Virtual simulation experiment system for onsite epidemiological investigation of sudden major respiratory infectious diseases

Abstract

The invention relates to the technical field of virtual simulation, and particularly discloses a sudden major respiratory infectious disease on-site epidemiological investigation virtual simulation experiment system which comprises a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module and an analysis and evaluation module. The virtual scene management module is used for creating and managing a simulation experiment environment and simulating an epidemic background and a development process of sudden major respiratory infectious diseases; the data management module is used for simulating the process of collecting case information, environment information and social contact data when students perform on-site epidemiological investigation; according to the system, big data analysis, the GIS technology and an AI intelligent analysis tool are integrated, a disease transmission path and a development trend are simulated, and virtual simulation not only helps students to master how to support a decision through scientific data, but also can improve the emergency response capability and the comprehensive analysis capability of the students in a complex epidemic situation.

Description

Technical Field

[0001] The invention belongs to the technical field of virtual simulation, and in particular relates to a virtual simulation experimental system for on-site epidemiological investigation of sudden major respiratory infectious diseases. Background Art

[0002] With the continuous development of social economy, the continuous acceleration of urbanization and the continuous improvement of urban transportation, while providing great convenience for people's travel and gathering, it also provides convenient conditions for the rapid spread and prevalence of infectious diseases. New and emerging infectious disease outbreaks occur from time to time. The timeliness and accuracy of emergency response to public health emergencies are crucial to reducing losses and effectively handling them. An important part of this is on-site epidemiological investigation.

[0003] Field epidemiological surveys can dynamically collect and integrate information such as sampling, testing, clinical characteristics of the injured and sick, isolation and quarantine of exposed people, risk factors, and treatment measures at the scene of the emergency in real time. It is particularly important for comprehensively grasping the occurrence and development of the event, delineation of the scope of pollution, hazard assessment, influencing factors, population vulnerability characteristics, health resource estimation, and evaluation of the effectiveness of intervention measures. It is the key to the success of medical rescue and treatment. Experimental teaching of infectious disease outbreaks requires coordination among multiple departments and switching between multiple scenarios. The process is costly and irreversible, and many links are difficult to simulate in reality. As a result, the practice of this part of the content in traditional teaching is limited to literature analysis and case discussion. Students lack practical training in on-site participation and response, and theory and practice are out of touch. The practical effect cannot meet the needs of maintaining public health safety with frequent disease outbreaks.

[0004] Therefore, it is necessary to propose a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases to solve the problem in the existing technology that students lack practical skills to operate in line with actual work.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases, comprising: a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module, and an analysis and evaluation module;

[0009] The virtual scene management module is used to create and manage the simulation experiment environment to simulate the epidemic background and development process of sudden major respiratory infectious diseases;

[0010] The data management module is used to simulate the process of students collecting case information, environmental information, and social contact data during field epidemiological surveys to obtain the data required for disease analysis;

[0011] The disease transmission simulation module is used to simulate the disease transmission path and development trend by using big data, GIS technology and AI intelligent analysis technology;

[0012] The epidemiological analysis module is used to provide real-time disease analysis reports based on the acquired data combined with epidemiological and medical statistical methods, and to provide prevention and control decision support;

[0013] The virtual on-site interactive module is used to enable students to interact with virtual characters in a virtual scene, simulating the real scene of on-site investigation;

[0014] The learning support module is used to provide support for knowledge related to epidemiology, field epidemiology, medical statistics, and infectious diseases, and to analyze various operations and analysis methods during the investigation process;

[0015] The analysis and evaluation module is used to evaluate students' performance in the virtual simulation process through big data analysis and provide improvement suggestions.

[0016] Preferably, the virtual scene management module is also used to create various disease scene models, including static scenes and dynamic elements, and add environmental details; render various disease scene models and environmental details to generate virtual scenes;

[0017] Based on the imported case information, virtual scenes are used to simulate the epidemic background and development process of sudden major respiratory infectious diseases. The integrated scene management tool can realize seamless switching between different virtual scenes, and the disease transmission simulation module is combined to update the disease data in real time. By designing a multi-role, multi-perspective interactive mode, multiple students can jointly cope with the challenges of disease transmission in virtual scenes.

