112 results about "Virtual patient" patented technology

The invention relates to the technical field of auxiliary training, and discloses an AR auxiliary training system for improving the diagnosis and treatment capability of general practitioners on undifferentiated diseases, and an immersive training environment is constructed by adopting an augmented reality technology. The system comprises an environment sensing module, a space positioning module, a case data module, a virtual rendering module, an interactive acquisition module, an intelligent response module, a state control module and an evaluation calculation module. The environment sensing module adopts an RGB-D camera and an inertial measurement unit to collect environment and head movement data of a user; the spatial positioning module determines a virtual patient placement position based on an SLAM algorithm; the virtual rendering module renders a three-dimensional virtual patient in a real environment; the interaction acquisition module supports voice and gesture multi-mode interaction; the intelligent response module generates an intelligent patient answer; the evaluation calculation module carries out comprehensive evaluation from three dimensions. The invention provides a highly immersive and intelligent training environment, and has the advantages of low cost, reusability and high standardization degree.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

The invention relates to the technical field of intelligent education, in particular to an AI-based nursing teaching scene construction and evaluation system. According to the system, a dynamic case script is generated through historical case data, and a virtual patient physiological model is constructed; creating three teaching scenes of community follow-up visit, emergency rescue and traditional Chinese medicine nursing fusion by using a mixed reality technology; a personalized learning portrait of the student is constructed through a pre-test, and accurate matching between the ability of the student and the teaching content is formed; a double-track system skill training module is built, and AI tutor guidance and teacher guidance are integrated; and an ability evaluation system based on operation data is established, and intelligent pushing of adaptive training content is realized. The problems that in traditional nursing teaching, scene authenticity is insufficient, personalized training is insufficient, and clinical thinking is difficult to cultivate are solved, and the clinical operation skill and decision judgment ability of students are improved.

The invention relates to the technical field of medical education, in particular to a virtual patient skin care teaching system with enhanced pathological features and a method thereof, the system comprises pathological feature generation, parameter control, physical modeling, intelligent evaluation and a multi-mode feedback module, and can edit and simulate various skin pathological features in real time, such as pressure sores and diabetic feet, so that the system can be applied to the field of medical education. The visual display of the lesion degree is realized through a five-segment color mapping system, the system also simulates percolate reflection and cuticle spalling, the sense of reality is enhanced, the intelligent assessment module provides risk assessment and healing prediction according to physiological and morphological parameters, the multi-modal feedback module integrates visual sense, tactile sense and user behavior feedback, the interactive experience is improved, and the system is suitable for popularization and application. And the adaptive optimization module dynamically adjusts system parameters according to user feedback and learning effects, and provides personalized teaching. Compared with a traditional method, the method is higher in parameter control precision, supports personalized teaching, and is of great significance to medical education.

The invention relates to the technical field of medical teaching assistance, and provides an artificial intelligence-based medical virtual patient simulation system, which comprises a virtual patient generation module, which is used for carrying out pathological fusion analysis on disease model data and an individual difference database based on an artificial intelligence model to obtain personalized pathological feature vectors, and carrying out spatial projection on the personalized pathological feature vectors through a spatial projection algorithm, generating a virtual patient based on the pathological feature vector, wherein the virtual patient is used for indicating feedback of a corresponding clinical operation instruction set; the real-time interaction module is used for acquiring and analyzing a clinical operation instruction set to obtain a virtual physiological parameter disturbance value, and performing dynamic deduction on the pathological feature vector according to the virtual physiological parameter disturbance value to obtain physical sign data of a virtual patient; and the intelligent evaluation feedback module is used for calculating a spatial similarity score of the clinical operation instruction set and the standard data set based on the multi-dimensional evaluation matrix, and generating a clinical operation quality report based on the spatial similarity score and the physical sign data. The quality and effect of medical education and training can be improved.

