285 results about "Virtual training" patented technology

The invention relates to the technical field of power distribution network recovery, in particular to an urban power distribution network toughness recovery management system under extreme weather, which comprises the steps of acquiring voltage, current, temperature, humidity and wind speed in real time, cleaning, formatting and standardizing data, identifying abnormal conditions influencing the stability of a power grid, evaluating the performance of the power grid under the extreme weather, and managing the toughness of the power distribution network under the extreme weather. An optimization suggestion is provided, various extreme weather scenes are simulated, feedback data are generated, comprehensive risk assessment is carried out, a coping strategy is generated, a fault point needing emergency processing is identified, recovery resources are dispatched, and a result is analyzed in real time to dynamically optimize an operation strategy of a power grid; according to the invention, by introducing the scene simulation and virtual training module, complex dynamic scene simulation is carried out, and coping strategies are verified and optimized. Feedback data generated by the system in simulation is used for further optimizing strategies in the toughness evaluation and decision support module, and adaptability and effectiveness of the strategies in actual extreme weather events are ensured.

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 model training system, a model training task execution method, a model training task execution device and a medium, a management cluster in the model training system can obtain real-time state data when a training cluster executes a model training task, and the model training task is executed through the real-time state data. The state of the training cluster during execution of a model training task in a set time period in the future is predicted, the management cluster determines simulators corresponding to the devices included in the training cluster, and the virtual training cluster corresponding to the training cluster is initialized through the simulators. The management cluster generates a plurality of execution strategies for the virtual training cluster according to the prediction state, simulates and executes the model training task through the virtual training cluster according to at least a part of the execution strategies in the execution strategies, obtains performance indexes corresponding to the at least a part of the execution strategies, determines a target strategy according to the obtained performance indexes, and sends the target strategy to the virtual training cluster. And the model training task is executed according to the target strategy, so that the efficiency of the whole training cluster is effectively improved.

The invention provides an intelligent driving skill training method and system based on behavior cloning and reinforcement learning, and relates to the technical field, and the method comprises the steps: collecting the driving behavior data of a human driver, dividing a training set and a verification set to train a behavior cloning model, and obtaining an initial driving model; and determining to-be-optimized driving skill indexes such as safety, efficiency and comfort according to the performance of the initial model, and constructing diversified training scenes in the virtual training environment. And performing reinforcement learning training on the initial model through a deep reinforcement learning algorithm by using a reward function containing the indexes to obtain an optimized driving model. The optimized model is tested in the test scene set, and when the safety index, the efficiency index and the comfort index all reach preset threshold values, training is completed. By combining behavior cloning and reinforcement learning, the comprehensive performance of the intelligent driving model in the aspects of safety, efficiency, comfort and the like can be effectively improved.

The invention belongs to the technical field of virtual reality, and discloses a virtual rich real environment training system based on benign stress, comprising: a database module for storing cognitive tasks and virtual scenes; the acceptance testing module is used for testing a preset cognitive task of the user and generating a scene acceptance index; and the initial training task generation module is used for generating a training task comprising two groups of scene parameters and one cognitive task according to the index. And the training execution module is used for constructing a dynamically changing virtual training scene according to the two groups of parameters and executing a cognitive task in the virtual training scene. And the training task updating module is used for generating a new training task based on the execution result and the acceptability index. And the training planning module formulates an overall training plan and controls an execution process. Personalized training content is provided by evaluating individual differences of the users, it is ensured that the training difficulty is matched with the capabilities of the users, and the training effect is optimized while the user participation degree and the possibility of insisting in training for a long time are improved.

The invention discloses an interactive control method for virtual simulation training of electric power operation and maintenance, and particularly relates to the technical field of electric power operation and maintenance simulation, which comprises the following steps of: acquiring operation behavior data of students, performing real-time capability grading on the students by utilizing a multi-dimensional capability evaluation model, and performing real-time capability grading on the students; a difference adaptation parameter model is constructed in combination with the difference between the virtual device and the target actual device, training content with corresponding difficulty is matched from a preset training task library, a knowledge graph of students is constructed, and a personalized training path is generated through a path recommendation engine. Iteratively updating the training difficulty and the recommended path according to the newly generated behavior data and the performance of the trainee in the aspect of the difference adaptive capacity, constructing a difference adaptive parameter model, bringing the difference into a training task adjustment and capacity evaluation mechanism, dynamically adjusting the training task parameters to fit the actual equipment complexity, and evaluating the performance of the trainee in the aspect of the difference adaptive capacity. And a migration barrier between virtual training and real operation and maintenance is broken through.

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 provides a virtual training and evaluation method and device for intelligent focusing of an operating microscope, and relates to the technical field of intelligent control of operating microscopes, and the method comprises the steps: constructing a virtual operation scene based on real operation video data; obtaining a current virtual microscopic texture image which is collected by the virtual camera at the current position and aims at the virtual operation target; determining a region of interest in the current virtual microscopic texture image, and calculating the definition of the region of interest; based on the definition of the region of interest, controlling the virtual camera to perform focusing motion to obtain a focusing distance after the position of the virtual camera is updated; and calculating performance index data based on the real focusing distance and the updated focusing distance so as to evaluate the focusing algorithm of the operating microscope based on the performance index data. According to the method, organic unification among virtual operation scene depth, microscope motion control and image quality evaluation is realized, and a safe and reliable quantitative evaluation means is provided for development of an intelligent focusing algorithm of the operation microscope.

