CN115409434B

CN115409434B - Regional demographic method, system and storage medium based on signaling big data

Info

Publication number: CN115409434B
Application number: CN202211359377.7A
Authority: CN
Inventors: 成立立; 于笑博; 张广志; 李铭哲
Original assignee: Beiling Rongxin Datalnfo Science and Technology Ltd
Current assignee: Beiling Rongxin Datalnfo Science and Technology Ltd
Priority date: 2022-11-02
Filing date: 2022-11-02
Publication date: 2023-03-24
Anticipated expiration: 2042-11-02
Also published as: CN115409434A

Abstract

The embodiment of the application provides a regional demographic method, a regional demographic system and a storage medium based on signaling big data. The method comprises the following steps: acquiring a signaling data information set of terminal users in an area within a preset time period, extracting and cleaning to obtain a base station sector dynamic track portrait, extracting base station sector coverage identification data, combining with signaling response time data, processing to obtain sector intersection density data, performing model processing according to the sector intersection density data of a second identification user, and aggregating to obtain a grid density distribution diagram in the area to convert to obtain population density distribution conditions; therefore, the sector intersection density data obtained by cleaning the signaling data of the terminal user are screened out based on the signaling big data, the grid density distribution data obtained by model processing are subjected to aggregation conversion to obtain population density distribution data, the user sector grid track parking density recognition is carried out according to the signaling data to obtain population density distribution conditions, and accurate statistics on the population resident density distribution in the region is realized.

Description

Region demographic method, system and storage medium based on signaling big data

Technical Field

The present application relates to the field of big data and signaling data technology, and in particular, to a method, system and storage medium for regional demographic based on signaling big data.

Background

The collection of signaling data is a continuous process, correspondingly, the storage of the signaling data is also a continuous process, each operator records the signaling data according to the time acquired by the signaling and the place where the service occurs, the data used for identifying the place where the service occurs in the signaling data is a base station sector connected when the signaling occurs, the base station sector has certain changes in the network optimization and service adjustment processes, which causes inconsistency of corresponding position information of sectors in different time periods, in addition, the signaling data is used for carrying out demographic statistics or related analysis, and the processing needs to be carried out according to certain spatial latitudes, such as communities, villages, towns, key areas and the like.

The existing urban area usually adopts information registration to acquire aiming at the identification and statistics of the distribution situation of the floating population, but the method is easy to generate technical loopholes of missing or statistical deviation, the statistical result lacks accuracy and rapidness, the identification and statistics of the user terminal information lacks accuracy and rapidness, the resident information of the floating population is difficult to accurately master, the effective identification and statistics of the distribution situation of the area population cannot be carried out, and the dynamic statistics technology of the distribution situation of the population is lacked.

In view of the above problems, an effective technical solution is urgently needed.

Disclosure of Invention

The embodiment of the application aims to provide a regional population statistical method, a system and a storage medium based on signaling big data, which can perform a technology for acquiring a regional population resident distribution situation according to signaling data information of a terminal user, and improve the accuracy of population distribution situation statistics.

The embodiment of the application also provides a regional demographic method based on the signaling big data, which comprises the following steps:

acquiring a signaling data information set of terminal users in an area within a preset time period;

according to the signaling data information set, data extraction is carried out to obtain signaling response time data and signaling response address data, and data cleaning is carried out to obtain a first identification user;

acquiring base station sector dynamic information corresponding to a signaling response address of the first identification user in the preset time period and generating a base station sector dynamic track portrait of the first identification user;

extracting base station sector coverage identification data according to the base station sector dynamic track portrait and combining corresponding signaling response time data of the identification data to process to obtain sector intersection density data;

comparing the threshold value according to the sector intersection density data and a preset resident intersection density threshold value, screening corresponding identification users meeting the threshold value comparison requirement, and marking the identification users as second identification users;

Inputting the sector intersection density data of the second identification user into a preset sector grid dense distribution model for processing to obtain grid density distribution data;

and aggregating according to the grid density distribution data to obtain an area grid density distribution map, and performing density value conversion according to the area grid density distribution map to obtain an area resident population density distribution condition.

