Micromodeling method and system for simulating market virus propagation based on multiple Agents

Abstract

The invention discloses a micro-modeling method for simulating market virus transmission based on multiple Agents, and belongs to the field of computer simulation. According to the method, through interaction among simulation personnel, open space and policy intervention, the process that viruses are transmitted to other people through air and the surface of an object can be effectively simulated, and influences on different indexes under different policies are revealed; through verification, the model has relatively high reliability, can well couple multiple transmission paths of viruses, comprehensively considers the influence of personnel, space and policy on the transmission of the viruses, and has a good application prospect. The method effectively solves the problems that propagation paths are not considered sufficiently, propagation factors only pay attention to interaction of personnel, theoretical knowledge cannot be broken through to provide practical value for virus prevention and control, and the like, and provides scientific and reasonable epidemic prevention strategy reference for public health decision makers such as market management personnel.

Description

Technical Field

[0001] The invention relates to a multi-agent-based microscopic modeling method for simulating market virus propagation, and belongs to the field of computer simulation. Background Art

[0002] At present, models that simulate virus transmission using agent-based micro-modeling methods often only consider the way the virus spreads between individuals, while ignoring the way the virus spreads through objects. These studies generally focus on the spread of the epidemic in public places, especially in densely populated areas such as schools and shops. Application No. 202211254628.5 proposes a simulation method and system for epidemic transmission based on the combined field of individual data of urban residents. Through actual data, human agents and urban space scenes are constructed. Researchers study the virus transmission process in urban space scenes. Although many models have been proposed, these models only consider one way of virus transmission, and fail to comprehensively consider the two situations of air transmission and indirect transmission on the surface of objects. The mutual influence between interpersonal transmission and surface transmission of objects is insufficiently considered, and the impact of different intervention measures has not been systematically evaluated. In addition, most models lack effective integration of spatial factors, but use people as the only agent to study the spread of the virus, and the spatial impact on the spread of the virus needs to be studied. How to effectively integrate the influence of individual factors, spatial factors and policy factors, comprehensively consider the two modes of transmission: airborne transmission and surface transmission, and on this basis systematically evaluate the impact of different intervention measures, so as to provide guidance and theoretical support for market managers to formulate epidemic prevention and control strategies, is still a problem that needs to be solved. Summary of the invention

[0003] In order to solve the complexity of virus transmission in public places, the present invention provides a multi-agent-based micro-modeling method for simulating market virus transmission. By simulating the interaction between people, open space and policy intervention, the method can effectively simulate the process of virus transmission to other people through the air and the surface of objects, and reveal the impact of different policies on different indicators. The model has been verified and has high reliability. It can well couple the various transmission pathways of the virus, comprehensively consider the influence of people, space and policy on the transmission of the virus, and effectively solve the problems of insufficient consideration of transmission pathways, transmission factors only focusing on the interaction of people, and inability to break through theoretical knowledge to provide practical value for virus prevention and control. It provides a scientific and reasonable reference for epidemic prevention strategies for public health decision makers such as market managers.

[0004] The technical solution of the present invention is:

[0005] According to a first aspect of the present invention, a multi-agent-based micro-modeling method for simulating market virus propagation is provided, comprising the following steps:

[0006] S1. Create three types of Agents; the three types of Agents are specifically the space module S_Agent, the personnel module P_Agent, and the policy intervention module I_Agent;

[0007] S2. Initialize the spatial module S_Agent: divide the market into different functional areas, take the center point of each area as a node, and build the market space layout of the model based on the nodes and edges corresponding to different functional areas; where the edge represents the path between nodes;

[0008] S3, initialization of the personnel module P_Agent: create a fixed population of the first preset size, define the initial state of the fixed population as susceptible; create a mobile population of the second preset size, randomly assign initial states to the personnel in the mobile population according to the initial infection rate, and initialize the personnel in the fixed population to enter the corresponding area in the market space, and the mobile population is located outside the market space; the fixed population is the stall owner, and the mobile population is the customer; the initial state is divided into infected and susceptible;

[0009] S4. Initialize the policy intervention module I_Agent: Initialize the intervention and control policy of the personnel module P_Agent, and initialize the intervention and control policy of the space module S_Agent;

[0010] S5. For fixed groups of people in the market space, intervention and regulation are carried out according to the intervention and regulation policy of the personnel module P_Agent;

[0011] S6. Perform intervention and control according to the input policy of limiting the maximum number of customers in the market, and determine the number of people allowed to enter the market space at each time step; at each independent time step, determine the total number of people going to the market space in the mobile crowd that changes over time according to the "total number of people going-time diagram"; for the people going to the market space, determine the people to be allowed to enter the market space; at each time step, determine the people who enter the market space according to the "number of people allowed to enter the market space" for the people to be allowed to enter the market space; for the people who enter the market space, perform intervention and control according to the intervention and control policy of the personnel module P_Agent;

