A Method and System for Quantitative Assessment of Infectious Disease Risk under Population Mobility

Abstract

This invention discloses a method and system for quantitatively assessing the risk of infectious diseases under population movement. The method includes scenario modeling, generating a simulation scenario using GIS data; determining the population movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in population movement risk; and, based on the simulation scenario, combining it with the population movement modeling and vehicle flow modeling, injecting epidemic data to realize the simulation and deduction of personnel flow, contact between people, and / or regional population density in the simulation scenario. This method enables the simulation and deduction of population transmission based on application scenarios, population movement, and droplet diffusion models, providing scientific assessment and reference for the formulation of prevention and control plans.

Description

Technical Field

[0001] This invention relates to the field of computer simulation technology, and in particular to a method and system for quantitatively assessing the risk of infectious diseases under population mobility. Background Technology

[0002] In recent years, respiratory infectious diseases have occurred frequently on a large scale, with population movement and gatherings being the core risks of epidemic transmission. Current technologies lack simulation and assessment of the epidemic transmission risks caused by population movement and gatherings, and also lack methods for using computer simulation to simulate population susceptibility and disease epidemic patterns in real-world scenarios based on geographic information. Summary of the Invention

[0003] The main technical problem solved by this invention is to provide a method and system for quantitative assessment of infectious disease risk under population mobility, which solves the problem of lack of risk assessment of epidemic spread in the population based on simulation scenario conditions in the existing technology.

[0004] To address the aforementioned technical problems, one technical solution adopted by this invention is to provide a method for quantitatively assessing the risk of infectious diseases under population mobility, comprising the following steps:

[0005] Step S1: Scene modeling, using GIS data to generate a simulation scene;

[0006] Step S2: Determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in the risk of population movement;

[0007] Step S3: Based on the simulation scenario, combined with the crowd movement modeling and vehicle flow modeling, inject epidemic data to realize the simulation and deduction of personnel flow, contact between personnel and / or regional population density in the simulation scenario.

[0008] Preferably, in step S1, the GIS data includes static scene data, travel data, video data, and / or risk area data.

[0009] Preferably, step S1 further includes preprocessing the risk area data: preprocessing the trajectory data of confirmed or suspected cases; if the trajectory data of confirmed or suspected cases is uniquely determined, the area involved in the trajectory data of confirmed or suspected cases is directly set as a high-risk area; if the trajectory data of confirmed or suspected cases is not uniquely determined, a gradient risk level is set for all possible driving trajectories according to the different lengths of the routes traversed; then the risk level of all possible driving trajectories is mapped to the road network data and building data in the corresponding static scene data, and finally the risk level of each location in the scene is reflected in the risk level of the building.

[0010] Preferably, in step S2, the crowd motion modeling includes determining the magnitude of the motion speed of individual i within the crowd. Solve the following:

[0011]

[0012] in: and Let E and ξ represent the expected speed and maximum speed of individual i under normal conditions (panic value of 0), respectively, and their values ​​can be 1.4 m / s and 2 m / s, respectively. Let E represent the individual's emotional value and ξ represent the individual's physical strength decay coefficient.

[0013] Preferably, high-risk areas are defined as potential sources of danger, and only the impact of these sources on emotional values ​​is considered. An individual's emotional value E is represented as:

[0014]

[0015] Where L represents the individual's distance from the hazard source. If the individual is located in a high-risk area, then L = 0; otherwise, L = L. s / R, where L s R is the distance between the current individual and the nearest high-risk area, and R is the diameter of the current high-risk area.

[0016] Preferably, in step S2, the droplet diffusion model is represented as follows:

[0017]

[0018]

[0019] in: ρ, u, p, t, and f represent density, velocity, pressure, time, and external force, respectively. Denotes divergence, The gradient is used; the default wind speed inside the vehicle is 0.

[0020] Preferably, in step S2, the risk situation modeling is represented as:

[0021]

[0022] Where: P represents the probability that the current individual is infected, p k Let represent the propagation probability at the k-th contact; K represents the number of effective contacts.

[0023] Preferably, in step S3, the epidemic data includes environmental data, scene data, personnel data, and risk source data.

[0024] Preferably, step S3 includes setting parameters such as personnel travel routes, personnel transportation modes, vehicle speed limits, and vehicle trajectory parameters within the scenario to simulate and verify the control plan.