Optionally, in the area demographic method based on signaling big data according to the embodiment of the present application, the acquiring a signaling data information set of an end user in an area within a preset time period, performing data extraction according to the signaling data information set to acquire signaling response time data and signaling response address data, and performing data cleaning to acquire a first identified user includes:

acquiring a signaling data information set of a terminal user in an area within a preset time period, wherein the signaling data information set comprises signaling time information, signaling positioning information, signaling interaction information and signaling service identification information;

extracting data according to the signaling data information set to obtain signaling response time data and signaling response address data of signaling response;

carrying out time point, time point positioning and duration data cleaning according to the signaling response time data and the signaling response address data to screen out signaling data information which does not meet the requirement;

And marking the terminal user corresponding to the cleaned and retained second signaling data information set as a first identification user.

Optionally, in the signaling big data-based regional demographic method according to the embodiment of the present application, the obtaining base station sector dynamic information corresponding to a signaling response address of the first identified user in the preset time period and generating a base station sector dynamic trajectory representation of the first identified user includes:

extracting base station response identification data according to the signaling response address data of the first identification user in the preset time period;

inquiring in a base station identification database according to the base station response identification data to obtain base station sectors corresponding to the identification in the area and extracting dynamic information of the base station sectors;

the base station sector dynamic information comprises base station sector response data, base station sector coverage data and base station dynamic interaction data;

and generating a base station sector dynamic track portrait of the first identification user according to the base station sector responsiveness data, the base station sector coverage data and the base station dynamic interaction data.

Optionally, in the area demographic method based on signaling big data according to the embodiment of the present application, the extracting, according to the base station sector dynamic trajectory representation, base station sector coverage identification data and processing with corresponding signaling response time data of the identification data to obtain sector intersection density data includes:

Extracting base station sector coverage identification data according to the base station sector dynamic track image, wherein the base station sector coverage identification data comprises base station arrangement identification data, sector intersection degree data, sector track identification data and sector residence data;

calculating according to the base station arrangement identification data, the sector intersection degree data, the sector track identification data and the sector residence data and combining with a signaling response time period node in a preset time period to obtain sector intersection density data in the preset time period;

the sector intersection density data calculation formula is as follows:

；

where P is the sector intersection density data,

sector resident data of the node for the ith signaling response time period,

sector-diversity-level data for the ith signaling response time period node,

identifying data for the sector trace of the ith signaling response time period node,

arranging identification data for a base station, wherein n is the number of signaling response time period nodes in a preset time period, i is the ith signaling response time period node in the n time period nodes,

is the track density coefficient.

Optionally, in the signaling big data-based regional demographic method according to the embodiment of the present application, the performing threshold comparison according to the sector intersection density data and a preset resident intersection density threshold to screen a corresponding identified user meeting a threshold comparison requirement, and marking the identified user as a second identified user includes:

Comparing a threshold value according to the sector intersection density data of the first identification user and a preset resident intersection density threshold value;

and identifying the first identification user which meets the threshold comparison result, and marking the first identification user as a second identification user.

Optionally, in the area demographic method based on the signaling big data according to the embodiment of the present application, the inputting the sector intersection density data of the second identified user into a preset sector grid dense distribution model for processing to obtain grid density distribution data includes:

acquiring a trained sector grid dense model;

and inputting the sector intersection density data of the second identification user in combination with the sector intersection degree data and the sector track identification data into a trained sector grid dense distribution model for processing to obtain grid density distribution data.

Optionally, in the area demographic method based on the large signaling data according to the embodiment of the present application, the aggregating according to the grid density distribution data to obtain an area grid density distribution map, and performing density value conversion according to the area grid density distribution map to obtain an area resident population density distribution condition includes:

aggregating according to the grid density distribution data to obtain grid density distribution data in the area;

Constructing a grid density distribution map according to the grid density distribution data;

performing population density value conversion on the grid density distribution map according to a preset grid density comparison value to obtain regional resident population density layout data;

and taking the area resident population density distribution data as the statistical area resident population density distribution situation in the preset time period.