[0012] S7. For those who are in the market space and are in a healthy state, they will spread the virus into the air in the market space through their breathing function to form airborne virus volume, and the virus in the air will also cover the surface of objects according to the drop rate to form ground virus volume;

[0013] S8. When a person whose health status is susceptible in the market space enters an area with the virus, his health status changes from susceptible to exposed;

[0014] S9. When the health status of people in the market space is exposed, when the sum of the viral load inhaled by the exposed person in the air and the viral load indirectly inhaled by contacting the surface of objects reaches the critical value of infection, the health status will change from exposed person to infected person;

[0015] S10. When there is a virus in an area of ​​the market space, the virus will spread to adjacent areas according to the dynamic mechanism;

[0016] S11. When there is a virus in a region of the market space, the virus will decay naturally over time;

[0017] S12. After the recession, the market space is intervened and regulated according to the intervention and regulation policy of the space module S_Agent;

[0018] S13. In each simulation time step, the customers in the market space will have a patience value, and the customers will determine their movement mode based on the patience value.

[0019] S14. Continuously update the time step according to the process of steps S6-S13. When the time step reaches the preset end time and there are no more customers in the market space, the simulation ends and the virus propagation situation of all personnel and the market space is output.

[0020] Furthermore, the initialization of the intervention and control policy of the personnel module P_Agent includes: initializing the detection and isolation policy value to false and the mask wearing policy value to 0; the initialization of the intervention and control policy of the space module S_Agent includes: initializing the policy value of limiting the maximum number of customers in the market to M max , the environmental disinfection policy value is initialized to false, the ventilation policy value is initialized to false; among them, M max Indicates the preset threshold.

[0021] Furthermore, S5 and S6 are specifically:

[0022] For fixed groups of people in the market space, intervention and regulation will be carried out according to the mask-wearing policy;

[0023] Intervention and regulation are carried out according to the input policy of limiting the maximum number of customers in the market to determine the number of people allowed to enter the market space at each time step; at each independent time step, the total number of people going to the market space in the mobile crowd that changes over time is determined according to the "total number of people going-time diagram"; for the people going to the market space, intervention and regulation are carried out according to the detection and isolation policy to determine whether the people are isolated, and the non-isolated people among the people going to the market space are regarded as people waiting to be allowed to enter the market space; at each time step, the people who are waiting to be allowed to enter the market space are determined according to the "number of people allowed to enter the market space" to enter the market space; for the people entering the market space, intervention and regulation are carried out according to the mask wearing policy; among them, the number of people allowed to enter the market space is the difference between the policy value of limiting the maximum number of customers in the market and the number of customers in the market space.

[0024] Furthermore, the specific method of intervening and regulating based on the policy of limiting the maximum number of customers in the market is: when the input policy value for limiting the maximum number of customers in the market is M, if the number of customers in the market space is less than the policy value for limiting the maximum number of customers in the market, customers are allowed to enter the market space until the number is equal to the policy value for limiting the maximum number of customers in the market; if the number of customers in the market space is equal to the policy value for limiting the maximum number of customers in the market, customers are refused entry into the market space.

[0025] Furthermore, the specific method of intervention and regulation based on the detection and isolation policy is: when the input detection and isolation policy value is true, the isolation policy is implemented for the infected persons among the people going to the market space based on the detection rate, so that the status of these people is changed from infected persons to isolated persons; when the input detection and isolation policy value is false, the people going to the market space are not isolated.

[0026] Furthermore, the specific method of intervening and regulating according to the mask wearing policy is: when the input mask wearing policy value is P1, the mask wearing policy value is used as the probability that the person entering the market space wears a mask; wherein, 0 <P1<1。

[0027] Furthermore, the virus diffusion ratio is intervened and regulated according to the ventilation policy, and the specific way in which the ventilation policy intervenes and regulates is: when the ventilation policy value is true, the virus diffusion ratio increases from the ratio value under natural conditions to the ratio value under ventilation conditions; when the ventilation policy value is false, the virus diffusion ratio adopts the ratio value under natural conditions.

[0028] Furthermore, the S12 is specifically: after the recession, intervening and regulating the market space according to the environmental disinfection policy;

[0029] There are three types of situations in which intervention and regulation are carried out according to the environmental disinfection policy, specifically:

[0030] First, when the first environment disinfection policy value is true, at each time step, the air virus quantity and ground virus quantity corresponding to all node areas in the entire market space are disinfected according to the disinfection effect and disinfection probability;

[0031] Second, when the second environment disinfection policy value is true, at each time step, the air virus amount and ground virus amount corresponding to the node area connected to the entrance and exit are disinfected according to the disinfection effect and disinfection probability;

[0032] Third, when the first environment disinfection policy value is false and the second environment disinfection policy value is false, the air virus level and ground virus level in the entire market space will not be disinfected.

