CN114999669A - Epidemic situation prevention, control and flow regulation system and method in big data scene - Google Patents
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Abstract
The invention discloses an epidemic situation prevention and control flow regulation system and method under a big data scene, which comprises the following steps: the system comprises a flow modulation data acquisition module, a database, a target analysis module, a field work planning module and a remote work planning module, wherein the flow modulation data acquisition module is used for acquiring visiting target data and field information investigation data, the database is used for storing all the acquired data, the target analysis module is used for analyzing the moving track data of the visiting target and classifying the visiting target, the visiting target is distributed by the field work planning module to enter different areas to receive information investigation, and the remote work planning module is used for distributing staff who carry out remote telephone investigation to carry out information investigation on the visiting target in different time periods, so that the phenomenon that key information is not received timely is reduced, and the efficiency of the field information investigation and the remote telephone investigation is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an epidemic situation prevention, control and flow regulation system and method under a big data scene.
Background
The epidemiological method is used for research and research, and is mainly used for researching distribution of diseases, health and sanitary events and determinants thereof, and the purpose of the epidemiological method is to carry out information research on targets: the method has the advantages that the activity track of the target, the encountered people and the happening events in a certain past time period are inquired, so that related personnel can be helped to quickly know the disease transmission path and control the disease transmission, and the flow regulation work can be applied to epidemic situation prevention and control and can be helped to do the epidemic situation prevention and control work;
however, the existing flow-tuning work has certain disadvantages: firstly, when the field information investigation is carried out, because the number and the space of investigation areas are limited, the phenomena of extremely more people in partial areas and extremely less people in partial areas often occur, the prior art can not distribute proper number of people to the corresponding areas to receive the information investigation, and the efficiency of the field information investigation work is reduced; secondly, when information is investigated on a target in a remote telephone investigation mode, the phenomenon that a worker is not connected due to conversation with other targets in the prior telephone access often occurs, and the data feedback receiving efficiency cannot be improved within a limited time, so that the telephone flow regulation efficiency is improved.
Therefore, a system and a method for epidemic prevention, control and flow regulation in a big data scene are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an epidemic situation prevention and control flow regulation system and method in a big data scene so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an epidemic prevention, control and flow regulation system under big data scene, the system comprises: the system comprises a flow modulation data acquisition module, a database, a target analysis module, a field work planning module and a remote work planning module;
the flow modulation data acquisition module is used for acquiring visiting target data and field information survey data;
the database is used for storing all the collected data;
the target analysis module is used for analyzing the activity track data of the visiting target and classifying the visiting target;
the field work planning module is used for distributing visiting targets to enter different areas to receive information investigation;
the remote work planning module is used for distributing staff who carry out remote telephone investigation to carry out information investigation on visiting targets in different time periods.
Furthermore, the traffic dispatching data acquisition module comprises an investigation information acquisition unit and a target information acquisition unit, wherein the investigation information acquisition unit is used for acquiring the area data of the divided regions when information investigation is carried out on the visiting target on site; the target information acquisition unit is used for acquiring the position information of the visiting target and transmitting all the acquired data to the database.
Furthermore, the target analysis module comprises a real-time position analysis unit and a target classification unit, wherein the real-time position analysis unit is used for calling and analyzing the position information of the visiting targets and analyzing the activity track coincidence data among the visiting targets; the target classification unit is used for classifying the visiting targets according to the activity track coincidence data among the visiting targets.
Further, the field work planning module comprises an investigation region query unit and a personnel allocation unit, wherein the investigation region query unit is used for querying region area data which is stored in the database and is used for carrying out information investigation on visiting targets; the personnel allocation unit is used for allocating the visit targets to the divided areas for information investigation.
Further, the remote work planning module comprises a telephone investigation analysis unit, a peak period prediction unit and an information investigation adjustment unit, wherein the telephone investigation analysis unit is used for analyzing the incoming call but not-connected data in the information investigation process when the information investigation is carried out on the visiting target in a remote telephone investigation mode; the peak period prediction unit is used for analyzing the time and the quantity of the incoming calls which are not connected, predicting data to feed back to the peak period and transmitting the prediction result to the information investigation and adjustment unit; the information survey adjusting unit is used for adjusting the working time of workers who survey the access target information.