In a second aspect, embodiments of the present application provide a region demographic system based on signaling big data, the system including: a memory and a processor, wherein the memory includes a program of a region demographic method based on signaling big data, and the program of the region demographic method based on signaling big data realizes the following steps when being executed by the processor:

comparing the sector intersection density data with a preset resident intersection density threshold value according to the threshold value, screening corresponding identification users meeting the threshold value comparison requirement, and marking the identification users as second identification users;

Optionally, in the area demographic system based on signaling big data according to the embodiment of the present application, the obtaining a signaling data information set of an end user in an area within a preset time period, performing data extraction according to the signaling data information set to obtain signaling response time data and signaling response address data, and performing data cleaning to obtain a first identified user includes:

In a third aspect, the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes a signaling big data based regional demographic method program, and when the signaling big data based regional demographic method program is executed by a processor, the method implements the steps of the signaling big data based regional demographic method as described in any one of the above.

As can be seen from the above, the area demographic method, system and storage medium based on signaling big data provided in the embodiment of the present application obtain a base station sector dynamic trajectory representation by obtaining a signaling data information set of a terminal user in an area within a preset time period and cleaning the signaling data set, extract base station sector coverage identification data and process the signaling response time data to obtain sector intersection density data, perform model processing according to the sector intersection density data of a second identified user and aggregate the data to obtain a grid density distribution map in the area, and obtain a population density distribution situation by conversion; therefore, the sector intersection density data obtained by cleaning the signaling data of the terminal user are screened out based on the signaling big data, the grid density distribution data obtained by model processing are subjected to aggregation conversion to obtain population density distribution data, the user sector grid track parking density recognition is carried out according to the signaling data to obtain population density distribution conditions, and accurate statistics on the population resident density distribution in the region is realized.

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a flowchart of a regional demographic method based on signaling big data according to an embodiment of the present application;

fig. 2 is a flowchart of screening out a first identified user according to a regional demographic method based on signaling big data provided in an embodiment of the present application;

FIG. 3 is a flow chart of generating a dynamic trajectory representation of a base station sector according to a signaling big data based regional demographic method provided in an embodiment of the present application;

Fig. 4 is a schematic structural diagram of a regional demographic system based on signaling big data according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flow chart of a signaling big data based regional demographic method in some embodiments of the present application. The regional demographic method based on the signaling big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The regional demographic method based on the signaling big data comprises the following steps:

s101, acquiring a signaling data information set of terminal users in an area within a preset time period;

s102, extracting data according to the signaling data information set to obtain signaling response time data and signaling response address data, and performing data cleaning to obtain a first identification user;

s103, acquiring base station sector dynamic information corresponding to a signaling response address of the first identification user in the preset time period and generating a base station sector dynamic track portrait of the first identification user;

s104, extracting base station sector coverage identification data according to the base station sector dynamic track portrait and combining corresponding signaling response time data processing of the identification data to obtain sector intersection density data;

s105, comparing the threshold value according to the sector intersection density data and a preset resident intersection density threshold value, screening corresponding identification users meeting the threshold value comparison requirement, and marking the identification users as second identification users;

S106, inputting the sector intersection density data of the second identification user into a preset sector grid dense distribution model for processing to obtain grid density distribution data;

and S107, aggregating according to the grid density distribution data to obtain an area grid density distribution map, and performing density value conversion according to the area grid density distribution map to obtain an area resident population density distribution condition.