[0018] Preferably, the disease transmission simulation module is also used to simulate the disease transmission process in the virtual scene through an integrated epidemiological model based on a preset infection mechanism to generate simulation results; adjust the disease data and events in the virtual scene according to the simulation results, and perform cross-scene disease diffusion response through a linkage mechanism based on GIS technology;

[0019] AI intelligent analysis technology is used to analyze the behavioral data of individuals in virtual scenes, establish a contact network between individuals in virtual scenes, and predict potential chains of infection; when an infected person is identified, the people who have been in contact with him are automatically marked and tracked through an integrated data update tool; according to the different prevention and control measures selected, the control parameters in the epidemiological model are used to simulate the prevention and control process of the prevention and control measures.

[0020] Preferably, the virtual scene interaction module is also used to simulate the interaction process between the system user and various disease scene models based on the virtual scene through the Havok Physics physics engine; and dynamically adjust the interaction content or difficulty according to the input content or behavior of the system user using AI intelligent analysis technology;

[0021] Combine VR / AR technology to integrate real-world elements with virtual scenes, and influence virtual scenes through the actions of real-world elements; use 3D audio technology to adjust the direction and intensity of sound in real time according to the positioning and perspective changes of the system user.

[0022] Preferably, the data management module is also used to record the operation of the system user in the virtual scene through the integrated event listener; collect input data from the system user device through the network connection and the deployed sensors;

[0023] The Havok Physics engine is used to collect and update the position changes and postures of system users in the virtual scene in real time, and to record the status of each dynamic element in the virtual scene; the Redis database is used to store short-term real-time data, and MongDB is used to store multi-student data;

[0024] Store the behavior data and scenario logs of system users in the data warehouse for historical data analysis and data mining; use the Kafka stream processing framework to process real-time data streams and generate real-time reports or interactive feedback on system user behavior.

[0025] Preferably, the epidemiological analysis module is also used to use big data to analyze the behavior data of system users and mine the behavior patterns of system users; use statistical analysis methods to analyze disease data and understand the distribution and fluctuation of disease data;

[0026] By clustering and analyzing the disease data in virtual scenarios, similar disease transmission patterns can be found, and targeted prevention and control measures can be formulated based on the behavioral patterns. AI intelligent analysis technology can be used to analyze historical data and predict the development trend of the disease within a preset time period.

[0027] Through real-time data monitoring operations, abnormal data that does not conform to the development trend can be automatically identified, alarms can be issued and response suggestions can be provided; the risks of different groups of people in the virtual scene can be evaluated in real time based on the simulation results, and corresponding intervention measures can be provided; multiple simulation results of virtual scenes can be integrated to evaluate the prevention and control effects of different prevention and control measures, thereby optimizing the priority and intensity of the implementation of prevention and control measures.

[0028] Preferably, the analysis and evaluation module is also used to obtain subjective evaluations of system users through questionnaires, scoring, and user communication, and analyze the deficiencies of the system in terms of user experience based on the subjective evaluations; analyze students' behavioral data through big data to evaluate their performance in virtual scenes;

[0029] Calculate students' completion rate, success rate, and accuracy rate, and compare the gap between students and the class average, curriculum standards or benchmark values ​​to determine students' relative performance; use sentiment analysis technology to evaluate students' learning motivation, participation, and emotional fluctuations based on their daily learning situation; use deep learning algorithms based on students' current performance to predict the difficulties they may encounter within a preset time period, and provide students with personalized improvement suggestions.

[0030] Preferably, the learning support module is also used to integrate an AI-based question-answering system to answer questions raised by students, and provide comprehensive suggestions based on students' current performance and previous problem records; provide real-time guidance to students through a virtual assistant, and set up a learning discussion area to achieve multi-person communication and experience sharing; track each student's performance in the virtual scene, and update their completed operations, mastered skills, and solved problems in real time to help students clearly understand their own learning path.

[0031] Preferably, the system uses robotic process automation technology to automatically process repetitive interactive processes and automatically adjust the interface layout according to the behavioral data of system users, thereby providing a personalized user experience; at the same time, the system can also store data and files in the cloud to achieve real-time synchronization across devices, so that system users can access, modify and share content anytime and anywhere, and add multilingual functions so that the system supports users in different regions.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The system of the present invention provides students with a highly simulated virtual scene, which allows them to simulate on-site epidemiological investigations in public health emergencies without actual risks. Based on the simulated virtual scene, students can actually operate in it, experience the challenges and decisions in the real investigation process, and thus deepen their understanding of epidemiological investigation methods; the system integrates big data analysis, GIS technology and AI intelligent analysis tools to simulate disease transmission paths and development trends. This virtual simulation not only helps students master how to support decision-making through scientific data, but also improves their emergency response capabilities and comprehensive analysis capabilities when facing complex epidemic situations.

