Urban virus propagation path simulation analysis method and system

Abstract

The invention provides an urban virus propagation path simulation analysis method and system. The method at least comprises the steps of S1, urban scene construction, S2, individual and crowd behavior simulation, S3, traffic network dynamic simulation, S4, propagation path and dynamic updating, S5, three-dimensional visualization and real-time feedback, S6, simulation result analysis and output and the like. According to the analysis method, a multi-agent model is matched with urban traffic network data, urban-level propagation rendering and an interactive simulation interface are achieved, the propagation situation after virus leakage is rapidly and accurately predicted, the public health coping capacity is improved, the safety consciousness of urban managers is improved, and leakage is prevented and evaluated. The method has the advantages of simple and visual operation, effective prevention of laboratory leakage accidents, accurate simulation, improvement of practical adaptability and the like.

Description

Technical Field

[0001] The present invention relates to the fields of public health safety and virtual simulation technology, and particularly to a method and system for simulating and analyzing the transmission path of urban viruses based on a multi-agent model. Background Art

[0002] With the acceleration of the urbanization process, the population in the urban environment is dense, the transportation system is complex and the interactions are frequent, which significantly increases the risk of virus transmission in the city. At present, a variety of computer simulation technologies have been introduced in the field of public health for simulating the transmission path and risk assessment of infectious diseases. By analyzing the population flow and virus transmission dynamics in the city through models, researchers can predict potential infection paths and diffusion trends in a short time, providing a reference basis for public health management.

[0003] Traditional virus transmission simulation systems are mainly based on mathematical modeling methods, such as the SIR model and the SEIR model, etc. These models can describe the overall transmission trend of the virus in the population, but they show certain limitations when capturing complex individual behavior patterns and urban traffic network interactions. For example, Chinese invention patent CN119400448A discloses a virus transmission path analysis system based on big data, including a data screening module, a virus transmission path module, a path screening module, a spatio-temporal network construction module, and a virus transmission influencing factor analysis module. However, this solution does not introduce an agent model in the virus transmission simulation field, and there are limitations in the interaction between its individual behavior and the urban traffic network, and it is impossible to achieve high-precision virus transmission path simulation.

[0004] In recent years, agent-based systems have gradually been introduced into this field. By introducing a multi-agent model, the behavior and interaction of individuals can be simulated at a relatively fine level, especially suitable for virus transmission simulation in the complex urban traffic network environment. However, existing multi-agent virus transmission simulation systems usually lack dynamic adaptability to traffic networks or fail to provide the ability for users to customize transmission scenarios, restricting their application effects in complex urban environments. Currently, there are mainly several problems in the existing technologies: ① Limitations in individual behavior simulation: Existing multi-agent modeling systems are not yet perfect in terms of the flexibility of individual behavior and rule setting, making it difficult to truly reflect the behavior patterns of individuals in complex environments such as public transportation. This limitation affects the accurate simulation of virus transmission routes, restricting the accuracy and practicality of simulation results. ② Insufficient dynamic interaction of traffic networks: Virus transmission is not only affected by population flow but also significantly restricted by urban traffic networks. In current simulation systems, traffic networks usually exist as static models, lacking interactive simulation of the dynamic changes in transportation systems such as buses and subways. This deficiency restricts the accuracy and flexibility of the system in simulating virus transmission through various transportation modes. ③ Insufficient support for custom scenarios: In practical applications, environmental factors, traffic facility layouts, and population densities vary significantly in different cities and regions, resulting in different virus transmission patterns and routes. Most existing systems lack support for custom scenarios and cannot flexibly adjust simulation parameters to meet the needs of specific urban environments, which poses an obstacle to the practicality of public health emergency management and virus diffusion path analysis. ④ Insufficient real-time rendering and interaction performance: The process of virus leakage and transmission requires rapid and accurate real-time feedback to support urban managers in responding in a timely manner. However, existing simulation systems have bottlenecks in real-time rendering and interaction performance, making it difficult to achieve efficient presentation of transmission routes and real-time decision support, thereby reducing the applicability of the system in actual emergency situations.