The invention discloses a medical training evaluation method and system based on an intelligent simulation patient, and relates to the field of artificial intelligence technology and medical simulation, and the method comprises a virtual patient model construction step, a multi-modal perception step, an intelligent interaction step and an intelligent evaluation step. By integrating voice recognition, natural language understanding, facial expression recognition, gesture recognition and virtual reality technologies, a high-simulation and high-interactivity intelligent patient model is constructed for medical students or clinical medical staff to perform diagnosis and treatment training and skill operation assessment. The virtual patient model supports custom information such as age, gender, race, medical history, living habits and the like, different virtual patient images and different pathophysiological states are generated, and personalized learning requirements of users are met. In addition, targeted training suggestions are provided, the training difficulty and content can be adjusted according to the performance of the user, and different training requirements are met.

The invention discloses an intelligent evaluation method and system for pharmaceutical service and teaching ability, and the method comprises the steps: building an interaction session between a scoring target and a virtual patient based on the generated virtual patient; marking key pharmaceutical behavior nodes and pharmaceutical related statement content of the scoring target in the interaction session; and based on the marked key pharmaceutical behavior node and the pharmaceutical related statement content, intelligently generating an evaluation score of the interactive performance ability of the scoring target in the pharmaceutical service scene. According to the application, the on-site strain capability of pharmacists or students can be effectively improved, the dependence on entity training resources is remarkably reduced, the fairness and reliability of assessment results are ensured, the training efficiency is greatly improved, and the differentiated training requirements of different scenes are met.

The invention belongs to the technical field of medical education training systems, and particularly relates to a generative AI-based personalized infection control training system and method, and the system comprises a data collection module, a capability portrait construction module, a knowledge generation module, a difficulty regulation and control module, an evaluation module, a personalized learning module, an output module and a virtual patient interaction module. According to the system, user behavior data are integrated through a federal learning mechanism, a multi-dimensional ability portrait is constructed, hierarchical training content is generated by using a generative large model, and content difficulty dynamic adjustment is realized in combination with a Q-learning algorithm. The system supports VR / AR immersive training and voice interaction control, and can simulate a real infection scene and present a 3D propagation consequence. Compared with a traditional static teaching scheme, the system has higher adaptability, interactivity and training closed-loop ability, and the individuation and effectiveness of infection control training are remarkably improved.

The present application provides an oral treatment table training system and method based on spatial computing technology, which relates to augmented reality technology and is used to solve the problems that existing simulators only support single-person use and only perform training based on oral models, lack full-process doctor-patient communication, and have low simulation authenticity. With the help of spatial computing technology, the virtual-reality integrated head-mounted viewer obtains the positioning data of entities and the dental chair of the treatment table in the real space, and the situation observation server arranges virtual entities and generates a scene file. The simulation computer generates a virtual-reality integrated treatment scene, virtual patients, and treatment tools according to the scene file, and can simulate the whole process of the patient from entry to treatment. And generate force sense information to control the force sense tool in the force sense information and match the pose of the virtual treatment tool with the force feedback tool. The user wears the virtual-reality integrated head-mounted viewer to perform treatment operations. With the help of network synchronization technology, it supports multiple people to watch the operation process of the same treatment table. The present application improves the comprehensive simulation authenticity.

A modular, virtual reality (VR)-based CPR training system with multimodal feedback mechanisms, including: a robust VR environment platform configured to generate real-time interactive simulations of realistic medical scenarios, with the platform monitoring compression depth and rate, ensuring real-time feedback for accurate skill assessment and correction; a VR headset operatively connected to the rugged VR environment platform and configured to display the realistic medical scenarios to the user, wherein the virtual scene generated by the rugged VR environment platform and displayed by the VR headset includes an interactive virtual patient that dynamically responds to the user's actions and provides visual and tactile cues to guide the correct technique for CPR; one or more visual feedback mechanisms within the virtual environment generated by the VR environment platform to provide real-time guidance to the user, wherein the visual feedback mechanism includes a dynamic UI slider and a color-coded chest overlay transition; a haptic feedback module configured to provide haptic cues to the user when the compression depth exceeds the predefined threshold, wherein the haptic feedback module comprises a linear resonant actuator (LRA) mounted on the back of the user's index finger to provide the haptic cues through vibrations, and a haptic feedback driver configured to control the tactile signal, wherein the haptic feedback module provides a strong vibration alert via the actuator, allowing the user to immediately correct over-compression; and a microcontroller acting as a core processing unit with a control interface configured to control the operation of the proposed feedback system, wherein the microcontroller communicates with the VR environment platform, the VR headset, and the haptic feedback module, wherein the VR environment platform communicates with the microcontroller via serial communication to trigger the haptic feedback module, wherein the microcontroller processes the received signals and generates appropriate pulse width modulation (PWM) signals for the haptic feedback driver to actuate the actuator.