[0034] The system of the present invention provides rich subject knowledge support, including relevant knowledge in the fields of epidemiology, field epidemiology, medical statistics, and infectious diseases, to help students understand and apply relevant academic theories and methods during the operation process; the system also provides personalized improvement suggestions through big data analysis and evaluation of students' performance in the virtual simulation process, which helps students better integrate into the actual environment in actual work and improve their professional ability and practical operation level.

[0035] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a framework diagram of the virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1:

[0039] See also Figure 1 As shown, a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases includes: a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module, and an analysis and evaluation module;

[0040] The virtual scene management module is used to create and manage the simulation experiment environment to simulate the epidemic background and development process of sudden major respiratory infectious diseases;

[0041] The data management module is used to simulate the process of students collecting case information, environmental information, and social contact data during field epidemiological surveys to obtain the data required for disease analysis;

[0042] The disease transmission simulation module is used to simulate the disease transmission path and development trend by using big data, GIS technology and AI intelligent analysis technology;

[0043] The epidemiological analysis module is used to provide real-time disease analysis reports based on the acquired data combined with epidemiological and medical statistical methods, and to provide prevention and control decision support;

[0044] The virtual on-site interactive module is used to enable students to interact with virtual characters in a virtual scene, simulating the real scene of on-site investigation;

[0045] The learning support module is used to provide support for knowledge related to epidemiology, field epidemiology, medical statistics, and infectious diseases, and to analyze various operations and analysis methods during the investigation process;

[0046] The analysis and evaluation module is used to evaluate students' performance in the virtual simulation process through big data analysis and provide improvement suggestions.

[0047] The virtual scene management module is also used to create various disease scenario models, including static scenes and dynamic elements (such as personnel activities, vehicle driving, etc.), and add environmental details, such as the appearance of different buildings, indoor facilities (wards, offices, etc.), equipment (hospital medical equipment, public facilities, etc.) and characters (patients, medical staff, investigators, etc.); these scenes can include different environments such as city streets, hospitals, schools, public transportation, quarantine areas, etc.

[0048] Use rendering engines (such as Unity and Unreal Engine) to render various disease scene models and environmental details to generate virtual scenes; factors such as lighting, shadows, and weather should also be simulated through the rendering engine to enhance the sense of reality.

[0049] Based on the imported case information, virtual scenes are used to simulate the epidemic background and development process of sudden major respiratory infectious diseases;

[0050] In simulation experiments, students may need to switch between different scenarios (such as from hospital to quarantine area, from case investigation to public places, etc.). The integrated scenario management tool enables seamless switching between different virtual scenarios, and combines the disease transmission simulation module to update disease data in real time.

[0051] By designing a multi-role, multi-perspective interactive mode, multiple students can jointly cope with the challenge of disease transmission in a virtual scene. For example, multiple students are allowed to work together in the same scene. In an epidemiological investigation experiment, one group of students is responsible for case tracking, while another group of students is responsible for data analysis and report generation.

[0052] The disease transmission simulation module is also used to simulate the disease transmission process in the virtual scene based on the preset infection mechanism by integrating epidemiological models (such as SIR model, SEIR model, Agent-based model, etc.) to generate simulation results;

[0053] SIR model (susceptible-infected-recovered): The classic SIR model simulates the spread of the disease and predicts the speed and scale of the epidemic by calculating the dynamic changes between different populations (susceptible, infected, and recovered).

[0054] SEIR model (susceptible-latent-infected-recovered): adds the incubation period factor to the SIR model and is suitable for modeling emerging infectious diseases (such as COVID-19).

[0055] Agent-based model: By simulating the behavior of each individual (such as travel, social interaction, medical treatment, etc.), it calculates how the disease spreads among different individuals, and then predicts the spread of the epidemic in different regions and populations.

