CN111383771A

CN111383771A - Epidemic disease virus field-based prevention and control system

Info

Publication number: CN111383771A
Application number: CN202010119944.6A
Authority: CN
Inventors: 汤一平; 汤晓燕; 窦文博
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-02-26
Filing date: 2020-02-26
Publication date: 2020-07-07
Anticipated expiration: 2040-02-26
Also published as: CN111383771B

Abstract

A control system based on an epidemic disease virus field comprises a spreading infectious disease city diffusion modeling unit, a collecting, cleaning and processing unit, a virus carrier position semantic information acquiring unit, a virus distribution density calculating unit, a time, space and virus distribution density data mapping unit, an epidemic disease virus field cloud platform building unit, an infection risk crowd investigation unit and a healthy crowd protection and guarantee unit, wherein the spreading infectious disease city diffusion modeling unit is sequentially connected and takes air as a medium, the collecting, cleaning and processing unit is used for collecting, cleaning and processing space trajectory data and infection morbidity data of virus carrier crowds, the virus carrier position semantic information acquiring unit is used as a position center, the time, space and virus distribution density data mapping unit is used for virus carrier crowds, the epidemic disease virus field cloud platform building unit is based on a temporal GIS, the infection risk crowd investigation unit is. The invention accurately positions the prevention and control objects, accurately implements the prevention and control measures in a classified manner, embodies the passivity of epidemic situations to the human temperature situation and gives consideration to epidemic situation prevention and control and life production.

Description

Epidemic disease virus field-based prevention and control system

Technical Field

The invention relates to application of mobile phone positioning, temporal GIS, big data and cloud computing in the field of epidemic infectious disease prevention and control, in particular to a prevention and control system based on an epidemic infectious disease virus field.

Background

The heart of an infection outbreak is its infectivity, which can be transferred from one person to another or more persons, either directly or indirectly. In response to such sudden outbreaks, humans have mastered a very old but exceptionally effective solution-namely isolation.

The isolated core has three:

one is to find and manage the source of infection. Scientists have made clear that the new coronavirus is the causative agent of this outbreak and that it is transmitted from person to person, and this is done by quickly identifying those who are already ill or suspected to be ill and isolating the treatment.

The second is to cut off the transmission path. The main transmission pathway of the new coronavirus, which is a respiratory virus, is via droplet transmission, but at present, other transmission pathways cannot be completely excluded. The most effective way to cut off the transmission route is therefore to avoid large-scale crowding and long-distance movement of people.

And thirdly, the susceptible people are protected. In the face of this novel coronavirus COVID-19, each individual can be said to be a susceptible group.

The Chinese patent application number of 201610060508.X discloses a method for tracking infection source and predicting epidemic trend of infectious disease by using mobile phone track, which comprises the following steps: obtaining new infected person data from a disease control center and determining new infected persons; acquiring mobile phone traffic data and relevant base station data of the new infected person within a period of time before and after the onset of disease; performing trajectory visualization analysis on the mobile phone telephone traffic data and the related base station data on a geographic information system platform for a new infected person; and analyzing high-risk areas and crowds with epidemic diseases to predict the epidemic trend of the infectious diseases.

The greatest confusion for people who control the COVID-19 line is as follows: does not know "who the virus carrier is? How many people are? Where they are? Where? We can't recognize, judge, predict, and prevent. It is also unknown whether these people can ascertain that they are not infected, cannot guarantee that they are not infected, and they cannot exclude from infection others. "

Since the prior art fails to solve the above problems, many extreme practices have resulted, which, of course, are very effective in effectively cutting off the route of infection. Meanwhile, the method also has great side effect, and the production and the survival of enterprises and the life of common people are greatly damaged, especially in cities with epidemic outbreaks; therefore, how to effectively isolate virus carriers and reduce the influence of epidemic situation on the life of healthy people to the minimum.

In the internet era, what we lack is not information, but what we lack is the ability to quickly and accurately screen out real and beneficial parts from massive information. The anti-epidemic situation needs to be provided with a dynamic real-time accurate map for reflecting the epidemic situation, timely obtains the space-time distribution information of a virus carrier and maps the space-time distribution information into corresponding virus field space-time distribution information, then accurately screens and extracts semantic scenes which may be infected or infect other people and updates the semantic scenes in real time, so that the information is reported, flows and is released at the speed of the win-win virus propagation speed.

As to who the virus carrier is? How many people are? The method is easy to realize on the technical level, and only the basic disease prevention and control center workers in various places input the confirmed or suspected patient data every day through the cloud platform of the epidemic infectious disease virus field based on the temporal GIS and issue the data in time; people with administrative access can know who the virus carrier is at any time anywhere, and the range of motion and the residence point information of the virus carrier; the number of people is easy to answer, relevant data can be obtained only by carrying out statistics according to the administrative region, and the number of people in the region, the city, the province and even the whole country can be answered; this is of course based on rapid diagnosis and timely collection of information.

About the viral carriers where they are? Where? If the patient is diagnosed, the above problems can be easily solved by the mobile phone positioning technology and the trajectory tracking. The health code technology which is currently promoted nationwide basically adopts the technology to determine that a person is one of blue code, yellow code and red code. The technology has a good effect on preliminary investigation. However, there is still much room for improvement in accuracy, for example, the life of many healthy people is greatly influenced by taking tens of millions of cities and hundreds of millions of provinces in a population as the standard for determining the red code.

Can these people know that they are not infected? Can you not guarantee that you are not infected? Can they not exclude infecting others? These problems are of general concern and even anxiety to many people, especially residents living in cities with outbreaks. Of course, the same needs are also felt for the residents of the infectious disease import city.

In a strict sense, infection by an infectious disease or infection of others is a probabilistic event. Without any precautionary measures, the higher the frequency of contact with the virus carrier, the closer the distance to the virus carrier, and the longer the residence time together, the greater the probability of infection by the virus, i.e., the greater the risk.