[0005] Therefore, aiming at the problems existing in the existing technologies, it is particularly important to provide a technology for simulating and analyzing urban virus transmission paths that can comprehensively simulate the transmission paths, consequences, and various experimental scenarios of virus leakage. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for simulating and analyzing urban virus transmission paths, which utilize a multi-agent model in cooperation with urban traffic network data to achieve city-level transmission rendering and an interactive simulation interface, aiming to quickly and accurately predict the transmission situation after virus leakage, enhance public health response capabilities, and improve the safety awareness of urban managers and the prevention and assessment of leakage occurrences.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions: The present invention provides a method for simulating and analyzing the urban virus transmission path, and the method includes the following steps: S1. Urban scene construction: Obtain the data of the GIS module to establish an urban scene for simulating virus transmission. The urban scene is a virtual environment, including at least building, road network, and public transportation line data; S2. Individual and crowd behavior simulation: Use the multi-agent modeling module to model individual behavior, set the activity trajectory for simulation, and record the reactions of individuals in different risk areas; and set the crowd density and contact mode to simulate the flow of urban crowds during the daily life, study, and work of individuals; S3. Dynamic simulation of traffic network: Set the dynamic traffic module, combine virus transmission with the dynamic traffic module, and simulate the transmission path of the virus in public transportation; when simulating the transmission path of the virus, set the traffic flow, traffic line, traffic schedule, and vehicle airtightness data; S4. Transmission path and dynamic update: Import the virus transmission data in real time through the backend data module, simulate the transmission path, the number of infected people, and the transmission speed; obtain the crowd flow data in step S2 and the traffic network change data in step S3, obtain the diffusion range of the virus and dynamically update it; analyze the transmission path and transmission risk in real time to form the transmission path data and risk data; S5. Three-dimensional visualization and real-time feedback: Use the three-dimensional rendering module to perform three-dimensional display of virus transmission, and read the virus transmission path data and risk data in real time, and display the transmission path, crowd density, and high-risk areas through the interaction interface; S6. Analysis and output of simulation results: Obtain the virus transmission path and risk data, and generate an analysis report; the analysis report includes at least the number of infected people, transmission speed, and diffusion range data.

[0008] Specifically, the urban scene construction is the basic step of the simulation analysis method, which provides the spatial background required for virus transmission. After the urban scene is constructed, the virus transmission path, influence range, and crowd flow can be truly reflected. It generates a three-dimensional map of the city through the data of the GIS module (Geographic Information System or Geo-Information system, GIS, Geographic Information System), including road network, bus lines, subways, etc., and simulates a realistic urban scene to provide a real environmental framework for virus transmission. The construction of the urban scene provides a spatial reference for subsequent virus transmission and crowd flow, enabling the simulation to be carried out in a space consistent with reality.

[0009] Specifically, virus transmission highly depends on individual contacts and population mobility behaviors. The infection status of individuals and their behaviors in high-risk areas (such as activity trajectories) determine the virus diffusion pattern. Therefore, the modeling of individual and population behaviors is the core of transmission simulation. In the present invention, a multi-agent modeling module is used to model individual behaviors and simulate their responses and activity trajectories in high-risk areas. Meanwhile, the mobility of urban populations is simulated, and combined with population density and contact patterns, it provides refined behavior and mobility modeling support for virus transmission.

[0010] Specifically, virus transmission is combined with the dynamic traffic system to simulate the virus transmission situation in public transportation such as buses and subways. This simulation method takes into account factors such as traffic flow, routes, schedules, and vehicle airtightness to improve the realism of the transmission simulation. By obtaining traffic routes and schedule frequencies, setting the schedule frequencies, average passenger volumes, and peak-hour flows of each traffic route, the virus transmission differences at different time periods are simulated. And pay attention to the dynamic changes in traffic flow, model the changes in traffic flow during the day and night, and combine the passenger flows during peak and off-peak travel periods to simulate the dynamic changes in the transmission speed with the traffic volume.