[0056] Based on the epidemiological model, the infection steps are designed as follows:

[0057] (1) Before starting the infection mechanism, the individual intelligent agent needs to sense the existence of other individuals around it and make a state prediction. If it determines that it is immune or the individuals around it are not infected, it chooses to maintain its own state, otherwise it continues;

[0058] (2) Determine whether the time and space conditions are met, that is, when t>1min; d≤2m, where t represents the contact time between different individual agents and d represents the distance between the infected individual and the infected person. If not, maintain the state, otherwise continue;

[0059] (3) Obtain the congestion index D of the current area and the correlation coefficient C of the individual agent's appearance mode to the virus infection, obtain the intervention control parameter H and the individual agent's inherent infection rate P provided by the selected prevention and control measures, and obtain the infection index ω of the individual agent in the current area based on the obtained data;

[0060] (4) Individual agents update their adaptive states based on the index. During the updating process, some infected individuals will be cured or die as the disease progresses and will be eliminated from the infected population.

[0061] Adjust disease data and events in the virtual scene (such as new cases, blocked areas, and medical resource shortages) based on simulation results, and respond to cross-scenario disease spread through a linkage mechanism based on GIS technology;

[0062] Depending on the development of the disease, the virtual scene will change dynamically. For example, some areas may set up quarantine areas, suspend transportation, or close public places due to the spread of the disease; hospitals may be overcrowded due to the increase in patients. There needs to be a linkage mechanism between different scenes. For example, the spread of an epidemic in a certain virtual area may affect the public health response in another area. The linkage mechanism can be used to simulate and demonstrate this cross-scene linkage effect.

[0063] Use AI intelligent analysis technology to analyze the behavioral data of individuals in virtual scenes, establish a contact network between individuals in virtual scenes, and predict potential infection chains;

[0064] When an infected person is identified, people who have come into contact with him or her are automatically marked and tracked through an integrated data update tool;

[0065] According to the different prevention and control measures selected, the control parameters in the epidemiological model (such as isolation rate, contact rate, immunization rate, etc.) are used to simulate the prevention and control process of the prevention and control measures.

[0066] Prevention and control strategies, for example: social distancing: simulating social distancing measures by changing the probability of individual contact; vaccination: simulating the impact of vaccination on the spread of the epidemic by setting vaccination rates, immune effects and herd immunity thresholds; blockade areas: controlling the speed of virus transmission by setting blockades and isolation measures in different areas; allocation of medical resources: how to optimize the allocation of medical resources (such as beds, medical staff, etc.) when resources are limited.

[0067] The virtual scene interaction module is also used to simulate the interaction process between system users and various disease scene models based on virtual scenes through the Havok Physics physics engine;

[0068] AI intelligent analysis technology is used to dynamically adjust the interactive content or difficulty according to the input or behavior of the system user; the system can give instant feedback based on the user's input or behavior. Feedback can be visual feedback (such as graphic changes, animation displays), auditory feedback (such as sound effects, voice responses), tactile feedback (such as through vibration or electrical feedback devices), etc.

[0069] Combine VR / AR technology to integrate real-world elements with virtual scenes, and influence virtual scenes through the actions of real-world elements;

[0070] Students can have conversations with virtual characters (such as patients, medical staff, government officials, etc.) to collect relevant information. When designing these characters, they need to be given certain behavior patterns and response mechanisms, such as the changes in patients' symptoms at different stages and the workflow of medical staff.

[0071] Use 3D audio technology to adjust the direction and intensity of sound in real time based on the positioning and viewing angle of the system user.

[0072] The data management module is also used to record the system user's operations in the virtual scene through integrated event listeners (such as mouse clicks, keyboard inputs, gesture operations, etc.);

[0073] Collect input data from system user devices through network connections and deployed sensors (such as VR headsets, controllers, motion capture devices, etc.);

[0074] The Havok Physics engine collects and updates the user's position changes and posture (such as speed, acceleration, rotation, etc.) in the virtual scene in real time, and records the status of each dynamic element in the virtual scene;

[0075] Use Redis database to store short-term real-time data, usually for real-time interaction data and system performance indicators. And use MongDB to store multi-student data, especially for scenarios that require horizontal expansion (such as multi-person online interaction, complex simulation systems);

[0076] Store system users' behavior data and scenario logs in the data warehouse for historical data analysis and data mining;

[0077] Use the Kafka stream processing framework to process real-time data streams and generate real-time reports or interactive feedback on system user behavior. This is especially important for multi-person collaborative virtual scenes, which can adjust system responses or feedback in real time during the interaction process.

[0078] Operation steps of the virtual simulation experiment of field epidemiological investigation:

[0079] Case introduction: The background of the case is that a county reported 4 cases of infectious diseases of unknown causes. The first case (Case 1) was a 34-year-old middle-aged man. Cases 2 and 3 were Case 1’s 6-year-old son and 32-year-old wife, respectively. Epidemiological investigation showed that Case 4 had a history of dining at the same table with Case 1.