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.

A customisable medical training system includes a processor and a memory, with the memory containing instructions that cause the processor to generate or modify a virtual environment based on a customisation input The virtual environment comprises a 3D virtual scene displaying a virtual patient, a state of the virtual patient, and a set of one or more available virtual medical resources displayable in the 3D virtual scene. The system communicates with a VR display system to render the generated or modified virtual environment to a training recipient via the VR display system. The customisation input is an input received to customise the state of the virtual patient and / or the set of available virtual medical resources.

The invention relates to the field of medicine and computing. Proposed is a method for training medical personnel in which an animated interactive patient is displayed on a display device to a user, said animated interactive patient being generated by a computing system on the basis of a virtual model of a patient. The user interacts with the virtual patient by means of an input device, and the computing system analyzes the trainee user's actions and responds to each one by producing an audio and video stream in the form of motor responses, vocal reactions and facial expressions of the virtual patient such that pathological changes in the patient's condition can be seen. At the end of a training session, the computing system provides an assessment of the accuracy of the trainee user's actions by comparing the saved actions and procedures performed by the user with corresponding predetermined correct actions and procedures stored in a library of nursing scenarios. The invention allows safe practical training of medical personnel.

The invention relates to a medical teaching training system based on AI-driven high-simulation SP medical history acquisition, belongs to the field of teaching tools, and is used for extracting corresponding case data according to teaching requirements and determining symptoms and physiological indexes of AI-driven high-simulation SP. Constructing an SP body model; extracting an entity of the medical history acquisition knowledge graph from the current question of the student, and searching a triple corresponding to the entity from the medical history acquisition knowledge graph to obtain a first set; obtaining an SP state entity according to the AI-driven high-simulation SP symptoms, the physiological indexes and the body parameters; searching a triple corresponding to the SP state entity from the first set to obtain a second set; and according to the second set, in combination with the SP character parameters and the emotion parameters, generating an answer of the SP. According to the invention, a large number of medical history collection training opportunities with AI-driven high-simulation SP diversification and low cost can be provided, the teaching cost of standardized patient medical history collection is reduced, and the performance of virtual patients is more stable and objective.

The invention belongs to the technical field of diagnosis training, and particularly relates to a traditional Chinese and western medicine clinical diagnosis training system, which constructs diversified virtual gynecological patients by integrating gynecological case data from different hospitals, creates detailed virtual diagnosis space for each virtual patient, and on the basis of the virtual diagnosis space, provides a clinical diagnosis system for the traditional Chinese and western medicine. A gradient pressure limiting scene is set for each virtual gynecological patient, so that a student selects virtual gynecological patients in different gradient pressure limiting scenes from the constructed virtual gynecological patients for diagnosis training, diagnosis training can be closer to an actual clinical situation, more opportunities for diagnosis training under extreme conditions are provided for the student, and the diagnosis training efficiency is improved. And meanwhile, in the process of performing diagnosis training on the selected virtual gynecological patient, simulation evaluation of the training scene is performed by collecting interaction data between the student and the virtual patient, so that interaction feedback defects existing in the virtual training scene can be found in time, and the student is prevented from generating a sense of disengagement.