[0033] Furthermore, S13 is specifically as follows: in each simulation time step, the customer personnel inside the market space will have a patience value. When the time the customer personnel stays inside the market space is less than the patience value, the customer personnel will randomly go to an adjacent area that is not a backtracking route or choose to stay in the current area; when the time the customer personnel stays inside the market space is greater than or equal to the patience value, the customer personnel will randomly select a shortest path to the nearest exit node according to the current position and move without stopping until leaving the exit node.

[0034] According to a second aspect of the present invention, a multi-agent-based micro-modeling system for simulating market virus propagation includes a module of any one of the above methods.

[0035] The beneficial effects of the present invention are:

[0036] First, the multi-agent-based micro-modeling method simulates the virus transmission process at the individual level, which can simulate in detail how individuals spread and are infected with the virus during activities, providing theoretical support for revealing the factors of human infection.

[0037] Second, based on the traditional method of studying the process of virus transmission by taking people as a single agent, a multi-dimensional agent research method is proposed, which studies people, space, and policies as agents. In addition to the spread and infection of viruses between people, the spatial environment will also interact with adjacent spaces and people in the space and become contagious, that is, the spatial environment will also be infected by the virus and spread the virus. On this basis, the regulation of different policies causes different changes in the viruses transmitted by people and the spatial environment, and the significance of such changes is studied.

[0038] Third, the present invention effectively integrates the impact of individual factors and spatial environmental factors on virus transmission. Individuals transmit and absorb viruses into the environment through respiration. Viruses in the spatial environment are distributed proportionally on the surface of objects and in the air. Viruses in the air are directly transmitted to susceptible people due to respiration or spread to the surrounding spatial environment due to diffusion. Viruses on the surface of objects enter the body indirectly through breathing and contact with people's behavior. This human-space coupled transmission pathway comprehensively considers two different transmission pathways and simulates various virus transmission methods in detail.

[0039] Fourth, different policies have different impacts on people and space, and this impact in turn guides policy formulation. Through the dynamic influence between policies, people and space, we can find feasible and effective pathogen prevention and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a model framework diagram for simulating virus transmission in farmers' markets;

[0041] Figure 2 Flowchart of the model for simulating virus transmission in farmers markets;

[0042] Figure 3 A map of the market layout to model the model;

[0043] Figure 4 It is the total number of customers entering the P module in the model-time diagram;

[0044] Figure 5 This is the cumulative infection number-time diagram simulated by the model without policy intervention;

[0045] Figure 6 This is a diagram of the S_Agent virus load simulated by the model without policy intervention;

[0046] Figure 7 This is the cumulative infection number-time diagram simulated by the model under different mask coverage ratio interventions;

[0047] Figure 8 This is the cumulative infection number-time diagram simulated by the model under the intervention of wearing masks by different types of personnel;

[0048] Fig. 9 This is the cumulative infection number-time diagram simulated by the model under the intervention of the maximum number of customers in different markets;

[0049] Fig.10 This is the cumulative infection number-time diagram simulated by the model under the intervention of comprehensive spatial disinfection;

[0050] Fig.11 This is the cumulative infection number-time diagram simulated by the model under local disinfection intervention in space;

[0051] Fig.12 This is a diagram of the S_Agent virus load simulated by the model under local disinfection intervention in space;

[0052] Fig.13 This is the cumulative infection number-time diagram simulated by the model under space ventilation intervention;

[0053] Fig.14 This is a diagram of the S_Agent virus load simulated by the model under space ventilation intervention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0055] Example 1: Figure 1-Figure 14 As shown, according to a first aspect of an embodiment of the present invention, a multi-agent-based micro-modeling method for simulating market virus propagation is provided, comprising the following steps:

[0056] S1. Create three types of Agents; the three types of Agents are specifically the space module S_Agent, the personnel module P_Agent, and the policy intervention module I_Agent;

[0057] S2. Initialize the spatial module S_Agent: divide the market into different functional areas, take the center point of each area as a node, and build the market space layout of the model based on the nodes and edges corresponding to different functional areas; where the edge represents the path between nodes;

[0058] For example, the nodes and edges corresponding to different functional areas are established as follows: Figure 3 The market space layout of the model shown in the figure, where the nodes represent the center points of the regions, the region size is 2m×5m (not shown in the figure), and the edges represent the paths between the center points of the regions. Figure 3 Four functional areas are shown in;

[0059] S3, initialization of the personnel module P_Agent: create a fixed population of the first preset size, define the initial state of the fixed population as susceptible; create a mobile population of the second preset size, randomly assign initial states to the personnel in the mobile population according to the initial infection rate, and initialize the personnel in the fixed population to enter the corresponding area in the market space, and the mobile population is located outside the market space; the fixed population is the stall owner, and the mobile population is the customer; the initial state is divided into infected and susceptible;

[0060] For example, Figure 3 Taking the market shown in the figure as an example, the fixed population is the stall owners and the mobile population is the customers; create a stall owner population with a population size of 33, define the initial state of the stall owner population as susceptible, and assign node areas; create a customer population with a population size of 531, and set the initial infection rate β to 0.021; randomly assign the initial state to the people in the mobile population based on the initial infection rate (the initial infection rate is the probability that the people in the customer population are initially infected).

