A method and device for identifying population in a jurisdiction based on spatio-temporal big data

Abstract

The present invention discloses a method and device for identifying the population in a jurisdiction based on spatio-temporal big data, belonging to the field of information technology, and is used to solve the technical problems of poor authenticity of data collection, low efficiency and timeliness in the method for controlling the floating population in cities. The method includes: collecting various types of resident information data of a preset area; wherein, the various types of resident information data at least include: base station data and resident interaction information; the preset area belongs to a preset jurisdiction; determining a household set in the preset area according to the various types of resident information data; identifying the household types in the household set according to the various types of resident information data; associating housing information with each household in the household set to realize data collection. Through technical collection means and data governance means, the household situation in the jurisdiction is automatically identified, the burden of door-to-door collection work is reduced, and the problem of insufficient grass-roots community grid members and police force is solved.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular, to a method and device for identifying population in a jurisdiction based on spatio-temporal big data. Background Art

[0002] With the rapid development of social urbanization, economies of scale and convenient transportation have reduced the cost of social personnel flow. More and more people are flowing into cities. Therefore, the management of urban floating population has attracted increasing attention from society.

[0003] The types of households in urban communities are becoming increasingly complex. Traditional management methods, relying solely on the strength of grass-roots police stations and grid administrators, not only make it difficult to figure out the number of community personnel, but also require grass-roots staff to manually collect information door-to-door. The authenticity and timeliness of data collection cannot be guaranteed, and it also greatly costs human and time costs, bringing difficulties to community work. Summary of the Invention

[0004] Embodiments of this application provide a method and device for identifying population in a jurisdiction based on spatio-temporal big data, which are used to solve the following technical problems: the authenticity of data collection in the urban floating population management method is poor, and the efficiency and timeliness are low.

[0005] Embodiments of this application adopt the following technical solutions:

[0006] On the one hand, embodiments of this application provide a method for identifying population in a jurisdiction based on spatio-temporal big data. The method includes: collecting various types of resident information data of a preset area; wherein, the various types of resident information data at least include: base station data and resident interaction information; the preset area belongs to a preset jurisdiction; determining a set of households in the preset area according to the various types of resident information data; identifying the types of households in the set of households according to the various types of resident information data; associating house information with each household in the set of households to realize data collection.

[0007] In a feasible implementation manner, collecting various types of resident information data in a preset area specifically includes: collecting mobile phone numbers and subscriber information that appear in the preset area during a specific time period based on a preset time interval; performing big data analysis on the mobile phone numbers and subscriber information obtained in each time interval to determine the information of frequently appearing personnel in the preset area, and obtaining the base station data; wherein, the types of personnel in the base station data include current residents and non-residents; obtaining the resident interaction information in the preset area in the system.

[0008] In a feasible implementation manner, determining the set of households within the preset area according to the various types of resident information data specifically includes: defining the base station data as the first set, and defining all real-name information in the resident interaction information as the second set; determining the intersection of the first set and the second set as the third set; defining the difference set between the first set and the third set as the non-household set, and defining the difference set between the second set and the third set as the set of households to be verified; analyzing the households to be verified in the set of households to be verified, and determining all actual households among the households to be verified as the fourth set; determining the union of the third set and the fourth set as the set of households within the preset area.

[0009] In a feasible implementation manner, analyzing the households to be verified in the set of households to be verified, and determining all actual households among the households to be verified as the fourth set, specifically includes: According to assigning weight scores to the households to be verified in the set of households to be verified; where, Score j is the score obtained by the j-th household to be verified, R i is the weighting factor assigned to the i-th data category in the set of households to be verified, T ij is the number of occurrences of the j-th household to be verified in the i-th data category; n is the coefficient of the occurrence situation of the j-th household to be verified in the data categories of the set of households to be verified, n>0, and the value of n is positively correlated with the number of data categories involved by the household to be verified; if the score obtained by the household to be verified is greater than or equal to the preset critical value, then determine the household to be verified as an actual household within the preset area and include it in the fourth set.

[0010] In a feasible implementation manner, identifying the household types in the set of households according to the various types of resident information data specifically includes: comparing the household information in the set of households with the information with obvious homeowner characteristics in the resident interaction information; if the comparison is successful, then determine the household type corresponding to the household as the homeowner; if the comparison is unsuccessful, then tentatively determine the household type corresponding to the household as the tenant.

[0011] In a feasible implementation manner, after if the comparison is unsuccessful, then tentatively determine the household type corresponding to the household as the tenant, the method further includes: obtaining auxiliary judgment data, and through the auxiliary judgment data, performing refined filtering and association on the household information with unsuccessful comparison to further verify the household type.

[0012] In a feasible implementation manner, housing information is associated with each household in the household set to achieve data collection, which specifically includes: according to the resident interaction information, associating each household in the household set with the corresponding housing address, and determining the household information and household type in each house; wherein, the housing address includes building, unit, and room number.