To further illustrate the gist of the present invention, it is shown in FIG. 4; here the population is first divided into 4 categories, A, B, C, D. Wherein population A is virus carrier; b is absolutely agnostic to the loss of A, some of which are latently infected; c is recognized with A, such as family, friends, colleagues, neighbors and the like, and belongs to close contact people; d is a healthy population who is not out of home during the epidemic situation. From the population, more than 99.99% of people belong to the group D, and A and C are rare. Under the current technical conditions, the biggest hidden trouble is B, which cannot be found at all, nobody knows who is B, and does not know that B is B. If D goes out, B may be touched, namely 2B (new B).

Therefore, there is a need for an information service for the group B people to inform the unknown people with higher risk of infection that they are infected by the virus carrier who has been in close contact with the virus carrier, and if there are any symptoms such as fever and fatigue, please go to the hospital for a doctor as soon as possible; in addition, before the infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people.

Another information service is primarily for class D or class B groups: some people who worry about being infected and have panic psychology, some healthy people need to go out for life and life, they can know and prove whether they have contact with the virus carrier through the network inquiry mode, if have contact, the probability of being infected is little.

Yet another application is to target those group C who are intentionally hidden from view, and isolate them in time by means of information technology, so as not to pose a greater risk to society and others.

How to avoid infection risk for 99.99% of people is the most important purpose and purpose of the invention to avoid or reduce disasters and secondary disasters caused by the infection.

Disclosure of Invention

In order to overcome the defects of unclear control objects, simple control method, lack of humanity care, high manpower, material resources and financial resources consumption, difficulty in balancing epidemic situation control and life production and the like of the existing infectious disease control method, the invention provides the epidemic situation control system which can accurately position the control objects, accurately implement control measures in a classified mode, embody the situation of epidemic situation absence to the human temperature situation and give consideration to epidemic situation control and life production.

The technical scheme adopted by the invention for solving the technical problems is as follows:

a control system based on an epidemic disease virus field comprises a spreading infectious disease city diffusion modeling unit, a collecting, cleaning and processing unit, a virus carrier position semantic information acquiring unit, a virus distribution density calculating unit, a time, space and virus distribution density data mapping unit, an epidemic disease virus field cloud platform building unit, an infection risk crowd investigation unit and a healthy crowd protection and guarantee unit, wherein the spreading infectious disease city diffusion modeling unit is sequentially connected and takes air as a medium, the collecting, cleaning and processing unit is used for collecting, cleaning and processing space trajectory data and infection morbidity data of virus carrier crowds, the virus carrier position semantic information acquiring unit is used as a position center, the time, space and virus distribution density data mapping unit is used for virus carrier crowds, the epidemic disease virus field cloud platform building unit is based on a temporal GIS, the infection risk crowd investigation unit is.

Furthermore, the urban spread model unit for spreading infectious diseases by taking air as a medium is used for dividing urban region functions and mapping spatial positions, and semanticizing each spatial position function in the city;

the main urban area of the city is firstly decomposed into areas and subspaces in the areas, and the functional information of the subspaces is obtained by the digital map content, navigation and position service solution providers; acquiring a base station position area identification number and a base station sector position identification number from a communication operator, and mapping the area and a subspace in the area;

the region is a city plot serving different purposes, and the city is divided into different types of agricultural regions, office regions, residential regions, hospitals, schools, universities and leisure and entertainment regions; regardless of the areas that have substantially no effect on the spread of infectious diseases, such as agricultural areas, the areas are mapped into seven types: residential, office, school, university, hospital, leisure and traffic areas; the traffic area is a special area and consists of movable independent spaces, such as train carriages, subway carriages and buses;

the subspace is a smaller space unit belonging to the region and corresponds to independent non-mobile spaces in real life, and the independent non-mobile spaces are a family, a hospital ward, an office, a leisure entertainment place, a classroom or a green space; the sub-space distinguishes between outdoor and indoor; the type of the subspace is determined by the function type of the region where the subspace is located; people perform corresponding types of activities in the subspace, such as home, hospitalization, work, leisure, entertainment and learning; generating different types of said subspaces in each of said regions with reference to actual data, such as a university region consisting of an office subspace, a living space, a classroom subspace, a leisure subspace; thus, the subspaces described here are divided into the following six types: residences, offices, classrooms, wards, leisure places, traffic subspaces; thus each subspace is provided with corresponding semantic information.

Furthermore, the collecting, cleaning and processing unit of the space-time trajectory data and the infection morbidity data of the virus-carrying population; acquiring infection morbidity data of each individual in the virus carrying population, namely the infection morbidity data of each virus carrier from a disease control center, wherein the infection morbidity data comprises a mobile phone number, morbidity time and isolated time, and acquiring mobile phone traffic data of the virus carrier and relevant base station data thereof at a time interval t from 1 day before morbidity, after morbidity and before isolation by using the mobile phone number from a communication operator, wherein the mobile phone traffic data of the virus carrier comprises user traffic triggering time, a user communication service type and a user ID number; the relevant base station data comprises a base station position area identification number and a base station sector position identification number which are relevant to the mobile phone traffic data; the data is then processed into a user ID, time of day, spatial location and written into a spatiotemporal data repository in time series, namely DATASET 1.

Furthermore, the semantic information of the position of the virus carrier is obtained by the semantic information obtaining unit accessing the urban area function division and spatial position mapping unit according to the spatial position of the virus carrier.

The virus distribution density calculation unit with the virus carrier as the location center calculates the virus distribution density with each user ID as the location center according to the user ID, the time and the spatial location information in the DATASET1, and the calculation formula is as follows;

in the formula, P (i)^tIs the spatial coordinate of the ith virus carrier at the sampling time t, t (i) is the time elapsed in the spatial coordinate of the ith virus carrier at the sampling time t, σ is a constant, f (P (i))^tAnd t (i)) is the ith virus at the sampling time tthThe virus distribution density of the carrier;

furthermore, the outdoor virus distribution density is not accumulated in consideration of different attenuation conditions of the indoor and outdoor virus distribution densities; for indoor, firstly, the space coordinate P (i) of the ith virus carrier at the sampling time t is judged^tAnd the spatial coordinate P (i) of the ith virus carrier at the sampling time t +1^t+1Whether the distance Δ D between is less than a threshold value T_DIf satisfied, the additive effect of virus density is taken into account; that is, the distribution density f (P (i)) of the residual viruses of the ith virus carrier at the sampling time t +1^t+1T (i) + Δ t and sampling time t +1 the virus distribution density f (P (i)) of the ith virus carrier^t+1T (i)) accumulating;