[0061] S4. Initialize the policy intervention module I_Agent: Initialize the intervention and control policy of the personnel module P_Agent, and initialize the intervention and control policy of the space module S_Agent;

[0062] The initialization of the intervention and control policy of the personnel module P_Agent includes: initializing the detection and isolation policy value to false and the mask wearing policy value to 0; the initialization of the intervention and control policy of the space module S_Agent includes: initializing the policy value of limiting the maximum number of customers in the market to M max , the environmental disinfection policy value is initialized to false, the ventilation policy value is initialized to false; among them, M max It represents a preset threshold value, which is set to 200 in the embodiment of the present invention. It should be noted that, according to the prevention and control policy of virus transmission, the present invention can also introduce other intervention and control policy values ​​that meet the prevention and control requirements.

[0063] S5. Intervene and regulate fixed groups of people in the market space based on the mask-wearing policy;

[0064] S6. According to the input policy of limiting the maximum number of customers in the market, intervention and regulation are carried out to determine the number of people allowed to enter the market space at each time step; at each independent time step, according to Figure 4The "Total number of people going - Time graph" shown determines the total number of people going to the market space among the moving population over time; for the people going to the market space, according to the detection and isolation policy, intervention and regulation are carried out to determine whether the people are isolated, and the non - isolated people among those going to the market space are regarded as the people waiting to be allowed to enter the market space; at each time step, for the people waiting to be allowed to enter the market space, the number of people entering the market space is determined according to the "Number of people allowed to enter the market space"; for the people entering the market space, intervention and regulation are carried out according to the mask - wearing policy; among them, the number of people allowed to enter the market space is the difference between the policy value of the maximum number of customers restricted in the market and the number of customers in the market space. It should be noted that in the present invention, a Poisson distribution is used to construct the "Total number of people going - Time graph" (the abscissa is time, and the ordinate is the total number of customers going to the market space), and in this way, it can effectively reflect the randomness and independence of customers entering the store.

[0065] The specific way of intervention and regulation according to the policy of restricting the maximum number of customers in the market is as follows: when the input policy value of the maximum number of customers restricted in the market is M, if the number of customers in the market space is less than the policy value of the maximum number of customers restricted in the market, customers are allowed to enter the market space until it equals the policy value of the maximum number of customers restricted in the market; if the number of customers in the market space equals the policy value of the maximum number of customers restricted in the market, customers are refused entry into the market space; exemplarily, M can take values of 40, 80, 160.

[0066] The specific way of intervention and regulation according to the detection and isolation policy is as follows: set the detection rate α to 0.8. When the input detection and isolation policy value is true, for the infected people among those going to the market space, the isolation policy is implemented according to the detection rate α, so that the status of this part of people changes from infected to isolated; when the input detection and isolation policy value is false, those going to the market space are not isolated.

[0067] The specific way of intervention and regulation according to the mask - wearing policy is as follows: when the input mask - wearing policy value is P1, the mask - wearing policy value is taken as the probability that the people entering the market space wear masks; among them, 0 < P1 < 1, for example, it can take 0.5; the mask wearers reduce γ inhale_air and γ exhale to simulate the implementation of the mask policy, and the mask effect is set to 83% (that is, γ inhale_air and γ exhale are adjusted to 17% of the original value).

[0068] Exemplarily, when the time step is 10 min, the input policy value of the maximum number of customers restricted in the market is 40, and the number of customers in the market space is 0. Therefore, it is determined that the number of people allowed to enter the market space at the time step is 40; according to Figure 4The "Total Number of People Going to the Market Space-Time Diagram" shown determines that the total number of people going to the market space in the mobile crowd with a time step of 10 minutes is 5; the detection and isolation policy value is set to false, and the 5 people going to the market space are not isolated, so the 5 people going to the market space are regarded as people waiting to be allowed to enter the market space; at the current time step, since the "number of people allowed to enter the market space" is 40, and the number of people waiting to be allowed to enter the market space is 5, the 5 people waiting to be allowed to enter the market space are all determined to be people entering the market space; the mask wearing policy value is set to 0.5, and intervention and regulation are carried out for people entering the market space based on the mask wearing policy value.

