WO2022142467A1 - 疫情防控方法、装置、设备和介质 - Google Patents

疫情防控方法、装置、设备和介质 Download PDF

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WO2022142467A1
WO2022142467A1 PCT/CN2021/117893 CN2021117893W WO2022142467A1 WO 2022142467 A1 WO2022142467 A1 WO 2022142467A1 CN 2021117893 W CN2021117893 W CN 2021117893W WO 2022142467 A1 WO2022142467 A1 WO 2022142467A1
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community
epidemic prevention
sub
person
control area
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French (fr)
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宋轩
莫宇
张浩然
冯德帆
唐之遥
云沐晟
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Southern University of Science and Technology
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Southern University of Science and Technology
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the embodiments of the present invention relate to the technical field of epidemic prevention and control, and in particular, to an epidemic prevention and control method, device, equipment and medium.
  • the epidemic prevention and control methods to curb the spread of the epidemic in the population can be roughly divided into three categories, namely: general prevention and control measures proposed from an epidemiological perspective; modeling and simulation based on existing data and existing measures and current prevention and control measures. assessment of control measures; and optimization of measures based on epidemiological models.
  • the general prevention and control measures proposed from an epidemiological point of view aim to curb the spread of the virus among people by allowing individuals or organizations to take theoretically effective measures;
  • the evaluation of control measures is mainly to predict the development trend of the virus epidemic in the future under the current measures through the extended general epidemiological model, and to analyze and evaluate the effectiveness of different measures;
  • the optimization of measures based on the epidemiological model is based on the previous two This method makes further and fuller use of data, proposes new epidemic prevention measures or combined epidemic prevention measures, and optimizes them according to the actual situation, aiming to achieve the best epidemic prevention effect with the least cost.
  • the embodiments of the present invention provide an epidemic prevention and control method, device, equipment and medium, which can effectively prevent and control the epidemic, reduce the transmission speed of the epidemic, and improve the effect of epidemic prevention and control.
  • an embodiment of the present invention provides an epidemic prevention and control method, including:
  • the community structure includes a plurality of communities, and each community includes at least one sub-region;
  • the community to which the confirmed person belongs is determined, so as to adjust the epidemic prevention measures of the community.
  • the embodiment of the present invention also provides an epidemic prevention and control device, including:
  • the data acquisition module is used to acquire the movement data of each person in the epidemic prevention and control area;
  • a first determination module configured to determine a network structure of the epidemic prevention and control area based on the movement data of each person, wherein the network structure includes a plurality of sub-areas;
  • a second determining module configured to determine the community structure of the epidemic prevention and control area based on the sub-areas in the network structure, wherein the community structure includes a plurality of communities, and each community includes at least one sub-area;
  • the adjustment module is used to determine the community to which the confirmed person belongs when there is a confirmed person in the community structure, so as to adjust the epidemic prevention measures of the community.
  • an embodiment of the present invention also provides an electronic device, including:
  • processors one or more processors
  • the one or more processors implement the epidemic prevention and control method described in any of the embodiments of the present invention.
  • an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements any of the epidemic prevention and control methods described in the embodiments of the present invention.
  • the network results of the epidemic prevention and control area are determined based on the movement data of each person, and the community structure of the epidemic prevention and control area is determined based on the sub-areas in the network results, and then when When a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs shall be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • Embodiment 1 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 1 of the present invention
  • FIG. 1A is a schematic diagram of determining the movement of people in different sub-areas in an epidemic prevention and control area according to Embodiment 1 of the present invention
  • FIG. 1B is a schematic diagram of converting the movement of personnel in different sub-areas in an epidemic prevention and control area into a network structure according to Embodiment 1 of the present invention
  • FIG. 1C is a schematic diagram of determining a community structure based on a network structure according to Embodiment 1 of the present invention.
  • 1D-FIG. 1I are another schematic diagram of determining a community structure based on a network structure provided by Embodiment 1 of the present invention.
  • Embodiment 2 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 2 of the present invention
  • Embodiment 3 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 3 of the present invention.
  • Embodiment 4 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 4 of the present invention.
  • Embodiment 5 is a schematic structural diagram of an epidemic prevention and control device provided in Embodiment 5 of the present invention.
  • FIG. 6 is a schematic structural diagram of an electronic device according to Embodiment 6 of the present invention.
  • Embodiment 1 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 1 of the present invention. This embodiment is applicable to a scenario where targeted epidemic prevention is performed on an epidemic prevention and control area.
  • the method can be executed by an epidemic prevention and control device.
  • the apparatus may consist of hardware and/or software and may be integrated into electronic equipment. The method specifically includes the following:
  • the epidemic prevention and control area refers to any area that needs to carry out epidemic prevention and control, such as different provinces and cities, where the provinces can be but are not limited to: Shaanxi province, Hebei province and Zhejiang province, etc.; cities can be but not limited to: Shenzhen, Shanghai and Beijing, etc.
  • Personnel movement data refers to data including personnel identification, longitude, latitude and time information.
  • the personnel identification refers to the information that uniquely determines the identity of the personnel, such as personnel ID, etc.
  • the time information is in units of frames. That is, in this embodiment, one day is decomposed into 480 frames, and each frame is 3 minutes, that is, the location of the person is recorded every three minutes.
  • a data acquisition request can be sent to an institution that has the movement data of each person in the epidemic prevention and control area, so that the institution can feed back the movement data of each person in the epidemic 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 time period may be determined according to other virus characteristics, which is not specifically limited here.
  • the mobile data format of each person can be as shown in Table 1 below:
  • S102 Determine a network structure of the epidemic prevention and control area based on the movement data of each person, wherein the network structure includes multiple sub-areas.
  • the epidemic prevention and control area can be divided into multiple sub-areas based on the Uber h3 model, and the movement trajectory of each person can be determined according to the movement data of each person in the epidemic prevention and control area.
  • each of the multiple sub-regions is a regular hexagonal region with the same size and non-overlapping with each other.
  • each sub-area has a certain area
  • the location of each time point in the movement trajectory of each person can be matched with multiple sub-areas to determine which sub-area the location of each person at each time point belongs to.
  • the location of each person at each time point is converted into the identification information of the sub-area to which it belongs, and the movement of each person within a preset time period (for example, 15 days) is counted to obtain the information of each person within the preset time period.
  • the total number of movements an individual has made between different sub-areas. Therefore, according to the total number of movements as the correlation between different sub-areas, the network structure of the epidemic prevention and control area is determined, which lays the foundation for effective prevention and control of the epidemic prevention and control area in the future.
  • the identification information of the sub-area may be identity information capable of being unique to the sub-area, such as a sub-area number or a sub-area name.
  • the position of each person at each time point is converted into the format of the identification information of the sub-region to which he belongs, as shown in Table 2 below:
  • this embodiment determines the network structure of the epidemic prevention and control area based on the movement data of each person, including: dividing the epidemic prevention and control area into multiple sub-areas based on the honeycomb hexagonal model; Based on the movement data of each person and the size of each sub-area, the sub-area to which each person belongs is determined; based on the sub-area to which each person belongs, the network structure of the epidemic prevention and control area is determined.
  • the network structure for determining the epidemic prevention and control area based on the movement data of each person in this embodiment will be exemplarily described below.
  • the epidemic prevention and control area is divided into 7 sub-areas based on the Uber h3 model, namely area1, area2, area3, area4, area5, area6 and area7, then when there are personnel in the epidemic prevention and control area
  • a and Person B are connected, according to the respective movement data of Person A and Person B, it can be determined that the location of Person A at each time point is located in area2, area4, area5 and area7 respectively; the location of Person B at each time point is located in area1, area1, area3, area4 and area6.