The invention discloses a clinical medicine practice skill evaluation system, and relates to the technical field of clinical medicine education, and the system comprises the following components: a comprehensive data processing module which collects and preprocesses real patient electronic medical record data and examinee multi-modal data, fuses all modal data features, cleans the electronic medical record data, and stores the cleaned electronic medical record data; providing data for subsequent modules; the personalized intelligent virtual patient is generated based on the electronic medical record data of the real patient, rich and diversified clinical simulation scenes are provided for examinees, the complexity and diversity of the real patient are achieved, the illness state can be adjusted in real time according to decisions of the examinees, the uncertainty and complexity of the clinical scenes are simulated, and the clinical simulation experience of the examinees is improved. The system has the advantages that the system is simple in structure and convenient to use, clinical thinking of examinees can be cultivated and improved in practice, the examinees can learn to make accurate judgment and decisions when the illness state changes, and meanwhile, the examinees can exercise the strain capacity of the examinees through interaction with virtual patients so as to better adapt to various challenges in actual clinical work.

The invention provides a standardized training system based on a large model and a virtual generation technology, belongs to the technical field of artificial intelligence medical education, deeply fuses a large model technology in the field of artificial intelligence and a virtual generation technology in the field of virtual reality, and improves the training efficiency by integrating an advanced voice interaction function. Voice instructions of intern doctors can be responded in real time, and natural and smooth dialogue communication is achieved. Meanwhile, by means of the virtual generation technology, the system can generate a highly-vivid virtual patient image, the appearance, expression and action of the patient are adjusted according to specific cases, and the reality sense of simulation is further enhanced. In addition, a synchronous mouth shape technology is also adopted, so that the mouth shape of the virtual patient is accurately matched with voice output, and the immersion experience of interaction is improved. By combining customized training and a controllable generation strategy of a large model, complex cases can be effectively simulated, real medical data can be fully utilized on the premise of protecting patient privacy, and simulation accuracy and practicability are improved.

The invention discloses a basic nursing skill training system based on a large language model and deep learning, and belongs to the technical field of nursing skill training. The user terminal equipment is provided with a training system application program and is used for user login, training module selection and operation content display; the image acquisition equipment is used for acquiring video data of a simulation operation area in a user operation process in real time; the voice interaction device is used for collecting voice input of a user and playing voice feedback of a virtual patient; the processing control unit is used for running a large language model and a deep learning algorithm and realizing intelligent processing of visual identification and voice interaction of an operation process; and the evaluation feedback module is used for performing multi-dimensional evaluation on the operation normalization and communication performance of the user and generating a real-time feedback report. According to the invention, a complete intelligent teaching closed loop is realized. And full-process, multi-dimensional and granulated automatic evaluation can be carried out on three core capabilities, namely operation normalization, clinical thinking and communication effectiveness.

The invention provides an immersive virtual patient diagnosis and treatment thinking training system for neural immune diseases. The immersive virtual patient diagnosis and treatment thinking training system comprises a multi-dimensional case holographic modeling module, a diagnosis and treatment thinking anchor point dynamic capturing module, a double-AI collaborative diagnosis and treatment deduction module, an evidence-based feedback and thinking correction module and a diagnosis and treatment capability quantitative evaluation module which are in data connection in sequence and form closed-loop optimization. The objective of the invention is to solve the pain points of scene staticization, thinking assessment deficiency, clinical separation of an AI scheme and no closed-loop optimization in existing neural immune disease diagnosis and treatment training.

The invention discloses a multi-modal artificial intelligence virtual patient inquiry teaching system. The system comprises a case arrangement agent, a dialogue planning agent, a portrait and emotion agent, an image acquisition module, a double-flow emotion representation fusion network, a medical verification agent and a teaching feedback agent. According to the invention, through multi-agent cooperative work of the case arrangement agent, the dialogue planning agent, the portrait and emotion agent, the medical verification agent and the teaching feedback agent, accurate presentation of medical information and true expression of a natural language of a virtual patient in an inquiry process are realized. The system can automatically identify questioning intentions of students, call a case manuscript fact unit and generate answers conforming to medical logic and humanistic features in combination with patient portraits and emotional states, thereby greatly improving authenticity of inquiry interaction and medical rationality.