An epidemic situation prevention and control flow regulation method in a big data scene comprises the following steps:
z01: collecting visiting target position data and field information investigation data;
z02: analyzing the activity track of the visiting target, and classifying the visiting target;
z03: analyzing the pre-divided regional data for field information investigation, and distributing the visiting target to enter a proper region for receiving information investigation;
z04: analyzing the incoming call but not incoming call situation when information investigation is carried out in a remote telephone investigation mode, and predicting a data feedback peak time period;
z05: and allocating workers to enter a data feedback peak section according to the prediction result to carry out information investigation.
Further, in step Z01: acquiring real-time position data of a visiting target to obtain a moving track of the visiting target, and acquiring a set of area areas for performing field information investigation as s ═ s1, s 2.., sn }, wherein n represents the number of the areas for performing the field information investigation, in step Z02: analyzing the activity track of the visiting target: obtaining q visiting targets of which the active tracks except the end point do not have intersection points, wherein the visiting targets are divided into q types: obtaining a random moving track of one visiting target in q visiting targets, counting that the number of targets, of which the moving track coincides with the corresponding visiting target, in the remaining visiting targets is M, dividing the M visiting targets into the categories to which the corresponding visiting targets belong, wherein the moving track does not coincide with the moving track of the corresponding visiting target and the number of targets with intersection points is k, counting that the number set of intersection points with the moving track of the corresponding visiting target is M { M1, M2., Mk } in the k targets, the remaining visiting targets refer to all the remaining visiting targets except q-1 visiting targets without intersection points with the moving track of the corresponding visiting target, and judging the probability Pi of the random one target in the k targets belonging to the category to which the corresponding visiting target belongs according to the following formula:
wherein Mi represents the moving track and pair of one random target in k targetsThe probability set is obtained by the number of intersections of the visiting target activity track, and is P ═ P1, P2
Comparing Pi and
if it is
The probability that the target corresponding to the Pi belongs to the category of the corresponding visiting target does not exceed the threshold value; if it is
Explaining that the probability of the object corresponding to Pi belonging to the category of the corresponding visiting object exceeds a threshold value, dividing the object corresponding to Pi into the category of the corresponding visiting object, after the division is finished, randomly selecting one object again in the remaining q-1 visiting objects, judging the probability of the object not divided into the categories of the corresponding visiting objects belonging to the category of the randomly selected object again in the same mode, dividing the undivided visiting objects until the division of all the visiting objects is finished, in the field information investigation process, preliminarily classifying the objects according to the activity tracks of the visiting objects, naturally dividing the objects with overlapped activity tracks into one category, and simultaneously judging whether the visiting objects belong to the corresponding category according to the number of the intersection points because the intersection points exist among part of the activity tracks and the number of the intersection points is different, the more the number of the intersection points among the tracks is, the more the information among the description objects is similar, and judging whether the visiting objects belong to the corresponding category according to the number of the intersection points, the method has the advantages that the visiting targets are preliminarily classified, so that the method is favorable for helping relevant personnel to subsequently sort visiting target survey information, and is more favorable for timely calling data when abnormal conditions are found out, tracking is carried out on the abnormal targets, and safety work is well carried out.
Further, in step Z03: analyzing the pre-divided regional data for field information investigation: obtaining the area si of a random area, predicting to contain ri people in the corresponding area, and obtaining the total number of people contained in all areas
Allocating visiting targets to enter proper areas to receive information investigation: and (3) carrying out secondary classification on the q types of visiting targets: randomly dividing q types of visiting targets into n types, counting the total number of the n types of visiting targets to be B ═ B1, B2., Bn }, and selecting an optimal classification mode according to the following formula:
wherein, Bj represents the total number of random visiting targets in n types of current classifications, Aj represents the number of people accommodated in a random area, H represents the difference between the standard deviation of the total number of visiting targets in n types of current classifications and the standard deviation of the number of people accommodated in n areas, the classification mode which enables H to be minimum is selected as the optimal classification mode in the random classification modes, and the n types of visiting targets are distributed into the corresponding areas: the n categories are sorted according to the sequence of the total number of the visiting targets in each category from large to small, the n areas are sorted according to the sequence of the number of the people accommodated in the areas from large to small, the n categories of the visiting targets in the corresponding sequence are distributed into the n areas to be subjected to information investigation, the number distribution of the visiting targets in the fixed area is particularly important due to the limited field investigation area and the limited space, the visiting targets which are preliminarily divided are secondarily classified, namely the visiting targets are classified according to the number, and the optimal classification mode is selected according to the difference between the standard difference of the number of the different categories of the visiting targets and the standard difference of the number of the expected accommodated people in the areas.
