CN113593713A - Epidemic situation prevention and control method, device, equipment and medium - Google Patents
Epidemic situation prevention and control method, device, equipment and medium Download PDFInfo
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Abstract
The embodiment of the invention discloses an epidemic prevention and control method, device, equipment and medium. The method comprises the following steps: acquiring mobile data of each person in an epidemic situation prevention and control area; determining a network structure of an epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas; determining a community structure of an epidemic situation prevention and control area based on sub-areas in a network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area; when confirmed persons appear in the community structure, the community to which the confirmed persons belong is determined so as to adjust epidemic prevention measures of the community. The embodiment of the invention realizes effective prevention and control of epidemic situation, reduces the spreading speed of the epidemic situation and improves the prevention and control effect of the epidemic situation.
Description
Technical Field
The embodiment of the invention relates to the technical field of epidemic prevention and control, in particular to an epidemic prevention and control method, device, equipment and medium.
Background
The outbreak of the epidemic situation causes serious negative effects on all aspects of the society, so that the restraint of the epidemic situation spread in people becomes a crucial problem in epidemic situation prevention and control.
At present, epidemic prevention and control modes for inhibiting the spread of the epidemic among people can be roughly divided into three categories, namely: general preventive measures from an epidemiological point of view; modeling simulation and current prevention and control measure evaluation based on existing data and current measures; and epidemiological model-based optimization of measures. Wherein, the general prevention and control measures proposed from the epidemiology perspective aim to inhibit the spread of the virus among people by leading individuals or organizations to take theoretically effective measures; modeling simulation and current prevention and control measure evaluation based on existing data and current measures are mainly used for predicting the development trend of future virus epidemic situation under the current measures through an expanded universal epidemiological model and analyzing and evaluating the effectiveness of different measures; and the measure optimization based on the epidemiological model further utilizes data more fully based on the two modes, provides new epidemic prevention measures or combined epidemic prevention measures, and optimizes according to actual conditions to achieve the optimal epidemic prevention effect by using the minimum cost.
However, the above epidemic prevention and control method has poor epidemic prevention and control effect due to incomplete understanding of the epidemic and complex behavior of the personnel.
Disclosure of Invention
The embodiment of the invention provides an epidemic prevention and control method, device, equipment and medium, which can effectively prevent and control an epidemic, reduce the epidemic propagation speed and improve the epidemic prevention and control effect.
In a first aspect, an embodiment of the present invention provides an epidemic prevention and control method, including:
acquiring mobile data of each person in an epidemic situation prevention and control area;
determining a network structure of the epidemic prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
when confirmed persons appear in the community structure, determining the community to which the confirmed persons belong so as to adjust epidemic prevention measures of the community.
In a second aspect, an embodiment of the present invention further provides an epidemic situation prevention and control apparatus, including:
the data acquisition module is used for acquiring the mobile data of each person in the epidemic situation prevention and control area;
the first determination module is used for determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
the second determination module is used for determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
and the adjusting module is used for determining the community to which the confirmed person belongs when the confirmed person appears in the community structure so as to adjust the epidemic prevention measures of the community.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the epidemic prevention and control method according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements any one of the epidemic prevention and control methods in the embodiments of the present invention.
The technical scheme disclosed by the embodiment of the invention has the following beneficial effects:
the method comprises the steps of obtaining mobile data of each person in an epidemic situation prevention and control area, determining a network result of the epidemic situation prevention and control area based on the mobile data of each person, determining a community structure of the epidemic situation prevention and control area based on a sub-area in the network result, and adjusting epidemic prevention measures of a community to which diagnosed persons belong when the diagnosed persons appear in the community structure of the epidemic situation prevention and control area. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect.
Drawings
Fig. 1 is a schematic flow chart of an epidemic prevention and control method according to an embodiment of the present invention;
fig. 1A is a schematic diagram illustrating a situation that a person moves in different sub-areas in an epidemic prevention and control area according to an embodiment of the present invention;
fig. 1B is a schematic diagram illustrating a network structure for converting the movement of people in an epidemic prevention and control area into different sub-areas according to an embodiment of the present invention;
fig. 1C is a schematic diagram of determining a community structure based on a network structure according to an embodiment of the present invention;
FIGS. 1D-1I are schematic diagrams of another network-based structure-based community structure determination provided in the first embodiment of the present invention;
fig. 2 is a schematic flow chart of an epidemic prevention and control method according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of an epidemic prevention and control method according to a third embodiment of the present invention;
fig. 4 is a schematic flow chart of an epidemic prevention and control method according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an epidemic situation prevention and control device according to a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad invention. It should be further noted that, for convenience of description, only some structures, not all structures, relating to the embodiments of the present invention are shown in the drawings.
