CN112364907A - Method, system, server and storage medium for general investigation of frequent station of user to be tested - Google Patents
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Abstract
The invention discloses a common station census method for a user to be tested, which comprises the following steps: acquiring communication information reported by a mobile terminal of a user to be tested and corresponding reporting time; determining the position information of the user to be detected based on the communication information; generating one or more track points of the user to be tested based on the position information and the reporting time; performing clustering analysis on one or more track points to generate one or more track point aggregation areas, and taking the track point aggregation areas as appearance areas of the users to be detected; judging whether the frequency of the user to be detected in the appearance area within a preset time interval is greater than or equal to a first preset threshold value or not; and if the current position is larger than or equal to the preset position, taking the appearance area as the normal station of the user to be detected. The embodiment clusters the user track points to be detected to obtain the frequent site of the user to be detected, so that the general census efficiency of the frequent population site is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of mobile communication, in particular to a method, a system, a server and a storage medium for general site survey of a user to be tested.
Background
The population is the main body of national economy and social development, and the human-oriented idea is the core of scientific development. Meanwhile, the nature of urbanization is that the population is urbanized, which determines the important role of the urban population number in the statistical city management. Urban residents are the main bodies of urban spaces, the change of behaviors and activities of residents can influence the spatial organization and structure of the city, and the urban resident quantity statistics is a routine work for urban planning, traffic planning and urban management, so that periodic resident population census work is always implemented.
The traditional census program mainly adopts a mode of home-entry sampling survey, the mode has the defects of long period, low frequency and lagged result, and the result of the permanent population statistics result is easily influenced by sample factors, so that the traditional census program cannot adapt to the high-speed development of the economic society and cannot meet the current requirement on the timeliness of resident population monitoring and management work.
The existing method positions a user to be tested by acquiring communication information reported by a mobile terminal of the user to be tested so as to acquire statistical information of a city frequent population. However, since the range of activities of the regular population is not limited to residential areas, but also appears around workplaces and other living streets, the positioning only by the mobile terminal of the user to be tested may cause inaccuracy in the resident demographic.
Disclosure of Invention
The invention provides a common station census method for a user to be tested, which can perform clustering operation on track points through mobile terminal communication information reported by the user to be tested, and take the track points which are concentrated by the user to be tested as a resident area so as to accurately position the position of the user to be tested and improve the efficiency of the common station demographics.
In a first aspect, the present invention provides a frequent-station census method for a user to be tested, including:
acquiring communication information reported by a mobile terminal of a user to be tested and corresponding reporting time;
determining the position information of the user to be detected based on the communication information;
generating one or more track points of the user to be tested based on the position information and the reporting time;
performing clustering analysis on one or more track points to generate one or more track point aggregation areas, and taking the track point aggregation areas as appearance areas of the users to be detected;
judging whether the frequency of the user to be detected in the appearance area within a first preset time interval is greater than or equal to a first preset threshold value or not;
and if the current position is larger than or equal to the preset position, taking the appearance area as the normal station of the user to be detected.
Further, the reporting time includes a working day and a non-working day, and the working day time includes a working time period and an accommodation time period, then performing cluster analysis on one or more track points to generate one or more track point aggregation areas, and using the track point aggregation areas as the appearance areas of the users to be detected includes:
if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested;
meanwhile, clustering track points appearing in the lodging time period, and taking the obtained area as the lodging area of the user to be detected;
and if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested.
Further, the communication information further includes identity information of a user to be tested, then, performing cluster analysis on one or more track points to generate one or more track point aggregation areas, and after the track point aggregation areas are used as appearance areas of the user to be tested, the method further includes:
generating an area number based on the identity information of the user to be tested, wherein the area number comprises a provincial number, a city number and a administrative district number, one or more city numbers are arranged under each provincial number in the area, and one or more administrative district numbers are arranged under each city number;
and establishing a partition information table based on the user to be detected, the appearance area and the area number.
Further, after the partition information table is established based on the user to be tested, the presence area, and the area number, the method further includes:
under each area number directory, judging whether the user to be detected is present in the area or not at intervals of a second preset time interval;
if so, generating a first number under the area number;
if not, generating a second number under the area number;
and taking a number string formed by one or more first numbers and/or second numbers as the area number of the appearance area of the user to be detected after a third preset time interval.
Further, after a third preset time interval, taking a number string formed by one or more first numbers and/or second numbers as an area number of an appearance area of the user to be tested, the method further includes:
and recording one or more area numbers of one or more users to be tested in a fourth preset time interval, wherein the fourth preset time interval is greater than the third preset time interval.