Further, in steps Z04-Z05: after on-site information investigation is carried out on the visiting target, a part of visiting targets are reminded in advance in a short message mode to actively return calls to receive the telephone information investigation, and the working time of one day is leveledEqually divided into f segments, and the number of the working personnel arranged in each time segment is collected to be X i ＝{X i1 ，X i2 ，...，X if D, the times of the incoming call but not the call is collected in each time period i ＝{D i1 ，D i2 ，...，D if And g days of data are collected, and according to a formula, the data are acquired
Calculating to obtain a data feedback coefficient C of a random time period of a random day ij The feedback coefficient of the comprehensive data of the corresponding time period in g days is obtained as
Obtaining a set of comprehensive data feedback coefficients C' ═ C in each time period in g days 1 ’，C 2 ’，...，C f ' }, screening out comprehensive data feedback coefficient exceeding
In addition, as the condition that the call back of the visiting target is not received exists in the telephone information investigation process, historical investigation data is analyzed through big data, the data feedback peak time period in the working time is analyzed according to the specific data feedback condition of the data, and the personnel in other time periods are allocated to receive the call back for information investigation, so that the phenomenon that key data are not received timely is reduced, and the remote telephone investigation efficiency is improved.
Compared with the prior art, the invention has the following beneficial effects:
in the field information investigation process, the invention firstly analyzes and matches the activity tracks of the visiting targets through big data, and preliminarily classifies the targets by taking different activity tracks as the reference, thereby facilitating the subsequent arrangement of the visiting target investigation information by related personnel, being more beneficial to calling data in time when finding out abnormal conditions, tracking the abnormal targets and doing safety work; the primarily divided visiting targets are classified secondarily according to the number, an optimal classification mode is selected according to the difference value between the standard deviation of the number of the targets in different categories and the standard deviation of the number of the expected accommodated persons in the areas, the number of the target persons distributed in each area is enabled to be closest to the expected accommodated persons in the corresponding area while field information investigation is carried out comprehensively, the synchronous operation of the information investigation work of each area is facilitated, the completion within close time is realized, and the efficiency of the field information investigation is improved; historical survey data are analyzed through big data, data feedback peak time periods in working hours are analyzed according to specific data feedback conditions of the data, personnel in other time periods are allocated to receive calls in the peak time periods to conduct information survey, the phenomenon that key data are not received timely is reduced, and remote telephone survey efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a structural diagram of an epidemic situation prevention and control flow regulation system in a big data scene;
FIG. 2 is a flowchart of an epidemic situation prevention and control flow regulation method in a big data scene.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-2, the present invention provides a technical solution: an epidemic situation prevention, control and flow regulation system under big data scene, the system includes: the system comprises a flow modulation data acquisition module, a database, a target analysis module, a field work planning module and a remote work planning module;
the flow modulation data acquisition module is used for acquiring visiting target data and field information survey data;
the database is used for storing all the collected data;
the target analysis module is used for analyzing the activity track data of the visiting target and classifying the visiting target;
the field work planning module is used for distributing visiting targets to enter different areas to receive information investigation;
the remote work planning module is used for distributing staff who carry out remote telephone investigation to carry out information investigation on visiting targets in different time periods.
The system comprises a flow modulation data acquisition module, a flow modulation data acquisition module and a data processing module, wherein the flow modulation data acquisition module comprises a survey information acquisition unit and a target information acquisition unit, and the survey information acquisition unit is used for acquiring area data of a divided region when information survey is carried out on a visiting target on site; the target information acquisition unit is used for acquiring the position information of the visiting target and transmitting all the acquired data to the database.
The target analysis module comprises a real-time position analysis unit and a target classification unit, wherein the real-time position analysis unit is used for calling and analyzing the position information of the visiting targets and analyzing the activity track coincidence data among the visiting targets; the target classification unit is used for classifying the visiting targets according to the activity track coincidence data among the visiting targets.