[0013] On the other hand, an embodiment of the present application also provides a jurisdiction population identification device based on spatio-temporal big data. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, so that the at least one processor can execute a jurisdiction population identification method based on spatio-temporal big data according to any of the above embodiments.

[0014] The jurisdiction population identification method and device provided by the embodiments of the present application automatically identify the household situation in the jurisdiction through technical collection means and big data governance means, reduce the burden on the household visit collection work, and solve the problem of insufficient grass-roots community grid members and police force. Through the effective collection and scientific utilization of various data, new value mining of data is realized, and dynamic management of the jurisdiction population is realized based on automatic calculation by algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings. In the drawings:

[0016] Figure 1 It is a flowchart of a jurisdiction population identification method based on spatio-temporal big data provided by an embodiment of the present application;

[0017] Figure 2 It is a schematic diagram of a household classification set provided by an embodiment of the present application;

[0018] Figure 3 It is a schematic diagram of the structure of a jurisdiction population identification device based on spatio-temporal big data provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0020] The embodiment of this application provides a method for identifying the population in a jurisdiction based on spatio-temporal big data, as Figure 1 shown, the method specifically includes steps S101 - S104:

[0021] S101. Collect various types of resident information data in a preset area.

[0022] First of all, this method needs to first collect various types of resident information data in a preset area, including at least base station data and resident interaction information. Among them, the preset area is the designated area, such as a certain community A in the jurisdiction, and the preset area belongs to the current jurisdiction.

[0023] Specifically, based on a preset time interval, collect the mobile phone numbers and the information of the mobile phone owners that appear in the preset area during a specific time period. Then, perform big data analysis on the mobile phone numbers and the information of the mobile phone owners obtained in each time interval to determine the information of the frequently appearing people in the preset area, and obtain the base station data; among them, the types of people in the base station data include current residents and non-residents.

[0024] Furthermore, in the system, obtain the resident interaction information in the preset area.

[0025] It should be noted that the resident interaction information refers to various data interaction information in the daily life of residents, such as WIFI information, express delivery recipient information, payment information, etc. The base station data and the resident interaction information obtained in this application are all obtained with the consent and authorization of the parties and are legally obtained in accordance with the law.

[0026] S102. Determine the set of residents in the preset area according to various types of resident information data.

[0027] Specifically, define the information of the frequently appearing people in the base station data as the first set, and define the resident interaction information as the second set. Determine the intersection of the first set and the second set as the third set. Then, define the difference set between the first set and the third set as the non-resident set, and define the difference set between the second set and the third set as the set of residents to be verified.

[0028] Further, analyze the households to be verified in the set of households to be verified, and determine all the actual households among the households to be verified as the fourth set. Determine the union of the third set and the fourth set as the set of households within the preset area.

[0029] In one embodiment, Figure 2 FIG. is a schematic diagram of a household classification set provided by an embodiment of the present application. As Figure 2 shown, set a is the set of information on frequently appearing persons in the base station data (i.e., the first set), and set b is the set of real-name information in the resident interaction information (i.e., the second set). It can be Figure 2 seen that the persons included in set a but not in set b are non-households in the community, indicating that this person only appears around the community regularly or visits the relatives and friends of community residents temporarily; the persons included in both set a and set b are the current households in the community; the persons not included in set a but included in set b are the persons to be verified or the persons having an associated relationship with the current households, defined as set c (i.e., the third set), c = a ∩ b.

[0030] As a feasible implementation method, due to the actual situation of missing collection or incomplete coverage in the base station data, it is necessary to analyze the households to be verified. In this application, by using the method of linear weights, weight scores are assigned to the households to be verified in the set of households to be verified. The higher the obtained score, the higher the possibility that this person is a household in this area. Specifically, it includes:

[0031] According to assign weight scores to the households to be verified in the set of households to be verified;

[0032] wherein, Score j is the score obtained by the jth household to be verified, R i is the assigned weight of the ith data category in the set of households to be verified, T ij is the number of occurrences of the jth household to be verified in the ith data category; n is the coefficient of the occurrence situation of the jth household to be verified in the data categories of the set of households to be verified, n > 0, and the value of n is positively correlated with the number of data categories involved by the household to be verified. For example, if the coefficient of the household to be verified j appearing in both the WIFI real-name user information and the express recipient real-name information is n1, and the coefficient of only appearing in the WIFI real-name user information is n2, then n1 > n2.

[0033] If the score obtained by the household to be verified is greater than or equal to the preset critical value, then determine the household to be verified as an actual household within the preset area and classify it into the fourth set. If the score obtained by the household to be verified is less than the preset critical value, then the household to be verified is not a community household.

[0034] S103. Identify the household types in the set of households according to various types of resident information data.