[0080] Epidemic verification: Students are assumed to be CDC staff, and the outbreak verification is completed by collecting and analyzing key information such as the time of the first onset of the disease, the number of cases, and patient characteristics;

[0081] On-site investigation: First, through intelligent operations in virtual scenarios, we complete preparations such as professional personnel selection, material preparation, and technical support. During the on-site investigation, we obtain clinical information on the disease and three-room distribution information through face-to-face interviews, on-site sample collection, and key information collection. We trace the source to find the first case and the source of infection, and analyze the disease transmission route.

[0082] The main symptoms of the four cases included dyspnea, fever, cough, sore throat, and muscle pain. A lung CT scan revealed multiple ground-glass shadows and infiltration shadows in the lungs. Case 3 had pulmonary consolidation and was in serious condition. He was admitted to the ICU for emergency treatment. The hospital where the first case was located realized at the beginning that the disease was different from previous cases, and similar clinical symptoms and signs had occurred in the medical staff in the hospital. The unexplained case was reported through the national infectious disease online reporting system within 2 hours, and was urgently reported to the local CDC and the main leaders of the hospital during the same period.

[0083] In the following week, three hospitals reported 10 cases of fever of unknown cause in succession, two of whom were medical staff. Serological tests such as influenza virus were conducted on 14 cases, all of which showed negative results. In a short period of time, the number of fever cases in all outpatient clinics of the county hospitals increased sharply, and the related symptoms were similar to those of previous cases. It was preliminarily judged that the unexplained disease was a respiratory infectious disease and could be transmitted from person to person.

[0084] On-site disposal: Based on the on-site investigation and analysis results, different disposal methods are carried out for the source of infection, transmission route and susceptible individuals to prevent the further spread of the disease;

[0085] After receiving the call, the Municipal Center for Disease Control and Prevention immediately activated the emergency response plan for major infectious diseases and organized four teams for emergency response to infectious diseases, which were responsible for on-site epidemiological investigation, on-site sampling and laboratory testing, data visualization and disease research and development, and implementation of on-site prevention and control measures. In accordance with their respective task requirements, the four teams brought protective equipment, specimen collection and transportation equipment, rescue equipment, etc., and rushed to the scene with the hospital medical rescue team to conduct an investigation.

[0086] Summary report: Write a closing report on the progress of the entire case.

[0087] Embodiment 2:

[0088] See also Figure 1 As shown, this embodiment is basically the same as the above embodiment, except that the epidemiological analysis module is also used to use big data to analyze the behavior data of system users and mine the behavior patterns of system users;

[0089] Use statistical analysis methods to analyze disease data to understand the distribution and fluctuation of disease data;

[0090] By clustering and analyzing the disease data in virtual scenarios, similar disease transmission patterns can be discovered, and targeted prevention and control measures can be formulated based on the behavioral patterns;

[0091] Apply AI intelligent analysis technology to analyze historical data and predict the development trend of the disease within a preset time period;

[0092] Through real-time data monitoring operations, abnormal data that does not conform to the development trend can be automatically identified, alarms can be issued, and response suggestions can be provided;

[0093] Assess the risks of different groups of people in the virtual scenario in real time based on the simulation results, and provide corresponding intervention measures (such as social isolation, city lockdown, school closures, etc.);

[0094] Integrate multiple simulation results of virtual scenarios to evaluate the effectiveness of different prevention and control measures, and then optimize the priority and intensity of the implementation of prevention and control measures.

[0095] Through GIS technology, disease data can be presented as heat maps to show the epidemic risks and trends in various regions, helping decision makers to accurately grasp the spatial distribution of the epidemic; through charts showing the historical trends and future forecasts of key indicators such as the number of infections, mortality rate, and recovery rate, managers can be helped to evaluate the effectiveness of epidemic control; through a comprehensive dashboard, current epidemic data, medical resource utilization, and the implementation status of public health measures can be displayed, providing policy makers with real-time decision-making basis.

