Health management method and system based on data analysis

Abstract

The invention relates to the technical field of health management, and particularly discloses a health management method and system based on data analysis, and the method comprises the following steps: dividing a monitoring region into a plurality of monitoring sub-regions, and collecting the crowd data information of the monitoring sub-regions in a unit collection time period; calculating to obtain a crowd flow rate and a crowd steady-state rate; calculating to obtain a disease case ratio; acquiring an infection rate, a recurrence rate and a recovery rate of the disease; calculating to obtain a regional risk solid-state value in the monitoring sub-region; calculating and obtaining a regional risk dynamic value in the monitored sub-region i; based on a regional risk solid-state value and a regional risk dynamic value; calculating to obtain a regional risk value of the monitoring sub-region; the regional risk value is compared with the risk assessment threshold value to obtain the risk signal, in the use process, the calculation method can be suitable for calculation of different infectious diseases, the regional risk value calculation is accurate, the assessment accuracy is high, and the system can adapt to the management and control requirements of different use scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and specifically relates to a health management method and system based on data analysis. Background Art

[0002] At present, for the prevention and control of infectious diseases, control personnel are separately equipped in different regions, and the control personnel verify passing personnel according to the set passing rules. It is impossible to automatically carry out overall hierarchical control of different regions. This prevention and control method requires a large amount of human resources, has low efficiency and cannot predict and control the risks of mobile personnel;

[0003] At the same time, due to the complex on-site situation and various problems in urban planning, many communities and surrounding buildings are intertwined, making it difficult to determine the divided areas, and the epidemiological investigation personnel are not familiar with the on-site situation. During large-scale epidemics, the support team members from other regions are unfamiliar with the local situation and it is difficult to quickly make judgments. At the same time, they do not understand the infectious disease risk assessment method, and it is difficult to quickly divide areas to block the flow of people, which easily leads to the existence of infection risks in this area;

[0004] Therefore, we propose a health management method and system based on data analysis, which are used to monitor and evaluate infectious diseases and personal risks in the monitored area, so as to better manage and reduce the infection risk. Summary of the Invention

[0005] The purpose of the present invention is to provide a health management method and system based on data analysis to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A health management method based on data analysis includes the following steps:

[0008] Divide the monitored area into several monitoring sub-areas, and label the monitoring sub-areas as i, where i is 1, 2, 3...;

[0009] Taking the unit time T as the unit of the collection time period, collect the population data information of the monitoring sub-area i within the unit collection time period T;

[0010] Calculate the population flow rate PL i and the population steady state rate PJ of the monitoring sub-area i at the moment of the unit collection time period T i ;

[0011] Obtain the disease data information of the monitoring sub-area i within the unit collection time period T, where the disease data information includes the confirmed case value BQ i and the recovered case value BK i ;

[0012] Through calculate the disease case ratio BB i ;

[0013] Obtain the infection rate a, recurrence rate b, and recovery rate c of the disease;

[0014] Through calculate to obtain the regional risk solid value QF within the monitored sub-region i ; Then calculate and obtain the regional risk dynamic value QD within the monitored sub-region i i ;

[0015] Based on the regional risk solid value QF i and the regional risk dynamic value QD i ; Calculate to obtain the regional risk value QYZ of the monitored sub-region i ;

[0016] Compare the regional risk value QYZ i with the risk assessment threshold QYY; Obtain the risk signal.

[0017] As a further solution of the present invention: The population data information includes: total population PZ, population density value PM i , population interaction value PH i and population stability value PW i .

[0018] As a further solution of the present invention: Through calculate to obtain the population flow rate PL i ;

[0019] Through calculate to obtain the population steady state rate PJ at time T within the unit collection time period in the monitored sub-region i i .

[0020] As a further solution of the present invention: The calculation method of the regional risk dynamic value QD i includes:

[0021] Step 051: Mark the inflowing flow person-times Pr i sequentially as j, where j is 1, 2, 3...;

[0022] Obtain the monitored sub-region trajectory of each inflowing flow person-time Pr i ;

[0023] Respectively calculate the personal risk value within the inflowing flow person-time Pr i and mark them respectively as Pg j ;

[0024] Step 052: Calculate the inflow risk value QL of the inflowing passenger flow i ;

[0025] Step 053: Based on the rehabilitation case value BK i and the outflow passenger flow Pc i , through calculate the regional recurrence risk value FF i ;

[0026] Step 054: Then, through QD i =QL i +FF i calculate the regional risk dynamic value QD within the monitored sub-region i .