[0025] This invention also provides a system for quantitatively assessing the risk of infectious diseases under population mobility, comprising:

[0026] The scene modeling module is used to generate simulation scenes using GIS data;

[0027] The risk factor modeling module is used to determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in the risk of population movement.

[0028] The simulation module is used to simulate and extrapolate the flow of people, contact between people, and / or the density of people in a region based on the simulation scenario, in conjunction with the crowd movement modeling and vehicle flow modeling.

[0029] The beneficial effects of this invention are as follows: This invention discloses a method and system for quantitatively assessing the risk of infectious diseases under population movement. The method includes scenario modeling, generating a simulation scenario using GIS data; determining the population movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in population movement risk; and, based on the simulation scenario, combining it with the population movement modeling and vehicle flow modeling, injecting epidemic data to realize the simulation and deduction of personnel movement, contact between people, and / or regional population density in the simulation scenario. This method achieves population transmission simulation and deduction based on application scenarios, population movement, and droplet diffusion models, providing scientific assessment and reference for the formulation of prevention and control plans. Attached Figure Description

[0030] Figure 1 This is a flowchart according to an embodiment of the present invention;

[0031] Figure 2 This is a topographic map of a region according to an embodiment of the present invention;

[0032] Figure 3 It is based on Figure 2 Buildings and road networks generated from GIS data of a central region topographic map;

[0033] Figure 4 It is a combination Figure 3 A visual diagram illustrating the risk warning and public trajectory risk assessment functions obtained from the simulation scenario;

[0034] Figure 5 It is a combination Figure 3 The schematic diagram of the planned route obtained from the simulation scenario. Detailed Implementation

[0035] To facilitate understanding of the present invention, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0036] It should be noted that, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0037] Figure 1 This invention illustrates an embodiment of the method and system for quantitative assessment of infectious disease risk under population mobility, including:

[0038] Step S1: Scene modeling, using GIS data to generate a simulation scene;

[0039] Step S2: Determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in the risk of population movement;

[0040] Step S3: Based on the simulation scenario, combined with the crowd movement modeling and vehicle flow modeling, inject epidemic data to realize the simulation and deduction of personnel flow, contact between personnel and / or regional population density in the simulation scenario.

[0041] This assessment method first reduces the difficulty of scenario construction by modeling scenarios using open-source GIS data. Then, it models and simulates core elements related to the risk management of respiratory infectious diseases, such as population movement, vehicle movement, and droplet spread, thereby improving the reliability of the simulation results. Furthermore, it can utilize Level of Dimension (LOD) technology for 3D visualization and visual analysis of population movement risks, and supports interactive editing by users, enhancing the method's practicality.

[0042] Preferably, in step S1, the GIS data includes static scene data, travel data, video data, and / or risk area data. Specifically, it includes static scene data such as open-source imagery, road network data, and building data; user travel data captured by mobile devices; and video data and risk area data captured by cameras. This data is open-source and can be easily imported into the simulation system.

[0043] Preferably, preprocessing is performed on the static scene data, travel data, video data, and risk area data. Preferably, preprocessing of the static scene data includes preprocessing of road network data and building data.

[0044] Furthermore, the preprocessing of road network data includes addressing issues such as road redundancy, road overlap, and road disconnection.

[0045] (1) Road redundancy refers to the existence of roads less than 5 meters long at intersections. Such roads almost do not exist in real life, and they will affect the efficiency of vehicle passage when modeling traffic flow. Therefore, roads at intersections are cut off and all roads less than 5 meters long are removed.

[0046] (2) Road overlap refers to two or more roads that overlap or nearly overlap in space. In this case, the overlapping road area is located through spatial topology analysis, and duplicate roads are removed.

[0047] (3) Disconnected roads refer to roads at intersections that are not connected or are overconnected in space. Here, a circular buffer is created with the road intersection as the center, and spatial topology analysis is performed on the vector roads in the buffer. Disconnected roads will be automatically connected to the center.

[0048] Furthermore, the preprocessing of building data primarily addresses the intersection of open-source building vector data and road vector data. To solve this problem, based on the road centerline, all buildings within the closed area generated by the intersection of road centerlines are merged into a single building vector surface.

[0049] Preferably, travel data includes pedestrian movement trajectory data and vehicle operation trajectory data. The main problems with travel data include missing data, logical errors (e.g., disconnected trajectories), and data exceeding boundaries (trajectory data exceeding the scene area). Preprocessing of travel data includes: performing connectivity and reachability checks on the input trajectory data based on the underlying road network data, and locally correcting and completing problematic trajectories. For data exceeding boundaries, the data is directly cropped proportionally to limit its activity range to the scene area.