whether the environment where the virus carrier is located is indoor or outdoor is realized according to the space semantics obtained by the virus carrier position semantic information acquisition unit;

considering the attenuation of the virus distribution density over the sampling time interval Δ t, the attenuation of the virus distribution density in the region where the virus distribution density survived over the sampling time interval Δ t is calculated by equation (2),

in the formula, P (i)^t+1Is the spatial coordinate of the ith virus carrier at the sampling time t +1, t (i) is the time elapsed in the spatial coordinate of the ith virus carrier at the sampling time t, σ is a constant, f (P (i))^t+1T (i) + Δ t) is the distribution density of the residual virus of the ith virus carrier at the sampling time t + 1;

the data are further processed into user ID, time, space position, virus distribution density and written into a virus distribution space-time database set, namely DATASET2 according to time sequence.

The time, space and virus distribution density data mapping unit of the virus-carrying crowd is used for mapping the data in the DATASET2 to a temporal GIS;

the temporal GIS is added with time dimension on the basis of the traditional GIS, and the GIS is expanded into three elements of space, time and attribute from the two elements of the traditional space and attribute; the temporal GIS can describe and express the distribution and the shape of the virus field in space, and can also describe and express the change of the virus field along with time to perform temporal analysis.

The epidemic infectious disease virus field cloud platform construction unit based on the temporal GIS is realized by adopting a cloud computing mode in the face of processing massive epidemic situation data generated every day around the country; the working personnel of the disease prevention and control center of each basement layer inputs the infection and morbidity data of the virus carriers through the cloud platform of the epidemic disease virus field based on the temporal GIS; and then, by the collecting, cleaning and processing unit of the space-time trajectory data and the infection morbidity data of the virus-carrying population, the position semantic information acquisition unit of the virus carrier is used as a virus distribution density calculation unit of a position center, the time, space and virus distribution density data mapping unit of the virus-carrying population performs cloud calculation, and finally, the epidemic infectious disease virus field based on the temporal GIS is automatically generated.

The inspection unit of the infection risk crowd based on the virus distribution density chart of the temporal GIS is used for inspecting the crowd who has close contact with a virus carrier and informing that the risk of infection exists;

firstly, focusing a relatively closed space of a public place according to a virus distribution density map of a temporal GIS, wherein the relatively closed space of the public place is mainly a railway station, an expressway service area, a supermarket, a restaurant, a shopping mall, an airport, a long-distance passenger station, a taxi, a high-speed railway carriage, a bus, a public washroom and an elevator room …; the regions and the subspaces in the regions are semantically processed in the urban spread modeling unit for the air-mediated infectious disease transmission; therefore, only the space with the virus field is screened from the relatively closed space of the public place and the time period for generating the virus field, and AC is used_n,TDWherein n represents the number of virus field spaces present in said region and subspaces in said region, TD represents the duration of the virus field in the region and the subspace of the region;

next, an incoming AC is acquired_n,TDThe mobile phone track information of all the personnel in the system is used as a primary investigation object;

generally, the longer the susceptible population stays in the infectious disease virus field and the more the susceptible population enters the infectious disease virus field, the greater the probability of infection;

first, the problem of the probability of infection of an epidemic is explained here, and the probability of a susceptible individual being infected when accessing a subspace depends on: how many infectious individuals exist in the subspace, how long each contact lasts, the activity type and the infectious class to which the infectious individuals belong;

recording the mean value of the infection events occurring in the contact between an individual with complete infectivity and an individual with complete susceptibility within a given time period t as lambda; for simplicity, it is assumed that each infectious individual has the same infectivity; for randomly occurring events, the number of times of occurrence within a given time period t obeys the randomly occurring events, the number of times of occurrence within the given time period t obeys poisson distribution, and the parameter is lambdat;

here, the value D of the virus field area range is used_iThe calculation is in meters, and the following are respectively specified: d₁Less than 1 high risk area, D is more than or equal to 1₂Less than 2 medium-risk area, 2 is less than or equal to D₃Less than 4 low-risk area, D is not less than 4₄A safer region;

therefore, the probability of not being infected in different areas of the virus field within the time interval t is

The probability of at least one infection occurring is

These virus field area range values D_iAdjustable for different infectious disease categories; when an infectious individual A and an infectious individual D are in different areas of the same subspaceWithin a specified time T_ADThe probability of occurrence of infection can be expressed by formula (3),

if expressed in a discrete manner, the calculation can be performed using equation (4),

in the formula, λ (D)_i) The value D of the range of a certain virus field area within the sampling time interval between an infectious individual and a completely susceptible individual_iThe mean number of infectious events occurring in moderate exposure,

the probability of infection of A to D within the time sampled by j;

therefore, the method of investigation is to detect whether the fully susceptible individual has entered any viral field within a sampling time interval and then calculate the probability of infection using equation (5) based on its different distances from the viral field

Where k denotes the number of the virus field, j denotes the sampling time number, and λ (D)_i(k) A value D of the range of the virus field region for an individual entering a certain virus field k, which is completely susceptible within a certain sampling time_i(k) The mean of the occurrence of the infected event in (c),

the completely susceptible individual in the time interval sampled for j enters a certain virus field area range value D of a certain virus field k_i(k) In the event of an infectious eventProbability.