[0011] Specifically, the transmission path and dynamic update ensure that the simulation can adjust the infected areas and the number of people in a timely manner with the population mobility and traffic changes; this is a key step in virus transmission simulation, making the simulation results more in line with the virus diffusion situation in reality. In the present invention, transmission data is injected in real time through the backend data module, such as the current number of infected people, infection rate, and transmission speed. And according to the population mobility and traffic network changes, the virus transmission path and diffusion range are dynamically updated; the addition of dynamic data ensures the real-time nature of the simulation, making the simulation results synchronized with the actual situation.

[0012] Specifically, the setting of 3D visualization and real-time feedback can provide users with intuitive transmission information and scenario displays, enabling users to clearly observe the development of the epidemic situation, which is a means to assist in monitoring and decision-making. Through 3D rendering, users can see the visualization displays of the transmission path, population density, and high-risk areas in real time. The interactive interface also provides heat maps and markers to show the crowd density and high-risk areas, facilitating users' quick analysis and judgment; the data update frequency can be adjusted to ensure that the visualization display is always consistent with the latest transmission data and supports users' real-time monitoring needs.

[0013] Specifically, the step setting of simulation result analysis and output can generate a data analysis report. With the support of basic data such as the transmission path, the number of infected people, and the transmission speed, a risk assessment report is generated to quantitatively evaluate the probability of infection, exposure risk, and diffusion range. The analysis results can be used as a scientific basis for emergency response and output as a visualization report to support public health management and decision-making.

[0014] For the above, the method for simulating the urban virus transmission path may further include the following steps: S7. Set custom and interactive functions: The user sets a custom scenario according to requirements, including setting the outbreak source, adjusting high-risk areas, changing individual behaviors or traffic conditions, and testing different virus transmission scenarios in real time.

[0015] Specifically, the system can be set to allow the user to customize the scenario according to requirements, such as setting the outbreak source, adjusting high-risk areas, changing individual behaviors or traffic conditions, etc. Through an interactive method, the user can test different virus transmission scenarios in real time. This interactive simulation function provides flexibility for the user, enabling customization for specific emergency situations or emergency drill scenarios, and enhancing the applicability and practical value of the system.

[0016] For the above, in the step S1, the GIS module data includes data resolution, update frequency, and data coverage to make the urban scene details conform to reality; the road network data includes backbone network data and pedestrian flow distribution data to ensure the rationality of urban traffic flow and population flow; the public transportation line data includes the distribution, schedules, congestion levels, and passenger flow data of bus or subway public transportation lines to provide a reasonable flow channel for the virus transmission path in the city.

[0017] For the above, in the step S1, the processing method for constructing the urban scene is as follows: S1-1: Select a map file with a required clarity and mark the POI points (Points of Interest); S1-2: Use the map database to select the full-element data of the roads in the map, construct the public transportation lines through the network analysis module, and draw the map pipelines; S1-3: Perform coloring processing on different maps and points of interest.

[0018] For the above, in the step S2, the processing method for simulating individual and crowd behaviors is as follows: S2-1: When modeling, set individual attributes; the individual attribute data includes individual health status and infection probability data; S2-2: Randomly process the reaction speeds of different individuals and generate activity trajectories using programming software; S2-3: Set the crowd density and set the contact parameters under different contact scenarios, and randomly select the contact mode for each contact event.

[0019] For the above, in the step S3, the processing method for dynamically simulating the traffic network is as follows: S3-1: Obtain the public transportation information and traffic information from the map database and write them into a pre-established database; S3-2: Obtain the system time during simulation, read information from the backend data generation database, perform downsampling processing on the read data, and then store the downsampled data in the front-end display database for display.

[0020] For the above, in the step S4, the processing method of the propagation path and dynamic update is as follows: S4-1: Set the injection frequency of virus propagation data, and update the propagation data of the virus in real time through the backend data module, including the infection rate and the diffusion range; S4-2: Set the flow data of the infected population, and dynamically update the propagation path according to the flow characteristics of the infected population in different regions; S4-3: Read the traffic network change data, adjust the dynamic diffusion area of the propagation model according to the real-time traffic data, obtain the influence data of the traffic density at different time periods on the virus propagation, and form the propagation path data and risk data.