It should be noted that, in order to obtain the resident distribution condition and distribution density of the terminal users in a certain preset time period in the area, the signaling data information set of the terminal users in the area in the preset time period is obtained to extract the data to obtain the signaling response time data and the signaling response address data, and the data is cleaned to obtain the first identification user, the signaling response time in the area which is not satisfied and the signaling data corresponding to the signaling response address distribution are screened out to obtain the first identification user, the base station sector dynamic track portrait generated according to the base station sector dynamic information corresponding to the signaling response address in the preset time period describes the marked base station sector of the user signaling response path to obtain the track information of the base station sector on the path, and the base station sector coverage identification data is extracted to be combined with the corresponding signaling response time data to obtain the sector intersection density data, the sector intersection density data reflects the residence time of a user in a sector overlapping area in an area, the number of nodes of the residence time and the residence frequency of each residence point in a time period, then the threshold comparison is carried out according to the sector intersection density data to screen a mark which meets the threshold comparison requirement as a second mark user, namely the residence time and the frequency of the user in the sector intersection area are insufficient, the user which frequently moves or does not move in a certain time period such as express delivery, area service, work access, temporary stop and the like is eliminated to obtain the resident user of the area, then the sector intersection density data of the second mark user is input into a preset sector grid dense distribution model to be processed to obtain grid density distribution data, namely the sector intersection information is rasterized, namely, the grid with the adaptive size is obtained according to the effective area of the sector or the intersection area, the intelligent screening statistical technology is convenient for performing data mapping and visual description on the sector parking condition through the grid density, then aggregating the grid density distribution data of the second identification users in the area to obtain an area grid density distribution map, wherein the distribution map is a data grid density distribution map and reflects the parking residence condition of the second identification users in the area, and then performing density value conversion according to the area grid density distribution map to obtain an area resident population density distribution condition, namely, the resident parking distribution condition of the second identification users in the area is presented through data conversion of preset density values, if a certain subarea A has more resident parking users and has high frequency, the density of the subarea A is displayed as large as deep as chromaticity, and the corresponding density value is large, and if a subarea B has less resident parking users, the density of the subarea A is displayed as small as shallow as chromaticity and the corresponding density value is small, namely, the intelligent screening statistical technology for obtaining the grid density distribution data aggregation and conversion of the area resident population density distribution condition through rasterization processing of the base station sector intersection density data of the signaling users is realized.

Referring to fig. 2, fig. 2 is a flowchart illustrating a method for filtering out first identified users based on regional demographics of signaling big data according to some embodiments of the present disclosure. According to the embodiment of the invention, the obtaining of the signaling data information set of the terminal user in the area within the preset time period, the data extraction according to the signaling data information set to obtain the signaling response time data and the signaling response address data, and the data cleaning to obtain the first identification user specifically comprises:

s201, acquiring a signaling data information set of a terminal user in an area within a preset time period, wherein the signaling data information set comprises signaling time information, signaling positioning information, signaling interaction information and signaling service identification information;

s202, extracting data according to the signaling data information set to obtain signaling response time data and signaling response address data of signaling response;

s203, performing time point, time point positioning and duration data cleaning according to the signaling response time data and the signaling response address data to screen out signaling data information which does not meet the requirement;

and S204, marking the terminal user corresponding to the cleaned and retained second signaling data information set as a first identification user.

It should be noted that, data extraction is performed according to a signaling data information set of an end user in an area within a preset time period to obtain signaling response time data and signaling response address data, that is, address location corresponding to signaling response time, and signaling interaction information and signaling service identifiers such as signaling information of a communication provider, a registration place, and the like are extracted to obtain signaling response time and data corresponding to a response address, data cleaning and screening are performed on the data according to signaling response time nodes, response time point locations, and signaling time lengths, and signaling data information that does not meet preset requirements are screened out, if the signaling response time does not meet the requirements, the number of times of the signaling response time nodes is insufficient, and signaling response location distribution, location points, associated location addresses, and other signaling data information that do not meet the requirements are cleaned, a user corresponding to a remaining signaling data information set is marked as a first identified user, so as to perform preliminary screening on users who are effectively resident in the area according to preset conditions of the signaling data information.