The field work planning module comprises an investigation region query unit and a personnel allocation unit, wherein the investigation region query unit is used for querying region area data which is stored in a database and is used for carrying out information investigation on a visiting target; and the personnel allocation unit is used for allocating the visit targets to the divided areas for information investigation.
The remote work planning module comprises a telephone investigation analysis unit, a peak period prediction unit and an information investigation adjustment unit, wherein the telephone investigation analysis unit is used for analyzing the incoming call but not the data in the information investigation process when the information investigation is carried out on the visiting target in a remote telephone investigation mode; the peak period prediction unit is used for analyzing the time and the quantity of the incoming calls which are not connected, predicting data to feed back to the peak period and transmitting the prediction result to the information investigation and adjustment unit; the information investigation adjusting unit is used for adjusting the working time of the staff investigating the access target information.
An epidemic situation prevention and control flow regulation method under a big data scene comprises the following steps:
z01: collecting visiting target position data and field information investigation data;
z02: analyzing the activity track of the visiting target, and classifying the visiting target;
z03: analyzing the pre-divided regional data for field information investigation, and distributing the visiting target to enter a proper region for receiving information investigation;
z04: analyzing the situation that an incoming call is not connected when information investigation is carried out in a remote telephone investigation mode, and predicting a data feedback peak time period;
z05: and allocating workers to enter a data feedback peak section according to the prediction result to carry out information investigation.
In step Z01: the method comprises the following steps of collecting real-time position data of a visiting target to obtain a moving track of the visiting target, collecting area sets of areas for carrying out site information investigation as s ═ s1, s 2. Where n denotes the number of areas where the field information survey is performed, in step Z02: analyzing the activity track of the visiting target: obtaining q visiting targets with the number of visiting targets without crossing points of the active tracks except the end point, wherein the visiting targets are divided into q types: obtaining a random moving track of a visiting target in q visiting targets, counting that the number of targets, of which the moving track coincides with a corresponding visiting target, in the remaining visiting targets is M, dividing the M visiting targets into the categories to which the corresponding visiting targets belong, wherein the moving track does not coincide with the moving track of the corresponding visiting target and the number of targets with intersection points is k, counting that the number of intersection points with the moving track of the corresponding visiting target in the k targets is M { M1, M2., Mk }, and the remaining visiting targets refer to all the remaining visiting targets except q-1 visiting targets without intersection points with the moving track of the corresponding visiting target, and judging the probability Pi of a random one target in the k targets belonging to the category to which the corresponding visiting target belongs according to the following formula:
wherein Mi represents the number of intersections between the activity track of one random target among the k targets and the activity track of the corresponding visiting target, the obtained probability set is P ═ P1, P2
Comparing Pi and
if it is
The probability that the target corresponding to Pi belongs to the category of the corresponding visiting target does not exceed the threshold value; if it is
The probability that the target corresponding to the Pi belongs to the category to which the corresponding visiting target belongs exceeds the threshold value is described, the target corresponding to the Pi is divided into the category to which the corresponding visiting target belongs, after the division is finished, one target is randomly selected again in the remaining q-1 visiting targets, the probability that the targets which are not divided into the category to which the corresponding visiting target belongs belong belongs to the category to which the target which is randomly selected again is judged in the same mode, the non-divided visiting targets are divided until all the visiting targets are divided, the visiting targets are preliminarily classified according to the activity track, relevant personnel are helped to subsequently sort visiting target investigation information, data can be called in time when the abnormal condition is found out, the abnormal targets are tracked, and safety work is done.