An epidemic prevention and control method, device, equipment and medium provided by the embodiments of the present invention are described below with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flowchart of an epidemic situation prevention and control method according to an embodiment of the present invention, which is applicable to a scenario of performing targeted epidemic prevention on an epidemic situation prevention and control area, and the method can be executed by an epidemic situation prevention and control device, which can be composed of hardware and/or software and can be integrated in an electronic device. The method specifically comprises the following steps:
s101, mobile data of each person in the epidemic situation prevention and control area are obtained.
The epidemic situation prevention and control area refers to any area in need of epidemic situation prevention and control, such as different provinces and cities, wherein the provinces can be but are not limited to: shaanxi province, Hebei province, Zhejiang province and the like; the markets may be, but are not limited to: shenzhen city, Shanghai city and Beijing city, etc.
The movement data of the person means data including person identification, longitude, latitude, and time information. The personnel identification refers to information for uniquely determining personnel identity, such as personnel ID and the like; the time information is in units of frames. That is, the present embodiment records the position of the person every three minutes by dividing one day into 480 frames, each frame being 3 minutes.
Specifically, a data acquisition request can be sent to a mechanism with mobile data of each person in the epidemic situation prevention and control area, so that the mechanism feeds back the mobile data of each person in the epidemic situation prevention and control area according to the received data acquisition request. The data acquisition request may carry a data acquisition time period. The data acquisition period may be determined according to virus characteristics, and is not particularly limited herein.
The mobile data format of each person in this embodiment can be shown in table 1 below:
TABLE 1
S102, determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas.
Specifically, the epidemic prevention and control area can be divided into a plurality of sub-areas based on a honeycomb hexagonal (Uber h3) model, and the movement track of each person in the epidemic prevention and control area can be determined according to the movement data of each person. Each sub-area in the plurality of sub-areas is a regular hexagon area with the same size and without overlapping with each other.
Since each sub-area has a certain area, the position of each time point in each person moving track can be matched with a plurality of sub-areas to determine which sub-area each person belongs to. Then, the position of each person at each time point is converted into the identification information of the sub-area to which the person belongs, and meanwhile, the moving condition of each person in a preset time period (for example, 15 days) is counted to obtain the total moving times of each person in different sub-areas in the preset time period. Therefore, the network structure of the epidemic situation prevention and control area is determined by taking the total number of times of movement as the degree of association between different sub-areas, and a foundation is laid for effective prevention and control of the epidemic situation prevention and control area in the follow-up process. The identification information of the sub-region may be identity information of the unique sub-region, such as a sub-region number or a sub-region name.
In this embodiment, the position of each person at each time point is converted into the format of the identification information of the sub-area, which may be shown in table 2 below:
TABLE 2
That is to say, the network structure of the epidemic situation prevention and control area is determined based on the mobile data of each person in the embodiment, which includes: dividing the epidemic situation prevention and control area into a plurality of sub-areas based on a honeycomb hexagonal model; determining the subzone of each person based on the movement data of each person and the size of each subzone; and determining the network structure of the epidemic situation prevention and control area based on the sub-area to which each person belongs.
Next, referring to fig. 1A and 1B, an exemplary description will be given of a network structure for determining an epidemic situation prevention and control area based on movement data of each person in this embodiment. First, as shown in fig. 1A, assuming that an epidemic prevention and control area is divided into 7 sub-areas, namely area1, area2, area3, area4, area5, area6 and area7, based on an Uber h3 model, when a person a and a person B exist in the epidemic prevention and control area, it can be determined that the position of each time point of the person a is located in the area2, the area4, the area5 and the area7, respectively, according to the respective movement data of the person a and the person B; person B is located at area1, area3, area4, and area6 at each time point, respectively. In fig. 1A, the black dots represent person a, and the gray dots represent person B. And the total number of times that the person a and the person B move between the different sub-areas is determined by the number of movement trajectories between any two sub-areas. In fig. 1A, the moving trajectory of person a is a black line of person a between area2, area4, area5, and area7, and the moving trajectory of person B is a gray line of person B between area1, area3, area4, and area 6.
That is, when the identification of the sub-area to which the person a and/or the person B respectively belong changes between adjacent frames, the person a and/or the person B are/is explained to move between different sub-areas, so that the relationship between the person a and/or the person B between different sub-areas is determined to be more intimate.