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 embodiment of the invention provides a robot autonomous programming system and method based on reinforcement learning, and belongs to the technical field of robot automation. The system comprises an autonomous programming engine module, a simulation environment training module, a real environment migration module, a sensor feedback and data processing module, and a controller and execution module. The autonomous programming engine module is used for converting a high-level task instruction into a low-level action program; the simulation environment training module is used for providing a virtual training platform and reinforcement learning training; the real environment migration module is used for migrating the strategy learned from the simulation environment to the real environment; the sensor feedback and data processing module is used for collecting and processing various sensor data of the robot; and the controller and execution module is used for receiving the instruction from the autonomous programming engine module and controlling the robot to execute the task. According to the method, the complexity of robot programming can be reduced, and the flexibility and applicability of a robot system are 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 VR-based power distribution network maintenance virtual training system and method in the technical field of electric power. A VR-based power distribution network maintenance virtual training system comprises a modeling module, a database module, a training platform module, an operation learning module, a comprehensive training module and a training evaluation module. A three-dimensional virtual environment is created for trainees through the VR technology, so that the trainees can feel the actual operation scene and the fault condition of the power distribution network in an immersive manner; students can deeply understand the structure and the working principle of the power distribution network and various phenomena and details when faults occur from different angles; in the virtual training environment, the trainee can perform various dangerous operations and fault processing exercises without causing any actual threat to the safety of the trainee. In combination with actual cases and data in a power distribution network fault database, the system can intelligently adjust the difficulty of training contents and fault scenes, so that the training contents and the fault scenes are matched with the ability of students.

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 provides an intelligent drill evaluation method and system based on a large model and RAG, a medium, a program product and a terminal. The method comprises the steps that a virtual training scene is constructed based on a large language model; the trainee carries out dialogue interaction with the virtual customer portrait in the virtual training scene, and dialogue interaction data is collected in real time; and analyzing and feeding back the virtual customer and the dialogue interaction data by adopting RAG, and evaluating the comprehensive ability of the trainee based on a multi-dimensional evaluation index. According to the invention, trainees are helped to identify portrait characteristics of virtual customers, determine questions possibly concerned by the portrait customers and provide answering strategies; and the professional ability of the trainee is comprehensively evaluated from multiple dimensions of answer fluency, objection question processing, portrait question pertinence, customer emotion processing, insurance professional knowledge and the like, so that the accuracy and fairness of evaluation are ensured. According to the method, virtual customer portrait analysis is combined with insurance industry information and enterprise private domain knowledge, the professional ability and service quality of trainees can be comprehensively improved, and a multi-dimensional evaluation mechanism is of great significance to improvement of customer service quality and satisfaction.

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 provides a management method and system suitable for enterprise training, and belongs to the field of enterprise training. The management method suitable for enterprise training comprises the following steps: S1, establishing an individual occupational development planning file; s2, evaluating a result according to the occupational quality; s3, creating a virtual training scene; s4, designing training course content, and providing immersive training experience; s5, performing real-time feedback and evaluation; according to the method, the virtual reality technology is utilized, a more real and vivid training environment is provided for employees, the learning interest and participation degree of the employees can be improved, and the training effect is improved; multi-person online cooperative training is supported, and the communication and cooperation ability between employees can be enhanced; learning conditions of employees are fed back and evaluated in real time, training strategies can be adjusted in time, and the training effect is improved; the virtual training scene is created, so that more immersive and interactive training experience can be provided, and the learning effect and the participation degree of students can be improved.

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 technical field of agents, in particular to an agent behavior generation system and method supporting skill drilling, and the method comprises the steps: obtaining environment data and interaction object features of an agent, constructing a multi-dimensional environment model, and carrying out the real-time updating; in a multi-interaction object and multi-device interaction scene, distributed control and collaborative learning are adopted to realize behavior coordination and information sharing among intelligent agents. Constructing an adversarial and collaborative training mechanism, and enabling the intelligent agent to optimize skills in an interaction process; and based on deep reinforcement learning, in combination with preset skill rules, interaction objects and environment data, intelligent agent behaviors are generated, including self actions, sound and picture contents, target reasoning, rule selection, task planning and exception handling. According to the method, the adaptability and decision-making ability of the intelligent agent in a complex environment are improved, the interaction intelligence and the skill drilling effect are enhanced, and the method is suitable for the fields of virtual training, intelligent interaction, automatic control and the like.