[0050] Preferably, video data refers to data such as pedestrian and vehicle movement captured by cameras within the scene. Preprocessing of the video data involves tracking people and vehicles in the video to obtain the pixel coordinates of their trajectories. Then, several static objects in the video are selected, and a feature point matching-based method is used to calculate the corresponding coordinates of their pixels in the 3D scene.

[0051] Preferably, the preprocessing of risk area data involves preprocessing the trajectory data of confirmed or suspected cases. If the trajectory data of a confirmed or suspected case can be uniquely determined, the area involved in that trajectory data is directly set as a high-risk area. If the trajectory data of a confirmed or suspected case cannot be uniquely determined, a gradient risk level is assigned to all possible travel trajectories based on different route lengths. Then, the risk levels of all possible travel trajectories are mapped to the road network data and building data in the corresponding static scene data. Finally, the risk level at each location within the scene is reflected in the risk level of enclosed areas such as buildings.

[0052] Using the aforementioned GIS data and preprocessing it, a relatively accurate three-dimensional simulation scenario can be established. Furthermore, these application scenarios can be combined with the trajectory data of confirmed / suspected cases to simulate and establish risk areas and determine risk levels.

[0053] Preferably, users can edit and modify the GIS data in the generated simulation scene.

[0054] Dynamic editing and modification of road network data mainly includes dynamic editing and modification of road segment data and connection relationship data within the region.

[0055] Dynamic editing and modification of building data mainly includes the dynamic editing and modification of the population count within enclosed buildings such as living areas / working areas.

[0056] Dynamic editing and modification of travel data mainly includes dynamic editing and modification of traffic light data, vehicle speed limits, and the number / type / route / departure frequency of public transportation.

[0057] Real-world maps based on GIS data, such as Figure 2 As shown, the buildings and road network generated based on the GIS data of this real-world scene are as follows: Figure 3 As shown. In Figure 3 In the diagram, the parts marked 1 are buildings, the parts marked 2 are road sections, and the parts marked 3 are intersections.

[0058] Furthermore, in step S2, the crowd motion modeling includes determining the magnitude of the motion speed of individual i within the crowd. Solve the following:

[0059]

[0060] in: and Let E and ξ represent the expected speed and maximum speed of individual i under normal conditions (panic value of 0), respectively, and their values ​​can be 1.4 m / s and 2 m / s, respectively. Let E represent the individual's emotional value and ξ represent the individual's physical strength decay coefficient, which can be a constant ξ = 0.9.

[0061] Preferably, in high-risk areas, individuals travel for shorter periods and minimize contact with other individuals during their trips. High-risk areas are defined as potential sources of danger; considering only the impact of these sources on emotional values, an individual's emotional value E is expressed as:

[0062]

[0063] Where L represents the individual's distance from the hazard source. If the individual is located in a high-risk area, then L = 0; otherwise, L = L. s / R, where L s R represents the distance of the current individual to the nearest high-risk area, and R is the diameter of the current high-risk area. The values ​​of these parameters can be determined by combining the aforementioned risk area data.

[0064] Furthermore, in step S2, the traffic flow modeling includes setting the driving decisions of the currently traveling vehicles, letting the driving decision set Q = [q1, q2], where q1 and q2 represent the categories of decisions, respectively. These represent the vehicle's acceleration, deceleration, constant speed, and stopping, respectively. Let these represent lane changes to the left, going straight, and lane changes to the right, respectively. The driving decision is then represented as:

[0065]

[0066] Where M×N equals the total amount of environmental information, where M can be 1 or 2, N can be 1-4, and p kj Let β be the probability that the driver ultimately perceives the j-th piece of information from class k. 00 ,β kj Let k ∈ M and j ∈ N represent the corresponding parameters.

[0067] Furthermore, in step S2, the droplet diffusion model is represented as follows:

[0068]

[0069]

[0070] in: ρ, u, p, t, and f represent density, velocity, pressure, time, and external force, respectively. Denotes divergence, The gradient is used. The default wind speed inside the vehicle is 0.