Next, referring to fig. 1B, based on the movement of the person a and the person B in the epidemic prevention and control area shown in fig. 1A in different sub-areas, the person a and the person B in the epidemic prevention and control area can be converted into a network structure in different sub-areas, as shown in fig. 1B. In fig. 1B, the nodes represent sub-regions in the epidemic situation prevention and control region, and the edge connecting any node represents the association degree between the sub-regions, and the association degree in this embodiment may be determined according to the total number of times of movement of people from the first sub-region to the second sub-region in a preset time period.
S103, determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area.
In practical applications, most people tend to travel more than within a specified range, for example, if a person wants to go to a common supermarket, he will choose a first supermarket closer to the company or away from the company rather than a second supermarket farther away. That is, the movement trajectory of the general person is in accordance with a modularity algorithm (Louvain algorithm) in a community discovery (Fasttunfolding) algorithm. That is, the mobility of people within a sub-area of the same community is stronger than the mobility between communities.
Based on this, the embodiment may determine the community structure of the epidemic situation prevention and control area based on the determined network structure, and may include the following steps:
step 1, taking each sub-area in the network structure as an independent community, and calculating the modularity between any two communities in all the communities in the network structure.
Specifically, calculating the modularity between any two communities in all the communities in the network structure can be implemented by the following formula (1):
wherein Q represents the modularity between any two communities in all communities; m represents the sum of the incoming degree weights of all the sub-areas in the network structure; a. thei,jRepresenting the weight of the connecting edge between the sub-area i and the sub-area j; k is a radical ofiWeights representing all edges connecting sub-regions iSumming; k is a radical ofjRepresents the sum of the weights of all edges connecting the sub-region j; delta (c)i,cj) Used for judging whether the sub-area i and the sub-area j belong to the same community, if so, delta (c)i,cj) 1, otherwise δ (c)i,cj) 0; c represents a community; Σ in represents the edge weight sum of the community c in the community; Σ tot represents the sum of the total edge weights of all subregions in the community c.
Step 2, because the modularity algorithm can find a hierarchical community structure, and the optimization goal is to maximize the modularity of the whole community structure, after the modularity between any two communities is calculated, any community can be added to the adjacent community, the modularity change value after the addition and before the addition is calculated, the maximum neighbor community is recorded, and the step of adding any community to the adjacent community is repeatedly executed until the communities of all the sub-areas do not change any more.
And optionally, the community with the smaller community weight value can be preferentially added to the adjacent communities.
And 3, compressing the network structure to compress all sub-regions in the same community into a new sub-region, wherein the weight of edges between the sub-regions in the community is the total weight and value of the edges between the original communities.
And 4, repeatedly executing the step 2 and the step 3 until the modularity of the whole network structure is not changed (namely is a fixed value), and determining the corresponding structure of the fixed modularity as the community structure of the epidemic situation prevention and control area.
That is, the determining the community structure of the epidemic situation prevention and control area based on the sub-area in the network structure in this embodiment includes: and taking each sub-region in the network structure as a community to perform iterative processing on the communities in the network community until the modularity of the community after iteration is a fixed value, thereby obtaining the community structure of the epidemic situation prevention and control region.
Continuing with the description of fig. 1B, as shown in fig. 1B, the network structure of the epidemic situation prevention and control area is provided, where the network structure includes a plurality of sub-areas, and then the network structure may be continuously iterated according to the foregoing steps 1 to 4 based on a modularity algorithm in the community discovery algorithm to obtain the community structure of the epidemic situation prevention and control area, which is specifically shown in fig. 1C.
For another example, assuming that the network structure of the epidemic prevention and control area includes 5270 sub-areas, the 5270 sub-areas can be iteratively processed, so as to obtain a first community structure having 1017 communities, as shown in fig. 1D in particular, and calculate the modularity of the first community structure. Then, iteration processing is performed on the first community structure with 1017 communities, so that a second community structure with 249 communities can be obtained, and as shown in fig. 1E in detail, it is calculated and determined whether the modularity of the second community is greater than that of the first community. If so, performing iterative processing on the second community structure with 249 communities to obtain a third community structure with 74 communities, specifically as shown in fig. 1F, calculating and determining whether the modularity of the third community structure is greater than that of the second community structure. If so, performing iterative processing on the third community structure with 74 communities to obtain a fourth community structure with 21 communities, specifically, as shown in fig. 1G, calculating and determining whether the modularity of the fourth community structure is greater than that of the third community structure. If so, performing iterative processing on the fourth community structure with 21 communities to obtain a fifth community structure with 15 communities, specifically as shown in fig. 1H, calculating and determining whether the modularity of the fifth community structure is greater than that of the fourth community structure. If so, performing iterative processing on the fifth community structure with 15 communities to obtain a sixth community structure with 14 communities, specifically as shown in fig. 1I, calculating and determining whether the modularity of the sixth community structure is greater than that of the fifth community structure. And if the modularity of the sixth community structure is equal to that of the fifth community structure, determining that the modularity of the sixth community structure is maximized, and determining the sixth community structure as the community structure of the epidemic situation and epidemic prevention area.
