182 results about "Virtual training" patented technology

The invention discloses a child electric motorcycle intelligent safety control system based on the Internet of Things, and relates to the technical field of electric motorcycle safety control. The problem of insufficient driving risk perception and control response of children is solved. The method comprises the following steps: firstly, fusing obstacle trajectory prediction, a road adhesion coefficient and an environment contour, constructing a dynamic risk vector field, and generating a collision probability and out-of-control coefficient fused risk assessment matrix; then, the matching degree of handle operation and vehicle body response is calculated through cross attention, and a third-level behavior label is output; according to the behavior level and the risk direction, anti-sideslip control or progressive power limitation is executed, and intervention characteristics are recorded; and finally, constructing a virtual training task by using parent operation data, extracting a personalized control threshold, dynamically optimizing system parameters, and realizing a child driving behavior-oriented closed-loop safety control system.

The invention discloses a PICC virtuality and reality combined simulation training system and method, solves the problems that puncture simulation training is not real enough and students cannot perform intensive training aiming at a puncture technology due to the fact that an existing venipuncture simulation platform lacks necessary puncture force feedback, and belongs to the technical field of virtual training. The system is composed of a personal information management module, a simulation training terminal, a virtual model management module and a man-machine interaction module, and student operation information is collected in real time based on a multi-dimensional sensor; the virtual model management module reconstructs real anatomical structures such as blood vessels and skin of a human body through a high-precision 3D modeling technology, a physical engine is matched to provide a vivid puncture hand feeling, students can experience a scene very close to real operation, and the simulation training terminal can simulate the real operation through a force feedback technology in a virtual scene. The trainee can feel the touch similar to that of real puncture, so that the understanding of the trainee on the puncture process and the control of the trainee on the puncture strength and angle are improved.

The invention provides a mine safety training method and system based on digital twinning and virtual reality, and the method comprises the steps: obtaining laser scanning point cloud and geological exploration data of an underground roadway of a mine, and constructing a high-precision virtual underground environment model based on the data; on the basis of the virtual underground environment model, combined with explosion condition judgment, disaster simulation is triggered, and a virtual training environment is generated by applying real-time rendering and dynamic labeling technologies; obtaining operation behavior data of trainees in the virtual training environment, and evaluating the operation behavior data to obtain a behavior evaluation result; and based on the behavior evaluation result and the environmental parameter change data, constructing and training an evaluation model by utilizing machine learning, and generating customized training content in combination with geological conditions and mining process characteristics of different mining areas. According to the method, a high-reduction-degree virtual scene can be constructed, the problem that a traditional training scene is single is solved, quantitative evaluation of the training effect is achieved, and training pertinence and effectiveness are improved.

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 provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a new energy automobile battery pack thermal management system multi-target adaptive control method, which comprises the following steps: constructing a thermal resistance-thermal capacity dynamic network model of a battery module and a cooling flow channel, and constructing a reinforcement learning virtual training environment; constructing a multi-target composite reward function; constructing a neural network model based on depth deterministic strategy gradient, and guiding gradient updating of strategy network parameters by using a multi-target composite reward function until the neural network model converges; solidifying the trained and converged strategy network parameters, deploying the solidified strategy network parameters to a vehicle thermal management controller, and outputting the optimal cooling liquid flow rate under the current working condition; and mapping the optimal cooling liquid flow rate into an electronic water pump rotating speed instruction. The method can solve the problems that in the prior art, due to the fact that the calculation load is large, the requirement for millisecond-level real-time response under the extreme working condition is difficult to meet, limitation is caused by linear deduction of temperature distribution only depending on head and tail modules, and a dynamic adjustment strategy cannot be fed back in real time.

The invention discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

The invention discloses an intelligent driving school mode virtual driving scene generation method based on an AI model, relates to the technical field of virtual driving scene generation, and is used for solving the problems of feedback lag and poor individualized adaptability of a training scheme caused by difficulty in dynamically adapting to the current stress level and operation habit of a driver. The method comprises the steps of monitoring the instantaneous speed and direction control amplitude of a simulated vehicle in real time, calculating a longitudinal G value and a steering change rate, collecting physiological data of a tested driver to analyze heart rate variability and cognitive load degree, generating driving behavior characteristics and stress characteristics by using an LSTM model, and judging whether to trigger a specific scene or not through a support vector machine based on the characteristics. The method comprises the following steps of: acquiring response time and misoperation frequency, generating a risk response score through fusion function joint quantification, and judging a driving level grade according to a score threshold value, thereby realizing screening and marking of tested drivers, dynamically adapting to actual performance, and improving pertinence and effectiveness of virtual training.