The calculation unit is used for calculating the probability that the fully susceptible individual is infected by the virus in the TD time period based on the virus distribution density map of the temporal GIS;

the probability of being infected by a virus is related to the frequency of entering a virus field area, the distance from the virus field area and the retention time, and formula (5) provides a range value D of a certain virus field area entering a certain virus field k within a certain sampling time_i(k) The probability of an infected event, therefore, the range value D of a certain virus field area entering a certain virus field k in the TD time period needs to be calculated_i(k) The probability of an infectious event occurring; as shown in equation (6);

in the formula (I), the compound is shown in the specification,

as a probability that no infection occurs during the TD period,

to trace the probability of an infection occurring at least once within the time period TD,

the completely susceptible individual in the time interval sampled for j enters a certain region range value D of a certain virus field k_i(k) K represents the serial number of the virus field, j is the serial number of the sampling time, and lambda (D)_i(k) A range of values D for a certain region of a certain virus field k for an individual who is completely susceptible within a certain sampling time_i(k) The mean value of the infected events, n is the starting sampling time of the TD time period, TD/delta t is the total sampling number in the time period TD, and delta t is the investigation sampling time;

then, use

The value classifies the completely susceptible individuals from viral infection in the TD time period into four categories of high probability, low probability and low probability;

secondly, editing the spatial position semantic information, time TD information and classification information of the kth virus field, finally accessing the database of the communication operator to obtain the mobile phone number of the user according to the ID number of the mobile phone user, and automatically sending the edited prompt information to the mobile phone of the mobile phone number by the system;

for the group with extremely high possibility and high possibility of infection, the edited prompt information provided by the system is as follows: the user contacts with the virus carrier at a short distance at time TD and spatial position semantic information of a kth virus field, and if the user does not wear a mask at that time, a high infection risk exists; if any symptoms such as fever and hypodynamia exist, please go to the hospital to see a doctor as soon as possible; in addition, before the infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people.

For the population with low possibility of infection, the compiled prompt information provided by the system is as follows: the user has some contact with the virus carrier at the time TD and the space position semantic information of the kth virus field, if the user does not wear a mask at that time, the infection risk cannot be completely eliminated, and if the user has any symptoms such as fever, weakness and the like, the user needs to go to a hospital for diagnosis as soon as possible; in addition, before infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people;

meanwhile, the following information is reported to a village and town health hospital located by a mobile phone or a street community health service center or a basic level disease control center, and users with mobile phone numbers of your district have close contact with virus carriers at one time, so that the risk of infection exists, and the system estimates the infected users at presentProbability is

Pre-judging the infection risk level as x level, and taking relevant measures;

through the above processing, suspected virus infectors are diverted from local residents, and are taken as key objects for prevention and control in the district.

The protection and guarantee unit of healthy people based on the virus distribution density map of the temporal GIS is used for protecting healthy people and guaranteeing production and life during epidemic outbreak;

during the outbreak of the epidemic situation, most residents generally concern, even are anxious about ① worrying about whether the residents are possibly infected by viruses or not, ② how to avoid the infection risk when the residents go out necessarily, such as buying living necessities, going to and from work and whether the residents are safe or not;

①, the system provides such an information service for people who do not wear masks when going out within a period of time after the epidemic situation appears, and as long as users send their mobile phone numbers to the platform, the platform will check whether the users enter any virus field during going out again according to the activity track of the users;

firstly, detecting whether the user enters the kth virus field once in a certain sampling time interval of Tdu within a period of time after the epidemic situation appears, and if yes, calculating the probability of infection by using a formula (5) according to different distances between the user and the virus field

If a plurality of virus fields exist or a plurality of virus fields enter, calculating the probability of all infections by using the formula (5)

Then, calculating the probability of the infected event entering all virus fields in Tdu within a period of time after the epidemic situation appears by using a formula (7); as shown in equation (7);

in the formula (I), the compound is shown in the specification,

is the probability that no infection occurs during the Tdu time period,

the probability that infection occurs at least once within Tdu within a period of time after the epidemic appears,

the user enters a certain region range value D of a certain virus field k in the time interval of j sampling_i(k) K represents the serial number of the virus field, j is the serial number of the sampling time, and lambda (D)_i(k) For a certain area range value D for a certain sampling time for a user to enter a certain virus field k_i(k) The mean value of the infected events, n is the time period Tdu starting sampling time, TD/delta t is the total number of samples in the time period Tdu, and delta t is the retrieval sampling time;

②, the system prompts the user to avoid the risk sections when the travel track is covered with a virus field according to the travel habit of the user, or provides the user with a virus distribution density map based on a temporal GIS (geographic information system) to enable the user to decide a travel route by himself.

The description of any one random event needs to be considered from a probabilistic and statistical perspective, with the understanding that random events are unlikely to achieve an absolute zero risk and avoid incurring additional costs for unrealistic pursuits. All people believe that the whole society can finally obtain the best epidemic prevention and control effect with the minimum cost. In order to achieve the goal, various data are required to be integrated to carry out accurate and dynamic collection, statistics and probability calculation, so that the scientific and rational strategy for controlling the epidemic situation is formulated.

In the invention, to realize the invention task, several core problems must be solved: (1) two-step visualization method, namely visualization of virus carriers and visualization of virus distribution emitted to the periphery; (2) collecting, cleaning and processing the space-time trajectory data and the infection morbidity data of virus-carrying people on the premise of fully protecting individual privacy; (3) calculating the virus distribution density with the virus carrier as the position center; (4) mapping time, space and virus distribution density data of virus-carrying crowd to a temporal GIS; (5) checking all suspicious important infectious disease virus fields, and identifying high-risk infectious disease virus fields according to the spatial position semantic information; (6) the method comprises the following steps of (1) checking infection risk people based on a virus distribution density graph of a temporal GIS, checking people who have close contact with a virus carrier as far as possible, and informing that the risk of infection exists; (7) the protection and guarantee of healthy people based on the virus distribution density graph of the temporal GIS can enable anyone to know and prove whether the person contacts with a virus carrier or not, and if the person contacts with the virus carrier, the probability of infection is high.

The invention has the following beneficial effects:

(1) through the information tool, the contact crowd who once had a short distance with the virus carrier can be found out fast to inform that the risk of being infected exists, also can provide accurate prevention and control object for the basic level disease control center of each level simultaneously.

(2) The system provided by the invention can ensure that the whole society obtains the best epidemic situation prevention and control effect with the minimum cost.

(3) The system can fully reflect the temperature of the people without epidemic situation, and can ensure the normal life and production of most urban residents while well performing epidemic situation prevention and control.