In step Z03: analyzing the pre-divided regional data for field information investigation: obtaining the area si of a random area, predicting the number of people accommodated in ri corresponding to the area, and obtaining the total number of people accommodated in all the areas
Allocating visiting targets into appropriate areas to receive information surveys: and (3) carrying out secondary classification on the q types of visiting targets: randomly dividing q types of visiting targets into n types, counting the total number of the n types of visiting targets to be B ═ B1, B2., Bn }, and selecting an optimal classification mode according to the following formula:
wherein, Bj represents the total number of random visiting targets in n types of current classifications, Aj represents the number of people accommodated in a random area, H represents the difference between the standard deviation of the total number of visiting targets in n types of current classifications and the standard deviation of the number of people accommodated in n areas, the classification mode which enables H to be minimum is selected as the optimal classification mode in the random classification modes, and the n types of visiting targets are distributed into the corresponding areas: the n categories are sorted according to the sequence of the total number of the visiting targets in each category from large to small, the n areas are sorted according to the sequence of the number of the people accommodated in the areas from large to small, the n types of visiting targets in the corresponding sequence are distributed to the n areas to receive information investigation, the visiting targets which are preliminarily divided are secondarily classified, the number of the target people distributed in each area is enabled to be closest to the number of the expected accommodating people in the corresponding area finally, the synchronous operation of information investigation of each area is facilitated, the information investigation work is completed in the close time, and the efficiency of the field information investigation is improved.
In steps Z04-Z05: after on-site information investigation is carried out on a visiting target, a part of visiting targets are reminded to actively return calls to receive telephone information investigation in a short message mode in advance, the working time of one day is averagely divided into f sections, the number of workers arranged in each time section is collected to be X in the working time of one day randomly in the past i ＝{X i1 ，X i2 ，...，X if D, the times of the incoming call but not the call is collected in each time period i ＝{D i1 ，D i2 ，...，D if And g days of data are collected, and according to a formula, the data are acquired
Calculating to obtain a data feedback coefficient C of a random time period of a random day ij The feedback coefficient of the comprehensive data of the corresponding time period in g days is obtained as
Obtaining a comprehensive data feedback coefficient set of C ═ C in each time period in g days 1 ’，C 2 ’，...，C f ' }, screening out comprehensive data feedback coefficient exceeding
In the time period, the time period screened out is predicted to be a data feedback peak time period, and in the staff who are not arranged in the data feedback peak time period, the staff is allocated to enter the data feedback peak time period for telephone information investigation, so that the phenomenon that key data are not received timely is reduced, and the remote telephone investigation efficiency is improved.
The first embodiment is as follows: acquiring real-time position data of a visiting target to obtain an activity track of the visiting target, acquiring a region area set for performing field information investigation as s ═ s1, s2, s3 ═ 100, 50, 80}, acquiring the number q of visiting targets without intersection points of the activity track except the terminal point as 5, and dividing the visiting targets into q ═ 5 types: obtaining the activity track of the first visiting target in 5 visiting targets, counting M to 10 in the rest visiting targets, dividing M visiting targets into the category of the corresponding visiting targets, wherein the activity track is not coincident with the activity track of the corresponding visiting target and the number of targets with intersection points is k to 5, counting M to 5 in the 5 targets the set of the intersection points with the activity track of the corresponding visiting target is M { M1, M2, M3, M4, M5} {4, 5, 1, 2, 6}, and calculating the target number of the first visiting target in the 5 targets according to the formula
Judging the probability P1 of the first object belonging to the category of the corresponding visiting object to be approximately equal to 0.98, obtaining the probability set as P { P1, P2, P3, P4, P5} {0.98, 0.99, 0.73, 0.88 and 1}, and setting the probability threshold value as
Comparing Pi and
dividing targets corresponding to P1, P2 and P5 into a category to which a first visiting target belongs, dividing non-divided visiting targets according to the same mode until all visiting targets are divided, and analyzing pre-divided regional data for field information investigation: acquiring the area of a random area as si-100, predicting to accommodate ri-64 persons in the corresponding area, acquiring the number of persons accommodated in all areas as a-1, a2, A3, 64, 32, 51, and allocating the visiting target to enter the appropriate area to receive information survey: and (3) carrying out secondary classification on 5 types of visiting targets: randomly classifying 5 types of visiting targets into 3 types in an optimal classification mode, sequencing 3 types according to the sequence that the total number of the visiting targets in each type is from large to small, sequencing 3 areas according to the sequence that the number of people accommodated in the areas is from large to small, and distributing the 3 types of visiting targets in the corresponding sequence to the 3 areas to receive information investigation;