  • the black point in Figure 1A represents Person A
  • the gray point represents Person B
  • the total number of movements of personnel A and personnel B between different sub-areas is determined by the number of movement trajectories between any two sub-areas.
  • the movement trajectory of person A is the black line between area2, area4, area5 and area7 of person A
  • the movement trajectory of person B is the gray line between area1, area3, area4 and area6 of person B.
  • FIG. 1B based on the movement of Person A and Person B in different sub-areas in the epidemic prevention and control area shown in Fig. 1A, Person A and Person B in the epidemic prevention and control area can be converted into different sub-areas into a network structure, as shown in Figure 1B.
  • the nodes in FIG. 1B represent sub-regions in the epidemic prevention and control area, and the edge connecting any node represents the degree of association between the sub-area and the sub-area. The total number of moves from the area to the second sub-area is determined.
  • S103 based on the sub-regions in the network structure, determine a community structure of the epidemic prevention and control region, wherein the community structure includes a plurality of communities, and each community includes at least one sub-region.
  • the travel mode of most people is more inclined to be fixed within a specified range. For example, if a person wants to go to an ordinary supermarket, he will choose the first supermarket that is closer to home or to the company with a high probability. , rather than a second supermarket further away. That is to say, the movement trajectory of the general person is consistent with the modularity algorithm (Louvain algorithm) in the community discovery (Fastunfolding) algorithm. That is, the mobility of people within sub-regions of the same community is stronger than the mobility between communities.
  • determining the community structure of the epidemic prevention and control area may include the following steps:
  • each sub-region in the network structure is regarded as an independent community, and the modularity between any two communities in all the communities in the network structure is calculated.
  • Q represents the degree of modularity between any two communities in all communities;
  • m represents the sum of the in-degree weights of all sub-regions in the network structure;
  • a i,j represents the connection weight between sub-region i and sub-region j ;
  • k i represents the sum of the weights of all the edges connecting the sub-region i;
  • k j represents the sum of the weights of all the edges connecting the sub-region j;
  • c represents the community;
  • ⁇ in represents the sum of the edge weights of the community c in the community;
  • ⁇ tot representss the sum of the total edge weights of all subregions in community c.
  • Step 2 since the modularity algorithm can discover the hierarchical community structure, and its optimization goal is to maximize the modularity of the entire community structure, after calculating the modularity between all any two communities, any community can be added. to the adjacent community, and calculate the modularity change value after adding and before adding, and record the largest neighbor community at the same time, repeat the step of adding any community to its adjacent community, until all sub-regions belong to The community doesn't change anymore.
  • any community is added to the adjacent community, and optionally, the community with a smaller weight can be preferentially added to the adjacent community.
  • Step 3 compress the network structure to compress all sub-regions in the same community into a new sub-region, and the weight of the edges between the sub-regions in the community is the total weight and value of the edges between the original communities.
  • Step 4 Repeat steps 2 and 3 until the modularity of the entire network structure no longer changes (that is, a fixed value), so that the corresponding structure of the fixed modularity is determined as the community structure of the epidemic prevention and control area.
  • the present embodiment determines the community structure of the epidemic prevention and control area based on the sub-areas in the network structure, including: taking each sub-area in the network structure as a community to iterate on the communities in the network community Process until the iterative community modularity is a fixed value, and the community structure of the epidemic prevention and control area is obtained.
  • the network structure of the epidemic prevention and control area is the network structure of the epidemic prevention and control area, where the network structure includes multiple sub-areas
  • this embodiment is based on the modularity algorithm in the community discovery algorithm, and can follow the aforementioned step 1.
  • the network structure is continuously iteratively processed to obtain the community structure of the epidemic prevention and control area, as shown in Figure 1C.
  • the network structure of the epidemic prevention and control area includes 5,270 sub-areas
  • the 5,270 sub-areas can be iteratively processed to obtain the first community structure with 1,017 communities, as shown in Figure 1D, and calculate the first community Modularity of the structure.
  • the first community structure with 1017 communities is iteratively processed, and the second community structure with 249 communities can be obtained.
  • this embodiment can analyze the community where the confirmed person is often active according to the movement data of the confirmed person, and then adjust the epidemic prevention measures of the community to realize the prevention and control of the epidemic.
  • Different communities in the region implement targeted epidemic prevention measures to improve the epidemic prevention effect.
  • the number of the community to which the confirmed person belongs is at least one.
  • the community to which the confirmed person belongs can be determined, and the level of epidemic prevention measures in that community can be determined. If the community's epidemic prevention measures are at the highest level, no adjustment will be made; if the community's epidemic prevention measures are not at the highest level, it means that the community's current epidemic prevention measures cannot effectively curb the spread of the epidemic, and an outbreak may occur at any time. .
  • this embodiment can upgrade the epidemic prevention measure level of the current embodiment of the community to the highest level, for example, adopting community blockade measures to suppress the spread of the epidemic and effectively prevent the spread of the epidemic.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • Embodiment 2 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 2 of the present invention.
  • the method is as follows:
  • S202 based on the movement data of each person, determine a network structure of the epidemic prevention and control area, wherein the network structure includes a plurality of sub-areas.
  • S203 based on the sub-regions in the network structure, determine a community structure of the epidemic prevention and control region, wherein the community structure includes a plurality of communities, and each community includes at least one sub-region.
  • the preset time period can be set according to the type of epidemic situation.
  • the flow of the confirmed person in the sub-region of the community to which the confirmed person belongs can also be determined according to a preset time period. That is, determine which sub-regions the confirmed person has passed through in the community to which they belong, and which sub-regions they have not passed through, so as to lay a foundation for adjusting the epidemic prevention measures corresponding to the sub-regions and sub-regions that the confirmed person has passed through.
  • determining the community to which the confirmed person belongs, as well as the sub-regions and sub-regions in which the confirmed person belongs can be determined according to the time information, longitude and latitude in the movement data of the confirmed person in the preset time period.
  • the longitude and latitude of each time point in the preset time period of the confirmed person is matched with each community in the community structure, and the successfully matched community is determined as the community to which the confirmed person belongs; in the same way, determine Which sub-areas and which sub-areas did the confirmed person pass through in the community to which they belonged, is also to match the longitude and latitude of each time point in the preset time period of the confirmed person with each sub-area in the community to which they belong, and match the
  • the successful sub-area is determined as the sub-area that the confirmed person passes through, and the sub-area that fails to match is determined as the sub-area that the confirmed person does not pass through.
  • the risk growth value of the pathway sub-region and the non- pathway sub-region can be determined, and then the risk value of the pathway sub-region and the non- pathway sub-region can be updated according to the risk growth value.
  • determining the risk growth value of the pathway sub-region can be achieved by the following formula (2):
  • T i the total time that the confirmed person stays in the ith sub-area, in units of frames
  • the maximum risk value can be selected from the received risk values sent by other sub-regions in the community to which it belongs, and the maximum risk value can be used as the risk growth value of the unpassed sub-region.
  • the electronic device can update the risk value of the path sub-region and the non-path sub-region of the confirmed person in the community to which they belong in different ways according to the risk increase value. Specifically, it includes: for the update operation of the risk value of the sub-area of the path of the confirmed person, by adding the increased risk value on the basis of the risk value of the sub-area of the path, and determining the sum value as the updated risk value of the sub-area of the path; for the confirmed person
  • the risk value update operation of the unpassed sub-area by adding the maximum risk growth value to the risk value of the unpassed sub-area, the sum value is determined as the updated risk value of the unpassed sub-area
  • this embodiment can be implemented by the following formula (3):
  • the updated risk values of the pathway sub-region and the non- pathway sub-region can also be updated respectively. Adjust the epidemic prevention measures in the sub-regions of the route and the sub-areas that are not routed.