S104, when confirmed personnel appear in the community structure, determining the community to which the confirmed personnel belong so as to adjust epidemic prevention measures of the community.
Specifically, when at least one confirmed person appears in the community structure, the situation spread of the community in which the confirmed person is located is indicated to have high risk. Therefore, when confirmed persons appear in the community structure, the community in which the confirmed persons frequently move can be analyzed according to the mobile data of the confirmed persons, and then epidemic prevention measures of the community are adjusted to implement specific implementation of epidemic prevention measures on different communities in an epidemic situation prevention and control area, so that the epidemic prevention effect is improved. Wherein, the number of communities to which the confirmed personnel belong is at least one.
When the method is specifically implemented, the community to which the confirmed personnel belongs can be determined, and the epidemic prevention measure level of the community can be determined. If the epidemic prevention measure level of the community is the highest level, no adjustment is made; if the grade of the epidemic prevention measure of the community is not the highest grade, the situation that the epidemic prevention measure currently implemented by the community cannot effectively inhibit the spread of the epidemic situation is shown, and the possibility of epidemic situation outbreak can occur at any time. Therefore, the embodiment can upgrade the epidemic prevention measure level of the current embodiment of the community to the highest level, for example, community blocking measures are adopted to inhibit spread of epidemic situations and effectively prevent spread of epidemic situations.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect.
Example two
Fig. 2 is a schematic flow chart of an epidemic situation prevention and control method according to a second embodiment of the present invention, and based on the second embodiment, further optimization is performed on "determining a community to which a confirmed person belongs when the confirmed person appears in the community structure, so as to adjust an epidemic prevention measure of the community" in this embodiment. As shown in fig. 2, the method specifically includes:
s201, mobile data of each person in the epidemic situation prevention and control area are obtained.
S202, determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas.
S203, determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area.
S204, when confirmed personnel appear in the community structure, determining the community to which the confirmed personnel belong, and determining the passage sub-area and the non-passage sub-area of the confirmed personnel in the community within a preset time period.
Wherein, the preset time quantum can be set according to epidemic situation types.
Because the mobility of people within sub-areas of a community is greater than the mobility from community to community. When the confirmed person is determined to be present in the community structure, besides determining the community to which the confirmed person belongs, the flow condition of the confirmed person in the sub-area of the community can be determined according to the preset time period. Namely, which sub-areas the confirmed person passes through in the community of the confirmed person and which sub-areas the confirmed person does not pass through are determined, so that a foundation is laid for adjusting epidemic prevention measures corresponding to the sub-areas the confirmed person passes through and the sub-areas the confirmed person does not pass through.
The community where the confirmed personnel belong to is determined, and the passage subarea and the non-passage subarea in the community can be determined according to the time information, the longitude and the latitude of the confirmed personnel in the movement data of the preset time period. During specific implementation, matching the longitude and the latitude of each time point within a preset time period of a confirmed diagnostician with each community in a community structure, and determining the community successfully matched as the community to which the confirmed diagnostician belongs; similarly, determining which sub-areas the confirmed person passes through and which sub-areas the confirmed person does not pass through in the community, matching longitude and latitude of each time point in a preset time period of the confirmed person with each sub-area in the community, determining the sub-area which is successfully matched as the confirmed person passing sub-area, and determining the sub-area which is not successfully matched as the confirmed person non-passing sub-area.
S205, updating the risk values of the path sub-region and the non-path sub-region based on the movement data of the confirmed personnel, and respectively adjusting epidemic prevention measures of the path sub-region and the non-path sub-region based on the updated risk values.
Specifically, the risk increase values of the approach sub-area and the non-approach sub-area can be determined according to the movement data of the confirmed personnel, and then the risk values of the approach sub-area and the non-approach sub-area are updated according to the risk increase values.