[0021] For the above, in the step S5, the processing method of 3D visualization and real-time feedback is as follows: S5-1: Generate a real-time monitoring interface, which includes an interactive panel that displays the dynamic update of the current number of infected people, the propagation path, the infection hotspots, and the high-risk areas; S5-2: Generate a crowd density and contact risk display interface, and display the crowd density distribution data and high-contact risk areas by means of a heat map or marking.

[0022] For the above, in the step S6, the processing method of simulation result analysis and output is as follows: S6-1: Trace and analyze the virus propagation path, and record the key diffusion nodes, the distribution of the infected population, and the change trend of the propagation speed; S6-2: Based on the data of the number of infected people, the propagation speed, and the diffusion range, establish a risk assessment model, output the assessment data of the infection probability, the exposure risk, and the affected area, and generate an analysis report.

[0023] The present invention also provides a system for simulating and analyzing the urban virus propagation path, which applies the method for simulating and analyzing the urban virus propagation path as described above; the system at least includes an urban scene construction unit, an individual and crowd behavior simulation unit, a traffic network dynamic simulation unit, a propagation path and dynamic update unit, a 3D visualization and real-time feedback unit, and a simulation result analysis and output unit; wherein, The urban scene construction unit is configured to obtain the data of the GIS module connected thereto to establish an urban scene for simulating virus propagation; The individual and crowd behavior simulation unit is configured to model individual behavior using a multi-agent modeling module, set activity trajectories for simulation, and record the reactions of individuals in different risk areas; and set crowd density and contact patterns to simulate the flow of urban crowds during the activity of individuals along the activity trajectories. The dynamic traffic network simulation unit is configured to set a dynamic traffic module to simulate the transmission path of the virus in public transportation; when simulating the transmission path of the virus, set traffic flow, traffic routes, traffic schedules, and vehicle airtightness data. The transmission path and dynamic update unit is configured to connect to an external or built-in backend data module, import virus transmission data in real time through the backend data module, simulate the transmission path, the number of infected people, and the transmission speed; obtain the crowd flow data transmitted by the individual and crowd behavior simulation unit and the traffic network change data transmitted by the dynamic traffic network simulation unit, obtain the diffusion range of the virus and dynamically update it; analyze the transmission path and transmission risk in real time to form transmission path data and risk data. The 3D visualization and real-time feedback unit is configured to use a 3D rendering module to perform a 3D display of virus transmission. The simulation result analysis and output unit is configured to obtain the virus transmission path and risk data and generate an analysis report.

[0024] Advantages of the present invention: The present invention provides a method and system for simulating and analyzing the transmission path of urban viruses. By providing an intelligent quantitative analysis report generation function, this analysis method and system can not only provide key data such as the number of infected people, transmission speed, and diffusion range, but also automatically generate risk assessments and emergency plan suggestions, with the advantages of being simple and intuitive to operate. By customizing the laboratory environment, operation steps, and specific leakage scenarios, it can simulate leakage scenarios that may be caused by different operation errors or equipment failures and dynamically quantify and evaluate their consequences. Researchers can identify potential leakage risk points in the simulation experiment, enhance accident prevention awareness, and formulate more rigorous safety measures, thereby reducing the leakage risk in actual experiments, with the advantage of effectively preventing laboratory leakage accidents. By introducing an agent module, it can simulate details such as individual health status, infection probability, and activity trajectories, improve the accuracy of individual reactions and crowd density distribution, make up for the deficiencies of traditional models in individual differences, and make the simulation results more personalized and detailed, with the advantage of being precise. In addition, it incorporates dynamic traffic networks such as buses and subways into virus transmission simulation, considers factors such as schedule frequency, airtightness, and passenger flow, can more realistically reflect the virus transmission situation in public transportation, is particularly suitable for complex urban environments, and provides more valuable simulation data for actual decision-making, with the advantage of enhancing real-world adaptability. Description of the Drawings

[0025] Figure 1 Schematic flow chart of the urban virus transmission path simulation and analysis method provided by the embodiment of the present invention; Figure 2 Schematic diagram of the principle of the urban virus transmission path simulation and analysis system provided by the embodiment of the present invention. Detailed implementation manners

[0026] The following further describes the detailed implementation manners of the present invention with reference to the accompanying drawings.