The invention relates to an AR projection interaction treadmill virtual scene generation method and device, equipment and a medium. The method comprises the following steps: acquiring motion inertia data and running belt motion parameters of a user on a running machine; respectively carrying out motion state extraction and gait phase extraction according to the motion inertia data and the running belt motion parameters to obtain a current motion state vector and gait phase information of the user; inputting the motion state vector and the gait phase information into a pre-trained motion state prediction model to obtain a preset predicted motion state of a plurality of frames in the future; based on the predicted motion state, performing layered scene generation through a scene generator to obtain a corresponding virtual scene; and performing projection mapping transformation processing on the virtual scene, and obtaining real-time AR projection data in combination with low-delay compensation. By adopting the method, a highly immersive and interactive virtual training environment can be provided in an AR projection mode.

The invention relates to the field of virtual reality, in particular to an adaptive virtual reality cognitive training method and system. The method comprises the steps of obtaining a user individual information set and a real-time multi-modal physiological data stream, performing real environment three-dimensional reconstruction and relative and friend face modeling based on the user individual information set, and generating an individual virtual training scene; based on the real-time multi-modal physiological data flow and the individualized virtual training scene, nerve-behavior feature extraction and digital twinning dynamic modeling are carried out, and a user cognitive state evaluation information set is generated; based on the user cognitive state evaluation information set, training task parameter real-time optimization and virtual scene element dynamic adjustment are carried out through an adaptive strategy, and a cognitive enhancement intervention instruction set is generated; and based on the cognitive enhancement intervention instruction set, performing neural regulation intervention and training data closed-loop feedback, and generating and outputting an individualized cognitive training report. According to the method and the device, accurate self-adaptive intervention is realized in a virtual reality cognitive training process.

The invention relates to an acupuncture rehabilitation training data processing method based on the combination of VR and digital therapy, and the method comprises the steps: obtaining an acupuncture stimulation signal and a VR training interaction signal, carrying out the synchronous association of the two signals based on a unified time reference, and dynamically determining a signal processing priority according to the state of a patient when the acupuncture operation and a virtual training task conflict, carrying out backtracking calibration on the offset signal; the task action rhythm, force and completion condition of a patient are monitored in real time in VR rehabilitation training, data of abnormal behaviors are collected and recorded, whether the abnormal behaviors are caused by acupuncture stimulation or not is analyzed by combining acupuncture stimulation parameters and time sequence information, and data confirmed to belong to acupuncture effect interference are marked as independent samples; real-time subjective feedback information of a patient is collected in a VR scene, subjective indexes of comfort, aching feeling and fatigue are associated with physiological and behavior data, analysis weights of corresponding data segments are adjusted according to feedback, and an individualized rehabilitation data processing strategy of the patient is formed based on historical feedback data.

The invention belongs to the technical field of tunnel construction, and particularly relates to a tunnel construction ventilation intelligent regulation and control method based on electrical automation. Distributed sensor arrays are deployed on a tunnel driving working face, a haulage roadway and a fan station through a multi-source heterogeneous sensor network, ventilation loads, environmental parameters, equipment states and construction event signals are collected in real time, and real-time and accurate original data support is provided for the system; a three-dimensional fluid mechanics-particulate matter diffusion coupling simulation model of a tunnel ventilation system is constructed, then a digital twin is used as a virtual training environment, an optimization instruction is converted into an electrical control signal, and a closed-loop PID-fuzzy coincidence controller dynamically compensates execution delay, controls operation of a fan and an air valve, and finally performs dynamic simulation on the tunnel ventilation system. And an execution result is fed back to the digital twin body for model calibration, so that finer and more efficient ventilation control is realized, the use problem in a high-altitude and alpine region environment is solved, the continuous effectiveness of ventilation regulation and control is effectively guaranteed, and the overall performance is improved.

The invention relates to the technical field of virtual training, in particular to a firefighter search, rescue and escape virtual training method and system, and the system comprises a virtual scene dynamic construction module which abstracts a room and a corridor intersection into nodes according to a planar graph of a building, abstracts a door and a channel into edges with geometric distance weights, and carries out the virtual scene dynamic construction module; and responding to a fire spreading or structure collapse event in the virtual training, adjusting the weight of a corresponding edge to be infinite, obtaining a shortest path, and establishing a reference rescue path set. According to the method, through dynamic analysis of building plan structure information, rooms and corridor intersections are abstracted as nodes, doors and channels are abstracted as edges with geometric weights, the accessibility of the edges is adjusted in real time after fire spreading or structure collapse occurs, impassable conditions are introduced during path calculation, and the path calculation accuracy is improved. And the real-time adaptability and rescue simulation precision of the path generation process are improved.