[0069] S7. For those who are in the market space and are in a healthy state and are infected, they will spread the virus into the air in the market space through their breathing function to form the amount of airborne virus. The virus in the air will also cover the surface of objects according to the drop rate to form the amount of ground virus. The specific calculation method is as follows, with people as the unit:

[0070] V exhale =V p ×γ exhale (1)

[0071] V air =V exhale ×(1-γ drop ) (2)

[0072] V ground =V exhale ×γ drop (3)

[0073] Among them, V exhale Represents the viral load exhaled by a single person, V p represents the viral load carried by the person, γ exhale Represents the virus exhalation rate of personnel, V air Indicates the amount of air virus in the area where the person belongs, γ drop Indicates the proportion of viruses falling onto the surface of objects, V ground Indicates the ground virus load in the area where the person belongs. It should be noted that the people in the market space include the stall owners and customers in the market space; exhale Set to 0.1, γ drop Set to 0.2.

[0074] S8. When a person whose health status is susceptible in the market space enters an area with the virus, his health status changes from susceptible to exposed;

[0075] S9. When the health status of people in the market space is exposed, when the sum of the viral load inhaled by the exposed person in the air and the viral load indirectly inhaled by contacting the surface of objects reaches the critical value of infection, the health status will change from exposed to infected. The specific calculation method is as follows:

[0076] V inhale_air =V air ×γ inhale_air (4)

[0077] V inhale_ground =V ground ×γ inhale_ground (5)

[0078] Among them, V inhale_air represents the viral load inhaled by the exposed person in the air, γ inhale_air V represents the proportion of viruses inhaled from the air by the exposed person. inhale_ground represents the viral load indirectly inhaled by the exposed person through contact with the surface of the object, γ inhale_ground Represents the proportion of viruses that are inhaled indirectly through surfaces. inhale_air Set to 0.4, γ inhale_ground Set to 0.6 and the infection threshold to 16000.

[0079] S10. When there is a virus in an area of ​​the market space, the virus will spread to adjacent areas according to the dynamic mechanism. The specific calculation method is as follows:

[0080] V air_diffusion =V air ×γ diffusion (6)

[0081] Among them, V air_diffusion represents the total viral load spreading from the current area to the adjacent areas, γ diffusion Indicates the virus spread ratio.

[0082] The virus diffusion ratio is intervened and regulated according to the ventilation policy. The specific way of intervention and regulation of the ventilation policy is as follows: when the ventilation policy value is true, the virus diffusion ratio γ diffusion From the ratio value under natural conditions to the ratio value under ventilation conditions; when the ventilation policy value is false, the virus diffusion ratio γ diffusion Adopt the ratio value in the natural situation; for example, when the ventilation policy value is true, the virus diffusion ratio γ diffusion It increased from 50% under natural conditions to 80% under ventilation conditions.

[0083] S11. When there is a virus in the market space, the virus will decay naturally over time, so that the amount of virus will gradually decrease under natural conditions. The specific calculation method is as follows:

[0084] V′ air =V air ×γ recession (7)

[0085] V′ ground =V ground ×γ recession (8)

[0086] Among them, V' air Indicates the amount of air virus in the current area after decay update, V' ground Indicates the ground virus volume in the current area after decay update; γ recession Represents the virus decay rate, 0<γ recession <1,γ recession Set to 0.04.

[0087] S12. After the recession, the market space will be intervened and regulated according to the environmental disinfection policy;

[0088] There are three types of situations in which intervention and regulation are carried out according to the environmental disinfection policy, specifically:

[0089] First, when the first environment disinfection policy value is true, at each time step, the air virus quantity and ground virus quantity corresponding to all node areas in the entire market space are disinfected according to the disinfection effect and disinfection probability;

[0090] Second, when the second environment disinfection policy value is true, at each time step, the air virus amount and ground virus amount corresponding to the node area connected to the entrance and exit are disinfected according to the disinfection effect and disinfection probability;

[0091] Third, when the first environment disinfection policy value is false and the second environment disinfection policy value is false, the air virus level and ground virus level in the entire market space will not be disinfected.

[0092] For example, taking the first case as an example, the disinfection effect is set to 45%, and the disinfection probability is 60%, that is, there is a 60% probability that all node areas in the market area will be disinfected with a disinfection effect of 45% (45% disinfection effect, that is, 45% of the air virus amount and the ground virus amount are eliminated).

[0093] S13. In each simulation time step, the customers in the market space will have a patience value. When the time spent by the customers in the market space is less than the patience value, the customers will randomly go to an adjacent area that is not a back-tracking route or choose to stay in the current area. When the time spent by the customers in the market space is greater than or equal to the patience value, the customers will randomly select a shortest path to the nearest exit node based on their current location and move without stopping until they leave the exit node. The patience value is set to 6-12. The customers will randomly go to an adjacent area that is not a back-tracking route or choose to stay in the current area, and the probability of the two choices is 0.5 respectively. In the above, going to an adjacent area that is not a back-tracking path refers to the adjacent area that is not the different node areas where the customer has stayed for the last two times (for example, customer A is currently located at node area 3, and its adjacent node areas are node area 1, node area 2, and node area 4, and the different node areas where the customer has stayed for the last two times are node area 1 and node area 2, then customer A goes to an adjacent area that is not a back-tracking path, which refers to the adjacent area selected when the area he goes to is not node area 1 or node area 2, that is, node area 4).