Drawings

FIG. 1 is a schematic view of a hierarchical urban model description constructed by temporal GIS technology, including 1-urban traffic network mapping layer, 2-urban area division mapping layer, 3-urban geographic information network mapping layer, and 4-virus fields distributed in urban areas;

FIG. 2 is a graph of the distribution of viruses produced by a virus carrier in the surrounding environment and the distribution of the viruses after decay over time;

FIG. 3 is a flow chart of a epidemic disease virus field-based prevention and control method and system;

FIG. 4 is a schematic diagram of a method for classifying different human subjects during the transmission of epidemic diseases;

FIG. 5 is an explanatory diagram of different regions of a virus field.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Referring to fig. 1 to 5, a control system based on an epidemic disease virus field comprises a spreading infectious disease urban diffusion modeling unit which takes air as a medium, a collecting, cleaning and processing unit of space-time trajectory data and infection morbidity data of virus carrying population, a position semantic information obtaining unit of a virus carrier, a virus distribution density calculating unit which takes the virus carrier as a position center, a time, space and virus distribution density data mapping unit of the virus carrying population, an epidemic disease virus field cloud platform construction unit based on a temporal GIS, an infection risk population investigation unit based on a virus distribution density map of the temporal GIS, and a protection and guarantee unit of healthy population based on the virus distribution density map of the temporal GIS, which are connected in sequence.

As shown in fig. 3, the processing steps of the system are as follows:

s1: urban spread modeling of infectious disease transmission by taking air as a medium;

s2: collecting, cleaning and processing the space-time trajectory data and the infection morbidity data of virus-carrying people;

s3: acquiring the position semantic information of a virus carrier;

s4: calculating the virus distribution density with a virus carrier as a position center;

s5: mapping time, space and virus distribution density data of virus carrying population;

s6: constructing an epidemic infectious disease virus field cloud platform based on a temporal GIS;

the following is divided into two branches, if the investigation of the infection risk group is carried out, S7 is executed, otherwise, S8 is executed;

s7: the virus distribution density chart based on the temporal GIS is used for examining infection risk groups;

s8: and protecting and guaranteeing healthy people based on the virus distribution density graph of the temporal GIS.

On the basis of the cloud platform of the epidemic infectious disease virus field based on the temporal GIS, people who have close contact with virus carriers are checked, and the risk of infection is informed;

on the basis of the epidemic infectious disease virus field cloud platform based on the temporal GIS, safety protection and guarantee services are provided for life counting and infection avoidance of most of healthy people in an information mode.

The air-mediated infectious disease spreading urban modeling unit is used for dividing urban region functions and mapping spatial positions, and semanticizing each spatial position function in the city; FIG. 1 is a schematic diagram showing a hierarchical urban model description constructed by temporal GIS technology, including 1-urban traffic network mapping layer, 2-urban area division mapping layer, 3-urban geographic information network mapping layer, and 4-virus field distributed in urban area; the urban regional division is preferably determined according to administrative regions governed by the disease prevention and control center of the most basic level of the country, so as to avoid loopholes in management;

the region is a city plot serving different purposes, and the city is divided into different types of agricultural regions, office regions, residential regions, hospitals, schools and leisure and entertainment regions; regardless of the areas that have substantially no effect on the spread of infectious diseases, such as agricultural areas, the areas are mapped into seven types: residential, office, school, hospital, leisure and traffic areas; the traffic area is a special area and consists of movable independent spaces, such as train carriages, subway carriages and buses;

the subspace is a smaller space unit belonging to the region and corresponds to independent non-mobile spaces in real life, and the independent non-mobile spaces are a family, a hospital ward, an office, a leisure entertainment place, a classroom or a green space; the sub-space distinguishes between outdoor and indoor; the type of the subspace is determined by the function type of the region where the subspace is located; people perform corresponding types of activities in the subspace, such as home, hospitalization, work, leisure, entertainment and learning; generating different types of said subspaces in each of said regions with reference to actual data, such as a university region consisting of an office subspace, a living space, a classroom subspace, a leisure subspace; thus, the subspaces described here are divided into the following six types: residences, offices, classrooms, wards, leisure places, traffic subspaces; thus, each subspace has corresponding semantic information;

the collecting, cleaning and processing unit is used for collecting, cleaning and processing the space-time trajectory data and the infection morbidity data of the virus-carrying population; acquiring infection morbidity data of each individual in the virus carrying population, namely the infection morbidity data of each virus carrier from a disease control center, wherein the infection morbidity data comprises a mobile phone number, morbidity time and isolated time, and acquiring mobile phone traffic data of the virus carrier and relevant base station data thereof at a time interval t from 1 day before morbidity, after morbidity and before isolation by using the mobile phone number from a communication operator, wherein the mobile phone traffic data of the virus carrier comprises user traffic triggering time, a user communication service type and a user ID number; the relevant base station data comprises a base station position area identification number and a base station sector position identification number which are relevant to the mobile phone traffic data; then processing the data into user ID, time and space position and writing the user ID, time and space position into a spatio-temporal database set, namely DATASET1 according to a time sequence;

in order to protect the individual privacy to the maximum extent, the platform only requires relevant personnel of disease control centers at all base levels to input the mobile phone numbers of virus carriers, the infection and morbidity data of the morbidity time and the isolated time, and does not contain any other personal information; only providing the mobile phone number when the access communication operator obtains the telephone traffic data and the relevant base station data; the urban regional epidemic virus farm is displayed on the temporal GIS without containing any personal information.

The semantic information acquisition unit of the position of the virus carrier accesses the urban area function division and spatial position mapping unit according to the spatial position of the virus carrier to obtain the semantic information of the spatial position of the virus carrier;

droplet infection and contact transmission are the main transmission channel of the novel coronavirus COVID-19 and the main transmission channel of a plurality of epidemic diseases; generally, outdoor air has good air circulation and is not suitable for the survival of microorganisms, and a virus field generated by a virus carrier can be attenuated relatively quickly; in a relatively closed space for the environment, a virus carrier brings pathogenic microorganisms into a room; cough, sneeze and even breath can discharge droplets into the air, the larger droplets fall to the ground before evaporation, the smaller droplets can form droplet nuclei due to the completion of water evaporation in a shorter time, the droplet nuclei with the diameter less than or equal to 10 mu m suspend in the air for several hours, and if people stay in the virus room for a longer time, the probability of contact with pathogenic microorganisms is higher, so that higher risk of disease infection is formed.