  • the updated risk values of the routed sub-regions and the non-passed sub-regions are compared with the corresponding epidemic prevention thresholds for each epidemic prevention measure level. If the updated risk value of the routed sub-areas and/or the non-routed sub-area is greater than the epidemic prevention threshold corresponding to the highest level of epidemic prevention measures, the routed sub-areas and/or the non-routed sub-area will be blocked to ensure that no outbreak will occur.
  • the route sub-areas and/or the non-passed sub-area take no action. If the updated risk value of the routed sub-area and/or the non-passed sub-area is smaller than the epidemic prevention threshold corresponding to the highest level of epidemic prevention measures, and greater than the epidemic prevention threshold of the next highest level of epidemic prevention measures Take temperature measurement measures of different intensities to achieve purposeful prevention; if the updated risk value of the route sub-areas and/or the non-pass sub-area is less than the epidemic prevention threshold corresponding to the lowest level of epidemic prevention measures, the route sub-areas and/or the non-pass sub-areas Pathway subregions take no action.
  • the highest level of epidemic prevention measures corresponds to the epidemic prevention threshold, and the optional value is 0.8; the next highest level of epidemic prevention measures corresponds to the epidemic prevention threshold, and the optional value is 0.4.
  • the highest level of epidemic prevention measures and the next highest level of epidemic prevention measures can also be corresponding to each other.
  • the epidemic prevention thresholds of 1 are adaptively adjusted according to actual needs, and there are no specific restrictions on them here.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • the epidemic prevention measures of the sub-regions and the sub-regions that have not been passed through are adjusted respectively, so as to realize the dynamic basis of the confirmed personnel.
  • the risk value of different sub-areas in the community to which you belong can adjust the epidemic prevention efforts of the sub-areas, so that stronger epidemic prevention efforts can be adopted in high-risk sub-areas and lower epidemic prevention efforts in safe sub-areas, which not only ensures the living comfort of people in safe sub-areas , and can effectively control the spread of the epidemic.
  • FIG. 3 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 3 of the present invention.
  • the method is as follows:
  • S303 based on the sub-regions in the network structure, determine a community structure of the epidemic prevention and control region, wherein the community structure includes a plurality of communities, and each community includes at least one sub-region.
  • the risk values of other people in the community can also be updated, so as to provide conditions for quickly locking all high-risk persons who may be infected in the future.
  • the method of updating the risk value of other personnel can count the number of times of all path sub-regions and the risk value of all path sub-regions within a preset time period for each frame of data of other personnel, and then according to the statistical results, other personnel value at risk is updated.
  • the risk value of other people in the community to which the confirmed person belongs can be updated through the following formula (4):
  • P_risk u represents the updated personal risk value of person u
  • T represents the set of all frame numbers in the previous preset time period of the frame
  • data[u][i] represents querying the location of person u at time i from the record, i represents the ith frame.
  • this embodiment may adjust the epidemic prevention measures of other persons according to the updated risk values of the other persons.
  • the updated risk values of other personnel are compared with the epidemic prevention thresholds corresponding to different levels of epidemic prevention measures. If the updated risk value of other personnel is greater than the epidemic prevention threshold corresponding to the highest level of epidemic prevention measures, compulsory accounting and detection measures will be taken for other personnel to prevent the possibility of multiple infections; if the updated risk value of other personnel is less than the highest level of epidemic prevention measures If the corresponding epidemic prevention threshold is greater than the epidemic prevention threshold corresponding to the next-highest level of epidemic prevention measures, other people should fill in the daily self-reporting measures, and recommend self-isolation at home; if the updated risk value of other people is less than the corresponding epidemic prevention threshold of the lowest level of epidemic prevention measures , no action is taken against other personnel.
  • the highest level of epidemic prevention measures corresponds to the epidemic prevention threshold, and the optional value is 0.8; the next highest level of epidemic prevention measures corresponds to the epidemic prevention threshold, and the optional value is 0.6.
  • the highest level of epidemic prevention measures and the next highest level of epidemic prevention measures can also be corresponding to each other.
  • the epidemic prevention threshold value of the virus is adaptively adjusted according to actual needs, and there is no specific restriction on it here.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • all possible risks can be quickly locked according to the risk value of each person in the community.
  • Infected high-risk individuals rather than only targeting groups who have been in close contact with confirmed individuals, can not only save time in finding people who may be infected, but also more comprehensively discover all susceptible populations.
  • FIG. 4 is a schematic flowchart of an epidemic prevention and control method provided in Embodiment 4 of the present invention, which is further optimized on the basis of the foregoing embodiment. As shown in Figure 4, the method is as follows:
  • S402 based on the movement data of each person, determine a network structure of the epidemic prevention and control area, wherein the network structure includes multiple sub-areas.
  • S403 based on the sub-regions in the network structure, determine a community structure of the epidemic prevention and control region, wherein the community structure includes a plurality of communities, and each community includes at least one sub-region.
  • S405 Determine the consumption cost corresponding to the adjusted epidemic prevention measures.
  • the consumption cost corresponding to the adjusted epidemic prevention measures includes: the adjusted consumption cost of the community epidemic prevention measures, the adjusted consumption cost of the epidemic prevention measures of sub-regions in the community, and/or the adjusted consumption cost of the epidemic prevention measures of other people in the community.
  • the sub-regions within the community refer to the sub-regions and non-pass sub-regions that confirm that the person is in the community to which they belong.
  • the indicators to measure the effect of epidemic prevention measures include not only the daily number of infected people, but also the consumption cost corresponding to the epidemic prevention measures. That is to say, in this embodiment, after adjusting the epidemic prevention measures of the community, the consumption cost corresponding to the adjusted epidemic prevention measures can also be determined.
  • the consumption cost corresponding to the adjusted epidemic prevention measures can be determined by the following formula (5):
  • Total Cost Cost of Equipment + Cost of Personnel + Cost of Hospitalization
  • Cost equipment consumption extracostA*testnum1...
  • Cost Personnel consumption avesalary*(tcost1*testnum1+tcost2*testnum2)
  • Cost Hospital consumption extracostB*testnum2+avesalary*(tcost3*testnum3+tcost4*testnum4)
  • the total cost of cost represents the consumption cost corresponding to the adjusted epidemic prevention measures
  • the cost of equipment consumption represents the consumption cost of the establishment and maintenance of the adjusted epidemic prevention measures, such as the purchase, maintenance and electricity costs of the checkpoint temperature measurement machine
  • the cost of personnel consumption represents the adjustment
  • the consumption cost of staff after the epidemic prevention measures such as those who help fill in the self-declaration of the test, or the medical staff who help to calculate the test, etc.
  • Cost hospital consumption represents the economic loss of hospitalized patients and those who self-isolate at home without working
  • extracostA represents statistical data.
  • the price of the temperature measuring machine is divided by the unit price per person who can measure the temperature; testnum1 represents the number of people performing temperature measurement; avesalary represents the average income per minute of personnel in the epidemic prevention and control area; tcost1, tcost2, tcost3 and tcost4 represent The time it takes to correspond to the epidemic prevention measures; testnum2 represents the number of people undergoing other inspections; extracostB represents the cost of nucleic acid testing; testnum3 represents the number of people undergoing nucleic acid testing; testnum4 represents the number of people hospitalized.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • determine the consumption cost corresponding to the adjusted epidemic prevention measures so as to realize the estimation of the economic losses caused by the epidemic prevention and control process, in order to avoid unnecessary economic losses in the epidemic prevention process. provide conditions.