Further, after the recording one or more area numbers of one or more users to be tested, the method further includes:
and counting the number of the area numbers in a fifth preset time interval, and taking the number of the area numbers as the resident population number of the area.
In a second aspect, the present invention provides a frequent-station census system for a user to be tested, including:
the first acquisition module is used for acquiring the communication information reported by the mobile terminal of the user to be tested and the corresponding reporting time;
the computing module is used for determining the position information of the user to be detected based on the communication information;
the track point generating module is used for generating one or more track points of the user to be detected based on the position information and the reporting time;
the clustering module is used for carrying out clustering analysis on one or more track points to generate one or more track point aggregation areas, and the track point aggregation areas are used as appearance areas of the user to be detected;
and the judging module is used for carrying out cluster analysis on one or more track points, taking the region of track point aggregation as the appearance region of the user to be detected, and if the appearance region is larger than or equal to the appearance region, taking the appearance region as the regular station of the user to be detected.
Further, the reporting time includes a working day and a non-working day, and the working day time includes a working time period and an accommodation time period, then the clustering module is further configured to:
if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested;
meanwhile, clustering track points appearing in the lodging time period, and taking the obtained area as the lodging area of the user to be detected;
and if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested.
In a third aspect, the present invention provides a server, including a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor implements the method for census of a frequent premises of a user to be tested as described in any one of the above when executing the program.
In a fourth aspect, a terminal-readable storage medium stores a program, and the program, when executed by a processor, can implement the customer premises screening method as described in any one of the above.
According to the invention, the trace points are clustered through the mobile terminal communication information reported by the user to be tested, and the trace points which are concentrated by the user to be tested are used as a resident area, so that the position of the user to be tested is accurately positioned, and the efficiency of the permanent demographics is improved.
Drawings
Fig. 1 is a flowchart of a frequent-premises general survey method for a user to be tested in the first embodiment of the present invention.
Fig. 2 is a flowchart of a frequent-premises general survey method for a user to be tested in the second embodiment of the present invention.
Fig. 3 is a block diagram of a customer premises general survey system to be tested according to a third embodiment of the present invention.
Fig. 4 is a block diagram of a customer premises general survey system to be tested according to an alternative embodiment of the third embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first preset threshold may be the second preset threshold, and similarly, the second preset threshold may be the first preset threshold, without departing from the scope of the present application. The first preset threshold and the second preset threshold are both preset thresholds used in the positioning process of the base station, but are not the same preset threshold. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "plurality", "batch" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
As shown in fig. 1, the present embodiment provides a method for general site survey of a user to be tested, which performs identification of a resident area by clustering track points of the user to be tested, and includes the following steps:
s101, communication information reported by a mobile terminal of a user to be tested and corresponding reporting time are obtained.
In this step, the user to be tested uses the mobile terminal to initiate a service (such as call, answer, switch, short message) each time, several pieces of MR and CDR data are generated, and the communication information refers to the MR and/or CDR data. In a terminal positioning mode, the identity information is determined to be the ID of the user to be detected through MR data, and terminal positioning is carried out through AGPS data in the MR data, latitude and longitude information of a base station in CDR data and the like so as to generate position information of the user to be detected.
And S102, determining the position information of the user to be detected based on the communication information.
S103, generating one or more track points of the user to be detected based on the position information and the reporting time.
And S104, carrying out clustering analysis on one or more track points to generate one or more track point aggregation areas, and using the track point aggregation areas as the appearance areas of the users to be detected.
In this step, cluster analysis refers to an analytical process that groups a collection of physical or abstract objects into classes composed of similar objects, with the purpose of collecting data for classification on a similar basis. Clustering analysis classifies a set of data into several categories for the similarity and difference of the data. The similarity between data belonging to the same class is large, but the similarity between data of different classes is small, and the cross-class data relevance is low. The number of classes required for clustering is unknown, for example, there may be one or more cluster regions where the user to be tested frequently appears in this step.
In an alternative embodiment of this step, because the reporting time includes a working day and a non-working day, the working day time includes a working time period and an accommodation time period, and areas where the user to be measured appears are different in different time periods, before performing cluster analysis on the track points, the track points are distinguished based on the reporting time, specifically: if the reporting time is a working day, the method further comprises the following steps: and clustering the trace points appearing in the working time period, and taking the obtained area as the working area of the user to be tested. Meanwhile, the track points appearing in the lodging time period are clustered, and the obtained region is used as the lodging region of the user to be tested. And if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested. The presence area includes a work area, an accommodation area, and/or a living area.