[0027] As a further solution of the present invention: In the said Step 052, the calculation method of the inflow risk value QL i is as follows:

[0028] Obtain the infection rate a and recurrence rate b of the disease;

[0029] Through calculate the inflow risk value QL within the monitored sub-region i i .

[0030] As a further solution of the present invention: Through QYZ i =β×QF i +γ×QD i calculate the regional risk value QYZ of the monitored sub-region i ; where β is the regional static risk assessment factor and γ is the regional dynamic risk assessment factor.

[0031] As a further solution of the present invention: The obtaining method of the personal risk value Pg includes:

[0032] Obtain the number of monitored sub-regions passed through in the personal movement trajectory, and mark the monitored sub-regions passed through by the personal movement trajectory as k;

[0033] Obtain the regional risk solid state value QF within each monitored sub-region passed through by the personal movement trajectory k ; where k is 1, 2, 3...;

[0034] Through calculate the personal risk value Pg.

[0035] As a further solution of the present invention: The risk signals include regional risk controllable signals and regional risk warning signals.

[0036] As a further solution of the present invention: If the regional risk value QYZi If it is less than the risk assessment threshold QYY, a regional risk controllable signal is generated;

[0037] If the regional risk value is QYZ i Greater than or equal to the risk assessment threshold QYY, a regional risk warning signal is generated.

[0038] As a further solution of the present invention: A health management system based on data analysis, comprising:

[0039] Data acquisition module: used to divide the monitored area into several monitored sub-areas, and mark the monitored sub-areas as i, where i is 1, 2, 3...;

[0040] Taking the unit time T as the unit collection time period, collect the population data information of the monitored sub-area i within the unit collection time period T;

[0041] Data processing module: used to calculate the population flow rate PL i And the population steady state rate PJ of the monitored sub-area i at the moment of the unit collection time period T i ;

[0042] Risk verification module: used to obtain the disease data information of the monitored sub-area i within the unit collection time period T, where the disease data information includes the confirmed case value BQ i And the recovered case value BK i ;

[0043] Through Calculate to obtain the disease case ratio BB i ;

[0044] Obtain the infection rate a, recurrence rate b and recovery rate c of the disease;

[0045] Through Calculate to obtain the regional risk solid state value QF within the monitored sub-area i ; Then calculate and obtain the regional risk dynamic value QD within the monitored sub-area i i ;

[0046] Based on the regional risk solid state value QF i And the regional risk dynamic value QD i ; Calculate to obtain the regional risk value QYZ of the monitored sub-area i ;

[0047] Risk assessment module: used to compare the regional risk value QYZ i With the risk assessment threshold QYY; obtain the risk signal.

[0048] The beneficial effects of the present invention:

[0049] In the present invention, by collecting the population data information in the monitored sub-region and then setting data such as the infection rate, recurrence rate, and recovery rate in the disease information transmission and recovery information by the managing doctor, the regional risk value in the monitored region can be calculated, and then it can be determined whether the region needs to be controlled and warned to avoid large-scale spread of the disease; better control and protect the health of the population in the monitored sub-region;

[0050] In addition, based on the calculation of the regional risk solid value passed by an individual, the individual risk value can be calculated. At this time, when the individual risk value is relatively high, the individual can be controlled, and at the same time, other monitored sub-regions can be reminded to prohibit the entry of this individual to avoid the risk of disease infection to other people, effectively reducing the impact of the individual on the population; and this system does not require the controlling personnel to make judgments, can conduct research and judgment based on the individual's whereabouts, and the evaluation is objective and accurate, avoiding the influence of subjective factors of the controlling personnel on the control;

[0051] At the same time, when the system divides the monitored region, the smaller the monitored sub-region is, the more accurate the risk assessment of the monitored sub-region is. At the same time, in the division of the monitored sub-region by the system, it can also be divided according to factors such as the population density in the monitored region; at the same time, in the use process, data such as the infection rate, recurrence rate, and recovery rate of different diseases are set by the managing doctor, so that this calculation method can be applied to the calculation of different infectious diseases, the calculation of the regional risk value is accurate, and the assessment accuracy rate is high, making this system able to adapt to the control requirements of different usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below with reference to the accompanying drawings.