The droplet core size containing the new coronavirus is in the submicron to micron range, similar to the particle size after tobacco combustion. These droplet nuclei propagate outward in brownian motion without any external disturbance. In a more popular way, smoking a cigarette in a closed room begins to produce smoke around the smoker, then slowly spreads around the smoker, and finally there is a smoke smell throughout the room. On the other hand, the smoke density gradually decreases with the passage of time. This is the basis for the calculation of the distribution density of the COVID-19 virus.

in the formula, P (i)^tIs the spatial coordinate of the ith virus carrier at the sampling time t, t (i) is the time elapsed in the spatial coordinate of the ith virus carrier at the sampling time t, σ is a constant, f (P (i))^tAnd t (i)) is the virus distribution density of the ith virus carrier at the sampling time tth;

FIG. 2 is a graph showing the distribution density of viruses calculated by the formula (1), in which the dotted line indicates the distribution density of viruses at the sampling time t; over time, if the virus carrier no longer shed virus to the surroundings, the virus distribution density will decay, as shown by the solid line in FIG. 2; if the virus carrier is still constantly distributing the virus to the surroundings, the virus distribution density needs to be accumulated.

Therefore, in an actual environment, the outdoor virus distribution density is not accumulated considering that the indoor and outdoor virus distribution density attenuation conditions are different; for indoor, firstly, the space coordinate P (i) of the ith virus carrier at the sampling time t is judged^tAnd the spatial coordinate P (i) of the ith virus carrier at the sampling time t +1^t+1Whether the distance Δ D between is less than a threshold value T_DIf satisfied, the additive effect of virus density is taken into account; that is, the distribution density f (P (i)) of the residual viruses of the ith virus carrier at the sampling time t +1^t+1T (i) + Δ t and sampling time t +1 the virus distribution density f (P (i)) of the ith virus carrier^t+1T (i)) accumulating;

then the data is further processed into user ID, time, space position and virus distribution density, and is written into a virus distribution spatiotemporal database set, namely DATASET2, according to time sequence;

the temporal GIS is added with time dimension on the basis of the traditional GIS, and the GIS is expanded into three elements of space, time and attribute from the two elements of the traditional space and attribute; the temporal GIS can describe and express the distribution and the shape of the virus field in space, and can also describe and express the change of the virus field along with time to carry out temporal analysis;

here, the value D of the virus field area range is used_iThe calculation is in meters, and the following are respectively specified: d₁Less than 1 high risk area, D is more than or equal to 1₂Less than 2 medium-risk area, 2 is less than or equal to D₃Less than 4 low-risk area, D is not less than 4₄A safer region, as shown in FIG. 5;

The probability of at least one infection occurring is

These virus field area range values D_iAdjustable for different infectious disease categories; when an infectious individual A and an infectious individual D are in different regional spaces of the same subspace for a certain period of time T_ADThe probability of occurrence of infection can be expressed by formula (3),

the probability of infection of A to D within the time sampled by j;

firstly, focusing a relatively closed space of a public place according to a virus distribution density map of a temporal GIS, wherein the relatively closed space of the public place is mainly a railway station, an expressway service area, a supermarket, a restaurant, a shopping mall, an airport, a long-distance passenger station, a taxi, a high-speed railway carriage, a bus, a public washroom and an elevator room …; the regions and the subspaces in the regions are semantically processed in the urban spread modeling unit for the air-mediated infectious disease transmission; therefore, only the space with the virus field is screened from the relatively closed space of the public place and the time period for generating the virus field, and AC is used_n,TDWherein n represents the number of virus field spaces present in said region and subspaces in said region, and TD represents the virus field duration in said region and subspaces in said region;

next, an incoming AC is acquired_n,TDThe mobile phone track information of all the personnel in the system is used as a preliminary investigation object Agent and corresponds to the completely susceptible individual;

in the unit for checking infection risk crowd based on the virus distribution density chart of the temporal GIS, the checking method is to detect whether the primary checking object Agent enters any virus field in a sampling time interval, and then calculate the probability p of infection by using a formula (5) according to different distances between the primary checking object Agent and the virus field_k,Di(j)；

Where k denotes the number of the virus field, j denotes the sampling time number, and λ (D)_i(k) For the initial inspected object Agent to enter the virus field area range value D of a certain virus field k in a certain sampling time_i(k) The mean of the occurrence of the infected event in (c),

the preliminary investigation object Agent enters a certain virus field area range value D of a certain virus field k in the time interval of j sampling_i(k) The probability of an infectious event occurring.

Further, calculating the probability that the preliminary Agent to be checked is infected by the virus in the TD time period based on the virus distribution density graph of the temporal GIS;

the probability of being infected by a virus is related to the frequency of entering a virus field area, the distance from the virus field area and the retention time, and formula (5) provides a range value D of a certain virus field area entering a certain virus field k within a certain sampling time_i(k) The probability of an infectious event occurring; therefore, it is also necessary to calculate a range value D of a certain virus field region entering a certain virus field k in the TD time period_i(k) The probability of an infectious event occurring; as shown in equation (6);

in the formula (I), the compound is shown in the specification,

as a probability that no infection occurs during the TD period,

the preliminary object to be examined in the time interval of j samplingThe Agent enters a certain region range value D of a certain virus field k_i(k) K represents the serial number of the virus field, j is the serial number of the sampling time, and lambda (D)_i(k) For a certain area range value D of the initial inspected object Agent entering a certain virus field k in a certain sampling time_i(k) The mean value of the infected events, n is the starting sampling time of the TD time period, TD delta t is the total number of samples in the time period TD, and delta t is the investigation sampling time.

Then use

The value of the preliminary investigation object Agent is classified by a formula (8) according to the probability of being infected by the virus in the TD time period, and the preliminary investigation object Agent is divided into four categories of extremely high possibility, low possibility and extremely low possibility;

in the formula, T_bpThreshold for a very high probability of being infected, T_gpThreshold value for high probability of infection, T_spIs a threshold at which the likelihood of infection is not high.