And S105, judging whether the frequency of the user to be detected in the appearance area in the first preset time interval is greater than or equal to a first preset threshold value.
In an alternative embodiment of this step, when the area where the user to be detected appears includes one or more of a work area, a lodging area and/or a living area, the times refer to the times that the user to be detected appears in one of the areas within the first preset time interval.
And S106, if the current position is larger than or equal to the preset position, taking the appearance area as the normal station of the user to be tested.
If the current value is less than the preset value, the user is not taken as the normal station of the user to be tested.
In this embodiment, the track points of the user to be tested are generated into a clustering region through a clustering algorithm, and the user to be tested, which appears in the clustering region for many times within a certain time, is identified as the user to be tested at the regular station of the region. Due to the fact that the activity ranges of the users to be tested on the working days and the non-working days are obviously different, the reporting time distinguishes the working days from the non-working days, and therefore the region division is more accurate.
Example two
Based on the above embodiment, the present embodiment performs classification and table building according to information of province, city, administrative district, etc. where the user to be tested is located and the number of times that the user to be tested appears in each area, so as to implement classification information management on a large number of frequent population places, as shown in fig. 2, including the following steps:
s201, communication information reported by a mobile terminal of a user to be tested and corresponding reporting time are obtained.
S202, determining the position information of the user to be detected based on the communication information.
And S203, generating one or more track points of the user to be detected based on the position information and the reporting time.
S2041, if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested. Meanwhile, the track points appearing in the lodging time period are clustered, and the obtained region is used as the lodging region of the user to be tested.
And S2042, if the reporting time is a non-working day, clustering the track points, and taking the clustered area as a living area of the user to be tested.
S205, creating a blank partition table, wherein the blank partition table comprises partition fields, and the partition fields comprise dates, provinces, cities and/or administrative districts.
In this step, specifically, a blank partition table is established for the dimension according to the three-level administrative regions of province and city administrative regions, and province codes, city codes, administrative region codes and dates day are used as partition fields. Alternatively, in the table building, the partition fields may be ranked with the date day as the top partition field and province code, city code, and administrative district code as the secondary partition fields.
S2061, in each administrative district, generating one or more district numbers based on the appearance area of one or more users to be tested, wherein each district number is used for representing a work area, a lodging area or a living area.
Illustratively, in an administrative district a, the appearance areas of the admin1 of the user to be tested are a work area G001, a lodging area Z001 and a living area S001, and the appearance areas of the admin2 of the user to be tested are a work area G002, a lodging area Z002 and a living area S001, then one or more area numbers in the administrative district a include: g001, G002, Z001, Z002 and S001.
And S2062, under the directory of each area number, judging whether the user to be detected is present in the area at intervals of a second preset time interval.
S2063, if present, generates the first number under the region number. If not, a second number is generated under the region number.
And S2064, after a third preset time interval, taking a numeric string formed by one or more first numbers and/or second numbers as an area field of the user to be tested in the directory of the area number.
In steps S2062-S2064, for example, the second preset time interval is one day, the first number is 1, the second number is 0, in the area with the area number G1, the track point of the first day of the user admin1 appears in the G1 area, which is marked as 1, the track point of the second day does not appear in the G1 area, which is marked as 0, the track point of the third day appears in the G1 area, which is marked as 1, the number string generated in 3 days is 101 … …, and so on, and a new number is added after the number string every second preset time interval. This numeric string is saved as an area field in the area number G1 directory.
Illustratively, setting the string length to 1000, it may record that the user admin1 appears in the G1 region within 1000 days. When another user to be tested admin2 appears, a numeric string corresponding to admin2 is generated based on the above manner, and is stored under the directory of the area number G1, and so on. One or more numeric strings can be stored in the directory of the area number G1, and each numeric string is used for describing the condition that a user to be tested appears in the G1 area within the days corresponding to the length of the numeric string.
S2065, filling the area number and the area field in the blank partition table to generate a resident information table of the user to be tested.
And filling the area numbers generated in the previous step and one or more corresponding area fields under each area number into a blank partition table, wherein the generated user resident information table comprises provinces, cities and administrative districts, each administrative district comprises one or more appearing areas, and each appearing area comprises one or more area fields for describing the appearance of one or more users in the appearing area.
After the resident information table of the user to be tested is generated, the statistics of the information of the resident population based on the resident information table of the user to be tested comprises the following steps:
and counting the number of the first digits appearing in the area field of each area number in the resident information table of the user to be detected in a fourth preset time interval, and generating a counting result of the user to be detected based on the fourth preset time interval, the number of the fields and a preset counting standard.