Referring to fig. 3, fig. 3 is a flow chart of generating a dynamic trajectory representation of a base station sector based on big data signaling processing according to some embodiments of the present application. According to the embodiment of the present invention, the obtaining of the base station sector dynamic information corresponding to the signaling response address of the first identified user in the preset time period and generating the base station sector dynamic trajectory sketch of the first identified user specifically includes:

s301, extracting base station response identification data according to the signaling response address data of the first identification user in the preset time period;

s302, inquiring in a base station identification database according to the base station response identification data to obtain base station sectors corresponding to identification in an area and extracting dynamic information of the base station sectors;

s303, the dynamic information of the base station sector comprises base station sector response data, base station sector coverage data and base station dynamic interaction data;

and S304, generating a base station sector dynamic track portrait of the first identification user according to the base station sector responsiveness data, the base station sector coverage data and the base station dynamic interaction data.

It should be noted that, in order to obtain the dynamic trajectory situation of the first identified user within the preset time period, the signaling response address data of the first identified user within the preset time period is used to extract the base station response identification data, that is, the identification data of the signaling corresponding to the response base station, the base station sector corresponding to the identification in the area is obtained by querying in the base station identification database according to the base station response identification data, and the base station sector dynamic information is extracted, that is, the base station sector is located in the base station identification database through the location identification of the base station and the dynamic interaction data between the signaling and the identified base station sector is obtained, the base station sector dynamic information includes base station sector response data, base station sector coverage data, and base station dynamic interaction data, each data reflects the interaction situation of the signaling response base station sector, the interaction frequency, the response intensity and the interaction duration of each base station sector or the intersection coverage area of the base station sector, and the parameter values corresponding to the interaction duration, the frequency, the coverage area, the repeatability, the base station sector dynamic trajectory of the base station which can reflect the interaction situation and the activity situation of the signaling corresponding to the first identified user signaling in the base station sector, and the dynamic trajectory of the signaling between the sectors and the user in the switching situation of the signaling trajectory.

According to the embodiment of the present invention, the extracting of the base station sector coverage identification data according to the base station sector dynamic trajectory sketch and the processing of the corresponding signaling response time data of the identification data to obtain the sector intersection density data specifically include:

extracting base station sector coverage identification data according to the base station sector dynamic track portrait, wherein the base station sector coverage identification data comprises base station arrangement identification data, sector intersection degree data, sector track identification data and sector residence data;

calculating according to the base station arrangement identification data, the sector intersection degree data, the sector track identification data and the sector residence data in combination with a signaling response time period node in a preset time period to obtain sector intersection density data in the preset time period;

the sector intersection density data calculation formula is as follows:

；

where P is the sector intersection density data,

sector resident data of the node for the ith signaling response time period,

sector-diversity-level data for the ith signaling response time period node,

arranging identification data for a base station, wherein n is a signaling response in a preset time periodAccording to the number of the time period nodes, i is the ith signaling response time period node in the n time period nodes,

Is the track density coefficient (

Obtained by querying the base station identification database according to the sector track identification data).

It should be noted that, the base station sector coverage identification data extracted according to the base station sector dynamic trajectory representation includes base station arrangement identification data, sector intersection degree data, sector trajectory identification data and sector residence data, the data reflects the base station path identification arrangement, the intersection area between sectors, the sector trajectory identification, the response residence time length and residence frequency of the sector and sector intersection area, and then the sector intersection density data in the preset time period is obtained by calculating the number in combination with the signaling response time period node in the preset time period, that is, the sector intersection density data of the user is obtained by performing the programmed calculation of the time node data accumulation on the base station sector intersection area distribution, the sector trajectory path, the sector response time length and the sector residence frequency corresponding to each signaling response time period node in the preset time period, and the density data reflects the activity intensity of the user in the sector or sector intersection area and the distribution density of the signaling response sector or sector intersection area in the preset time period, and the residence condition of each point of the user in the area and the residence point can be measured by the sector density.

According to the embodiment of the present invention, the step of performing threshold comparison according to the sector intersection density data and the preset resident intersection density threshold to screen the corresponding identified user meeting the threshold comparison requirement, and marking the identified user as a second identified user specifically includes:

It should be noted that, a threshold value comparison is performed according to the obtained sector intersection density data and a preset resident intersection density threshold value, users who do not meet the activity intensity of the sector or sector intersection area and the distribution density of the sector or sector intersection area of the signaling response within a preset time period are removed, and users who do not meet the requirements of parking and route within the area are removed, wherein the resident intersection density threshold value can be set according to the area attribute information, for example, the threshold value is set to 85%.