  • FIG. 5 is a schematic structural diagram of an epidemic prevention and control device provided in Embodiment 5 of the present invention.
  • the epidemic prevention and control device of this embodiment may be composed of hardware and/or software, and may be integrated into electronic equipment.
  • the epidemic prevention and control device 500 provided by 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 used to acquire the movement data of each person in the epidemic prevention and control area;
  • a first determining module 520 configured to determine a network structure of the epidemic prevention and control area based on the movement data of each person, wherein the network structure includes a plurality of sub-areas;
  • the second determination module 530 is configured to determine the community structure of the epidemic prevention and control area based on the sub-regions in the network structure, wherein the community structure includes a plurality of communities, and each community includes at least one sub-region;
  • the adjustment module 540 is used to determine the community to which the confirmed person belongs when a confirmed person appears in the community structure, so as to adjust the epidemic prevention measures of the community.
  • the first determining module 520 is specifically configured to:
  • the network structure of the epidemic prevention and control area is determined.
  • the second determining module 530 is specifically configured to:
  • Each sub-area in the network structure is used as a community to iteratively process the community in the network community until the iterative community modularity is a fixed value, and the community structure of the epidemic prevention and control area is obtained.
  • the adjustment module 540 is specifically configured to:
  • the apparatus 500 further includes: a third determining module;
  • the third determination module is used to determine the path sub-region and the non-path sub-region of the confirmed person in the community to which they belong within a preset time period;
  • the adjustment module 540 is further configured to update the risk values of the pathway sub-region and the non- pathway sub-region based on the movement data of the confirmed person, and based on the updated risk values, respectively adjust the pathway sub-regions.
  • the epidemic prevention measures in the region and the said non-passage sub-regions are adjusted.
  • the adjustment module 540 is further configured to:
  • the risk value of other personnel in the community to which the confirmed person belongs is updated, and the epidemic prevention measures of the other personnel are adjusted based on the updated risk value of the other personnel.
  • the apparatus 500 further includes: a fourth determining module
  • the fourth determination module is used to determine the consumption cost corresponding to the adjusted epidemic prevention measures.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • FIG. 6 is a schematic structural diagram of an electronic device according to Embodiment 6 of the present invention.
  • Figure 6 shows 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 impose any limitation on the function and scope of use of the embodiments of the present invention.
  • electronic device 600 takes the form of a general-purpose computing device.
  • Components of electronic device 600 may include, but are not limited to, one or more processors or processing units 16 , system memory 28 , and a bus 18 connecting various system components including system memory 28 and processing unit 16 .
  • Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures.
  • these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.
  • Electronic device 600 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 600, including volatile and non-volatile media, removable and non-removable media.
  • 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 .
  • Electronic device 600 may further include other removable/non-removable, volatile/non-volatile computer system storage media.
  • storage system 34 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive”).
  • a disk drive may be provided for reading and writing to removable non-volatile magnetic disks (eg "floppy disks"), as well as removable non-volatile optical disks (eg CD-ROM, DVD-ROM) or other optical media) to read and write optical drives.
  • each drive may be connected to bus 18 through one or more data media interfaces.
  • Memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present invention.
  • a program/utility 40 having a set (at least one) of program modules 42, which 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 or some combination of these examples may include an implementation of a network environment.
  • Program modules 42 generally perform the functions and/or methods of the described embodiments of the present invention.
  • the electronic device 600 may also communicate with one or more external devices 14 (eg, 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 device (eg, network card, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may take place through input/output (I/O) interface 22 . Also, the electronic device 600 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through the network adapter 20 . As shown, network adapter 20 communicates with other modules of electronic device 600 via bus 18 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with 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.
  • the processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the epidemic prevention and control method provided by the embodiment of the present invention, including:
  • the community structure includes a plurality of communities, and each community includes at least one sub-region;
  • the community to which the confirmed person belongs is determined, so as to adjust the epidemic prevention measures of the community.
  • the technical solution provided by the embodiments of the present invention determines the network result of the epidemic prevention and control area based on the movement data of each person by acquiring the movement data of each person in the epidemic prevention and control area, and determines the epidemic situation based on the sub-areas in the network result.
  • the community structure of the prevention and control area and then when a confirmed person appears in the community structure of the epidemic prevention and control area, the epidemic prevention measures of the community to which the confirmed person belongs will be adjusted.
  • the community structure of the epidemic prevention and control area can be determined based on the movement data of people.
  • targeted protection can be carried out for the community based on the community to which the confirmed person belongs, so as to effectively prevent and control the epidemic and reduce the epidemic situation.
  • the speed of transmission can improve the effectiveness of epidemic prevention and control.
  • Embodiment 7 of the present invention further provides a computer-readable storage medium.
  • the computer-readable storage medium provided by the embodiment of the present invention stores a computer program thereon, and when the program is executed by the processor, implements the epidemic prevention and control method according to the embodiment of the present invention, including:
  • the community structure includes a plurality of communities, and each community includes at least one sub-region;
  • the community to which the confirmed person belongs is determined, so as to adjust the epidemic prevention measures of the community.
  • the computer storage medium in the embodiments of the present invention may adopt any combination of one or more computer-readable mediums.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above.
  • a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted using any suitable 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 of the present invention may be written in one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, and also conventional procedures, or a combination thereof programming languages such as "C" 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.
  • the remote computer may be connected to the user's computer through any kind of network including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider to connect over the Internet) .