In the statistical process, for example, in the area with the area number G1, the area field of the user admin1 is the number string 1000001111011, and if the fourth time interval is 7 days, the number of times of occurrence of the first number 1 in the last 7 days is 6.
Based on a preset statistical criterion that the population which appears for 4 days or more in nearly 7 days and is considered as the resident population of the region in the week, a first statistical result is generated as follows: user admin1 was designated as the resident population of the week for the region with region number G1.
Or
And inquiring the area number of the user to be detected in the resident information table of the user to be detected based on the identity information of the user to be detected in a fifth preset time interval. And determining a working area, a lodging area and/or a living area of the user to be tested based on the area number, and taking the working area, the lodging area and/or the living area as a resident area of the user to be tested in the province, the city and the administrative district within a fifth preset time interval.
According to the embodiment, the recording is carried out by using a character string splicing 0 or 1 mode aiming at the personnel residence condition of a certain area, the storage space is reduced, the residence condition of a user to be detected in a certain area for thousands of days can be recorded through one field and one record, and the storage and data processing efficiency is improved.
EXAMPLE III
As shown in fig. 3, a third embodiment of the present invention provides a frequent-premises general survey system 3 for a user to be tested, which includes the following modules:
a first obtaining module 301, configured to obtain communication information reported by a mobile terminal of a user to be tested and corresponding reporting time;
a calculating module 302, configured to determine location information of the user to be tested based on the communication information;
a trace point generating module 303, configured to generate one or more trace points of the user to be detected based on the location information and the reporting time;
and the clustering module 304 is used for clustering and analyzing one or more track points to generate one or more track point aggregation areas, and the track point aggregation areas are used as appearance areas of the users to be detected. The module is further configured to: if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested; meanwhile, clustering track points appearing in the lodging time period, and taking the obtained area as the lodging area of the user to be detected; and if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested.
The judging module 305 performs cluster analysis on one or more track points, takes the region of track point aggregation as the appearance region of the user to be detected, and if the appearance region is larger than or equal to the appearance region, takes the appearance region as the regular station of the user to be detected.
As in fig. 4, in an alternative embodiment, further comprising:
a creation module 306 for creating a white space table comprising partition fields including dates, provinces, cities and/or administrative districts.
Further comprising:
a region number module 307, configured to generate one or more region numbers in each administrative district based on an occurrence region of one or more users to be tested, where each region number is used to represent a work region, a lodging region, or a living region;
the area field generation module 308 is configured to determine, every second preset time interval, whether the user to be detected appears in the area under the directory of each area number; if so, generating a first number under the area number; if not, generating a second number under the area number; and taking a number string formed by one or more first numbers and/or second numbers as an area field of the user to be tested under the catalog of the area number after a third preset time interval.
And an information table generating module 309, configured to fill the area number and the area field in the blank partition table, so as to generate a resident information table of the user to be tested.
In an alternative embodiment, as shown in fig. 4, further comprising:
the demographic module 310 is configured to count, within a fourth preset time interval, the number of the first number appearing in the area field of each area number in the resident information table of the user to be tested; and generating a statistical result of the user to be tested based on the fourth preset time interval, the field number and a preset statistical standard.
A resident area counting module 311, configured to query, in a fifth preset time interval, an area number of the user to be tested in the resident information table of the user to be tested based on the identity information of the user to be tested; and determining a working area, a lodging area and/or a living area of the user to be tested based on the area number, and taking the working area, the lodging area and/or the living area as a resident area of the user to be tested in the province, the city and the administrative district within a fifth preset time interval.
The frequent-station general survey system for the user to be tested can execute the frequent-station general survey method for the user to be tested provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 5 is a schematic structural diagram of a server according to a fourth embodiment of the present invention, and as shown in fig. 5, the server includes a processor 401, a memory 402, an input device 403, and an output device 404; the number of the processors 401 in the server may be one or more, and one processor 401 is taken as an example in the figure; the processor 401, the memory 402, the input device 403 and the output device 404 in the device/terminal/server may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The memory 402, as a computer-readable storage medium, may be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the frequent-premises census method for the user to be tested in the embodiment of the present invention (for example, the first obtaining module 501, the calculating module 502, and the like in the foregoing embodiment). The processor 401 executes various functional applications and data processing of the device/terminal/server by running the software programs, instructions and modules stored in the memory 402, that is, the above-mentioned customer premise census system to be tested is realized.