[0035] Specifically, looking at the household types, all households in the community include homeowners and renters. Compare the household information in the household set obtained in S102 with the data with obvious homeowner characteristics. If the comparison is successful, determine that the household type corresponding to the household is a homeowner; if the comparison is unsuccessful, tentatively determine that the household type corresponding to the household is a renter.

[0036] Furthermore, obtain auxiliary judgment data, and through the auxiliary judgment data, perform refined filtering and association on the household information with unsuccessful comparison to further verify the household type.

[0037] In one embodiment, the auxiliary judgment data may be information related to the housing property rights.

[0038] S104. Associate housing information with each household in the household set to achieve data collection.

[0039] Specifically, according to various real-name information in the resident interaction information, associate each household in the household set with the corresponding housing address, and determine the household information and household type in each house; wherein, the housing address includes building, unit, and room number.

[0040] In addition, the embodiment of the present application also provides a jurisdiction population identification device based on spatio-temporal big data, as Figure 3 shown. The jurisdiction population identification device based on spatio-temporal big data specifically includes:

[0041] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute:

[0043] Collect various types of resident information data in a preset area; wherein, the various types of resident information data at least include: base station data and resident interaction information; the preset area belongs to a preset jurisdiction;

[0044] Determine a household set in the preset area according to the various types of resident information data;

[0045] Identify the household types in the household set according to the various types of resident information data;

[0046] Associate housing information with each household in the household set to achieve data collection.

[0047] A method and device for identifying population in a jurisdiction based on spatio-temporal big data provided by an embodiment of the present application uses data means to identify personnel in modern communities, solving the problems that in the current stage, grass-roots staff need to manually collect information door to door, and the timeliness and accuracy cannot be guaranteed, and it also greatly costs labor costs and time costs, etc.

[0048] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment.

[0049] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the embodiments of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for identifying the population in a jurisdiction based on spatio-temporal big data, characterized in that, The method includes: Collecting various types of resident information data in a preset area; wherein, the various types of resident information data at least include: base station data and resident interaction information; the preset area belongs to a preset jurisdiction area; Determining a household set within the preset area according to the various types of resident information data, specifically including: Defining the base station data as a first set and the resident interaction information as a second set; Determining the intersection of the first set and the second set as a third set; Defining the difference set between the first set and the third set as a non-household set, and defining the difference set between the second set and the third set as a set of households to be verified; Analyzing the households to be verified in the set of households to be verified, and determining all actual households among the households to be verified as a fourth set, specifically including: According to weight scores are assigned to the households to be verified in the set of households to be verified; where, Score j is the score obtained by the j-th household to be verified, and R i is the scoring weight of the i-th data category in the set of households to be verified, and T ij is the number of occurrences of the j-th household to be verified in the i-th data category; n is the coefficient of the occurrence of the j-th household to be verified in the data categories of the set of households to be verified, n > 0, and the value of n is positively correlated with the number of data categories involved by the household to be verified; If the obtained score of a household to be verified is greater than or equal to a preset critical value, then determining the household to be verified as an actual household within the preset area and including it in the fourth set; Determining the union of the third set and the fourth set as the household set within the preset area; Identifying the household types in the household set according to the various types of resident information data; Associating housing information with each household in the household set to achieve data collection.

2. The method for identifying the population in a jurisdiction based on spatio-temporal big data according to claim 1, wherein, Collecting various types of resident information data within a preset area, specifically including: Based on a preset time interval, collecting mobile phone numbers and their owners' information that appear in the preset area during a specific time period; Performing big data analysis on the mobile phone numbers and their owners' information obtained in each time interval to determine the information of frequently appearing personnel within the preset area and obtaining the base station data; wherein, the types of personnel in the base station data include current residents and non-residents; In the system, obtaining the resident interaction information within the preset area.

3. A method for identifying the population in a jurisdiction based on spatio-temporal big data according to claim 1, characterized in that, Identifying the household types in the household set according to the various types of resident information data, specifically including: Comparing the household information in the household set with the information with obvious owner characteristics in the resident interaction information; If the comparison is successful, then determining the household type corresponding to the household as an owner; If the comparison is unsuccessful, then tentatively determining the household type corresponding to the household as a tenant.

4. The method for identifying the population in a jurisdiction based on spatio-temporal big data according to claim 3, wherein, After if the comparison is unsuccessful, then tentatively determining the household type corresponding to the household as a tenant, the method further includes: Obtaining auxiliary judgment data, and through the auxiliary judgment data, performing refined filtering and association on the household information with unsuccessful comparison to further verify the household type.

5. A method for identifying the population of an administrative region based on spatio-temporal big data according to claim 1, characterized in that Associating housing information with each household in the household set to achieve data collection, specifically including: According to the resident interaction information, associating each household in the household set with the corresponding housing address, and determining the household information and household type in each house; wherein, the housing address includes building, unit, and room number.

6. An area population identification device based on spatio-temporal big data, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a method for identifying the population of a jurisdiction area based on spatio-temporal big data according to any one of claims 1-5.

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