[0071] Furthermore, in step S2, the risk situation modeling is expressed as:

[0072]

[0073] Where: P represents the probability that the current individual is infected, p k This represents the transmission probability at the k-th contact. K represents the number of effective contacts. An effective contact is defined as a situation where the spatial distance between an uninfected individual and an infected individual, or when droplets from an infected individual are within a safe distance, is less than the safe distance. The safe distance can be preset, such as 1 meter, 2 meters, etc., or it can be derived by combining crowd movement modeling and droplet diffusion behavior modeling.

[0074] Preferably, in step S3, the epidemic data includes environmental data, scene data, personnel data, and risk source data.

[0075] Environmental data refers to environmental information within the controlled area, and the corresponding parameters include wind direction / wind force, atmospheric visibility, etc.

[0076] Scene data refers to scene information within the controlled area, with corresponding parameters including buildings, road network traffic, and closed areas. Building parameters include attributes such as the number of people inside and whether the area is ventilated. Road network traffic parameters include road restrictions / bans / speed limits / risk levels, vehicle load / optimal speed for various types of vehicles, and public transportation frequency / runtime / routes / stops. Closed area parameters refer to areas where people have various unavoidable and difficult-to-trace direct / indirect contacts due to living, working, or other needs. This mainly refers to schools, workplaces, independent residential areas, independent office areas, and independent buildings. Closed areas also include attribute parameters such as risk level. The risk level within a closed area is divided into three levels: high risk, medium risk, and low risk.

[0077] Personnel data refers to information about individuals requiring management. Corresponding parameters include macro-level attributes such as population density by region, population flow, travel time distribution, and age / gender distribution, as well as micro-level attributes such as initial location, height, risk level, whether self-protection measures were taken (wearing a mask), whether individuals went out, time spent out, walking speed, travel route (including origin, destination, and path), and type of transportation. Risk levels are divided into three levels: Level 1 indicates infection, Level 2 indicates contact with someone who has not been infected, and Level 3 indicates no infection.

[0078] Risk source data refers to the risk sources and risk propagation radius within the control area, and risk sources refer to personnel with a risk level of Level 1.

[0079] Furthermore, in step S3, the epidemic data can be edited and modified in the simulation scenario.

[0080] Preferably, for environmental data, wind direction and wind force directly affect the survival period of droplets in the simulation scenario. Preferably, for scenario data, environmental attribute parameters are used in outdoor scenarios, and indoor scenarios are divided into indoor ventilated scenarios and indoor unventilated scenarios. Environmental attribute parameters are used in indoor ventilated scenarios, while in indoor unventilated scenarios, droplets are not affected by any force in the horizontal direction.

[0081] Preferably, for personnel data, this refers to the individual exhaling the droplets. The individual's height determines the initial location of the droplets, and the frequency of sneezing determines the frequency of droplet ejection. These parameters can be set to improve the accuracy of droplet diffusion modeling.

[0082] Preferably, in step S3, based on the application scenario constructed in step S1, and based on the crowd movement modeling and vehicle flow modeling in step S2, the injected epidemic data enables simulation and intelligent analysis of personnel movement, contact between personnel, and regional population density within the scenario, as well as tracking and retrospective analysis of personnel movement trajectories within the region. The engine supports users in setting parameters such as personnel travel routes, modes of transportation, vehicle speed limits, and vehicle trajectories within the scenario to perform refined simulation and verification of the control plan.

[0083] Furthermore, in step S3, droplet diffusion modeling and / or risk situation modeling can be implemented, allowing users to set environmental attributes (wind direction / force), scene (indoor / outdoor), and host information (height, sneezing frequency). For environmental attributes, wind direction and force directly affect the lifespan of droplets within the scene. For scene attributes, environmental attribute parameters are used in outdoor scenes, while indoor scenes are divided into ventilated and non-ventilated scenarios. Indoor ventilated scenes use environmental attribute parameters, while in non-ventilated scenes, droplets are not affected by any force in the horizontal direction. The host refers to the individual exhaling the droplets; the individual's height determines the initial position of the droplets, and the sneezing frequency determines the frequency of droplet ejection.

[0084] Preferably, in step S3, three-dimensional Levels of Detail (LOD) technology is used to realize a three-dimensional interactive virtual visual simulation of the risk of respiratory infectious diseases spreading in the population.

[0085] Specifically, when presenting large-scale population transmission in cities, heat maps are used to display the distribution density of individuals such as crowds and traffic flow, where the density of individuals in a unit area is equal to the total number of individuals in the current area; when presenting population transmission trends in small communities, indoors, etc., 3D group animation rendering technology is used to achieve visual presentation of population transmission; when the field of view is focused on a single individual or several adjacent individuals, a method combining droplet diffusion behavior animation and human movement behavior animation simulation is used to achieve visualization of individual breathing, orientation, movement, and other behaviors.