The invention discloses a power distribution network fault maintenance VR scene processing method and training system in the technical field of electric power, and the method comprises the following steps: collecting the fault types of a previous power distribution network, building a power distribution network fault database, and carrying out the training classification; establishing a power distribution network fault simulation unit, and performing power distribution network fault scene simulation according to the data of the power distribution network fault data; the VR device generates a corresponding virtual training scene, and the student wears the VR device to prepare for training; real-time data transmission is carried out during VR equipment operation, and student operation data is compared with internal data of a power distribution network fault database. Through the virtual training scene generated by the VR equipment, the real-time data transmission during the operation of the VR equipment and the comparison with the internal data of the power distribution network fault database, operation feedback can be provided for students in time, the students are helped to rapidly improve the fault maintenance skills, and errors and mistakes in actual work are reduced.

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 provides a GIS equipment partial discharge detection system based on deep learning, and the system comprises an edge layer which is used for collecting sensing data collected by each sensor node, extracting a sensing feature vector, and transmitting a compressed waveform fragment; the fog calculation layer is used for determining defect positions by using a time difference of arrival algorithm according to the sensing data uploaded by the edge layer, and obtaining defect types and confidence coefficients according to the compressed waveform fragments and the sensing feature vectors in combination with a double-flow deformable attention convolutional network model; and the cloud platform is used for updating parameters of the double-flow deformable attention convolutional network model by adopting a meta-learning method according to the defect position, the defect type and the confidence which are uploaded by the fog computing layer in combination with virtual training data generated by the digital twin simulation model, and sending the updated parameters to the fog computing layer. The GIS equipment partial discharge detection system provided by the invention is light in weight and low in cost.

The invention relates to the technical field of artificial intelligence and automatic driving, in particular to a reinforcement learning-based virtual edge scene automatic generation system and a reinforcement learning-based virtual edge scene automatic generation method, comprising a generative adversarial network module for generating a virtual scene by using a dual-channel generator and a discriminator, an interaction module for inputting the scene into a driving strategy model and obtaining an evaluation result, the reinforcement learning module automatically adjusts scene generation weight, reduces manual intervention and improves training efficiency and generation quality, the scene screening module evaluates and screens a high-quality scene based on a deep neural network, the scene reconstruction module adaptively reconstructs a low-quality area, and the multi-modal fusion module integrates multi-source environment constraint data. An innovative double-channel confrontation generation mechanism optimizes scene authenticity and dangerousness at the same time, the quality and practicability of the generated scene are improved, the system fuses multidisciplinary knowledge such as the generative confrontation network, reinforcement learning and the deep neural network, and an efficient and practical virtual training scene is provided for automatic driving.

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 present application provides a data management system and method for stoma nursing teaching, which relates to the field of nursing teaching. It first stores the identity information of nursing students and teachers, as well as stoma nursing teaching video collections and virtual training tasks, then collects and transmits the training operation video of the target nursing student object during the virtual training process, and then performs a core information significant interactive analysis based on the training operation characteristics and the skeletal point position characteristics on the received training operation video to obtain the stoma nursing training feedback result of the target nursing student object, and finally generates the training operation suggestion based on the obtained stoma nursing training feedback result, and displays and stores it. In this way, not only can the teaching effect be improved and the cost be reduced, but also the risks in the practice of nursing students are avoided and the safety of teaching is enhanced. Nursing students can use virtual training to improve the proficiency and accuracy of stoma nursing operations, thereby better serving patients and improving patients' trust and satisfaction.

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 discloses a safety production virtual training method and system for a mine underground scene, and the method comprises the steps: creating a model library, and storing a preset part model through the model library; based on the mine underground mine hole excavation design drawing, the spatial orientation of each component model is obtained, and a virtual practical training scene of the mine underground is generated; based on a received practical training instruction, a virtual practical training scene is obtained from the scene library according to the practical training instruction, and the virtual practical training scene is displayed on the interaction module; adjusting and transforming the virtual practical training scene based on an operation feedback signal of a practical training miner, labeling the component model with a high danger coefficient in the virtual practical training scene, judging the safety condition of each component model under the mine, calculating the average labeling number in the virtual practical training scene, and comprehensively judging the safety coefficient of the mine. The system has the advantages that the mine underground environment is simulated through the virtual reality technology, miners are helped to carry out safety operation training under the risk-free condition, and the safety consciousness and the emergency response capacity of the miners are 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 discloses a VR fire-fighting training system, method and device based on Unity, and the system comprises a scene simulation module, a user interaction module, a personalized feedback module and the like, the system simulates a plurality of immersive fire virtual training scenes based on a Unity engine, and deploys a VR fire-fighting training system; receiving a user gesture or a controller instruction by means of VR equipment, and guiding the user to perform interactive training in the immersive fire virtual training scene; in user interaction training, the system records and intelligently evaluates user interaction training performance, and individually adjusts and optimizes an immersive fire virtual training scene according to the user performance. According to the invention, a vivid fire environment is created, a user can feel the temperature, smoke and sound of a fire scene in an immersive manner, a strong crisis feeling is formed, and the highly immersive experience can help the user to master fire-fighting knowledge more quickly and make a correct judgment when a fire occurs in reality.