  • LAN local area network
  • WAN wide area network
  • Internet service provider to connect over the Internet

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Abstract

一种疫情防控方法、装置、设备和介质。方法包括：获取疫情防控区域中每个人员的移动数据(S101)；基于每个人员的移动数据，确定疫情防控区域的网络结构，其中网络结构包括多个子区域(S102)；基于网络结构中的子区域，确定疫情防控区域的社区结构，其中社区结构包括多个社区，且每个社区包括至少一个子区域(S103)；在社区结构中出现确诊人员时，确定确诊人员所属社区，以对社区的防疫措施进行调整(S104)。实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。

Description

疫情防控方法、装置、设备和介质 技术领域
本发明实施例涉及疫情防控技术领域，尤其涉及一种疫情防控方法、装置、设备和介质。
背景技术
疫情的爆发给社会各个方面造成严重的负面影响，因此遏制疫情在人群中传播成为疫情防控中至关重要的问题。
目前，遏制疫情在人群中传播的疫情防控方式大致可分为三类，分别为：从流行病学角度提出的一般性防控措施；基于已有数据与现行措施的建模模拟与现行防控措施评估；以及基于流行病学模型的措施优化。其中，从流行病学角度提出的一般性防控措施，旨在通过让个人或组织采取理论上有效的措施来遏制病毒在人群间传播；基于已有数据与现行措施的建模模拟与现行防控措施评估，主要是通过拓展的通用流行病学模型来预测在现行措施下未来病毒疫情的发展趋势，并分析及评估不同措施的有效性；基于流行病学模型的措施优化，则基于前面两种方式更进一步更充分的利用数据，提出新的防疫措施或组合防疫措施，并根据实际情况进行优化，旨在利用最少成本达到最优的防疫效果。
然而，上述疫情防控方式因对疫情掌握不全面，以及人员的行为复杂性等因素，导致疫情防控效果不佳。
技术问题
本发明实施例提供一种疫情防控方法、装置、设备和介质，实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。
技术解决方案
第一方面，本发明实施例提供了一种疫情防控方法，包括：
获取疫情防控区域中每个人员的移动数据；
基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
第二方面，本发明实施例还提供了一种疫情防控装置，包括：
数据获取模块，用于获取疫情防控区域中每个人员的移动数据；
第一确定模块，用于基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
第二确定模块，用于基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
调整模块，用于在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
第三方面，本发明实施例还提供了一种电子设备，包括：
一个或多个处理器；
存储装置，用于存储一个或多个程序，
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现本发明实施例中任一所述的疫情防控方法。
第四方面，本发明实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现本发明实施例中任一所述的疫情防控方法。
有益效果
本发明实施例公开的技术方案，具有如下有益效果：
通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。
附图说明
图1是本发明实施例一提供的一种疫情防控方法的流程示意图；
图1A是本发明实施例一提供的一种确定疫情防控区域中人员在不同子区域中移动情况的示意图；
图1B是本发明实施例一提供的一种将疫情防控区域中人员在不同子区域中移动情况转换成网络结构的示意图；
图1C是本发明实施例一提供的一种基于网络结构确定社区结构的示意图；
图1D-图1I是本发明实施例一提供的另一种基于网络结构确定社区结构的示意图；
图2是本发明实施例二提供的一种疫情防控方法的流程示意图；
图3是本发明实施例三提供的一种疫情防控方法的流程示意图；
图4是本发明实施例四提供的一种疫情防控方法的流程示意图；
图5是本发明实施例五提供的一种疫情防控装置的结构示意图；
图6是本发明实施例六提供的一种电子设备的结构示意图。
本发明的实施方式
下面结合附图和实施例对本发明实施例作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本发明实施例，而非对本发明实施例的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本发明实施例相关的部分而非全部结构。
下面参考附图对本发明实施例提供的一种疫情防控方法、装置、设备和介质进行说明。
实施例一
图1是本发明实施例一提供的一种疫情防控方法的流程示意图，本实施例可适用于对疫情防控区域进行针对性防疫的场景，该方法可以由疫情防控装置来执行，该装置可由硬件和/或软件组成，并可集成于电子设备中。该方法具体包括如下：
S101，获取疫情防控区域中每个人员的移动数据。
其中，疫情防控区域是指任意需要进行疫情防控的地区，例如不同省市，其中省可以为但不限于：陕西省、河北省和浙江省等；市可以为但不限于：深圳市、上海市和北京市等等。人员的移动数据是指包括人员标识、经度、纬度及时间信息的数据。其中，人员标识是指唯一确定人员身份的信息，例如人员ID等；时间信息是以帧为单位。即本实施例通过将一天分解成480帧，每一帧为3分钟，即每隔三分钟会记录一次人员的所处位置。
[根据细则9更正 15.11.2021]　
具体的，可通过向具有疫情防控区域中每个人员移动数据的机构发送数 据获取请求，以使机构根据接收到数据获取请求反馈疫情防控区域中每个人员的移动数据。其中，数据获取请求中可携带有数据获取时间段。可以根据其他病毒特性，确定数据获取时间段，此处对其不做具体限定。
[根据细则9更正 15.11.2021]　
在本实施例中每个人员的移动数据格式，可如下表1所示：
表1
Figure PCTCN2021117893-appb-000001
S102，基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域。
具体的，可将该疫情防控区域，基于蜂巢六边形(Uber h3)模型划分成多个子区域，并根据该疫情防控区域中每个人员的移动数据，确定每个人员的移动轨迹。其中，多个子区域中每个子区域为大小相同且互相不重叠的正六边形区域。
由于每个子区域具有一定的面积，因此可将每个人员移动轨迹中每个时间点所在位置，与多个子区域进行匹配，以确定每个人员每个时间点所在位置属于哪一个子区域。然后，将每个人员在每个时间点所在位置转换成所属子区域的标识信息，同时统计每个人员在预设时间段(例如15天)内的移动情况，以获取预设时间段内每个人员在不同子区域之间的移动总次数。从而根据移动总次数作为不同子区域之间的关联度，确定出该疫情防控区域的网络结构，以为后续对疫情防控区域进行有效防控奠定基础。其中，子区域的标识信息可以是能够唯一子区域的身份信息，例如子区域编号或子区域名称等。
本实施例中，将每个人员在每个时间点所在位置转换成所属子区域的标识信息的格式，可如下表2所示：
表2
Figure PCTCN2021117893-appb-000002
也就是说，本实施例基于每个人员的移动数据，确定疫情防控区域的网络结构，包括：基于蜂巢六边形模型，将所述疫情防控区域划分成多个子区域；基于所述每个人员的移动数据和每个子区域的大小，确定所述每个人员的所属子区域；基于所述每个人员的所属子区域，确定所述疫情防控区域的网络结构。
下面结合图1A和图1B所示，对本实施例中基于每个人员的移动数据，确定疫情防控区域的网络结构进行示例性说明。首先，如图1A所示，假设基于Uber h3模型将疫情防控区域划分成7个子区域，分别为area1、area2、area3、area4、area5、area6和area7，那么当该疫情防控区域中存在人员A和人员B时，可根据人员A和人员B各自的移动数据，确定人员A每个时间点所在位置分别位于area2、area4、area5和area7中；人员B每个时间点所在位置分别位于area1、area3、area4和area6。其中，图1A中黑色点代表人员A，灰色点代表人员B。并且，人员A和人员B在不同子区域之间的移动总次数由任意两个子区域之间的移动轨迹数量确定。在图1A中，人员A的移动轨迹为人员A在area2、area4、area5和area7之间的黑色线路，人员B的移动轨迹为人员B在area1、area3、area4和area6之间的灰色线路。
也就是说，在相邻帧之间人员A和/或人员B各自所属子区域标识发生变化时，说明人员A和/或人员B在不同子区域之间移动，从而确定上述人员A和/或人员B在不同子区域之间的关系更为密切。
其次，结合图1B所示，基于图1A所示疫情防控区域中人员A和人员B，分别在不同子区域的移动情况，可将疫情防控区域中人员A和人员B在不同子区域转换成网络结构，具体如图1B。其中，图1B中节点代表疫情防控区域中的子区域，连接任意节点的边代表子区域与子区域之间的关联度，本实施例关联度可根据预设时间段内人员从第一子区域到第二子区域的移动总次数确定。
S103，基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域。