[0027] As Figure 1 shown, this embodiment provides an urban virus transmission path simulation and analysis method, and the method includes the following steps: Step S1. Urban scene construction: Obtain data from the GIS module to establish an urban scene for simulating virus transmission. The urban scene is a virtual environment, including at least building, road network, and public transportation line data. Among them, the GIS module data includes data resolution, update frequency, and data coverage to make the details of the urban scene consistent with the reality; the road network data includes trunk network data and pedestrian flow distribution data to ensure the rationality of urban traffic flow and population flow; the public transportation line data includes the distribution, schedules, congestion levels, and passenger flow data of bus or subway public transportation lines to provide a reasonable flow channel for the virus transmission path in the city. In the embodiment, the specific processing method for urban scene construction is: S1-1: Select a map file with a qualified clarity and mark the POI points; S1-2: Use OpenStreetMap (map database) to select the full-element data of the roads in the map, construct public transportation lines through the Network Analyst (network analysis module), and draw the map pipelines; S1-3: Perform coloring processing on different maps and points of interest.

[0028] The urban scene construction in step S1 is the basic step of the simulation and analysis method, which provides the spatial background required for virus transmission. After the urban scene is constructed, the virus transmission path, influence range, and population flow can be truly reflected. It generates a three-dimensional map of the city through the GIS module data, including road networks, bus lines, subways, etc., and simulates a realistic urban scene to provide a real environmental framework for virus transmission. The construction of the urban scene provides a spatial reference for subsequent virus transmission and population flow, enabling the simulation to be carried out in a space consistent with the reality.

[0029] Step S2. Individual and crowd behavior simulation: Use the multi-agent modeling module to model individual behavior, set activity trajectories for simulation, and record the reactions of individuals in different risk areas; and set crowd density and contact patterns to simulate the flow of urban crowds during the process of individuals moving along the activity trajectories. In this embodiment, in step S2, the specific processing method of individual and crowd behavior simulation is as follows: S2-1: When modeling, set individual attributes; the individual attribute data includes individual health status and infection probability data. S2-2: Randomly process the reaction speeds of different individuals, and use unity3D to generate activity trajectories. S2-3: Set crowd density and set contact parameters under different contact scenarios, and randomly select contact patterns for each contact event.

[0030] The settings in step S2 are because the spread of the virus highly depends on individual contacts and crowd movement behaviors. The infection status of individuals and their behaviors (such as activity trajectories) in high-risk areas determine the spread pattern of the virus. Therefore, the modeling of individual and crowd behaviors is the core of transmission simulation. In this embodiment, the multi-agent modeling module is used to model individual behavior, simulate their reactions and activity trajectories in high-risk areas. At the same time, the flow of urban crowds is simulated, combined with crowd density and contact patterns, to provide refined behavior and flow modeling support for virus transmission.

[0031] Step S3. Dynamic simulation of transportation network: Set up a dynamic transportation module, combine virus transmission with the dynamic transportation module, and simulate the transmission path of the virus in public transportation; when simulating the transmission path of the virus, set traffic flow, traffic routes, traffic schedules, and vehicle airtightness data. In this embodiment, the specific processing method of dynamic simulation of transportation network is as follows: S3-1: Obtain public transportation information and traffic flow information from OpenStreetMap and write them into the SQL database. S3-2: When performing simulation, obtain the system time, read information from the database, perform downsampling processing on the read backend data, and then store the downsampled data in the front-end database (MySQL database) and present it on the visualization platform.

[0032] Step S3 combines virus transmission with the dynamic traffic system to simulate the spread of the virus in public transportation such as buses and subways. This simulation method takes into account factors such as traffic flow, routes and schedules, as well as the airtightness of vehicles, etc., to improve the realism of the transmission simulation. By obtaining traffic routes and schedule frequencies, setting the schedule frequencies, average passenger volumes and peak-hour flows of each traffic route, the differences in virus transmission at different time periods are simulated. And pay attention to the dynamic changes in traffic flow, model the changes in traffic flow during the day and at night, and combine the passenger flows during peak and off-peak travel periods to simulate the dynamic changes in the transmission speed with the traffic volume.

