranteed. By setting copy health detection and copy switching logic, fault copy elimination and automatic taking-over of healthy copies are realized, and the fault-tolerant capability and service continuity of the system are enhanced.

Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.