在实际应用中，大部分人员的出行方式更偏向于固定在一个指定范围内，例如一个人如果要去一个普通超市时，他将大概率的选择离家或者离公司更近的第一家超市，而非距离较远的第二家超市。也就是说，一般人员的移动轨迹是符合社区发现(Fastunfolding)算法中模块度算法(Louvain算法)。即，人员在同一个社区的子区域内部流动性要强于社区与社区之间的流动性。
基于此，本实施例可基于确定的网路结构，确定疫情防控区域的社区结构可包括以下步骤：
步骤1，将该网络结构中每个子区域作为一个独立社区，并计算该网络结构中所有社区中任意两个社区之间的模块度。
具体的，计算该网络结构中所有社区中任意两个社区之间的模块度，可通过如下公式(1)实现：
Figure PCTCN2021117893-appb-000003
其中，Q代表所有社区中任意两个社区之间的模块度；m代表网络结构中所有子区域的入度权值之和；A i,j代表子区域i和子区域j之间的连边权重；k i代表连接子区域i的所有边的权值之和；k j代表连接子区域j的所有边的权值之和；δ(c i,c j)用于判断子区域i和子区域j是否属于同一社区，如果属于则δ(c i,c j)＝1，否则δ(c i,c j)＝0；c代表社区；∑in代表社区c在社区内的边权总和；∑tot代表社区c中所有子区域的总边权值之和。
步骤2，由于模块度算法能够发现层次性的社区结构，且其优化目标是最大化整个社区结构的模块度，因此在计算出所有任意两个社区之间的模块度之后，可将任意社区添加至与其相邻的社区中，并计算添加后与添加前的模块度变化值，同时记录最大的邻居社区，重复执行将任意社区添加至与其相邻的社区这一步骤，直到所有子区域的所属社区不再变化。
其中，将任意社区添加至与其相邻的社区中，可选的可将社区权值较小者优先添加至与其相邻的社区中。
步骤3，对网络结构进行压缩，以将所有在同一个社区的子区域压缩成为一个新子区域，社区内子区域之间的边的权重为原社区之间边的总权重和值。
步骤4，重复执行步骤2和步骤3，直到整个网络结构的模块度不再发生变化(即为固定值) 为止，从而将固定不变的模块度对应结构确定为疫情防控区域的社区结构。
即，本实施例基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，包括：将所述网络结构中每个子区域作为社区，以对所述网络社区中社区进行迭代处理，直到迭代后的社区模块度为固定值，得到所述疫情防控区域的社区结构。
继续以上述图1B进行说明，如图1B所示为疫情防控区域的网络结构，其中网络结构中包括多个子区域，那么本实施例基于社区发现算法中的模块度算法，可按照前述步骤1至步骤4对该网络结构进行不断迭代处理，得到该疫情防控区域的社区结构，具体如图1C所示。又例如，假设疫情防控区域的网络结构包括5270个子区域，那么可对5270个子区域进行迭代处理，可得到具有1017个社区的第一社区结构，具体如图1D所示，并计算第一社区结构的模块度。然后，对具有1017个社区的第一社区结构进行迭代处理，可得到具有249个社区的第二社区结构，具体如图1E所示，计算并确定第二社区的模块度是否大于第一社区的模块度。若大于则对具有249个社区的第二社区结构进行迭代处理，可得到具有74个社区的第三社区结构，具体如图1F所示，计算并确定第三社区结构的模块度是否大于第二社区结构的模块度。若大于则对具有74个社区的第三社区结构进行迭代处理，可得到具有21个社区的第四社区结构，具体如图1G所示，计算并确定第四社区结构的模块度是否大于第三社区结构的模块度。若大于则对具有21个社区的第四社区结构进行迭代处理，可得到具有15个社区的第五社区结构，具体如图1H所示，计算并确定第五社区结构的模块度是否大于第四社区结构的模块度。若大于则对具有15个社区的第五社区结构进行迭代处理，可得到具有14个社区的第六社区结构，具体如图1I所示，计算并确定第六社区结构的模块度是否大于第五社区结构的模块度。若第六社区结构的模块度等于第五社区结构的模块度，则确定第六社区结构的模块度为最大化，此时将第六社区结构确定为疫情防疫区域的社区结构。
S104，在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
具体的，当社区结构中出现至少一个确诊人员时，说明该确诊人员所在社区的疫情传播存在高风险。为此，当社区结构中出现确诊人员时，本实施例可根据该确诊人员的移动数据，分析出该确诊人员经常活动的社区，然后对该社区的防疫措施进行调整，以实现对疫情防控区域中的不同社区有针对性的实施防疫措施，从而改善防疫效果。其中，确诊人员所属社区的数量为至少一个。
具体实现时，可确定确诊人员所属社区，并确定该社区的防疫措施等级。如果该社区的防疫措施等级为最高级，则不做调整；如果该社区的防疫措施等级不为最高级别，则说明该社区当前实施的防疫措施不能有效遏制疫情传播，随时可能出现疫情爆发的可能。为此，本实施例可将该社区当前实施例的防疫措施等级升级成为最高等级，例如采取社区封锁措施，以抑制疫情的扩散，有效阻止疫情的传播。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。
实施例二
图2是本发明实施例二提供的一种疫情防控方法的流程示意图，在上述实施例的基础上，对本实施例中“在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社 区的防疫措施进行调整”进行进一步优化。如图2所示，该方法具体如下：
S201，获取疫情防控区域中每个人员的移动数据。
S202，基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域。
S203，基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域。
S204，在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，并确定预设时间段内所述确诊人员在所属社区内的途径子区域和未途径子区域。
[根据细则9更正 15.11.2021]　
其中，预设时间段可根据疫情种类进行设置。
因为人员在一个社区的子区域内部流动性要强于社区与社区之间的流动性。那么当确定社区结构出现确诊人员时，除了确定该确诊人员所属社区之外，还可根据预设时间段，确定该确诊人员在所属社区的子区域中的流动情况。即，确定该确诊人员在所属社区内途径了哪些子区域，以及未途径哪些子区域，以对确诊人员途径的子区域和未途径的子区域对应的防疫措施进行调整奠定基础。
其中，确定确诊人员所属社区，以及在所属社区内途径子区域和未途径子区域均可根据确诊人员在预设时间段的移动数据中的时间信息、经度和纬度来确定。具体实现时，将确诊人员预设时间段内每个时间点的经度和纬度，与社区结构中的每个社区进行匹配，并将匹配成功的社区确定为该确诊人员所属社区；同理，确定确诊人员在所属社区内途径了哪些子区域以及未途径哪些子区域，也是将确诊人员预设时间段内每个时间点的经度和纬度，与所属社区中的每个子区域进行匹配，并将匹配成功的子区域确定为该确诊人员途径子区域，将匹配不成的子区域确定为该确诊人员未途径子区域。
S205，基于所述确诊人员的移动数据，对所述途径子区域和所述未途径子区域的风险值进行更新，并基于更新后的风险值，分别对所述途径子区域和所述未途径子区域的防疫措施进行调整。
具体的，可根据确诊人员的移动数据，确定途径子区域和未途径子区域的风险增长值，然后根据风险增长值，对途径子区域和未途径子区域的风险值进行更新。
作为一种可选的实现方式，确定途径子区域的风险增长值可通过如下公式(2)实现：
Figure PCTCN2021117893-appb-000004
其中，
Figure PCTCN2021117893-appb-000005
代表第i个子区域第t帧的风险增长值；T i代表确诊人员在第i个子区域所停留的总时间，单位为帧；T sum代表确诊人员在预设时间段内有移动数据的时间。在本实施例中，如果预设时间段内的每个时间点均有移动数据，则T sum＝7200帧。
确定未途径子区域的风险增长值，可从接收到的所属社区中其他子区域发送的风险值，选取最大风险值，并将该最大风险值作为未途径子区域的风险增长值。
进一步的，在确定出风险增长值后，电子设备可根据风险增长值采用不同方式，对确诊人员在所属社区内的途径子区域和未途径子区域的风险值进行更新。具体包括：对于确诊人员途径子区域的风险值更新操作，通过在途径子区域的风险值的基础上加上风险增加值，并将和值确定为途径子区域更新后的风险值；对于确诊人员未途径子区域的风险值更新操作，通过在未途径子区域的风险值的基础上加上最大风险增长值，将和值确定为未途径子区域更新后 的风险值
作为一种可选实现方式，本实施例可通过如下公式(3)实现：
Figure PCTCN2021117893-appb-000006
其中，
Figure PCTCN2021117893-appb-000007
代表确诊人员在所属社区内途径子区域和未途径子区域更新后的风险值；max代表取最大值；η代表将确诊人员所属社区中的子区域风险值衰减成原来的η倍；
Figure PCTCN2021117893-appb-000008
代表第i个子区域第t-1帧的风险值；j代表确诊人员在所属社区内第j个子区域；cn代表第i个子区域直接相连且属于同一社区的子区域集合；
Figure PCTCN2021117893-appb-000009
代表第j个子区域第t-1帧的风险值；
Figure PCTCN2021117893-appb-000010
代表第i个子区域第t-1帧的风险增长值。
在本发明的一个实施例中，对确诊人员所属社区内的途径子区域和未途径子区域的风险值进行更新后，还可根据途径子区域和未途径子区域更新后的风险值，分别对途径子区域和未途径子区域的防疫措施进行调整。