The invention relates to the technical field of medical training, and discloses a sputum suction operation training method and system based on a virtual reality technology. The method comprises the steps that historical operation records of a trainee are collected, key operation nodes are extracted, and an airway state model of a virtual patient is constructed based on the nodes; secondly, setting a virtual sputum suction equipment model, performing a multi-working-condition simulation test on the virtual sputum suction equipment model to obtain operation response characteristics, and mapping the equipment model into the virtual airway state model; then calculating a secretion distribution parameter of a virtual airway in a training period and a theoretical clearance load of virtual equipment, and combining operation response characteristics to obtain an operation intensity parameter; generating an operation triggering instruction sequence based on the operation intensity parameter, and performing staged operation control on the virtual equipment according to the sequence; and finally, carrying out quantitative evaluation on the operation effect of the virtual equipment in the virtual airway. According to the method, a targeted virtual training scene can be constructed, a real operation response is restored, and accurate tracking and effect quantitative evaluation of the operation process are realized.

The embodiment of the invention discloses a cerebral apoplexy upper limb rehabilitation training system and method based on dynamic reward feedback. A data acquisition module is used for acquiring physiological signals, motion signals, emotional state data and training performance data; the motion intention decoding module is used for performing motion intention feature extraction and reliability evaluation on the physiological signals according to an improved MREE-Net + + fusion algorithm, performing weighted fusion after feature weights are adjusted according to a reliability result, obtaining a unified motion intention feature vector, performing motion intention decoding, and obtaining a motion intention intensity index and a motion intention vector; the dynamic reward decision module is used for processing the facial micro-expression data according to an improved VGG-Face model to obtain an emotion titer, and obtaining an emotion awakening degree according to the voice signal; a reward action is generated according to an improved deep reinforcement learning algorithm; and the adaptive training regulation and control module is used for generating a personalized virtual training scene according to the generative adversarial network and regulating and controlling the training intensity according to the fatigue index. The rehabilitation training effect can be improved.

The invention provides an aircraft refueling virtual-real combined practical training method and platform based on AR and scrapped equipment, and relates to the technical field of aircraft refueling training, and the method comprises the steps that a processing module loads digital twin scene data according to a training subject; the AR display module carries out space registration and superposition on the virtual airplane model, the virtual instrument interface and real scrapped refueling equipment; collecting operation action data of the trainee in real time; inputting the operation action data into the digital twin model, updating the state information of the virtual refueling operation, and sending the state information to an AR (Augmented Reality) display module; the AR display module displays the state information in an overlapping manner; when the butt joint operation is carried out, the relative position and posture of the physical joint and the physical refueling panel are monitored in real time, the correctness of the operation is judged, and the result is displayed through the AR display module; and the processing module records, analyzes and generates a quantitative evaluation report. The method can effectively solve the problems that pure virtual training lacks sense of reality, and pure physical training is high in cost and high in risk.

The invention provides an anesthesia risk simulation method and simulator based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: constructing a risk event sample database in anesthesia; carrying out generative learning by utilizing the risk event sample database, and embedding an emergency generative model into a virtual reality simulation platform; collecting initial force feedback data of initial interaction operation of a user according to a force feedback interaction module in the simulation scene, and calling an emergency generative model to simulate a risk event chain based on the initial force feedback data in the simulation scene; recording operation track data of the user in the simulation scene under the risk event chain; and performing simulation operation scoring on the user, and outputting a simulation operation scoring report. Through the method and the device, the technical problem that the actual combat ability of a user to cope with a complex clinical risk event is further affected due to the fact that a virtual training platform is seriously insufficient in the aspects of simulation authenticity, skill evaluation accuracy and the like in the prior art can be solved.

The invention belongs to the technical field of bioengineering, and particularly relates to a biomass fermentation safety reinforcement learning control method and system, and the method comprises the steps: training an initial strategy network through imitation learning by employing historical data, and providing safety weight initialization for a reinforcement learning agent; and constructing a virtual training environment fusing the mechanism model and the data-driven residual error correction network, applying domain randomization, training an agent in the environment to maximize long-term accumulated rewards, and obtaining a robust control strategy. A training strategy is deployed to a real fermentation system, a control instruction is corrected in real time through an independent safety layer according to a hard constraint rule to ensure operation safety, and meanwhile online fine tuning and updating are conducted on the strategy based on actual operation data. According to the method, safe and smooth transition from virtual training to practical application is realized, and the final ethanol concentration and the overall control performance in the fermentation process are remarkably improved.

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.