Further, editing the spatial position semantic information, time TD information and classification information of the kth virus field, finally accessing the database of the communication operator to obtain the mobile phone number of the user according to the mobile phone user ID number, and automatically sending the edited prompt information to the mobile phone of the mobile phone number by the system;

for the group with the highest possibility or high possibility of infection, the edited prompt information provided by the system is as follows: the user contacts with the virus carrier at a short distance at time TD and spatial position semantic information of a kth virus field, and if the user does not wear a mask at that time, a high infection risk exists; if any symptoms such as fever and hypodynamia exist in the near term, please go to the hospital for a diagnosis as soon as possible; in addition, before the infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people.

Further, reporting the troubleshooting information to a health service center of a village and town where the mobile phone is positioned, or a street community, or a base-level disease control center; "you district's mobile phone number is the user, when has had close contact person with virus carrier, has the risk of infection, and the probability that present system estimate to receive infection is p_k,Di(TD), prejudging the infection risk level as x level, and taking relevant measures; "

during an outbreak, a great majority of residents generally care about worrying about whether or not they may be infected by viruses;

the system provides such information service for people who do not wear masks when going out at that time within a period of time after an epidemic situation appears, and as long as a platform user sends a mobile phone number of the platform user to a platform, the platform checks whether the platform user enters any virus field during a trip or not according to the mobile phone track of the platform user; if the virus field is encountered, according to the frequency, intensity and time length of the virus field, finally calculating the probability of infection on a certain trip and the probability of infection in the period of time; the specific algorithm is as follows:

firstly, detecting whether the platform user enters the kth virus field once in a certain sampling time interval of Tdu within a period of time after the epidemic situation appears, and if yes, calculating the probability of infection by using a formula (5) according to different distances between the platform user and the virus field

in the formula (I), the compound is shown in the specification,

is the probability that no infection occurs during the Tdu time period,

the user enters a certain region range value D of a certain virus field k in the time interval of j sampling_i(k) K represents the serial number of the virus field, j is the serial number of the sampling time, and lambda (D)_i(k) For a certain area range value D of a certain virus field k entered by said platform user within a certain sampling time_i(k) The mean value of the infected events, n is the time period Tdu starting sampling time, TD/delta t is the total number of samples in the time period Tdu, and delta t is the retrieval sampling time;

finally, the system obtains the result according to the calculation

Value, classified by equation (9),

in the formula, T_bpThreshold for a very high probability of being infected, T_gpIs highly likely to be infectedThreshold value of (1), T_spIs a threshold at which the likelihood of infection is not high.

For platform users with a high or high possibility of infection, the edited prompt information provided by the system is as follows: the user contacts with the virus carrier at a short distance at the time Tdu and the space position semantic information of the kth virus field, and if the user does not wear a mask at that time, the user has a high risk of infection; if any symptoms such as fever and hypodynamia exist in the near term, please go to the hospital for a diagnosis as soon as possible; in addition, before the infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people.

Further, reporting the troubleshooting information to a health service center of a village and town where the mobile phone is positioned, or a street community, or a base-level disease control center; "the user who has a mobile phone number of your jurisdiction has been in close contact with the virus carrier at one time, there is a risk of infection, and the probability of infection is estimated by the current system

Pre-judging the infection risk level as x level, and taking relevant measures; "

For platform users who are unlikely to be infected, the edited prompt information provided by the system is as follows: the system finds that you have some contact with the virus carrier, and cannot eliminate the risk of infection at present; if any symptoms such as fever and hypodynamia exist in the near term, please go to the hospital for a diagnosis as soon as possible; in addition, before the infection is not completely eliminated, self-isolation is actively performed so as to prevent the infection to family members and surrounding people.

For platform users with little possibility of infection, the edited prompt information provided by the system is as follows: the system does not find that you have contact with virus carriers, please keep good travelling habits, and people can take a mask when the virus is out of the door during the virus epidemic period to pay attention to self protection.

Embodiments of the present invention are equally applicable to the prevention and control of epidemics transmitted by airborne droplets, such as avian flu, SRAS, and the like.

The embodiments described in this specification are merely illustrative of implementations of the inventive concepts, which are intended for purposes of illustration only. The scope of the present invention should not be construed as being limited to the particular forms set forth in the examples, but rather as being defined by the claims and the equivalents thereof which can occur to those skilled in the art upon consideration of the present inventive concept.

Claims

1. The prevention and control system is characterized by comprising a spreading infectious disease urban diffusion modeling unit, a collection, cleaning and processing unit, a position semantic information acquisition unit, a virus distribution density calculation unit, a time, space and virus distribution density data mapping unit, a epidemic infectious disease virus field cloud platform construction unit, an infection risk crowd investigation unit and a healthy crowd protection and guarantee unit, wherein the spreading infectious disease urban diffusion modeling unit, the collection, cleaning and processing unit, the infection morbidity data acquisition unit, the virus carrier acquisition unit, the time, space and virus distribution density data mapping unit, the epidemic infectious disease virus field cloud platform construction unit, the infection risk crowd investigation unit and the healthy crowd protection and guarantee unit are sequentially connected and take air as a medium.

2. The epidemic virus disease viral farm-based prevention and control system of claim 1, wherein the air-mediated urban spread of infectious diseases modeling unit is configured to perform urban area function partitioning and spatial location mapping to semantically map each spatial location function in the city;

3. The epidemic disease virus farm-based prevention and control system of claim 1, wherein the collection, cleaning and processing unit of the spatiotemporal trajectory data and the infection morbidity data of the virus-carrying population; acquiring infection morbidity data of each individual in the virus carrying population, namely the infection morbidity data of each virus carrier from a disease control center, wherein the infection morbidity data comprises a mobile phone number, morbidity time and isolated time, and acquiring mobile phone traffic data of the virus carrier and relevant base station data thereof at a time interval t from 1 day before morbidity, after morbidity and before isolation by using the mobile phone number from a communication operator, wherein the mobile phone traffic data of the virus carrier comprises user traffic triggering time, a user communication service type and a user ID number; the relevant base station data comprises a base station position area identification number and a base station sector position identification number which are relevant to the mobile phone traffic data; the data is then processed into a user ID, time of day, spatial location and written into a spatiotemporal data repository in time series, namely DATASET 1.