As an alternative implementation, determining the risk increase value of the pathway sub-region may be implemented by the following equation (2):
wherein the content of the first and second substances,
a risk increase value representing the ith sub-region th frame; t isiRepresenting the total time of the confirmed person staying in the ith sub-area, and the unit is a frame; t issumRepresenting the time when the diagnostician has movement data within a preset time period. In the present embodiment, if there is moving data at each time point within a preset time period, Tsum7200 frames.
And determining the risk growth value of the non-path sub-region, selecting the maximum risk value from the received risk values sent by other sub-regions in the community to which the user belongs, and taking the maximum risk value as the risk growth value of the non-path sub-region.
Further, after the risk increase value is determined, the electronic device can update the risk values of the passage sub-area and the non-passage sub-area of the confirmed person in the community according to the risk increase value in different modes. The method specifically comprises the following steps: for the updating operation of the risk value of the path subregion of the confirmed diagnostician, adding a risk increase value on the basis of the risk value of the path subregion, and determining a sum value as the updated risk value of the path subregion; for the updating operation of the risk value of the non-path subarea of the confirmed diagnostician, the sum value is determined as the updated risk value of the non-path subarea by adding the maximum risk increasing value on the basis of the risk value of the non-path subarea
As an alternative implementation, the present embodiment can be implemented by the following equation (3):
wherein the content of the first and second substances,
representing the updated risk values of the sub-areas of the paths and the sub-areas of the paths of the confirmed personnel in the community; max represents taking the maximum value; eta represents that the sub-region risk value in the community where the confirmed personnel belongs is attenuated to be eta times of the original value;
representing the risk value of the t-1 frame of the ith sub-area; j represents the jth sub-area of the confirmed person in the community; c. CnRepresenting a sub-area set which is directly connected with the ith sub-area and belongs to the same community;
representing the risk value of the t-1 frame of the jth sub-region;
representing the risk growth value of the ith sub-region frame t-1.
In an embodiment of the present invention, after updating the risk values of the pathway sub-region and the non-pathway sub-region in the community to which the confirmed diagnostician belongs, the epidemic prevention measures of the pathway sub-region and the non-pathway sub-region can be respectively adjusted according to the updated risk values of the pathway sub-region and the non-pathway sub-region.
Specifically, the updated risk values of the approach sub-area and the non-approach sub-area are respectively compared with epidemic prevention threshold values corresponding to various epidemic prevention measure levels. If the updated risk value of the path sub-region and/or the non-path sub-region is larger than the epidemic prevention threshold corresponding to the highest-level epidemic prevention measure, adopting a region blocking measure for the path sub-region and/or the non-path sub-region to ensure that the epidemic situation does not outbreak; if the updated risk value of the approach subregion and/or the non-approach subregion is smaller than the epidemic prevention threshold corresponding to the highest-level epidemic prevention measure and is larger than the epidemic prevention threshold corresponding to the second-level high-level epidemic prevention measure, temperature measurement measures with different intensities are taken for the approach subregion and/or the non-approach subregion, and purposeful prevention is realized; and if the updated risk value of the path sub-region and/or the non-path sub-region is less than the epidemic prevention threshold corresponding to the lowest-level epidemic prevention measure, no measure is taken on the path sub-region and/or the non-path sub-region.
Wherein, the highest grade epidemic prevention measure corresponds to the epidemic prevention threshold value, and the optional value is 0.8; the second-level epidemic prevention measure corresponds to an epidemic prevention threshold, which is optionally 0.4, and certainly, the epidemic prevention thresholds corresponding to the highest-level epidemic prevention measure and the second-level epidemic prevention measure can be adaptively adjusted according to actual needs, and no specific limitation is imposed here.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect. In addition, the epidemic prevention measures of the path subregion and the non-path subregion are respectively adjusted by updating the risk values of the confirmed person path subregion and the non-path subregion according to the updated risk values, so that the epidemic prevention strength of the subregions is dynamically adjusted according to the risk values of different subregions in the community to which the confirmed person belongs, and therefore, stronger epidemic prevention strength can be adopted in the high-risk subregion, the epidemic prevention strength is reduced in the safety subregion, the living comfort of the person in the safety subregion is guaranteed, and the spread of the epidemic situation can be effectively controlled.
EXAMPLE III
Fig. 3 is a schematic flow chart of an epidemic situation prevention and control method according to a third embodiment of the present invention, and based on the third embodiment, further optimization is performed on "determining a community to which a confirmed person belongs when the confirmed person appears in the community structure, so as to adjust an epidemic prevention measure of the community" in this embodiment. As shown in fig. 3, the method is as follows:
s301, mobile data of each person in the epidemic situation prevention and control area are obtained.