[0053] Figure 1 is a schematic flowchart of the method of the present invention;

[0054] Figure 2 is a block diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Please refer to Figure 1 as shown, the present invention is a health management method based on data analysis, including the following steps:

[0057] Step 1: Divide the monitoring area into several monitoring sub-areas, and label the monitoring sub-areas as i, where i = 1, 2, 3...;

[0058] Collect the time period in units of unit time T, and collect the data information in the monitoring sub-area, including: the population data information of the monitoring sub-area i within the unit collection time period T;

[0059] Among them, the population data information includes: total population value PZ, population density value PM i , population interaction value PH i and population stability value PW i ;

[0060] It should be noted that: the total population value PZ refers to the total population of the monitoring sub-area i at the moment of the unit collection time period T; the population density value PM i refers to the population density in the monitoring sub-area, that is, the ratio of the population quantity to the area of the monitoring sub-area; the population interaction value PH i refers to the total population movement value in the monitoring sub-area, that is, the total number of outgoing flow person-times Pc i and the total number of incoming flow person-times Pr i sum; the population stability value PW i refers to the population quantity value of the people in the monitoring sub-area who have not flowed out of the area within the unit time T;

[0061] Step 2: Calculate the population flow rate PL through i ; that is, the population flow volume of the monitoring sub-area i within the unit collection time period. Here, it should be noted that regarding the population interaction value PH i , it refers to the total population movement value in the monitoring sub-area, that is, the total number of outgoing flow person-times Pc i and the total number of incoming flow person-times Pr i sum; that is, the outgoing flow person-times Pc i and the incoming flow person-times Pr i , both are statistical quantity values. For example, when a person goes out once and comes in once, the population interaction value is 2;

[0062] Based on the population stability value PW i and the total population value PZ, calculate the population steady-state rate PJ of the monitoring sub-area i at the moment of the unit collection time period T through i ; that is, the ratio of the people in the monitoring sub-area who only move within the monitoring sub-area and do not flow out;

[0063] Step 3: Obtain the disease data information of the monitoring sub-area i within the unit collection time period T. Among them, the disease data information includes the confirmed case value BQi and the rehabilitation case value BK i ;

[0064] Based on the disease data information, through calculate the disease case ratio BB i ; that is, the proportion of the population at risk of infection in the area;

[0065] Obtain the infection rate a, recurrence rate b, and recovery rate c of the disease; it should be noted that in this management method, the infection rate a, recurrence rate b, and recovery rate c of the disease are all input by the management doctor, and the infection rate a, recurrence rate b, and recovery rate c are set according to the characteristics of different infectious diseases; so that this calculation method can be applied to the calculation of different infectious diseases;

[0066] Through calculate to obtain the regional risk solid value QF within the monitored sub - area i ; used to evaluate the regional risk value after the population enters and exits during the unit collection time period T, and is used to calculate the regional risk value at the end of the unit collection time period T;

[0067] Step four: Obtain the number of inflow and outflow people Pr within the monitored sub - area i i Based on the trajectory data of an individual, calculate the individual risk value Pg;

[0068] Specifically include:

[0069] Obtain the number of monitored sub - areas passed through in the individual's movement trajectory, and mark the monitored sub - areas passed through by the individual's movement trajectory as k;

[0070] Obtain the regional risk solid value QF within each monitored sub - area passed through by the individual's movement trajectory k ; where k is 1, 2, 3...; it should be noted that when k = 1, it means that the individual only moves within the monitored sub - area during the unit collection time period T;

[0071] Through calculate to obtain the individual risk value Pg;

[0072] That is, calculate according to the individual's movement trajectory to obtain the disease infection risk value generated by the individual after passing through multiple monitored areas. It should be noted that the higher the regional risk solid value of the monitored sub - areas passed through by the individual, and the more monitored sub - areas passed through, the greater the individual risk value. At this time, it means that the individual has a greater disease infection risk, and at the same time, it also indicates that the risk of being infected by others is higher;

[0073] Step five: Calculate and obtain the regional risk dynamic value QD within the monitored sub - area i i ;

[0074] Specifically include:

[0075] Step 051: Sequentially label the inflowing passenger flow Pr i as j, where j is 1, 2, 3...;

[0076] Obtain the trajectory of the monitoring sub-region for each inflowing passenger flow Pr i ; It should be noted that the trajectory value of the monitoring sub-region refers to the monitoring sub-regions passed by the trajectory of this inflowing passenger flow;

[0077] Respectively calculate the personal risk value within the inflowing passenger flow Pr i and label them as Pg j ;

[0078] Step 052: Calculate and obtain the inflow risk value QL of the inflowing passenger flow i ;

[0079] Specifically include: Obtain the infection rate a and recurrence rate b of the disease;