具体的，通过将途径子区域和未途径子区域更新后的风险值，分别与各防疫措施等级对应防疫阈值进行比较。如果途径子区域和/或未途径子区域更新后的风险值，大于最高等级防疫措施对应防疫阈值，则对途径子区域和/或未途径子区域采取区域封锁措施，以确保疫情不会发生爆发；如果途径子区域和/或未途径子区域更新后的风险值，小于最高等级防疫措施对应防疫阈值，且大于次高等级防疫措施对应防疫阈值，则对途径子区域和/或未途径子区域采取不同强度的测温措施，实现有目的性的预防；如果途径子区域和/或未途径子区域更新后的风险值，小于最低等级防疫措施对应防疫阈值，则对途径子区域和/或未途径子区域不采取任何措施。
其中，最高等级防疫措施对应防疫阈值，可选的为0.8；次高等级防疫措施对应防疫阈值，可选的为0.4，当然本实施例还可将最高等级防疫措施及次高等级防疫措施各自对应的防疫阈值根据实际需要进行适应性调整，此处对其不做具体限制。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。此外，通过对确诊人员途径子区域和未途径子区域的风险值进行更新，并根据更新后的风险值，分别对途径子区域和未途径子区域的防疫措施进行调整，实现动态的依据确诊人员所属社区中不同子区域的风险值来调整子区域的防疫力度，从而能够在高风险子区域采用更强的防疫力度，在安全子区域降低防疫力度，不仅保证了安全子区域的人员生活舒适度，还能有效控制疫情的传播。
实施例三
图3是本发明实施例三提供的一种疫情防控方法的流程示意图，在上述实施例的基础上，对本实施例中“在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社 区的防疫措施进行调整”进行进一步优化。如图3所示，该方法具体如下：
S301，获取疫情防控区域中每个人员的移动数据。
S302，基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域。
S303，基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域。
S304，在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区。
S305，对所述确诊人员所属社区中其他人员的风险值进行更新，并基于所述其他人员更新后的风险值，对所述其他人员的防疫措施进行调整。
本实施例中，在确定出确诊人员所属社区之后，还可对该社区中其他人员的风险值进行更新，以为后续快速锁定所有可能被感染的高风险人员提供条件。具体的，对其他人员的风险值进行更新的方式，可以为其他人员每一帧数据统计预设时间段内所有途径子区域的次数以及所有途径子区域的风险值，进而根据统计结果对其他人员的风险值进行更新。
具体实现时，可通过如下公式(4)对确诊人员所属社区中其他人员的风险值进行更新：
Figure PCTCN2021117893-appb-000011
其中，P_risk u代表人员u的更新后的个人风险值；T代表该帧的前预设时间段的所有帧数集合；data[u][i]代表从记录中查询i时刻人员u所在位置，i代表第i帧。
进一步的，对确诊人员所属社区中其他人员的风险值进行更新后，本实施例可根据其他人员更新后的风险值，对其他人员的防疫措施进行调整。
具体的，将其他人员更新后的风险值，与不同等级防疫措施对应防疫阈值进行比较。如果其他人员更新后的风险值，大于最高等级防疫措施对应防疫阈值，则对其他人员采取强制进行核算检测措施，杜绝多次传染的可能；如果其他人员更新后的风险值，小于最高等级防疫措施对应防疫阈值，且大于次高等级防疫措施对应防疫阈值，则对其他人员采取填写每日自主申报措施，并推荐居家自我隔离；如果其他人员更新后的风险值，小于最低等级防疫措施对应防疫阈值，则对其他人员不采取任何措施。
其中，最高等级防疫措施对应防疫阈值，可选的为0.8；次高等级防疫措施对应防疫阈值，可选的为0.6，当然本实施例还可将最高等级防疫措施及次高等级防疫措施各自对应的防疫阈值根据实际需要进行适应性调整，此处对其不做具体限制。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。此外，通过对确诊人员所属社区内其他人员的风险值进行更新，并根据更新后的风险值，对其他人员的防疫措施进行调整，从而依据社区内每个人员的风险值，能够快速锁定所有可能被感染的高风险人员，而并非只锁定与确诊人员密切接触过的群体，这样既能节约寻找可能被感染的人员时间，还能更全面的发现所有易感人群。
实施例四
图4是本发明实施例四提供的一种疫情防控方法的流程示意图，在上述实施例的基础上进行进一步优化。如图4所示，该方法具体如下：
S401，获取疫情防控区域中每个人员的移动数据。
S402，基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域。
S403，基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域。
S404，在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
S405，确定调整后的防疫措施对应的消费成本。
其中，调整后的防疫措施对应的消费成本包括：社区防疫措施调整后的消费成本、社区内子区域防疫措施调整后的消费成本和/或社区内其他人员防疫措施调整后的消费成本。本实施例中社区内子区域是指确认人员在所属社区内的途径子区域和未途径子区域。
通常，衡量防疫措施效果的指标除了包括每日感染人数之外，还包括防疫措施对应的消费成本。也就是说，本实施例在对社区的防疫措施进行调整之后，还可确定调整后的防疫措施对应的消费成本。
具体实现时，可通过如下公式(5)确定调整后的防疫措施对应的消费成本：
Cost 总消费＝Cost 设备消费+Cost 人员消费+Cost 住院消费
Cost 设备消费＝extracostA*testnum1…
Cost 人员消费＝avesalary*(tcost1*testnum1+tcost2*testnum2)
Cost 住院消费＝extracostB*testnum2+avesalary*(tcost3*testnum3+tcost4*testnum4)
………………………………….(5)
其中，Cost 总消费代表调整后的防疫措施对应的消费成本；Cost 设备消费代表调整后的防疫措施建立及维护的消费成本，例如关卡测温机器购置，维护以及电费等支出；Cost 人员消费代表调整后的防疫措施工作人员消费成本，例如帮助填报检测自主申报的人员，或帮助核算检测的医护人员等；Cost 住院消费代表住院确诊人员以及在家自我隔离人员没有进行工作的经济损失；extracostA代表统计得出的测温机器价格除以能够测温人数的每人测温单价；testnum1代表进行测温的人员数量；avesalary代表疫情防控区域中人员平均每分钟的收入；tcost1、tcost2、tcost3和tcost4代表对应防疫措施所要花费的时间；testnum2代表进行其他检查的人员数量；extracostB代表核酸检测的费用；testnum3代表进行核酸检测的人员数量；testnum4代表住院治疗的人员数量。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。此外，通过对社区的防疫措施进行调整之后，确定调整后的防疫措施对应的消费成本，以实现对疫情防控过程中所造成的经济损 耗进行预估，为避免防疫过程中不必要的经济损失提供条件。
实施例五
图5是本发明实施例五提供的一种疫情防控装置的结构示意图。本实施例疫情防控装置，可由硬件和/或软件组成，并可集成于电子设备中。如图5所示，本发明实施例提供的疫情防控装置500包括：数据获取模块510、第一确定模块520、第二确定模块530和调整模块540。其中，数据获取模块510，用于获取疫情防控区域中每个人员的移动数据；
第一确定模块520，用于基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
第二确定模块530，用于基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
调整模块540，用于在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
作为本发明实施例的一种可选的实现方式，所述第一确定模块520，具体用于：
基于蜂巢六边形模型，将所述疫情防控区域划分成多个子区域；
基于所述每个人员的移动数据和每个子区域的大小，确定所述每个人员的所属子区域；
基于所述每个人员的所属子区域，确定所述疫情防控区域的网络结构。
作为本发明实施例的一种可选的实现方式，所述第二确定模块530，具体用于：
将所述网络结构中每个子区域作为社区，以对所述网络社区中社区进行迭代处理，直到迭代后的社区模块度为固定值，得到所述疫情防控区域的社区结构。
作为本发明实施例的一种可选的实现方式，所述调整模块540，具体用于：
确定所述社区的防疫措施等级；
当所述社区的防疫措施等级不为最高级别，则将所述社区的防疫措施等级升级成最高等级。作为本发明实施例的一种可选的实现方式，所述装置500，还包括：第三确定模块；
其中，第三确定模块，用于确定预设时间段内所述确诊人员在所属社区内的途径子区域和未途径子区域；
所述调整模块540还用于基于所述确诊人员的移动数据，对所述途径子区域和所述未途径子区域的风险值进行更新，并基于更新后的风险值，分别对所述途径子区域和所述未途径子区域的防疫措施进行调整。
作为本发明实施例的一种可选的实现方式，所述调整模块540，还用于：
对所述确诊人员所属社区中其他人员的风险值进行更新，并基于所述其他人员更新后的风险值，对所述其他人员的防疫措施进行调整。
作为本发明实施例的一种可选的实现方式，所述装置500，还包括：第四确定模块；