The invention provides a training method of an inquiry large model and an inquiry method based on the large model, and relates to the field of artificial intelligence, in particular to the technical field of large models, deep learning and AI medical treatment. Based on the sample chief complaint information, performing retrieval enhancement in medical references to generate RAG so as to generate an inquiry knowledge base; acquiring a virtual patient portrait corresponding to the sample chief complaint information; simulating a patient role according to the virtual patient portrait through a multi-modal large language model, and simulating a doctor role according to the inquiry knowledge base; guiding the doctor role and the patient role to carry out inquiry dialogue interaction according to the inquiry knowledge base through a multi-mode large language model, and obtaining inquiry dialogue data; according to the inquiry dialogue data, supervision fine tuning SFT is carried out on the base large model, and a target inquiry large model is obtained.According to the method, multi-round inquiry and cross-modal reasoning can be carried out on the basis of the target inquiry large model, the inquiry ability in a complex inquiry scene is remarkably improved, and the practicability and accuracy of an inquiry result are improved.

The application relates to the technical field of virtual training, and provides a simulation data analysis method and system for virtual training of first aid personnel, which is applied to a simulation server and comprises the following steps: obtaining target information and first aid information of the first aid personnel on a target virtual patient; the first aid information comprises original time stamps of each first aid event in multiple first aid events and transmission delay time lengths of each first aid event; for a target first aid event in the multiple first aid events, original time stamps of the target first aid event and transmission delay time lengths of the target first aid event are determined according to the target information, the original time stamps of the target first aid event and the transmission delay time lengths of the target first aid event; the target first aid event is any one of the multiple first aid events; the adjusted time stamps of the multiple first aid events are arranged in chronological order to obtain training process data of virtual training of the first aid personnel. The simulation data analysis timing accuracy of virtual training of the first aid personnel can be improved.

The application discloses a kind of virtual standardization patient image generation and dialogue method and system, comprising: using large language model to extract the multidimensional information of patient from original medical data and construct structured case data, infer the visual features of patient, and integrate visual features as portrait description prompt word;Using text encoder, portrait description prompt word is converted into high-dimensional semantic vector, guide latent diffusion model to generate patient portrait image in line with medical data;Convert user voice signal into natural language text as role prompt word;According to the structured case data of patient and preset emotional factor, system prompt word is constructed;Using large language model and based on role and system prompt word, the reply text of user voice signal is generated;Start voice engine to convert reply text into reply voice, and output reply voice through loudspeaker.The application can generate virtual patient entity with visual, audible and intelligent interaction capability according to static medical text.

The application discloses a real scene interactive anesthesia simulation training management method and system based on VR technology, and relates to the technical field of intelligent medical simulation, which comprises the following steps: generating a virtual three-dimensional anatomical view by using a generative adversarial network based on MRI / CT images and medical records of a patient, configuring a linear mapping function by using a linear regression method, generating virtual patient physiological parameters, and embedding the virtual patient physiological parameters into the virtual three-dimensional anatomical view to obtain a virtual patient model; calculating the interaction force of a puncture needle in the virtual patient model in real time by macroscopic finite element analysis and microscopic molecular dynamics; and calculating the interaction force deviation value and the anesthetic deviation value based on the interaction force and the pharmacodynamic curve, and giving real-time prompts to a trainee through a VR head-mounted display and a tactile glove. By generating the virtual patient model, the application realizes accurate modeling of individualized anatomical structures and physiological parameters, and significantly improves the authenticity and individualization of the patient simulation.

A system for generating a virtual model of a virtual patient including: a template module to provide a morphable virtual 3D mesh template for a virtual model of a human organ; an anatomical parameters module to provide a range of values for each adjustable anatomical parameters of the virtual model, each value associated with a morphed state of the template; a plausibility module to provide at least one relation for the template in at least one morphed state, the relation being between at least two of the parameters; an instantiation module to provide, using a pseudo-random number generator, a parameter set including, for each parameter of the virtual model, a value of the corresponding range of values, while fulfilling the at least one relation; and a generating module to generate the virtual model based on the template in the morphed state corresponding to the selected parameters.