[0080] Through calculate and obtain the inflow risk value QL within the monitoring sub-region i i ;

[0081] Step 053: Based on the recovered case value BK i and the outflowing passenger flow Pc i , through calculate and obtain the regional recurrence risk value FF i ;

[0082] Step 054: By calculating the inflow risk value of the population within this monitoring sub-region, simultaneously obtain the influence of the recurrence risk of the population within the monitoring sub-region during the population movement process, that is, the regional recurrence risk value; Then, through QD i =QL i +FF i calculate to obtain the regional risk dynamic value QD within the monitoring sub-region i ; At this time, the regional risk dynamic value within this monitoring sub-region can be used to represent the dynamic risk brought by the population movement within the unit collection time period;

[0083] Step Six: Based on the regional risk solid value QF i and the regional risk dynamic value QD i ;

[0084] Through QYZ i =β×QF i +γ×QD iCalculate the regional risk value QYZ of the monitored sub-region i ; where β is the regional static risk assessment factor and γ is the regional dynamic risk assessment factor; it should be noted that both the regional static risk assessment factor β and the regional dynamic risk assessment factor γ are empirically set by the managing doctor based on the actual situation; for example, if the regional dynamic risk is large, then γ is set to be large;

[0085] Then, compare the regional risk value QYZ i with the risk assessment threshold QYY; obtain a risk signal, where the risk signal includes a regional risk controllable signal and a regional risk warning signal;

[0086] If the regional risk value QYZ i is less than the risk assessment threshold QYY, generate a regional risk controllable signal; indicating that the risk of this region is in a relatively low state and is overall controllable, and activities such as the personnel flow within the monitored sub-region can proceed normally;

[0087] If the regional risk value QYZ i is greater than or equal to the risk assessment threshold QYY, generate a regional risk warning signal; at this time, it indicates that the risk of this region is in a relatively high state, and personnel control needs to be carried out on this monitored sub-region; to avoid a large-scale disease transmission situation;

[0088] By collecting the population data information within the monitored sub-region, and then setting data such as the infection rate, recurrence rate, and recovery rate in the disease information transmission and recovery information by the managing doctor, the regional risk value within this monitored region can be calculated, and then it can be judged whether this region needs to be controlled and warned to avoid large-scale disease transmission; better control and protect the health of the population within this monitored sub-region;

[0089] In addition, based on the calculation of the regional risk solid value passed by an individual, the personal risk value can be calculated. At this time, when the personal risk value is high, the individual can be controlled, and at the same time, other monitored sub-regions can be reminded to prohibit this individual from entering to avoid the risk of disease infection to other populations and effectively reduce the impact of the individual on the population.

[0090] Embodiment 2

[0091] Based on the above embodiment, to avoid the problem of disease transmission in the monitored sub-region due to the excessive regional risk value QYZ i being too large, personnel can be controlled in advance to avoid excessive regional risk. Specifically, in the way of controlling personnel, this embodiment provides the following control methods based on the above method:

[0092] A01: By obtaining the number of monitored sub-regions passed through in an individual's movement trajectory, mark the monitored sub-regions passed through by the individual's movement trajectory as k;

[0093] A02: Obtain the regional risk solid value QFk within each monitored sub-region passed through by the individual's movement trajectory; where k is 1, 2, 3...; It should be noted that when k = 1, it means that the individual only moves within the monitored sub-region during the unit collection time period T;

[0094] Through Calculate to obtain the individual risk value Pg;

[0095] That is, calculate based on the individual's movement trajectory to obtain the disease transmission risk value generated by the individual after passing through multiple monitored regions. It should be noted that the higher the regional risk solid value of the monitored sub-regions passed through by the individual and the more monitored sub-regions passed through, the greater the individual risk value. At this time, it indicates that the individual has a greater disease transmission risk, and at the same time, it also indicates that the risk of being infected by the individual himself is greater; the risk to others is also higher;

[0096] A03: Compare the individual risk value Pg with the individual risk threshold Pgy;

[0097] If the individual risk value Pg is less than the individual risk threshold Pgy, generate an individual low-risk signal;

[0098] If the individual risk value Pg is greater than or equal to the individual risk threshold Pgy, generate an individual high-risk signal;

[0099] At this time, when the individual needs to enter the monitored sub-region, if the individual risk value of the individual is relatively high, prohibit the individual from entering the monitored sub-region;