S302, determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas.
S303, determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area.
S304, when confirmed persons appear in the community structure, determining the community to which the confirmed persons belong.
S305, updating the risk values of other people in the community where the confirmed person belongs, and adjusting epidemic prevention measures of the other people based on the updated risk values of the other people.
In this embodiment, after the community to which the confirmed person belongs is determined, the risk values of other persons in the community may be updated, so as to provide conditions for subsequently and quickly locking all possibly infected persons with high risk. Specifically, the method for updating the risk values of other people may count the number of times of all the path sub-regions and the risk values of all the path sub-regions in a preset time period for each frame of data of other people, and then update the risk values of other people according to the statistical result.
In specific implementation, the risk values of other persons in the community to which the confirmed person belongs can be updated through the following formula (4):
wherein, P _ riskuAn updated personal risk value on behalf of person u; t represents all frame number sets of the frame in the previous preset time period; data u][i]Representing the position of the person u at the moment i is inquired from the record, and i represents the ith frame.
Furthermore, after the risk values of other people in the community to which the confirmed person belongs are updated, the epidemic prevention measures of other people can be adjusted according to the updated risk values of other people.
Specifically, the updated risk values of other people are compared with epidemic prevention threshold values corresponding to different levels of epidemic prevention measures. If the updated risk value of other personnel is larger than the epidemic prevention threshold corresponding to the highest-level epidemic prevention measure, the other personnel are forced to take the accounting detection measure, and the possibility of multiple infections is avoided; if the updated risk value of other personnel is smaller than the epidemic prevention threshold corresponding to the highest-level epidemic prevention measure and is larger than the epidemic prevention threshold corresponding to the second-level high-level epidemic prevention measure, filling daily autonomous declaration measures for other personnel and recommending home self-isolation; and if the updated risk value of other personnel is smaller than the epidemic prevention threshold corresponding to the lowest-level epidemic prevention measure, no measure is taken on other personnel.
Wherein, the highest grade epidemic prevention measure corresponds to the epidemic prevention threshold value, and the optional value is 0.8; the second-level epidemic prevention measure corresponds to an epidemic prevention threshold, which is optionally 0.6, and certainly, the epidemic prevention thresholds corresponding to the highest-level epidemic prevention measure and the second-level epidemic prevention measure can be adaptively adjusted according to actual needs, and no specific limitation is imposed here.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect. In addition, the risk values of other people in the community to which the confirmed person belongs are updated, and epidemic prevention measures of other people are adjusted according to the updated risk values, so that all high-risk people possibly infected can be quickly locked according to the risk value of each person in the community, instead of only the group which is in close contact with the confirmed person, the time for searching the possibly infected people can be saved, and all susceptible people can be found more comprehensively.
Example four
Fig. 4 is a schematic flow chart of an epidemic situation prevention and control method provided by the fourth embodiment of the present invention, which is further optimized based on the foregoing embodiments. As shown in fig. 4, the method specifically includes:
s401, mobile data of each person in the epidemic situation prevention and control area are obtained.
S402, determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas.
S403, determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area.
S404, when confirmed personnel appear in the community structure, determining the community to which the confirmed personnel belong so as to adjust epidemic prevention measures of the community.
S405, determining the consumption cost corresponding to the adjusted epidemic prevention measure.
Wherein, the consumption cost corresponding to the adjusted epidemic prevention measure comprises: the consumption cost after the community epidemic prevention measure adjustment, the consumption cost after the sub-area epidemic prevention measure adjustment in the community and/or the consumption cost after the other personnel epidemic prevention measure adjustment in the community. The sub-region in the community in the embodiment refers to a path sub-region and a non-path sub-region in the community where the person is confirmed.
Generally, the measure of the effect of the epidemic prevention measure includes the consumption cost corresponding to the epidemic prevention measure in addition to the number of people infected per day. That is to say, in this embodiment, after the epidemic prevention measure of the community is adjusted, the consumption cost corresponding to the adjusted epidemic prevention measure can be determined.