[0094] S14, continuously updating the time step according to the process of steps S6-S13, when the time step reaches the preset end time and there are no more customers in the market space, the simulation ends and the virus transmission situation of all people and the market space is output;

[0095] Taking the market space constructed by the present invention as an example, the termination time may be the end of business hours of the day.

[0096] According to the above technical solution, it is possible to output the virus transmission situation of all personnel and market spaces under the intervention and regulation of different policies, and then integrate the virus transmission situation output under various policies, and output statistical analysis results to provide market managers with a basis for epidemic prevention and control strategies.

[0097] According to a second aspect of an embodiment of the present invention, there is provided a multi-agent-based simulated market virus propagation micro-modeling system, comprising a module of any of the above-mentioned methods. The term "module" used above can implement a combination of software and / or hardware for a predetermined function. Although the system described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable. For parts that are not described in detail in each module, please refer to the relevant description of this embodiment.

[0098] According to a third aspect of an embodiment of the present invention, there is provided a terminal, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute any one of the above-mentioned methods for micro-modeling of simulated market virus propagation based on multi-agent. In an exemplary embodiment, the terminal may further comprise a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0099] The present invention simulates the market virus transmission process under different policies respectively. First, it simulates the situation without policy intervention. Secondly, on this basis, it simulates the intervention and regulation of the personnel module P_Agent, including detection and isolation, and wearing masks. The intervention and regulation of the space module S_Agent includes limiting the maximum number of customers in the market, environmental disinfection, and ventilation. Each situation is simulated 1000 times, and the average is taken as the simulation result data.

[0100] The simulation results without policy intervention are shown in Table 1. Figure 5 , Figure 6 As shown, the data shows that without intervention, the infection rate of people will be very high, and the virus level in the market space will also be high, especially in the node areas adjacent to the entrances and exits, where the virus level will increase significantly.

[0101] Table 1 Simulation results without intervention

[0102] Recording Metrics average value Total number of simulated people 564 Total number of new infections 471.48 The average number of newly infected people at each time step 6.04 Average number of customer moves 12.32 Average infection rate of personnel β 0.84 <![CDATA[Average airborne virus quantity V air > 13002.51 <![CDATA[Average ground virus quantity V ground > 2881.42

[0103] The results of intervention and regulation using the detection and isolation policy are shown in Table 2. The data show that when the epidemic situation is complicated, the detection and isolation policy can effectively reduce the impact of the source of infection on the region. This policy mainly reduces the infection rate by reducing the source of infection, which is suitable for situations where the epidemic is more serious.

[0104] Table 2 Detection and isolation intervention simulation results

[0105] Interventions Infection rate β Vair Vground Detection and Isolation 0.43 2712.83 541.07

[0106] The intervention results are shown in Table 3. Figure 7 , Figure 8 As shown, it can be seen that when the mask wearing ratio is too low, it will not have a significant impact on the infection rate. When the mask wearing ratio is around 90%, the effect will be significantly improved, significantly reducing the infection rate, and the amount of virus in the market space will also be reduced. In addition, the model also considers the implementation of a mask wearing policy for stall owners P_W or customers P_C. The simulation found that the effect of implementing a mask wearing policy for customers P_C is better than that of implementing a mask wearing policy for stall owners P_W.

[0107] Table 3 Mask intervention simulation results

[0108] Interventions Infection rate β <![CDATA[V air ]]> <![CDATA[V ground ]]> Mask ratio (30%) 0.83 11740.65 2564.27 Mask ratio (60%) 0.77 8733.25 1866.77 Mask ratio (90%) 0.45 2817.67 553.69 All the stall owners are wearing 0.78 10593.00 2311.89 Customers all wear 0.68 6069.30 1234.86

[0109] The intervention results are shown in Table 4. Fig. 9 As shown, the simulation shows that limiting the maximum number of customers in the market can effectively reduce the infection rate, but this intervention will have an impact on the market's operating profits, and whether to implement it needs to be decided based on actual needs.