[0033] Step S4. Transmission path and dynamic update: Real-time import virus transmission data through the backend data module to simulate the transmission path, the number of infected people and the transmission speed; obtain the population flow data in Step S2 and the traffic network change data in Step S3 to obtain the diffusion range of the virus and dynamically update it; analyze the transmission path and transmission risk in real time to form transmission path data and risk data; in this embodiment, the specific processing method of the transmission path and dynamic update is as follows: S4-1: Set the virus transmission data injection frequency, and update the virus transmission data in real time through the backend data module, including the infection rate and the diffusion range; S4-2: Set the mobile data of the infected population, and dynamically update the transmission path according to the mobility characteristics of the infected population in different regions; S4-3: Read the traffic network change data, adjust the dynamic diffusion area of the transmission model according to the real-time traffic data, obtain the influence data of traffic density at different time periods on virus transmission, and form transmission path data and risk data.

[0034] The dynamic update of the transmission path in Step S4 ensures that the simulation can timely adjust the infected area and the number of people with the changes in population flow and traffic; this is a key step in virus transmission simulation, making the simulation results more in line with the actual virus diffusion situation. In this embodiment, transmission data such as the current number of infected people, the infection rate and the transmission speed are injected in real time through the backend data module. And according to the population flow and traffic network changes, dynamically update the transmission path and diffusion range of the virus; the addition of dynamic data ensures the real-time nature of the simulation, making the simulation results synchronized with the actual situation.

[0035] Step S5. Three-dimensional visualization and real-time feedback: Use the three-dimensional rendering module to perform three-dimensional display of virus transmission, and read the virus transmission path data and risk data in real time, and display the transmission path, population density and high-risk areas through the interactive interface; in this embodiment, the specific processing method of three-dimensional visualization and real-time feedback is as follows: S5-1: Generate a real-time monitoring interface which includes an interactive panel that displays the dynamic updates of the current number of infected people, transmission routes, infection hotspots, and high-risk areas; S5-2: Generate a crowd density and contact risk display interface, and use a heat map or marking method to display the crowd density distribution data and high-contact-risk areas.

[0036] In step S5, the setting of 3D visualization and real-time feedback can provide users with intuitive transmission information and scenario displays, enabling users to clearly observe the development of the epidemic situation. It is a means to assist monitoring and decision-making. Through 3D rendering, users can see the visual displays of transmission routes, crowd density, and high-risk areas in real time. The interactive interface also provides heat maps and markings to show the degree of crowd density and high-risk areas, facilitating users' quick analysis and judgment; the frequency of data updates can be adjusted to ensure that the visual display is always consistent with the latest transmission data and support users' real-time monitoring needs.

[0037] Step S6. Simulation result analysis and output: Obtain the virus transmission route and risk data, and generate an analysis report; the analysis report includes at least the number of infected people, transmission speed, and spread range data. In this embodiment, the specific processing method for simulation result analysis and output is as follows: S6-1: Trace and analyze the virus transmission route, and record the key diffusion nodes, the distribution of infected populations, and the changing trend of transmission speed; S6-2: Based on the data of the number of infected people, transmission speed, and spread range, establish a risk assessment model, the SEIR model, output the assessment data of infection probability, exposure risk, and affected areas, and generate an analysis report.

[0038] With the support of basic data such as the transmission route, the number of infected people, and transmission speed in step S6, a risk assessment report is generated to quantitatively evaluate the infection probability, exposure risk, and spread range. The analysis results can be used as a scientific basis for emergency response and output as a visual report to support public health management and decision-making.

[0039] Step S7. Set custom and interactive functions: Users set custom scenarios according to their needs, including setting the outbreak source, adjusting high-risk areas, changing individual behaviors, or traffic conditions, and testing different virus transmission scenarios in real time.

[0040] By setting step S7, users can customize scenarios according to their needs, such as setting the outbreak source, adjusting high-risk areas, changing individual behaviors, or traffic conditions, etc. Through an interactive method, users can test different virus transmission scenarios in real time. This interactive simulation function provides users with flexibility, enabling customization for specific emergency situations or emergency drill scenarios, and enhancing the applicability and practical value of the system.