[0096] The analysis and evaluation module is also used to obtain subjective evaluations of system users through questionnaires, ratings, and user communication, and to analyze the deficiencies of the system in terms of user experience based on the subjective evaluations;

[0097] Analyze students' behavioral data through big data and evaluate their performance in virtual scenarios;

[0098] Calculate students' completion rate, success rate, and accuracy rate, and compare students with the class average, course standards, or benchmark values ​​to determine their relative performance;

[0099] Combined with students' daily learning situation, sentiment analysis technology is used to assess students' learning motivation, engagement and emotional fluctuations;

[0100] Based on students’ current performance, deep learning algorithms are used to predict the difficulties they may encounter within a preset time period, providing students with personalized improvement suggestions.

[0101] The learning support module is also used to integrate an AI-based question-answering system to answer questions raised by students and provide comprehensive suggestions based on students’ current performance and previous problem records;

[0102] Provide real-time guidance to students through virtual assistants, and set up learning discussion areas to enable multi-person communication and experience sharing;

[0103] Track each student's performance in the virtual scene, and update their completed operations, mastered skills, and solved problems in real time to help students clearly understand their learning path.

[0104] Through robotic process automation technology, repetitive interactive processes can be automatically processed, and the interface layout can be automatically adjusted according to the behavioral data of system users to provide a personalized user experience.

[0105] By storing data and files in the cloud and achieving real-time synchronization across devices, system users can access, modify and share content anytime and anywhere, and add multilingual capabilities to enable the system to support users in different regions.

[0106] From the above, it can be seen that the system of the present invention provides students with a highly simulated virtual scene, which allows students to simulate on-site epidemiological investigations in public health emergencies without actual risks. Based on the simulated virtual scene, students can actually operate in it, experience the challenges and decisions in the real investigation process, and thus deepen their understanding of epidemiological investigation methods; the system integrates big data analysis, GIS technology and AI intelligent analysis tools to simulate disease transmission paths and development trends. This virtual simulation not only helps students master how to support decision-making through scientific data, but also improves their emergency response capabilities and comprehensive analysis capabilities when facing complex epidemic situations.

[0107] The system of the present invention provides rich subject knowledge support, including relevant knowledge in the fields of epidemiology, field epidemiology, medical statistics, and infectious diseases, to help students understand and apply relevant academic theories and methods during the operation process; the system also provides personalized improvement suggestions through big data analysis and evaluation of students' performance in the virtual simulation process, which helps students better integrate into the actual environment in actual work and improve their professional ability and practical operation level.

[0108] Embodiment 3:

[0109] The embodiment of the present invention also provides a computer-readable storage medium, on which is stored a program of a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases as described above, which, when executed by a processor, implements each process of the above virtual simulation experiment system embodiment and can achieve the same technical effect. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0110] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0111] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0112] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases, characterized in that: include: Virtual scene management module, data management module, disease transmission simulation module, epidemiological analysis module, virtual site interaction module, learning support module, analysis and evaluation module; The virtual scene management module is used to create and manage a simulation experiment environment to simulate the epidemic background and development process of sudden major respiratory infectious diseases; The data management module is used to simulate the process of students collecting case information, environmental information, and social contact data during field epidemiological surveys to obtain the data required for disease analysis; The disease transmission simulation module is used to simulate the disease transmission path and development trend by using big data, GIS technology and AI intelligent analysis technology; The epidemiological analysis module is used to provide real-time disease analysis reports based on the acquired data in combination with epidemiological and medical statistical methods, and to provide prevention and control decision support; The virtual scene interaction module is used to enable students to interact with virtual characters in the virtual scene to simulate the real scene of the field investigation; The learning support module is used to provide support for knowledge related to epidemiology, field epidemiology, medical statistics, and infectious diseases, and to analyze various operations and analysis methods during the investigation process; The analysis and evaluation module is used to evaluate the performance of students in the virtual simulation process through big data analysis and provide improvement suggestions.

2. According to claim 1, a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases is characterized in that: The virtual scene management module is also used for: Create various disease scenario models, including static scenes and dynamic elements, and add environmental details; Rendering various disease scene models and environmental details to generate a virtual scene; Based on the imported case information, the virtual scene is used to simulate the epidemic background and development process of the sudden major respiratory infectious disease; An integrated scene management tool enables seamless switching between different virtual scenes, and updates disease data in real time in conjunction with the disease transmission simulation module; By designing a multi-role, multi-perspective interactive mode, multiple students can jointly cope with the challenge of disease transmission in the virtual scenario.