[0110] Table 4 Results of interventions to limit the maximum number of customers in the market

[0111] Interventions Infection rate β <![CDATA[V air ]]> <![CDATA[V ground ]]> M＝40 0.52 1771.7 351.66 M＝80 0.68 3879.63 794.35 M＝160 0.75 7487.04 1594.54

[0112] Under the intervention and regulation of environmental disinfection policy, the intervention results are shown in Table 5. Fig.10 , Fig.11 , Fig.12 As shown (E air represents the average disinfection benefit of air virus content, E ground represents the average disinfection benefit of the ground virus load, and the average disinfection benefit refers to the ratio of the average virus reduction to the number of disinfection nodes). Through analysis, when disinfection resources are limited, compared with comprehensive disinfection, concentrated disinfection of areas near entrances and exits can make the average disinfection benefit of node areas higher. Local disinfection / disinfection of severe areas makes the air virus load V near the entrances and exits with the highest concentration of people air With ground virus volume V ground The number of people infected with the virus has been significantly reduced, which has reduced the problem of increased infection rates caused by the gathering of people to a certain extent. Therefore, it can be concluded that when resources are limited, disinfection in crowded places will bring better results than comprehensive disinfection, while when resources are sufficient, comprehensive disinfection will have a better effect in inhibiting the spread of the virus.

[0113] Table 5 Results of environmental disinfection intervention

[0114]

[0115] When ventilation policy is adopted for intervention and regulation, the intervention results are shown in Table 6. Fig.13 , Fig.14 As shown, the data show that under well-ventilated conditions, the risk of virus transmission and the probability of infection are significantly reduced, especially for V air For example, since most of the virus will be discharged from the room, the number of infected people will be reduced, thereby reducing the amount of virus emission, which indirectly makes V ground The simulation results show that regular ventilation can effectively reduce the accumulation of viruses indoors and reduce the chance of infection.

[0116] Table 6 Ventilation intervention results

[0117] Interventions Infection rate β <![CDATA[V air ]]> <![CDATA[V ground ]]> ventilation 0.62 2066.88 1464.19

[0118] In summary, the advantages of the method proposed in the present invention are:

[0119] First, innovation: This model is based on agent-based design, comprehensively considers human behavior, open space and policy factors, and provides a comprehensive simulation of virus transmission.

[0120] Second, practicality: By simulating a variety of intervention strategies (such as wearing masks, limiting the maximum number of customers in the market, etc.), the model demonstrates effective methods to reduce the infection rate in the market and has practical application value.

[0121] Third, resource optimization: Through strategies such as centralized disinfection and local disinfection, the model proposes an optimized intervention plan under conditions of limited resources, which helps to improve the efficiency of resource utilization.

[0122] Fourth, research orientation: It clearly points out the limitations of the model, provides improvement directions for future research, and encourages deeper exploration and data analysis to improve the accuracy and applicability of the model.

[0123] Fifth, market application potential: This model provides effective theoretical support and practical guidance for epidemic prevention and control in the market environment, and has broad market application prospects.

[0124] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A multi-agent-based micro-modeling method for simulating market virus propagation, characterized in that: The following steps are involved: S1. Create three types of Agents; the three types of Agents are specifically the space module S_Agent, the personnel module P_Agent, and the policy intervention module I_Agent; S2. Initialize the spatial module S_Agent: divide the market into different functional areas, take the center point of each area as a node, and build the market space layout of the model based on the nodes and edges corresponding to different functional areas; where the edge represents the path between nodes; S3, initialization of the personnel module P_Agent: create a fixed population of the first preset size, define the initial state of the fixed population as susceptible; create a mobile population of the second preset size, randomly assign initial states to the personnel in the mobile population according to the initial infection rate, and initialize the personnel in the fixed population to enter the corresponding area in the market space, and the mobile population is located outside the market space; the fixed population is the stall owner, and the mobile population is the customer; the initial state is divided into infected and susceptible; S4. Initialize the policy intervention module I_Agent: Initialize the intervention and control policy of the personnel module P_Agent, and initialize the intervention and control policy of the space module S_Agent; S5. For fixed groups of people in the market space, intervention and regulation are carried out according to the intervention and regulation policy of the personnel module P_Agent; S6. Perform intervention and control according to the input policy of limiting the maximum number of customers in the market, and determine the number of people allowed to enter the market space at each time step; at each independent time step, determine the total number of people going to the market space in the mobile crowd that changes over time according to the "total number of people going-time diagram"; for the people going to the market space, determine the people to be allowed to enter the market space; at each time step, determine the people who enter the market space according to the "number of people allowed to enter the market space" for the people to be allowed to enter the market space; for the people who enter the market space, perform intervention and control according to the intervention and control policy of the personnel module P_Agent; S7. For those who are in the market space and are in a healthy state, they will spread the virus into the air in the market space through their breathing function to form airborne virus volume, and the virus in the air will also cover the surface of objects according to the drop rate to form ground virus volume; S8. When a person whose health status is susceptible in the market space enters an area with the virus, his health status changes from susceptible to exposed; S9. When the health status of people in the market space is exposed, when the sum of the viral load inhaled by the exposed person in the air and the viral load indirectly inhaled by contacting the surface of objects reaches the critical value of infection, the health status will change from exposed person to infected person; S10. When there is a virus in an area of ​​the market space, the virus will spread to adjacent areas according to the dynamic mechanism; S11. When there is a virus in a region of the market space, the virus will decay naturally over time; S12. After the recession, the market space is intervened and regulated according to the intervention and regulation policy of the space module S_Agent; S13. In each simulation time step, the customers in the market space will have a patience value, and the customers will determine their movement mode based on the patience value. S14. Continuously update the time step according to the process of steps S6-S13. When the time step reaches the preset end time and there are no more customers in the market space, the simulation ends and the virus propagation situation of all personnel and the market space is output.

2. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 1 is characterized in that: The initialization of the intervention and control policy of the personnel module P_Agent includes: initializing the detection and isolation policy value to false and the mask wearing policy value to 0; the initialization of the intervention and control policy of the space module S_Agent includes: initializing the policy value of limiting the maximum number of customers in the market to M max , the environmental disinfection policy value is initialized to false, the ventilation policy value is initialized to false; among them, M max Indicates the preset threshold.

3. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 2 is characterized in that: The S5 and S6 are specifically: For fixed groups of people in the market space, intervention and regulation will be carried out according to the mask-wearing policy; Intervention and control are carried out according to the input policy of limiting the maximum number of customers in the market to determine the number of people allowed to enter the market space at each time step; at each independent time step, the total number of people going to the market space in the mobile crowd that changes over time is determined according to the "total number of people going-time diagram"; for the people going to the market space, intervention and control are carried out according to the detection and isolation policy to determine whether the people are isolated, and the non-isolated people among the people going to the market space are regarded as people waiting to be allowed to enter the market space; at each time step, the people who are waiting to be allowed to enter the market space are determined according to the "number of people allowed to enter the market space" to enter the market space; for the people entering the market space, intervention and control are carried out according to the mask wearing policy; among them, the number of people allowed to enter the market space is the difference between the policy value of limiting the maximum number of customers in the market and the number of customers in the market space.

4. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 3 is characterized in that: The specific method of intervention and regulation based on the policy of limiting the maximum number of customers in the market is: when the input policy value for limiting the maximum number of customers in the market is M, if the number of customers in the market space is less than the policy value for limiting the maximum number of customers in the market, customers are allowed to enter the market space until the number is equal to the policy value for limiting the maximum number of customers in the market; if the number of customers in the market space is equal to the policy value for limiting the maximum number of customers in the market, customers are refused entry into the market space.

5. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 3 is characterized in that: The specific method of intervention and regulation based on the detection and isolation policy is: when the input detection and isolation policy value is true, the isolation policy is implemented for the infected persons among the people going to the market space based on the detection rate, so that the status of these people is changed from infected persons to isolated persons; when the input detection and isolation policy value is false, the people going to the market space are not isolated.

6. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 3 is characterized in that: The specific method of intervening and regulating according to the mask-wearing policy is as follows: when the input mask-wearing policy value is P1, the mask-wearing policy value is used as the probability that the person entering the market space wears a mask; wherein, 0 <P1<1。 7. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 1 is characterized in that: The virus diffusion ratio is intervened and regulated according to the ventilation policy. The specific way in which the ventilation policy intervenes and regulates is: when the ventilation policy value is true, the virus diffusion ratio increases from the ratio value under natural conditions to the ratio value under ventilation conditions; when the ventilation policy value is false, the virus diffusion ratio adopts the ratio value under natural conditions.

8. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 1 is characterized in that: The S12 is specifically: after the recession, the market space is intervened and regulated according to the environmental disinfection policy; There are three types of situations in which intervention and regulation are carried out according to the environmental disinfection policy, specifically: First, when the first environment disinfection policy value is true, at each time step, the air virus quantity and ground virus quantity corresponding to all node areas in the entire market space are disinfected according to the disinfection effect and disinfection probability; Second, when the second environment disinfection policy value is true, at each time step, the air virus amount and ground virus amount corresponding to the node area connected to the entrance and exit are disinfected according to the disinfection effect and disinfection probability; Third, when the first environment disinfection policy value is false and the second environment disinfection policy value is false, the air virus level and ground virus level in the entire market space will not be disinfected.

9. The multi-agent-based micro-modeling method for simulating market virus propagation according to claim 1 is characterized in that: Specifically, S13 is as follows: in each simulation time step, the customer personnel inside the market space will have a patience value. When the time the customer personnel stays inside the market space is less than the patience value, the customer personnel will randomly go to an adjacent area that is not a backtracking route or choose to stay in the current area; when the time the customer personnel stays inside the market space is greater than or equal to the patience value, the customer personnel will randomly select a shortest path to the nearest exit node according to the current position and move without stopping until leaving the exit node.

10. A multi-agent-based micro-modeling system for simulating market virus propagation, characterized in that: A module comprising the method according to any one of claims 1 to 9.

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