According to the embodiment of the present invention, the sector intersection density data of the second identified user is input into a preset sector grid dense distribution model for processing to obtain grid density distribution data, specifically:

Acquiring a trained sector grid dense model;

It should be noted that, the sector intersection density data of the second identification user is input to a trained sector grid density distribution model in combination with the sector intersection density data and the sector track identification data to obtain grid density distribution data, that is, model rasterization is performed according to the activity intensity of the sector or the sector intersection region in a preset time period and the distribution density of the sector or the sector intersection region responded by the signaling to obtain a density grid which is adaptive and reflects the activity intensity and the activity distribution density, rasterization is performed according to the activity intensity and the density condition of the signaling in the sector to obtain an adaptive density grid and grid density distribution data, that is, the grid density distribution is performed on the activity intensity and the activity distribution condition of the sector and the sector intersection region of the base station, wherein the sector grid density distribution model is obtained by performing training processing on a large number of historical data samples of sector intersection density data, sector track identification data and grid density distribution data, the more accurate the processing result obtained by the model is obtained, the sector grid density distribution model is output to a preset grid density distribution model according to the historical data of the sector intersection density data, the sector intersection density data and the sector track identification data are input to the preset threshold value, and the learning threshold value is obtained by the learning threshold value is output to the sector grid density learning threshold value.

According to the embodiment of the present invention, the aggregating is performed according to the grid density distribution data to obtain the grid density distribution map in the area, and the density value conversion is performed according to the grid density distribution map in the area to obtain the density distribution situation of the resident population in the area, specifically:

aggregating according to the grid density distribution data to obtain grid density distribution data in the region;

The method includes the steps of obtaining grid density distribution data of second identification users in an area by aggregating the obtained grid density distribution data of the second identification users in the area, enabling activity intensity and activity density of the second identification users at each point and each sub-area in the area to be reflected in a centralized mode, constructing an area grid density distribution map according to the data, enabling the distribution map to be a digitalized grid density distribution map, reflecting parking, activity intensity and activity density of the second identification users in the area, conducting density value conversion according to the area grid density distribution map to obtain an area resident population density distribution situation, namely displaying the activity distribution and the parking distribution situation of the second identification users in the area through data conversion of preset density values, and reflecting the area resident population density distribution situation.

As shown in fig. 4, the present invention also discloses a region demographic system based on signaling big data, which includes a memory 41 and a processor 42, wherein the memory includes a region demographic method program based on signaling big data, and when the processor executes the region demographic method program based on signaling big data, the following steps are implemented:

acquiring a signaling data information set of a terminal user in an area within a preset time period;

According to the embodiment of the invention, the obtaining of the signaling data information set of the terminal user in the area within the preset time period, the data extraction according to the signaling data information set to obtain the signaling response time data and the signaling response address data, and the data cleaning to obtain the first identification user specifically comprises:

performing data extraction according to the signaling data information set to obtain signaling response time data and signaling response address data of signaling response;

According to the embodiment of the present invention, the obtaining of the base station sector dynamic information corresponding to the signaling response address of the first identified user in the preset time period and generating the base station sector dynamic track portrait of the first identified user specifically include:

the sector intersection density data calculation formula is as follows:

；

where P is the sector intersection density data,

sector resident data of the node for the ith signaling response time period,

sector-diversity-level data for the ith signaling response time period node,

Is the track density coefficient (

According to the embodiment of the present invention, the step of performing threshold comparison according to the sector intersection density data and a preset resident intersection density threshold to screen a corresponding identified user meeting a threshold comparison requirement, and marking the identified user as a second identified user specifically includes:

According to the embodiment of the present invention, the sector intersection density data according to the second identified user is input into a preset sector grid dense model for processing to obtain grid density distribution data, and the specific steps are as follows:

Acquiring a trained sector grid dense model;

A third aspect of the present invention provides a readable storage medium, which includes a signaling big data based regional demographic method program, and when the signaling big data based regional demographic method program is executed by a processor, the method implements the steps of the signaling big data based regional demographic method as described in any one of the above.