3. According to claim 2, a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases is characterized in that: The disease spread simulation module is also used to: Based on the preset infection mechanism, the disease propagation process in the virtual scenario is simulated by integrating the epidemiological model to generate simulation results; Adjust the disease data and events in the virtual scene according to the simulation results, and perform cross-scene disease spread response through a linkage mechanism based on GIS technology; Analyze the behavioral data of individuals in the virtual scene using AI intelligent analysis technology, establish a contact network between individuals in the virtual scene, and predict potential infection chains; When an infected person is identified, the people who have come into contact with him or her are automatically marked and tracked through an integrated data update tool; According to the different prevention and control measures selected, the control parameters in the epidemiological model are used to simulate the prevention and control process of the prevention and control measures.

4. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 3, characterized in that: The virtual site interaction module is also used for: Based on the virtual scene, the interaction process between the system user and the various disease scene models is simulated by the Havok Physics physics engine; Using AI intelligent analysis technology to dynamically adjust the interactive content or difficulty based on the input content or behavior of the system users; Combine VR / AR technology to integrate real-world elements with the virtual scene, and influence the virtual scene through the actions of the real-world elements; 3D audio technology is used to adjust the direction and intensity of sound in real time based on the positioning and viewing angle changes of the user of the system.

5. According to claim 4, a virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases is characterized in that: The data management module is also used for: Recording the operation of the system user in the virtual scene through an integrated event listener; Collecting input data from user devices of the system through network connections and deployed sensors; The Havok Physics engine is used to collect and update the position change and posture of the system user in the virtual scene in real time, and record the status of each dynamic element in the virtual scene; Use Redis database to store short-term real-time data, and use MongDB to store the multi-student data; Storing the behavior data and scenario logs of the system users in a data warehouse for historical data analysis and data mining; Use the Kafka stream processing framework to process real-time data streams and generate real-time reports or interactive feedback on user behavior of the system.

6. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 5, characterized in that: The epidemiological analysis module is also used for: Use big data to analyze the behavioral data of the system users and mine the behavioral patterns of the system users; Analyze the disease data using statistical analysis methods to understand the distribution and fluctuation of the disease data; By clustering the disease data of the virtual scene, similar disease transmission patterns are discovered, and targeted prevention and control measures are formulated in combination with the behavior patterns; Apply AI intelligent analysis technology to analyze the historical data and predict the development trend of the disease within a preset time period; Through real-time data monitoring operations, abnormal data that does not conform to the development trend can be automatically identified, alarms can be issued, and response suggestions can be provided; According to the simulation results, the risks of different groups of people in the virtual scene are evaluated in real time, and corresponding intervention measures are provided; Integrate the multiple simulation results of the virtual scene, evaluate the prevention and control effects of different prevention and control measures, and then optimize the priority and intensity of the implementation of the prevention and control measures.

7. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 6, characterized in that: The analysis and evaluation module is also used for: Obtain subjective evaluations of the system users through questionnaires, ratings, and user communications, and analyze deficiencies of the system in terms of user experience based on the subjective evaluations; Analyzing the student's behavioral data through big data to evaluate his / her performance in the virtual scene; Calculate the student's completion rate, success rate, and accuracy rate, and compare the student with the class average, course standard, or benchmark value to determine the student's relative performance; Evaluate the student's learning motivation, engagement and emotional fluctuations by using sentiment analysis technology in combination with the student's daily learning situation; Based on the student's current performance, a deep learning algorithm is used to predict the difficulties that the student may encounter within a preset time period, and personalized improvement suggestions are provided to the student.

8. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 7, characterized in that: The learning support module is also used to: Integrate an AI-based question-answering system to answer questions raised by the students and provide comprehensive suggestions based on the students’ current performance and previous problem records; Providing real-time guidance to the students through a virtual assistant, and setting up a learning discussion area to enable multi-person communication and experience sharing; The performance of each student in the virtual scene is tracked, and the completed operations, mastered skills, and solved problems are updated in real time to help the students clearly understand their own learning path.

9. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 8, characterized in that: The system uses robotic process automation technology to automatically process repetitive interactive processes and automatically adjusts the interface layout based on the behavioral data of the system users, thereby providing a personalized user experience.

10. A virtual simulation experiment system for on-site epidemiological investigation of sudden major respiratory infectious diseases according to claim 9, characterized in that: The system can also store data and files in the cloud to achieve real-time synchronization across devices. System users can access, modify and share content anytime and anywhere, and add multilingual functions to enable the system to support users in different regions.

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