[0100] It should be noted that when the individual's risk threshold is relatively high, if the individual belongs to the monitored sub-region during the unit collection time period T and finally cannot enter other monitored sub-regions, then the individual should be released into the monitored sub-region. In the actual monitored region, the monitored sub-region can be set as a community, and the individual's residence is in this community; when the individual's individual risk value causes the individual to be unable to enter other monitored sub-regions, this community should release the individual;

[0101] When evaluating and calculating the individual risk, if the individual risk value is relatively high, early control can be carried out to avoid the increase of the regional risk value of the monitored sub-region entered due to the relatively high individual risk value. Similarly, early control of individuals with relatively high risks can reduce the regional risk value of the monitored sub-region and avoid obtaining a risk warning signal due to the excessive risk value of the regional risk value;

[0102] Similarly, when the regional risk value in the monitored sub-region is too high, individuals with too high personal risk values are prohibited from entering. On the one hand, it can prevent the monitored sub-region from affecting the individual, and on the other hand, it can also prevent the individual from affecting the monitored sub-region, further reducing the risk of disease transmission.

[0103] Embodiment III

[0104] Based on the above embodiments, referring to Figure 2 as shown, this embodiment provides a health management system based on data analysis, including:

[0105] Data acquisition module: used to divide the monitored area into several monitored sub-regions and label the monitored sub-regions as i, where i is 1, 2, 3...;

[0106] Taking the unit time T as the unit of the acquisition time period, collect the population data information of the monitored sub-region i within the unit acquisition time period T;

[0107] Data processing module: used to calculate the population flow rate PL i and the population steady state rate PJ of the monitored sub-region i at the moment of the unit acquisition time period T i ;

[0108] Risk verification module: used to obtain the disease data information of the monitored sub-region i within the unit acquisition time period T, where the disease data information includes the confirmed case value BQ i and the recovered case value BK i ;

[0109] Through calculate to obtain the disease case ratio BB i ;

[0110] Obtain the infection rate a, recurrence rate b, and recovery rate c of the disease;

[0111] Through calculate to obtain the regional risk solid state value QF within the monitored sub-region i ; then calculate and obtain the regional risk dynamic value QD within the monitored sub-region i i ;

[0112] Based on the regional risk solid state value QF i and the regional risk dynamic value QD i ; calculate to obtain the regional risk value QYZ of the monitored sub-region i ;

[0113] Risk assessment module: used to compare the regional risk value QYZ i with the risk assessment threshold QYY; obtain a risk signal.

[0114] In the present invention, by collecting the population data information in the monitored sub-region and then setting data such as the infection rate, recurrence rate, and recovery rate in the disease information transmission and recovery information by the managing doctor, the regional risk value in the monitored region can be calculated, and then it can be determined whether the region needs to be controlled and warned to avoid large-scale transmission of diseases; better control and protect the health of the population in the monitored sub-region.

[0115] In addition, based on the calculation of the regional risk solid value passed by an individual, the individual risk value can be calculated. At this time, when the individual risk value is relatively high, the individual can be controlled, and at the same time, other monitored sub-regions can be reminded to prohibit the entry of this individual to avoid the risk of disease infection to other people, effectively reducing the impact of the individual on the population; and this system does not require the controlling personnel to make judgments, and can conduct research and judgment based on the individual's whereabouts, with objective and accurate evaluation, avoiding the influence of the subjective factors of the controlling personnel on the control.

[0116] At the same time, when the system divides the monitored region, the smaller the monitored sub-region is, the more accurate the risk assessment of the monitored sub-region is. At the same time, in the division of the monitored sub-region by the system, it can also be divided according to factors such as the population density in the monitored region; at the same time, during the use process, data such as the infection rate, recurrence rate, and recovery rate of different diseases are set by the managing doctor, so that this calculation method can be applied to the calculation of different infectious diseases, with accurate calculation of the regional risk value and high evaluation accuracy, enabling this system to adapt to the control requirements of different usage scenarios.

[0117] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data and performing software simulation to obtain a formula closest to the real situation. The preset parameters in the formula are calculated and set by those skilled in the art according to the actual situation, a large amount of data, and work experience.