The invention discloses a region population statistical method, a system and a storage medium based on signaling big data.A base station sector dynamic track portrait is obtained by obtaining a signaling data information set of terminal users in a region within a preset time period, and then extracting base station sector coverage identification data and combining with signaling response time data to process so as to obtain sector intersection density data, then model processing is carried out according to the sector intersection density data of a second identification user, and aggregation is carried out so as to obtain a grid density distribution map in the region to convert so as to obtain population density distribution conditions; therefore, the sector intersection density data obtained by cleaning the signaling data of the terminal user are screened out based on the signaling big data, the grid density distribution data obtained by model processing are subjected to aggregation conversion to obtain population density distribution data, the user sector grid track parking density recognition is carried out according to the signaling data to obtain population density distribution conditions, and accurate statistics on the population resident density distribution in the region is realized.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

Alternatively, the integrated unit of the present invention may be stored in a readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Claims

1. A regional demographic method based on signaling big data, comprising the steps of:

aggregating according to the grid density distribution data to obtain an area grid density distribution map, and performing density value conversion according to the area grid density distribution map to obtain an area resident population density distribution condition;

The method for extracting the base station sector coverage identification data according to the base station sector dynamic track portrait and processing the base station sector coverage identification data by combining the corresponding signaling response time data of the identification data to obtain the sector intersection density data comprises the following steps:

the calculation formula of the sector intersection density data is as follows:

where P is the sector intersection density data,

sector resident data of the node for the ith signaling response time period,

sector-diversity-level data for the ith signaling response time period node,

Is the track density coefficient.

2. The method for regional demographic statistics based on signaling big data as claimed in claim 1, wherein the obtaining of the signaling data information set of the end user in the region within the preset time period, performing data extraction according to the signaling data information set to obtain the signaling response time data and the signaling response address data, and performing data cleaning to obtain the first identified user comprises:

3. The signaling big data based regional demographic method as claimed in claim 2, wherein the obtaining base station sector dynamic information corresponding to the signaling response address of the first identified user in the preset time period and generating a base station sector dynamic trajectory representation of the first identified user comprises:

4. The signaling big data-based regional demographic method as claimed in claim 1, wherein the selecting, according to the threshold comparison between the sector intersection density data and a preset resident intersection density threshold, a corresponding identified user meeting a threshold comparison requirement as a second identified user comprises:

5. The signaling big data based regional demographic method as recited in claim 4, wherein the inputting sector intersection density data of the second identified user into a preset sector grid dense distribution model for processing to obtain grid density distribution data comprises:

acquiring a trained sector grid dense model;

6. The regional demographic method as claimed in claim 5, wherein the aggregating according to the grid density distribution data to obtain an intra-regional grid density distribution map and performing density value conversion according to the intra-regional grid density distribution map to obtain a regional resident population density distribution condition comprises:

performing population density value conversion on the grid density distribution map according to a preset grid density comparison value to obtain regional resident population density distribution data;

7. A regional demographic system based on signaling big data, the system comprising: a memory and a processor, wherein the memory includes a program of a region demographic method based on signaling big data, and the program of the region demographic method based on signaling big data realizes the following steps when being executed by the processor:

performing data extraction according to the signaling data information set to obtain signaling response time data and signaling response address data, and performing data cleaning to obtain a first identification user;

the sector intersection density data calculation formula is as follows:

where P is the sector intersection density data,

Sector resident data of the node for the ith signaling response time period,

sector-diversity-level data for the ith signaling response time period node,

is the track density coefficient.

8. The system of claim 7, wherein the acquiring signaling data information sets of end users in a region within a preset time period, performing data extraction according to the signaling data information sets to acquire signaling response time data and signaling response address data, and performing data cleaning to acquire a first identified user comprises:

9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a signaling big data based region demographic method program, which when executed by a processor implements the steps of the signaling big data based region demographic method according to any one of claims 1 to 6.