example two: after on-site information investigation is carried out on a visiting target, a part of visiting targets are reminded to actively return calls to receive telephone information investigation in a short message mode in advance, the working time of one day is averagely divided into f-4 sections, the number of workers arranged in each time section in the random working time of one day is collected and is integrated into X i ＝{X i1 ，X i2 ，X i3 ，X i4 -8, 10, 5, 6, the number of times an incoming but not connected situation occurs per time period being D i ＝{D i1 ，D i2 ，D i3 ，D i4 5, 2, 3, 8, i 1, 2, 3, collecting g 3 days data,according to the formula
Calculating to obtain a data feedback coefficient C of a random time period of a random day ij Approximately equals to 2.2, and the feedback coefficient of the obtained comprehensive data in the corresponding time period within 3 days is
Obtaining a comprehensive data feedback coefficient set of C ═ C in each time period in g days 1 ’，C 2 ’，C 3 ’，C 4 ' } {7.2, 8.4, 5.1, 4.8}, and screening out the feedback coefficient of the comprehensive data exceeding
The time period of (a): and dividing a first time period and a second time period, predicting the first time period and the second time period as data feedback peak time periods, and allocating staff to enter the first time period and the second time period for telephone information investigation among staff scheduled in a third time period and a fourth time period.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An epidemic situation prevention and control flow regulation system under big data scene is characterized in that: the system comprises: the system comprises a flow modulation data acquisition module, a database, a target analysis module, a field work planning module and a remote work planning module;
the flow modulation data acquisition module is used for acquiring visiting target data and field information survey data;
the database is used for storing all the collected data;
the target analysis module is used for analyzing the activity track data of the visiting target and classifying the visiting target;
the field work planning module is used for distributing visiting targets to enter different areas to receive information investigation;
the remote work planning module is used for distributing staff who carry out remote telephone investigation to carry out information investigation on visiting targets in different time periods.
2. The epidemic situation prevention and control flow regulation system under big data scene according to claim 1, characterized in that: the system comprises a flow modulation data acquisition module, a flow modulation data acquisition module and a data acquisition module, wherein the flow modulation data acquisition module comprises a survey information acquisition unit and a target information acquisition unit, and the survey information acquisition unit is used for acquiring area data of a divided region when information survey is carried out on a visiting target on site; the target information acquisition unit is used for acquiring the position information of the visiting target and transmitting all the acquired data to the database.
3. The epidemic situation prevention and control flow regulation system under big data scene according to claim 1, characterized in that: the target analysis module comprises a real-time position analysis unit and a target classification unit, wherein the real-time position analysis unit is used for calling and analyzing the position information of the visiting targets and analyzing the activity track coincidence data among the visiting targets; the target classification unit is used for classifying the visiting targets according to the activity track coincidence data among the visiting targets.
4. The epidemic situation prevention and control flow regulation system under big data scene according to claim 1, characterized in that: the field work planning module comprises an investigation region query unit and a personnel allocation unit, wherein the investigation region query unit is used for querying region area data which is stored in the database and is used for carrying out information investigation on visiting targets; the personnel allocation unit is used for allocating visit targets to the divided areas for information investigation.
5. The epidemic situation prevention and control flow regulation system under big data scene according to claim 1, characterized in that: the remote work planning module comprises a telephone investigation analysis unit, a peak period prediction unit and an information investigation adjustment unit, wherein the telephone investigation analysis unit is used for analyzing the incoming call but not the data in the information investigation process when the information investigation is carried out on the visiting target in a remote telephone investigation mode; the peak period prediction unit is used for analyzing the time and the quantity of the incoming calls which are not switched on, predicting data feedback peak periods and transmitting prediction results to the information investigation and adjustment unit; the information survey adjusting unit is used for adjusting the working time of workers who survey the access target information.
6. An epidemic situation prevention and control flow regulation method in a big data scene is characterized in that: the method comprises the following steps:
z01: collecting visiting target position data and field information investigation data;
z02: analyzing the activity track of the visiting target, and classifying the visiting target;
z03: analyzing the pre-divided regional data for field information investigation, and distributing the visiting target to enter a proper region for receiving information investigation;
z04: analyzing the situation that an incoming call is not connected when information investigation is carried out in a remote telephone investigation mode, and predicting a data feedback peak time period;
z05: and allocating workers to enter a data feedback peak section according to the prediction result to carry out information investigation.