[0041] As Figure 2 shown, this embodiment also provides a simulation analysis system for the urban virus transmission path. The system includes an urban scene construction unit, an individual and crowd behavior simulation unit, a traffic network dynamic simulation unit, a transmission path and dynamic update unit, a three-dimensional visualization and real-time feedback unit, and a simulation result analysis and output unit. Among them, the urban scene construction unit is configured to obtain the data of the GIS module connected thereto to establish an urban scene for simulating virus transmission; the individual and crowd behavior simulation unit is configured to use a multi-agent modeling module to model individual behaviors, set activity trajectories for simulation, and record the reactions of individuals in different risk areas; and set crowd density and contact patterns to simulate the flow of urban crowds during the process of individuals moving along the activity trajectories; the traffic network dynamic simulation unit is configured to set a dynamic traffic module to simulate the transmission path of the virus in public transportation; when simulating the transmission path of the virus, set traffic flow, traffic routes, traffic schedules, and vehicle airtightness data; the transmission path and dynamic update unit is configured to connect to an external or built-in backend data module, import virus transmission data in real time through the backend data module, simulate the transmission path, the number of infected people, and the transmission speed; obtain the crowd flow data transmitted by the individual and crowd behavior simulation unit and the traffic network change data transmitted by the traffic network dynamic simulation unit, obtain the diffusion range of the virus and dynamically update it; analyze the transmission path and transmission risk in real time to form transmission path data and risk data; the three-dimensional visualization and real-time feedback unit is configured to use a three-dimensional rendering module to perform three-dimensional display of virus transmission; the simulation result analysis and output unit is configured to obtain the virus transmission path and risk data and generate an analysis report.

[0042] According to the disclosure and teachings of the above specification, those skilled in the art to which the present invention pertains can also make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. In addition, although some specific terms are used in this specification, these terms are only for convenience of description and do not constitute any limitation to the present invention.

Claims

1. A method for simulating and analyzing the transmission path of urban viruses, characterized in that, The method includes the following steps: S1. Urban scene construction: Obtain data from the GIS module to establish an urban scene for simulating virus transmission. The urban scene is a virtual environment, including at least building, road network, and public transportation line data. S2. Individual and crowd behavior simulation: Use the multi-agent modeling module to model individual behavior, set activity trajectories for simulation, and record the reactions of individuals in different risk areas. Set crowd density and contact patterns to simulate the flow of urban crowds during individuals' daily life, study, and work. S3. Dynamic simulation of transportation network: Set up a dynamic traffic module, combine virus transmission with the dynamic traffic module, and simulate the transmission path of the virus in public transportation. When simulating the transmission path of the virus, set traffic flow, traffic routes, traffic schedules, and vehicle airtightness data. S4. Transmission path and dynamic update: Import virus transmission data in real time through the backend data module to simulate the transmission path, the number of infected people, and the transmission speed. Obtain the crowd flow data in step S2 and the transportation network change data in step S3 to obtain the spread range of the virus and dynamically update it. Analyze the transmission path and transmission risk in real time to form transmission path data and risk data. S5. Three-dimensional visualization and real-time feedback: Use the three-dimensional rendering module to display the virus transmission in three dimensions, and read the virus transmission path data and risk data in real time. Display the transmission path, crowd density, and high-risk areas through the interaction interface. S6. Analysis and output of simulation results: Obtain the virus transmission path and risk data, and generate an analysis report. The analysis report includes at least the number of infected people, the transmission speed, and the spread range data.

2. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein It also includes step S7. Setting custom and interactive functions: Users can set custom scenarios according to their needs, including setting the outbreak source, adjusting high-risk areas, changing individual behavior, or traffic conditions, and testing different virus transmission scenarios in real time.

3. The method for simulating and analyzing the urban virus transmission path according to claim 1, characterized in that It also includes: In step S1, the GIS module data includes data resolution, update frequency, and data coverage. The road network data includes backbone network data and pedestrian flow distribution data. The public transportation line data includes the distribution, schedule, congestion, and passenger flow data of bus or subway public transportation lines.

4. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In step S1, the processing method of urban scene construction is: S1-1: Select a map file with qualified clarity and mark POI points. S1-2: Use the map database to select the full-element road data in the map, construct public transportation lines through the network analysis module, and draw map pipelines. S1-3: Perform coloring processing on different maps and points of interest.

5. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In step S2, the processing method of individual and crowd behavior simulation is: S2-1: When modeling, set individual attributes. The individual attribute data includes individual health status and infection probability data. S2-2: Randomly process the reaction speeds of different individuals and generate activity trajectories using programming software. S2-3: Set crowd density and contact parameters for different contact scenarios, and randomly select contact modes for each contact event.

6. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In the step S3, the processing method of the traffic network dynamic simulation is as follows: S3-1: Obtain public transportation information and traffic flow information from the map database and write them into a pre-established database; S3-2: When performing the simulation, obtain the system time, read information from the back-end data generation database, perform downsampling processing on the read data, and then store the downsampled data in the front-end display database for display.

7. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In the step S4, the processing method of the propagation path and dynamic update is as follows: S4-1: Set the virus propagation data injection frequency, and update the virus propagation data in real time through the back-end data module, including the infection rate and the diffusion range; S4-2: Set the mobile data of the infected population, and dynamically update the propagation path according to the mobility characteristics of the infected population in different regions; S4-3: Read the traffic network change data, adjust the dynamic diffusion area of the propagation model according to the real-time traffic data, obtain the influence data of the traffic density at different time periods on the virus propagation, and form the propagation path data and risk data.

8. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In the step S5, the processing method of the three-dimensional visualization and real-time feedback is as follows: S5-1: Generate a real-time monitoring interface, which includes an interactive panel that displays the dynamic update of the current number of infected people, the propagation path, the infection hotspots, and the high-risk areas; S5-2: Generate a crowd density and contact risk display interface, and display the crowd density distribution data and the high-contact risk areas by means of a heat map or marking.

9. The method for simulating and analyzing the urban virus transmission path according to claim 1, wherein In the step S6, the processing method of the simulation result analysis and output is as follows: S6-1: Trace and analyze the virus propagation path, and record the key diffusion nodes, the distribution of the infected population, and the change trend of the propagation speed; S6-2: Based on the data of the number of infected people, the propagation speed, and the diffusion range, establish a risk assessment model, output the assessment data of the infection probability, the exposure risk, and the affected area, and generate an analysis report.

10. A system for simulating and analyzing the transmission path of urban viruses, characterized in that, Apply the method for simulating and analyzing the urban virus propagation path according to any one of claims 1 to 9; the system at least includes an urban scene construction unit, an individual and crowd behavior simulation unit, a traffic network dynamic simulation unit, a propagation path and dynamic update unit, a three-dimensional visualization and real-time feedback unit, and a simulation result analysis and output unit; wherein, The urban scene construction unit is configured to obtain the data of the GIS module connected thereto to establish an urban scene for simulating virus propagation; The individual and crowd behavior simulation unit is configured to use a multi-agent modeling module to model individual behaviors, set the activity trajectories for simulation, and record the responses of individuals in different risk areas; and set the crowd density and contact patterns to simulate the flow of urban crowds during the process of individuals moving along the activity trajectories; The traffic network dynamic simulation unit is configured to set a dynamic traffic module to simulate the virus propagation path in public transportation; when simulating the virus propagation path, set the traffic flow, traffic routes, traffic schedules, and vehicle airtightness data; The propagation path and the dynamic update unit are configured to be connected to an external or built-in backend data module, and import virus propagation data in real time through the backend data module to simulate the propagation path, the number of infected people, and the propagation speed; obtain the population flow data transmitted by the individual and population behavior simulation unit and the traffic network change data transmitted by the traffic network dynamic simulation unit, obtain the diffusion range of the virus and dynamically update it; analyze the propagation path and propagation risk in real time to form propagation path data and risk data; The three-dimensional visualization and real-time feedback unit is configured to use a three-dimensional rendering module to perform a three-dimensional display of virus propagation; The simulation result analysis and output unit is configured to obtain the virus propagation path and risk data and generate an analysis report.

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