[0086] Preferably, a heatmap method is used to visualize the number and risk level of risk sources within a unit of time in any enclosed area, thereby achieving a visual representation of the transmission risk. For example... Figure 4 As shown, the quantity distribution density of risk source 4 is displayed in the form of a heat map.

[0087] Number of Risk Sources: The number of risk sources is mainly generated or eliminated through two aspects: in-scene contact infection and input of control plans. Individuals who fall within the risk transmission radius of a risk source or within the risk transmission radius of droplets exhaled by a risk source become new risk sources. Risk sources exist as attributes of the personnel within the scene. If you want to change a risk source individual to a non-risk source individual, the user needs to manually adjust the personnel (individual) attributes.

[0088] Transmission Risk Level: The transmission risk level refers to the dynamic change in the risk level of a closed area due to the continuous movement of risk sources within the controlled area. It is mainly affected by two factors: intra-scenario transmission and the input of control plans. All areas traversed by an individual with a risk source are high-risk areas. Individuals who fall into high-risk areas but do not become risk sources themselves have their risk level changed to Level 2. Individuals with a Level 2 risk level traverse medium-risk areas, and other areas are low-risk areas.

[0089] Preferably, interactive visual editing, based on epidemic data and a presented 3D virtual simulation of population transmission risks, allows for interactive editing of control plans and is the core of developing such plans. Users can interactively edit relevant parameters related to data such as environment, scene, personnel, and risk sources to gradually improve the plan.

[0090] Preferably, in step S3, the spatiotemporal movement trajectories of publicly confirmed cases and the travel trajectories of public users within the region can be uniformly and structurally organized based on technologies such as data alignment, correlation knowledge discovery, and trajectory data completion. This data can then be made available to relevant users via mobile devices or web pages, enabling users to query the regional epidemic infection risk. Based on the spatiotemporal trajectory data input by the user, the risk timeline of the regions involved in the trajectory is compared to provide a travel risk warning to the user. Furthermore, the user-input trajectory is saved, enabling the aggregation of crowdsourced information. A visual diagram of regional risk alerts and public trajectory risk assessment functions is provided, allowing users to query the risk situation of their surrounding environment.

[0091] It provides interactive editing of all control data, including road network lane width / number of lanes / traffic permission status, personnel departure point / departure time / key points passed through, vehicle departure point / departure time / speed / path, number / distribution / trajectory tracing of risk sources, and risk propagation radius. Among them, trajectory tracing is the tracking and playback of personnel trajectories. By entering the personnel ID, the travel path, starting area, and ending area of ​​that person can be displayed on the map.

[0092] Preferably, users can interactively select and set the movement trajectory of people in the areas where work, production, and school are resuming. Figure 5 The diagram shows the interactive route setting function within the school reopening area. Users can click on any area within the scene to set the school reopening attributes and planned routes within that area. Figure 5 The diagram shows the planned route 7 (dashed line) from one location 5 to another location 6.

[0093] Based on the same concept, the present invention also provides a system for quantitatively assessing the risk of infectious diseases under population mobility, comprising:

[0094] The scene modeling module is used to generate simulation scenes using GIS data;

[0095] The risk factor modeling module is used to determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in the risk of population movement.

[0096] The simulation module is used to simulate and extrapolate the flow of people, contact between people, and / or the density of people in a region based on the simulation scenario, in conjunction with the crowd movement modeling and vehicle flow modeling.

[0097] Therefore, this invention discloses a method and system for quantitatively assessing the risk of infectious diseases under population movement. The method includes scenario modeling, generating a simulation scenario using GIS data; determining the population movement modeling, vehicle flow modeling, droplet diffusion modeling, and / or risk situation modeling involved in population movement risk; and, based on the simulation scenario, combining it with the population movement modeling and vehicle flow modeling, injecting epidemic data to realize the simulation and deduction of personnel movement, contact between people, and / or regional population density in the simulation scenario. This method achieves population transmission simulation and deduction based on application scenarios, population movement, and droplet diffusion models, providing scientific assessment and reference for the formulation of prevention and control plans.