7. The epidemic situation prevention and control flow regulation method under the big data scene according to claim 6, characterized in that: in step Z01: the method comprises the following steps of collecting real-time position data of a visiting target to obtain a moving track of the visiting target, collecting area sets of areas for carrying out site information investigation as s ═ s1, s 2. Where n denotes the number of areas where the field information survey is performed, in step Z02: analyzing the activity track of the visiting target: obtaining q visiting targets of which the active tracks except the end point do not have intersection points, wherein the visiting targets are divided into q types: obtaining a random moving track of one visiting target in q visiting targets, counting that the number of targets, of which the moving track coincides with the corresponding visiting target, in the remaining visiting targets is M, dividing the M visiting targets into the categories to which the corresponding visiting targets belong, wherein the moving track does not coincide with the moving track of the corresponding visiting target and the number of targets with intersection points is k, counting that the number set of intersection points with the moving track of the corresponding visiting target is M { M1, M2., Mk } in the k targets, the remaining visiting targets refer to all the remaining visiting targets except q-1 visiting targets without intersection points with the moving track of the corresponding visiting target, and judging the probability Pi of the random one target in the k targets belonging to the category to which the corresponding visiting target belongs according to the following formula:
wherein Mi represents the number of intersections between the activity track of one random target among the k targets and the activity track of the corresponding visiting target, the obtained probability set is P ═ P1, P2
Comparing Pi and
if it is
The probability that the target corresponding to Pi belongs to the category of the corresponding visiting target does not exceed the threshold value; if it is
Indicating that the probability that the target corresponding to Pi belongs to the category to which the corresponding visiting target belongs exceeds the threshold value, dividing the target corresponding to Pi into the category to which the corresponding visiting target belongsAnd after the classification is finished, randomly selecting one target again from the remaining q-1 visiting targets, judging the probability of the target which is not classified into the category to which the corresponding visiting target belongs and belongs to the category to which the randomly selected target belongs in the same way, and classifying the non-classified visiting targets until all the visiting targets are classified completely.
8. The epidemic situation prevention and control flow regulation method under the big data scene according to claim 7, characterized in that: in step Z03: analyzing the pre-divided regional data for field information investigation: obtaining the area si of a random area, predicting the number of people accommodated in ri corresponding to the area, and obtaining the total number of people accommodated in all the areas
n<q, allocating visiting targets to enter proper areas to receive information investigation: and (3) carrying out secondary classification on the q types of visiting targets: randomly dividing q types of visiting targets into n types, counting the total number of the n types of visiting targets to be B ═ B1, B2., Bn }, and selecting an optimal classification mode according to the following formula:
wherein, Bj represents the total number of random visiting targets in n types of current classifications, Aj represents the number of people accommodated in a random area, H represents the difference between the standard deviation of the total number of visiting targets in n types of current classifications and the standard deviation of the number of people accommodated in n areas, the classification mode which enables H to be minimum is selected as the optimal classification mode in the random classification modes, and the n types of visiting targets are distributed into the corresponding areas: and sorting the n types according to the sequence of the total number of the visiting objects in each type from large to small, sorting the n regions according to the sequence of the number of the accommodated persons in the regions from large to small, and distributing the n types of visiting objects in the corresponding sequence to the n regions to receive information investigation.
9. The epidemic situation prevention and control flow regulation method under big data scene according to claim 6, characterized in that: in steps Z04-Z05: after on-site information investigation is carried out on a visiting target, a part of visiting targets are reminded to actively return calls to receive telephone information investigation in a short message mode in advance, the working time of one day is averagely divided into f sections, the number of workers arranged in each time section is collected to be X in the working time of one day randomly in the past i ＝{X i1 ，X i2 ，...，X if D, the times of incoming call but not connection in each time interval are collected i ＝{D i1 ，D i2 ，...，D if And g days of data are collected, and according to a formula, the data are acquired
Calculating to obtain a data feedback coefficient C of a random time period of a random day ij The feedback coefficient of the comprehensive data of the corresponding time period in g days is obtained as
Obtaining a set of comprehensive data feedback coefficients C' ═ C in each time period in g days 1 ’，C 2 ’，...，C f ' }, screening out comprehensive data feedback coefficient exceeding
And in the time period of (4), predicting the screened time period as a data feedback peak time period, and allocating staff to enter the data feedback peak time period to investigate telephone information among the staff who are not arranged in the data feedback peak time period.
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