In specific implementation, the consumption cost corresponding to the adjusted epidemic prevention measure can be determined by the following formula (5):
among them, CostTotal consumptionRepresenting the consumption cost corresponding to the adjusted epidemic prevention measure; costDevice consumptionThe consumption cost for establishing and maintaining the adjusted epidemic prevention measures, such as expenditure of purchase, maintenance, electricity charge and the like of a checkpoint temperature measuring machine; costConsumption by personnelThe adjusted consumption cost of the anti-epidemic measures staff is represented, for example, the staff who help to fill in the self declaration of the detection or the medical staff who help to check and detect, etc.; costConsumption in hospitalEconomic losses representing no work for in-hospital confirmed personnel and at-home self-isolation personnel; the extracostA represents the price of the temperature measuring machine obtained by statistics divided by the unit price of the temperature measuring of each person capable of measuring the temperature; testnum1 represents the number of people taking thermometry; avesalary represents the average of people in epidemic prevention and control areaIncome per minute; tcost1, tcost2, tcost3, and tcost4 represent the time taken for the corresponding epidemic prevention measure; testnum2 represents the number of people who performed other examinations; extracostB represents the cost of nucleic acid detection; testnum3 represents the number of persons who performed nucleic acid testing; testnum4 represents the number of hospitalized personnel.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect. In addition, after the epidemic prevention measures of the community are adjusted, the consumption cost corresponding to the adjusted epidemic prevention measures is determined, so that the economic loss caused in the epidemic prevention and control process is estimated, and conditions are provided for avoiding unnecessary economic loss in the epidemic prevention process.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an epidemic situation prevention and control device provided by the fifth embodiment of the present invention. The epidemic prevention and control device of the embodiment can be composed of hardware and/or software and can be integrated in electronic equipment. As shown in fig. 5, an epidemic situation prevention and control apparatus 500 provided in the embodiment of the present invention includes: a data acquisition module 510, a first determination module 520, a second determination module 530, and an adjustment module 540.
The data acquisition module 510 is configured to acquire movement data of each person in the epidemic situation prevention and control area;
a first determining module 520, configured to determine a network structure of the epidemic situation prevention and control area based on the movement data of each person, where the network structure includes a plurality of sub-areas;
a second determining module 530, configured to determine a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, where the community structure includes multiple communities, and each community includes at least one sub-area;
an adjusting module 540, configured to determine, when a confirmed person appears in the community structure, a community to which the confirmed person belongs, so as to adjust an epidemic prevention measure of the community.
As an optional implementation manner of the embodiment of the present invention, the first determining module 520 is specifically configured to:
dividing the epidemic situation prevention and control area into a plurality of sub-areas based on a honeycomb hexagonal model;
determining the subzone of each person based on the movement data of each person and the size of each subzone;
and determining the network structure of the epidemic situation prevention and control area based on the sub-area to which each person belongs.
As an optional implementation manner of the embodiment of the present invention, the second determining module 530 is specifically configured to:
and taking each sub-region in the network structure as a community to perform iterative processing on the communities in the network community until the modularity of the community after iteration is a fixed value, thereby obtaining the community structure of the epidemic situation prevention and control region.
As an optional implementation manner of the embodiment of the present invention, the adjusting module 540 is specifically configured to:
determining an epidemic prevention measure level of the community;
and when the epidemic prevention measure level of the community is not the highest level, upgrading the epidemic prevention measure level of the community to the highest level.
As an optional implementation manner of the embodiment of the present invention, the apparatus 500 further includes: a third determination module;
the third determining module is used for determining a path subregion and a non-path subregion of the diagnosed person in the community within a preset time period;
the adjusting module 540 is further configured to update the risk values of the pathway sub-zone and the non-pathway sub-zone based on the movement data of the diagnosed person, and adjust the epidemic prevention measures of the pathway sub-zone and the non-pathway sub-zone based on the updated risk values, respectively.
As an optional implementation manner of the embodiment of the present invention, the adjusting module 540 is further configured to:
updating the risk values of other people in the community where the confirmed person belongs, and adjusting epidemic prevention measures of the other people based on the updated risk values of the other people.
As an optional implementation manner of the embodiment of the present invention, the apparatus 500 further includes: a fourth determination module;
and the fourth determining module is used for determining the consumption cost corresponding to the adjusted epidemic prevention measure.
It should be noted that the above explanation of the embodiment of the epidemic prevention and control method is also applicable to the epidemic prevention and control device of the embodiment, and the implementation principle is similar, and is not repeated here.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect.
EXAMPLE six
Fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary electronic device 600 suitable for use in implementing embodiments of the present invention. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 600 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The electronic device 600 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 20. As shown, the network adapter 20 communicates with the other modules of the electronic device 600 over the bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, to implement the epidemic situation prevention and control method provided by the embodiment of the present invention, which includes:
acquiring mobile data of each person in an epidemic situation prevention and control area;
determining a network structure of the epidemic prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
when confirmed persons appear in the community structure, determining the community to which the confirmed persons belong so as to adjust epidemic prevention measures of the community.