[0098] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for quantitatively assessing the risk of infectious diseases under population mobility, characterized in that, Including the following steps: Step S1: Scene modeling, using GIS data to generate a simulation scene; The GIS data includes static scene data, travel data, video data, and risk area data. It also includes preprocessing of the risk area data: preprocessing the trajectory data of confirmed or suspected cases; if the trajectory data of a confirmed or suspected case is uniquely determined, the area involved in the trajectory data is directly set as a high-risk area; if the trajectory data of a confirmed or suspected case is not uniquely determined, a gradient risk level is set for all possible travel trajectories based on the different lengths of the routes traversed; then, the risk levels of all possible travel trajectories are mapped to the road network data and building data in the corresponding static scene data, and finally, the risk level at each location within the scene is reflected in the risk level of the building. Step S2: Determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and risk situation modeling involved in the risk of population movement; The crowd movement modeling includes the magnitude of the movement speed of individual i within the crowd. Solve the following: ， in: and Let represent the expected speed and maximum speed of individual i under normal conditions, respectively, and E represent the individual's emotional value. Indicates the individual's physical strength decay coefficient; High-risk areas are defined as potential danger sources. Considering only the impact of these danger sources on emotional values, an individual's emotional value E is represented as: Where L represents the individual's distance from the hazard source. If the individual is located in a high-risk area, then L=0; otherwise, L=L s / R, where L s R is the distance between the current individual and the nearest high-risk area, and R is the diameter of the current high-risk area. Step S3: Based on the simulation scenario, combined with the crowd movement modeling and vehicle flow modeling, inject epidemic data to realize the simulation and deduction of personnel flow, contact between personnel and / or regional population density in the simulation scenario.

2. The method for quantitatively assessing the risk of infectious diseases under population mobility according to claim 1, characterized in that, In step S2, the droplet diffusion model is represented as follows: in: ; , u , p , t , f These are density, velocity, pressure, time, and external force, respectively. Denotes divergence, The gradient is used; the default wind speed inside the vehicle is 0.

3. The method for quantitatively assessing the risk of infectious diseases under population mobility according to claim 1, characterized in that, In step S2, the risk situation modeling is represented as follows: Where: P represents the probability that the current individual is infected. Let represent the propagation probability at the k-th contact; K represents the number of effective contacts.

4. The method for quantitatively assessing the risk of infectious diseases under population mobility according to any one of claims 1-3, characterized in that, In step S3, the epidemic data includes environmental data, scenario data, personnel data, and risk source data.

5. The method for quantitatively assessing the risk of infectious diseases under population mobility according to claim 4, characterized in that, In step S3, the control plan is simulated and verified by setting parameters such as the travel routes of people in the scenario, the mode of transportation of people, the speed limit of vehicles, and the trajectory of vehicles.

6. A system for quantitatively assessing the risk of infectious diseases under population mobility, characterized in that, include: The scene modeling module is used to generate simulation scenes using GIS data. The GIS data includes static scene data, travel data, video data, and risk area data. It also includes preprocessing the risk area data: preprocessing the trajectory data of confirmed or suspected cases; if the trajectory data of a confirmed or suspected case is uniquely determined, the area involved in the trajectory data is directly set as a high-risk area; if the trajectory data of a confirmed or suspected case is not uniquely determined, a gradient risk level is set for all possible travel trajectories based on the different lengths of the routes traversed; then, the risk levels of all possible travel trajectories are mapped to the road network data and building data in the corresponding static scene data, and finally, the risk level at each location within the scene is reflected in the risk level of the buildings. The risk factor modeling module is used to determine the crowd movement modeling, vehicle flow modeling, droplet diffusion modeling, and risk situation modeling involved in population flow risks; the crowd movement modeling includes the magnitude of the movement speed of individual i in the crowd. Solve the following: ， in: and Let represent the expected speed and maximum speed of individual i under normal conditions, respectively, and E represent the individual's emotional value. Indicates the individual's physical strength decay coefficient; High-risk areas are defined as potential danger sources. Considering only the impact of these danger sources on emotional values, an individual's emotional value E is represented as: Where L represents the individual's distance from the hazard source. If the individual is located in a high-risk area, then L=0; otherwise, L=L s / R, where L s R is the distance of the current individual from the nearest high-risk area, and R is the diameter of the current high-risk area; the simulation module is used to combine the simulation scenario with the crowd movement modeling and vehicle flow modeling, inject epidemic data, and realize the simulation and deduction of personnel flow, contact between personnel and / or regional population density in the simulation scenario.

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