4. The epidemic disease virus farm-based prevention and control system of claim 1, wherein the virus carrier location semantic information obtaining unit accesses the urban area function partitioning and spatial location mapping unit according to the spatial location of the virus carrier to obtain the semantic information of the spatial location of the virus carrier.

5. The epidemic disease virus farm-based prevention and control system according to claim 3, wherein the virus carrier is a location-centered virus distribution density calculation unit, which calculates the location-centered virus distribution density for each user ID based on the user ID, time of day, and spatial location information in the DATASET1, respectively, as follows;

furthermore, the outdoor virus distribution density is not accumulated in consideration of different attenuation conditions of the indoor and outdoor virus distribution densities; for indoor, firstly, the space coordinate P (i) of the ith virus carrier at the sampling time t is judged^tAnd the spatial coordinate P (i) of the ith virus carrier at the sampling time t +1^t+1Whether the distance Δ D between is less than a threshold value T_DIf satisfied, the virus is consideredThe additive effect of density; that is, the distribution density f (P (i)) of the residual viruses of the ith virus carrier at the sampling time t +1^t+1T (i) + Δ t and sampling time t +1 the virus distribution density f (P (i)) of the ith virus carrier^t+1T (i)) accumulating;

6. The epidemic disease virus field-based prevention and control system of any one of claims 1-5, wherein the time, space and virus distribution density data mapping unit of the virus-carrying population is used for mapping the data in DATASET2 to a temporal GIS;

7. The epidemic disease virus field-based prevention and control system according to any one of claims 1 to 5, wherein the cloud platform construction unit for the epidemic disease virus field based on the temporal GIS is implemented by cloud computing for processing massive epidemic situation data generated every day across the country; the working personnel of the disease prevention and control center of each basement layer inputs the infection and morbidity data of the virus carriers through the cloud platform of the epidemic disease virus field based on the temporal GIS; and then, by the collecting, cleaning and processing unit of the space-time trajectory data and the infection morbidity data of the virus-carrying population, the position semantic information acquisition unit of the virus carrier is used as a virus distribution density calculation unit of a position center, the time, space and virus distribution density data mapping unit of the virus-carrying population performs cloud calculation, and finally, the epidemic infectious disease virus field based on the temporal GIS is automatically generated.

8. The epidemic disease virus farm-based prevention and control system according to any one of claims 1 to 5, wherein the temporal GIS-based virus distribution density map is used for screening people at risk of infection who have come into close contact with the virus carrier and informing that there is a risk of infection;

firstly, focusing a relatively closed space of a public place according to a virus distribution density map of a temporal GIS, wherein the relatively closed space of the public place is mainly a railway station, an expressway service area, a supermarket, a restaurant, a shopping mall, an airport, a long-distance passenger station, a taxi, a high-speed railway carriage, a bus, a public washroom and an elevator room …; the regions and the subspaces in the regions are semantically processed in the urban spread modeling unit for the air-mediated infectious disease transmission; therefore, only the space with the virus field is screened from the relatively closed space of the public place and the time period for generating the virus field, and AC is used_n,TDWherein n represents the number of virus field spaces present in said region and subspaces in said region, and TD representsA virus field duration in said region and subspaces in said region;

the longer the retention time of the susceptible population in the infectious disease virus field is, the more the susceptible population enters the infectious disease virus field, the greater the probability of infection is;

The probability of at least one infection occurring is

These virus field area range values D_iAdjustable for different infectious disease categories; when an infectious individual A and an infectious individual D are in different regional spaces of the same subspaceTiming T_ADThe probability of occurrence of infection can be expressed by formula (3),

the probability of infection of A to D within the time sampled by j;

the completely susceptible individual in the time interval sampled for j enters a certain virus field area range value D of a certain virus field k_i(k) The probability of an infectious event occurring.

9. The epidemic disease virus field-based prevention and control system according to any one of claims 1-5, wherein the calculation unit for calculating the probability of infection of the completely susceptible individual by the virus in the TD time period is used for calculating the probability of infection of the completely susceptible individual by the virus in the TD time period based on the virus distribution density map of the temporal GIS;

in the formula (I), the compound is shown in the specification,

as a probability that no infection occurs during the TD period,

the completely susceptible individual in the time interval sampled for j enters a certain region range value D of a certain virus field k_i(k) K represents the serial number of the virus field, j is the serial number of the sampling time, and lambda (D)_i(k) A range of values D for a certain region of a certain virus field k for an individual who is completely susceptible within a certain sampling time_i(k) Mean of the occurrence of infectious events, n is TD time periodStarting sampling time, wherein TD/delta t is the total number of samples in the time period TD, and delta t is investigation sampling time;

then, use

for the group with extremely high possibility and high possibility of infection, the edited prompt information provided by the system is as follows: the user contacts with the virus carrier at a short distance at time TD and spatial position semantic information of a kth virus field, and if the user does not wear a mask at that time, a high infection risk exists; if any symptoms such as fever and hypodynamia exist, please go to the hospital to see a doctor as soon as possible; in addition, before infection is not eliminated, self-isolation is actively carried out so as to prevent the infection to family members and surrounding people;

meanwhile, the following information is reported to the health hospital or street society of the villages and towns where the mobile phone is positionedThe district health service center or the basic disease control center, the users with mobile phone numbers in the district of your jurisdiction have close contact with the virus carriers at any time, the risk of infection exists, and the probability of infection is estimated by the current system

Pre-judging the infection risk level as x level, and taking relevant measures;

10. The epidemic disease virus field-based prevention and control system according to any one of claims 1 to 5, wherein the temporal GIS-based virus distribution density map protection and guarantee unit for healthy people is used for protection and guarantee of production and life of healthy people during epidemic outbreak;

in the formula (I), the compound is shown in the specification,

is the probability that no infection occurs during the Tdu time period,