It should be noted that the above explanation of the embodiment of the epidemic situation prevention and control method is also applicable to the electronic device of the embodiment, and the implementation principle is similar, and is not described herein again.
According to the technical scheme provided by the embodiment of the invention, the mobile data of each person in the epidemic situation prevention and control area is obtained, the network result of the epidemic situation prevention and control area is determined based on the mobile data of each person, the community structure of the epidemic situation prevention and control area is determined based on the sub-area in the network result, and then when confirmed persons appear in the community structure of the epidemic situation prevention and control area, the epidemic prevention measures of the community to which the confirmed persons belong are adjusted. From this, through the community structure that confirms epidemic situation prevention and control region based on personnel's removal data, can carry out corresponding protection to the community based on the community that the personnel of diagnosing belong to when appearing the personnel of diagnosing to the realization is effectively prevented and is controlled the epidemic situation, reduces epidemic situation propagation speed, improves epidemic situation prevention and control effect.
EXAMPLE seven
To achieve the above object, a seventh embodiment of the present invention further provides a computer-readable storage medium.
The computer-readable storage medium provided in the embodiment of the present invention stores thereon a computer program, and when the computer program is executed by a processor, the method for controlling epidemic situations according to the embodiment of the present invention is implemented, including:
acquiring mobile data of each person in an epidemic situation prevention and control area;
determining a network structure of the epidemic prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
when confirmed persons appear in the community structure, determining the community to which the confirmed persons belong so as to adjust epidemic prevention measures of the community.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An epidemic prevention and control method is characterized by comprising the following steps:
acquiring mobile data of each person in an epidemic situation prevention and control area;
determining a network structure of the epidemic prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
when confirmed persons appear in the community structure, determining the community to which the confirmed persons belong so as to adjust epidemic prevention measures of the community.
2. The method of claim 1, wherein determining the network structure of the epidemic prevention and control area based on the movement data of each person comprises:
dividing the epidemic situation prevention and control area into a plurality of sub-areas based on a honeycomb hexagonal model;
determining the subzone of each person based on the movement data of each person and the size of each subzone;
and determining the network structure of the epidemic situation prevention and control area based on the sub-area to which each person belongs.
3. The method of claim 1, wherein the determining the community structure of the epidemic prevention and control area based on the sub-areas in the network structure comprises:
and taking each sub-region in the network structure as a community to perform iterative processing on the communities in the network community until the modularity of the community after iteration is a fixed value, thereby obtaining the community structure of the epidemic situation prevention and control region.
4. The method of claim 1, wherein the determining the community to which the confirmed person belongs to adjust the epidemic prevention measure of the community comprises:
determining an epidemic prevention measure level of the community;
and when the epidemic prevention measure level of the community is not the highest level, upgrading the epidemic prevention measure level of the community to the highest level.
5. The method of any one of claims 1-4, wherein determining the community to which the diagnostician belongs further comprises:
determining a path subregion and a non-path subregion of the diagnosed person in the community within a preset time period;
updating the risk values of the pathway sub-zone and the non-pathway sub-zone based on the movement data of the diagnosed personnel, and respectively adjusting the epidemic prevention measures of the pathway sub-zone and the non-pathway sub-zone based on the updated risk values.
6. The method of any one of claims 1-4, wherein said determining the community to which the diagnostician belongs further comprises:
updating the risk values of other people in the community where the confirmed person belongs, and adjusting epidemic prevention measures of the other people based on the updated risk values of the other people.
7. The method according to any one of claims 1-4, wherein the determining the community to which the confirmed person belongs to adjust the epidemic prevention measure of the community further comprises:
and determining the consumption cost corresponding to the adjusted epidemic prevention measure.
8. An epidemic prevention and control device, comprising:
the data acquisition module is used for acquiring the mobile data of each person in the epidemic situation prevention and control area;
the first determination module is used for determining a network structure of the epidemic situation prevention and control area based on the mobile data of each person, wherein the network structure comprises a plurality of sub-areas;
the second determination module is used for determining a community structure of the epidemic situation prevention and control area based on the sub-areas in the network structure, wherein the community structure comprises a plurality of communities, and each community comprises at least one sub-area;
and the adjusting module is used for determining the community to which the confirmed person belongs when the confirmed person appears in the community structure so as to adjust the epidemic prevention measures of the community.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the epidemic prevention and control method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the epidemic prevention and control method according to any one of claims 1-7.
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