其中，第四确定模块，用于确定调整后的防疫措施对应的消费成本。
需要说明的是，前述对疫情防控方法实施例的解释说明也适用于该实施例的疫情防控装置，其实现原理类似，此处不再赘述。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。
实施例六
图6是本发明实施例六提供的一种电子设备的结构示意图。图6示出了适于用来实现本发明实施方式的示例性电子设备600的框图。图6显示的电子设备600仅仅是一个示例，不应对本发明实施例的功能和使用范围带来任何限制。
如图6所示，电子设备600以通用计算设备的形式表现。电子设备600的组件可以包括但不限于：一个或者多个处理器或者处理单元16，系统存储器28，连接不同系统组件(包括系统存储器28和处理单元16)的总线18。
总线18表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(ISA)总线，微通道体系结构(MAC)总线，增强型ISA总线、视频电子标准协会(VESA)局域总线以及外围组件互连(PCI)总线。
电子设备600典型地包括多种计算机系统可读介质。这些介质可以是任何能够被电子设备600访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器28可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(RAM)30和/或高速缓存存储器32。电子设备600可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统34可以用于读写不可移动的、非易失性磁介质(图6未显示，通常称为“硬盘驱动器”)。尽管图6中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如CD-ROM,DVD-ROM或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线18相连。存储器28可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本发明各实施例的功能。
具有一组(至少一个)程序模块42的程序/实用工具40，可以存储在例如存储器28中，这样的程序模块42包括但不限于操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块42通常执行本发明所描述的实施例中的功能和/或方法。
电子设备600也可以与一个或多个外部设备14(例如键盘、指向设备、显示器24等)通信，还可与一个或者多个使得用户能与该电子设备600交互的设备通信，和/或与使得该电子设备600能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口22进行。并且，电子设备600还可以通过网络适配器20与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器20通过总线18与电子设备600的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备600使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
处理单元16通过运行存储在系统存储器28中的程序，从而执行各种功能应用以及数据处理，例如实现本发明实施例所提供的疫情防控方法，包括：
获取疫情防控区域中每个人员的移动数据；
基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
需要说明的是，前述对疫情防控方法实施例的解释说明也适用于该实施例的电子设备，其实 现原理类似，此处不再赘述。
本发明实施例提供的技术方案，通过获取疫情防控区域中每个人员的移动数据，以基于每个人员的移动数据确定疫情防控区域的网络结果，并基于网络结果中的子区域确定疫情防控区域的社区结构，进而当疫情防控区域的社区结构中出现确诊人员时，对该确诊人员所属社区的防疫措施进行调整。由此，通过基于人员的移动数据确定疫情防控区域的社区结构，当出现确诊人员时能够基于确诊人员所属社区，对社区进行有针对性的防护，从而实现对疫情进行有效防控，降低疫情传播速度，提高疫情防控效果。
实施例七
为了实现上述目的，本发明实施例七还提出了一种计算机可读存储介质。
本发明实施例提供的计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本发明实施例所述的疫情防控方法，包括：
获取疫情防控区域中每个人员的移动数据；
基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
本发明实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本发明操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言诸如”C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络包括局域网(LAN)或广域网(WAN)连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
注意，上述仅为本发明的较佳实施例及所运用技术原理。本领域技术人员会理解，本发明不限于这里所述的特定实施例，对本领域技术人员来说能够进行各种明显的变化、重新调整和 替代而不会脱离本发明的保护范围。因此，虽然通过以上实施例对本发明进行了较为详细的说明，但是本发明不仅仅限于以上实施例，在不脱离本发明构思的情况下，还可以包括更多其他等效实施例，而本发明的范围由所附的权利要求范围决定。

Claims (10)

  1. 一种疫情防控方法，其特征在于，包括：
    获取疫情防控区域中每个人员的移动数据；
    基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
    基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
    在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
  2. 根据权利要求1所述的方法，其特征在于，所述基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，包括：
    基于蜂巢六边形模型，将所述疫情防控区域划分成多个子区域；
    基于所述每个人员的移动数据和每个子区域的大小，确定所述每个人员的所属子区域；
    基于所述每个人员的所属子区域，确定所述疫情防控区域的网络结构。
  3. 根据权利要求1所述的方法，其特征在于，所述基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，包括：
    将所述网络结构中每个子区域作为社区，以对所述网络社区中社区进行迭代处理，直到迭代后的社区模块度为固定值，得到所述疫情防控区域的社区结构。
  4. 根据权利要求1所述的方法，其特征在于，所述确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整，包括：
    确定所述社区的防疫措施等级；
    当所述社区的防疫措施等级不为最高级别，则将所述社区的防疫措施等级升级成最高等级。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，确定所述确诊人员所属社区之后，还包括：
    确定预设时间段内所述确诊人员在所属社区内的途径子区域和未途径子区域；
    基于所述确诊人员的移动数据，对所述途径子区域和所述未途径子区域的风险值进行更新，并基于更新后的风险值，分别对所述途径子区域和所述未途径子区域的防疫措施进行调整。
  6. 根据权利要求1-4任一项所述的方法，其特征在于，所述确定所述确诊人员所属社区之后，还包括：
    对所述确诊人员所属社区中其他人员的风险值进行更新，并基于所述其他人员更新后的风险值，对所述其他人员的防疫措施进行调整。
  7. 根据权利要求1-4任一项所述的方法，其特征在于，所述确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整之后，还包括：
    确定调整后的防疫措施对应的消费成本。
  8. 一种疫情防控装置，其特征在于，包括：
    数据获取模块，用于获取疫情防控区域中每个人员的移动数据；
    第一确定模块，用于基于所述每个人员的移动数据，确定所述疫情防控区域的网络结构，其中所述网络结构包括多个子区域；
    第二确定模块，用于基于所述网络结构中的子区域，确定所述疫情防控区域的社区结构，其中所述社区结构包括多个社区，且每个社区包括至少一个子区域；
    调整模块，用于在所述社区结构中出现确诊人员时，确定所述确诊人员所属社区，以对所述社区的防疫措施进行调整。
  9. 一种电子设备，其特征在于，包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序，
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-7中任一所述的疫情防控方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-7中任一所述的疫情防控方法。
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