A system for generative artificial intelligence based adaptive training. The system includes an electronic processor. The electronic processor is configured to receive a prompt to generate a virtual training assistant, the prompt including a description of one or more training aids and a format for responses corresponding to the one or more training aids. The electronic processor is also configured to retrieve, using a retrieval model, personnel information from a personnel database, retrieve, using the retrieval model, knowledge information corresponding to the personnel information from a knowledge database, and augment the prompt to include a compliance rule regulating responses of the virtual training assistant. The electronic processor is also configured to generate, using a generator model, the virtual training assistant based on the prompt and configured to generate responses corresponding to the personnel information, the knowledge information, the responses being in compliance with the compliance rule.

The invention relates to the technical field of rail traffic safety operation and maintenance, in particular to a rail traffic training method and system based on an AR intelligent head-mounted terminal, and the method comprises the steps: classifying rail traffic equipment into high-risk equipment and common equipment based on a preset fault library; generating a virtual training scene aligned with the physical space coordinates; based on a preset student skill portrait, a preset fault library and a preset operation instruction library, configuring a modular training course in the virtual training scene; acquiring operation data when a student executes the modular training course through an AR head-mounted terminal, wherein the operation data comprises spatio-temporal trajectory data, decision delay and an interaction log; based on the spatio-temporal trajectory data and the decision delay, a compliance score is generated through fusion; and generating a skill short board positioning report. The method has the advantages that effectiveness and safety of operation training of the rail transit equipment are improved, and high-precision positioning and targeted strengthening of short skill of trainees are achieved.

The invention discloses an optimization method for senile cognitive impairment intervention. The optimization method comprises the following steps: S1, a multi-modal evaluation stage: acquiring neurophysiology, behavior and language data of a patient through a multi-source sensing module; s2, personalized prescription generation: calculating a comprehensive cognitive impairment index (CCI), and generating a personalized cognitive training scheme; s3, the intervention module executes the steps that a virtual training scene is generated in the AR environment; and S4, effect verification and iteration: generating a neuroplasticity evaluation report after training, and quantifying the parahippocampal gyrus-prefrontal cortex function connection enhancement degree. Neurophysiology, behaviors and language data of a patient are synchronously collected by adopting a multi-source sensing module, and a personalized cognitive training scheme is generated based on a comprehensive cognitive disorder index. Meanwhile, a virtual scene is constructed to execute an intervention module, and the parahippocampal gyrus-prefrontal lobe function connection enhancement degree is quantitatively analyzed after intervention, so that an evaluation-decision-intervention-verification closed-loop system is constructed, and the intervention effect of the senile cognitive impairment is remarkably improved.

The invention relates to the technical field of data analysis, in particular to a VR-based CT room operation virtual training method and system. The method comprises the following steps: acquiring an equipment model and a patient model in current CT training operation; analyzing a relative position relationship between the equipment model and the patient model at the initial position to obtain an initial abnormal value; analyzing posture changes of a plurality of limb parts of the patient model before and after the limb adjustment operation to obtain a plurality of posture anomaly indexes; according to the initial abnormal value, analyzing the change of an overlapping region between a plurality of limb parts of the patient model and the equipment model in the examination process of the current CT training operation, and obtaining a plurality of position abnormal indexes; and obtaining an operation evaluation result of the current CT training operation according to the plurality of attitude anomaly indexes and the plurality of position anomaly indexes. According to the invention, the evaluation accuracy of the abnormal operation condition of the CT training operation can be improved.

The invention discloses a brain-computer interface-based hand function rehabilitation auxiliary training method, which belongs to the technical field of rehabilitation training, and specifically comprises the following steps of: acquiring an original electroencephalogram signal of hand action of a patient, and synchronously recording an actual movement track of a healthy side hand; separating the original electroencephalogram signal into an action intention signal and an action control signal; activating a mirror image hand model in the virtual training scene based on the action intention signal, and mapping the action control signal into a three-dimensional motion parameter; dynamically correcting the joint movement range of the mirror image hand model according to the actual movement track of the uninjured side hand, and generating virtual hand actions matched with the physiological features of the patient; extracting track key point coordinates of the mirror image action and a joint angle change sequence, and converting the track key point coordinates and the joint angle change sequence into a driving instruction of affected side exoskeleton equipment; the exoskeleton resistance change is fed back to the virtual training scene in real time, and the motion damping of the mirror image hand model is adjusted; according to the invention, personalized precise assistance of hand function rehabilitation training is realized, and the adaptability and effectiveness of training are improved.