The application discloses a traditional Chinese medicine chronic disease family nursing method based on a cloud platform, relates to the technical field of intelligent health services, and comprises the following steps: performing deep fusion and intelligent analysis on a family nursing data package by constructing a virtual patient model, generating personalized data of patient physiological indexes and traditional Chinese medicine syndrome states, generating a customized family nursing candidate scheme based on the personalized data of the patient physiological indexes and the traditional Chinese medicine syndrome states, combining behavior prediction analysis, intelligently evaluating and real-time feeding back, and forming a traditional Chinese medicine chronic disease family nursing management scheme according to a nursing scheme set, combining physiological index and symptom data analysis correction. The application can accurately reflect the current health state of a patient by collecting physiological data and traditional Chinese medicine syndrome information of the patient, combining family environment data, constructing a dynamically updated virtual patient model, and realizing the personalization and dynamic adaptation of a nursing scheme.

The invention provides a pharmacist clinical ability assessment method and system based on multi-scene self-adaption, and relates to the technical field of pharmacist ability assessment, and the method comprises the steps: configuring a standard diagnosis and treatment process, a mapping rule of an operation event and a diagnosis and treatment step, and a reference weight vector of five ability dimensions for various assessment scenes in advance; performing multi-modal data acquisition on the assessment interaction process of the pharmacist and the virtual patient to obtain a dialogue text sequence; performing semantic coding and context modeling on the dialogue text sequence to obtain a global dialogue abstract vector; obtaining a five-dimensional score vector based on the dialogue text sequence, the diagnosis and treatment step sequence and the pharmaceutical knowledge graph; adopting an adaptive weight network to generate fusion weight vectors of five capability dimensions; and carrying out weighted aggregation on the five-dimensional score vector by using the fusion weight vector to obtain a comprehensive score, and outputting a structured evaluation report. According to the method, the defects of single evaluation scene, shallow scoring dimension and weight logic solidification in the prior art can be overcome.

The invention relates to the field of medical data processing, and discloses an imaginary medical data generation method, which comprises the following steps: S1, constructing a dynamic medical knowledge graph containing rare disease knowledge; s2, constructing a virtual patient model, and carrying out probability modeling on a disease development process; s3, using a deep learning algorithm to generate virtual patient data under the guidance of the virtual patient model, and synchronously recording associated knowledge path metadata; s4, performing post-processing on the generated data; s5, providing the data to a medical diagnosis platform; s6, establishing a performance feedback module, and collecting and evaluating performance indexes of the downstream medical diagnosis model; and S7, executing attribution analysis and adaptive optimization. According to the method, the medical logicality of the data is ensured through the strong constraint of the knowledge graph, and the continuous iterative improvement of the data quality according to the application effect is realized through a closed-loop feedback mechanism.

The application discloses a kind of based on knowledge graph's simulation clinical experiment environment interaction method, specifically related to medical artificial intelligence and clinical simulation technical field;Initial disease entity node and its corresponding symptom feature set are acquired, and initial pathological state graph with time factor is constructed;Based on user interaction behavior extraction operation feature vector, utilize graph neural inference model to carry out multi-hop semantic path inference, generate post-interaction state graph;Further calculate the semantic shift degree caused by user operation, dynamically adjust graph inference weight;Extract relevant etiological entity and generate pathological evolution chain, calculate response difference degree in combination with user subsequent behavior, dynamically adjust virtual patient feedback, trigger path rollback operation if necessary;The application can realize the precise modeling and response control based on knowledge graph in clinical simulation environment, improve the interaction intelligence, explainability and stability of system, applicable to medical teaching, virtual diagnosis training and the like scene.

This application provides a dental simulation workbench training system and method based on spatial computing technology, for solving problems that prior simulator only supports single-player use, only trains based on an oral model, lacks full-process doctor-patient communication. A virtual-real fusion head-mounted display acquires positioning data of physical objects in real space. A situation observation server arranges virtual entities and generates a scene file. A simulation computer generates a mixed-reality treatment scenario, a virtual patient, and dental instruments from the scene file, allowing a complete patient journey, from entry to the end of treatment, to be replicated. Force sensation information is generated, and both poses of a force tool and a virtual tool are controlled to match with a pose of a force feedback tool. A user wears the head-mounted display to perform treatment. Multiple people are supported to watch the treatment of the same dental simulation workbench through a network.