[0118] The above has described a detailed description of an embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as used to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A health management method based on data analysis, characterized in that: The following steps are involved: Divide the monitoring area into several monitoring sub-areas, and mark the monitoring sub-areas as i, where i is 1, 2, 3, etc.; Taking unit time T as the unit collection time period, collect crowd data information in the monitoring sub-area i within the unit collection time period T; Calculate the crowd flow rate PL i And the steady-state rate PJ of the crowd within the unit acquisition time period T in the monitoring sub-area i i ; Obtain the disease data information of the monitoring sub-area i within the unit collection time period T, where the disease data information includes the confirmed case value BQ i and recovered case value BK i ; pass Calculate the disease case ratio BB i ; Obtain the disease's infection rate a, recurrence rate b, and recovery rate c; pass Calculate the regional risk solid value QF in the monitoring sub-area i ; Then calculate and obtain the regional risk dynamic value QD within the monitoring sub-area i i ; Based on the regional risk solid value QF i and regional risk dynamic value QD i ; Calculate the regional risk value QYZ of the monitored sub-area i ; The regional risk value QYZ i Compare with the risk assessment threshold QYY; obtain a risk signal.

2. A health management method based on data analysis according to claim 1, characterized in that: The crowd data information includes: total population value PZ, crowd density value PM i , crowd interaction value PH i and population stability value PW i .

3. A health management method based on data analysis according to claim 2, characterized in that: pass Calculate the crowd flow rate PL i ; pass Calculate the steady-state rate PJ of the crowd within the unit acquisition time period T in the monitoring sub-area i i .

4. A health management method based on data analysis according to claim 1, characterized in that: The regional risk dynamic value QD i The calculation methods include: Step 051: Convert the inflow flow number Pr i Marked as j in sequence, where j is 1, 2, 3, etc.; Get each inflow flow number Pr i The monitoring sub-area trajectory; Calculate the number of inflows Pr i The individual risk values ​​within the range are marked as Pg j ; Step 052: Calculate the inflow risk value QL of the inflow flow number of people i ; Step 053: Based on the recovered case value BK i and outflow flow Pc i ,pass Calculate the regional recurrence risk value FF i ; Step 054: Pass QD again i =QL i +FF i Calculate the regional risk dynamic value QD in the monitoring sub-area i .

5. A health management method based on data analysis according to claim 4, characterized in that: In step 052, the inflow risk value QL i The calculation method is: Obtain the disease's infectious rate a and recurrence rate b; pass Calculate the inflow risk value QL in the monitoring sub-area i i .

6. A health management method based on data analysis according to claim 1, characterized in that: By QYZ i =β×QF i +γ×QD i Calculate the regional risk value QYZ of the monitored sub-area i ; Among them, β is the regional static risk assessment factor, and γ is the regional dynamic risk assessment factor.

7. A health management method based on data analysis according to claim 1, characterized in that: The method of obtaining the personal risk value Pg includes: Obtain the number of monitoring sub-areas that the individual's movement trajectory passes through, and mark the monitoring sub-areas that the individual's movement trajectory passes through as k; Get the regional risk solid value QF in each monitoring sub-area where the individual's movement trajectory passes k ; Where k is 1, 2, 3…; pass Calculate the personal risk value Pg.

8. A health management method based on data analysis according to claim 1, characterized in that: The risk signals include regional risk controllable signals and regional risk early warning signals.

9. A health management method based on data analysis according to claim 1, characterized in that: If the regional risk value QYZ i If it is less than the risk assessment threshold QYY, a signal that the regional risk is controllable is generated; If the regional risk value QYZ i If it is greater than or equal to the risk assessment threshold QYY, a regional risk warning signal is generated.

10. A health management system based on data analysis, characterized in that: The system is used to execute the health management method according to any one of claims 1 to 9, comprising: Data acquisition module: used to divide the monitoring area into several monitoring sub-areas, and mark the monitoring sub-areas as i, where i is 1, 2, 3...; Taking unit time T as the unit collection time period, collect crowd data information in the monitoring sub-area i within the unit collection time period T; Data processing module: used to calculate the crowd flow rate PL i And the steady-state rate PJ of the crowd within the unit acquisition time period T in the monitoring sub-area i i ; Risk assessment module: used to obtain disease data information of monitoring sub-area i within the unit collection time period T, where the disease data information includes the confirmed case value BQ i and recovered case value BK i ; pass Calculate the disease case ratio BB i ; Obtain the disease's infection rate a, recurrence rate b, and recovery rate c; pass Calculate the regional risk solid value QF in the monitoring sub-area i ; Then calculate and obtain the regional risk dynamic value QD within the monitoring sub-area i i ; Based on the regional risk solid value QF i and regional risk dynamic value QD i ; Calculate the regional risk value QYZ of the monitored sub-area i ; Risk assessment module: used to convert regional risk value QYZ i Compare with the risk assessment threshold QYY; obtain a risk signal.

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