The invention provides a goaf tunnel surrounding rock stability progressive evaluation method and system, and relates to the technical field of geotechnical engineering. The method comprises the following steps: constructing a virtual training sample set by utilizing an optimization process fused with active learning based on goaf and tunnel engineering key parameters and a Knoth time function; establishing a two-stage prediction architecture comprising a rapid screening model and an accurate rechecking model; carrying out risk primary judgment and shunting on the target section by utilizing a rapid screening model; for a section needing to be rechecked, outputting a surrounding rock safety coefficient and a probability prediction value of convergence displacement by using an accurate rechecking model in combination with Bayesian inference; outputting a differentiated support scheme in combination with the multi-level decision matrix; and executing parameter reverse reconstruction and model increment evolution based on the form deviation index of the field actual measurement data and the prediction data. The problem that the stability of the surrounding rock is difficult to accurately quantify under the small sample condition is solved, and self-adaptive evolution of an evaluation system is realized.

The invention relates to an unmanned ship intelligent navigation method and system based on deep reinforcement learning, and the method employs the construction of a virtual training environment to cooperate with the real-time sensing data collected by a real unmanned ship sensing system, thereby greatly reducing the training cost and risk. The autonomous navigation intelligent agent trained by using a deep reinforcement learning algorithm improves the adaptive and decision-making ability of the unmanned ship to a complex environment, reduces the dependence on a communication link, and improves the motion precision and stability of the unmanned ship.

The invention relates to the technical field of exercise rehabilitation, and discloses a hemodialysis patient exercise rehabilitation virtual training platform which comprises a dialysis state acquisition module used for acquiring dialysis parameters reflecting the dialysis tolerance state of a patient in real time in the hemodialysis process; the mapping module is connected with the dialysis state acquisition module and is used for mapping the dialysis parameters into virtual training constraint parameters; according to the hemodialysis patient exercise rehabilitation virtual training platform, dialysis parameters reflecting the dialysis tolerance state of a patient are collected in real time in the hemodialysis process, the dialysis parameters are mapped into virtual training constraint parameters, the action amplitude, the action rhythm and the training process in virtual training are controlled to be executed, and the hemodialysis patient exercise rehabilitation virtual training platform has the advantages that the training efficiency is improved; the virtual training content can be dynamically adjusted along with the change of the hemodialysis state, so that potential safety hazards caused by adopting a fixed training scheme are avoided, and the safety of exercise rehabilitation training during dialysis is improved.

The invention relates to the technical field of small sample unmanned aerial vehicle fault diagnosis, in particular to a small sample unmanned aerial vehicle sensor fault diagnosis method based on multi-auxiliary task learning. Comprising the following steps: S1, obtaining an original unmanned aerial vehicle sensor fault task and a training data set thereof, generating a plurality of auxiliary tasks according to fault categories in the training data set, taking a unique fault category corresponding to each auxiliary task as a target category, and generating a virtual training set of each auxiliary task; s2, training the virtual training set through auxiliary task learners corresponding to the auxiliary tasks to complete separation of a target category and a non-target category; s3, after category feature learning training of the corresponding target category of each auxiliary task is completed, performing weighted fusion on all the auxiliary tasks, learning fusion features through a general task learner to obtain a general task model, and outputting a prediction score of each sample on each fault category; and S4, optimizing the general task model.

The invention relates to the technical field of environment modeling, in particular to an underground safety production simulation modeling method based on the VR technology, and the method comprises the steps: obtaining all RGB images of an underground scene and corresponding depth images, and the attitude information and position coordinates of a camera when each frame of RGB image is shot, so as to generate a dense point cloud, and constructing a triangular mesh model; calculating the screening value of each point cloud, and obtaining the feature point cloud of each triangular patch; and obtaining all candidate images of each triangular patch, determining a mapping deviation value, a texture distortion degree and a quality evaluation value of each triangular patch in each candidate image, screening the candidate images of each triangular patch, performing texture mapping on the triangular mesh model, and importing the texture mapping result into a VR engine to construct a virtual underground environment. According to the method, the overall effect of texture mapping of the model can be improved, so that a highly vivid virtual underground scene is constructed, and the immersion and reality of underground scene safety production virtual practical training are enhanced.

The present invention relates to an electronic apparatus which may dynamically select a hyperparameter configuration via which the highest utility can be expected, by: for each of a plurality of virtual training conditions that assumes that a machine learning model is trained for each of a plurality of candidate epochs by using each of a plurality of hyperparameter configurations, calculating each predicted utility value for improvement in an actual performance value of the machine learning model by considering a training cost, and then performing an optimization procedure for selecting an optimal hyperparameter configuration from among the plurality of hyperparameter configurations on the basis of the predicted utility value and selecting an optimal epoch from among the plurality of candidate epochs.