The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 402 may further include memory located remotely from the processor 401, which may be connected to the device/terminal/server through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 403 may be used to receive input numeric or character information and generate key signal inputs related to the device/terminal/server's user settings under test and function control. The output device 404 may include a display device such as a display screen.
The server in the embodiment of the invention finds out the base station with wrong longitude and latitude through data analysis and calculation, and performs targeted check, thereby achieving the effects of improving the overhaul efficiency and saving resources.
EXAMPLE five
The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for census of a frequent premises of a user to be tested, where the method includes:
acquiring communication information reported by a mobile terminal of a user to be tested and corresponding reporting time;
determining the position information of the user to be detected based on the communication information;
generating one or more track points of the user to be tested based on the position information and the reporting time;
performing clustering analysis on one or more track points to generate one or more track point aggregation areas, and taking the track point aggregation areas as appearance areas of the users to be detected;
judging whether the frequency of the user to be detected in the appearance area within a first preset time interval is greater than or equal to a first preset threshold value or not;
and if the current position is larger than or equal to the preset position, taking the appearance area as the normal station of the user to be detected.
The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted over any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer under test, partly on the user's computer under test, as a stand-alone software package, partly on the user's computer under test and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user computer under test through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A common station census method for a user to be tested is characterized by comprising the following steps:
acquiring communication information reported by a mobile terminal of a user to be tested and corresponding reporting time;
determining the position information of the user to be detected based on the communication information;
generating one or more track points of the user to be tested based on the position information and the reporting time;
performing clustering analysis on one or more track points to generate one or more track point aggregation areas, and taking the track point aggregation areas as appearance areas of the users to be detected;
judging whether the frequency of the user to be detected in the appearance area within a first preset time interval is greater than or equal to a first preset threshold value or not;
and if the current position is larger than or equal to the preset position, taking the appearance area as the normal station of the user to be detected.
2. The customer premises general survey method to be tested according to claim 1, wherein the reporting time includes a working day and a non-working day, and the working day time includes a working time period and a lodging time period, then the clustering analysis is performed on one or more of the track points to generate one or more track point aggregation areas, and the track point aggregation areas are used as appearance areas of the customer to be tested, including:
if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested;
meanwhile, clustering track points appearing in the lodging time period, and taking the obtained area as the lodging area of the user to be detected;
and if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested.
3. The customer premises general survey method to be tested according to claim 1, wherein the clustering analysis is performed on one or more of the track points to generate one or more track point aggregation regions, and after the track point aggregation regions are used as appearance regions of the customer to be tested, the method further comprises:
creating a white space table, wherein the white space table comprises partition fields, and the partition fields comprise dates, provinces, cities and/or administrative regions.
4. The customer premises census method to be tested according to claims 2 and 3, wherein the communication information further includes identity information of the customer to be tested, and after the creating the blank partition table, further includes:
in each administrative district, generating one or more district numbers based on the appearance area of one or more users to be tested, wherein each district number is used for representing a work area, a lodging area or a living area;
judging whether the user to be detected appears in the area or not at intervals of second preset time under the directory of each area number;
if so, generating a first number under the area number;
if not, generating a second number under the area number;
taking a number string formed by one or more first numbers and/or second numbers as an area field of the user to be tested under the catalog of the area number after a third preset time interval;
and filling the identity information, the area number and the area field of the user to be detected in the blank partition table to generate a resident information table of the user to be detected.
5. The method for frequent premise screening of a user to be tested according to claim 4, wherein after the blank partition table is filled with the identity information, the area number and the area field of the user to be tested to generate a resident information table of the user to be tested, the method further comprises:
counting the number of the first digits appearing in the area field of each area number in the resident information table of the user to be tested within a fourth preset time interval;
and generating a statistical result of the user to be tested based on the fourth preset time interval, the field number and a preset statistical standard.
6. The method for frequent premise screening of a user to be tested according to claim 4, wherein after the blank partition table is filled with the identity information, the area number and the area field of the user to be tested to generate a resident information table of the user to be tested, the method further comprises:
inquiring the area number of the user to be detected in the resident information table of the user to be detected based on the identity information of the user to be detected in a fifth preset time interval;
and determining a working area, a lodging area and/or a living area of the user to be tested based on the area number, and taking the working area, the lodging area and/or the living area as a resident area of the user to be tested in the province, the city and the administrative district within a fifth preset time interval.
7. A customer premises census system to be tested is characterized by comprising:
the first acquisition module is used for acquiring the communication information reported by the mobile terminal of the user to be tested and the corresponding reporting time;
the computing module is used for determining the position information of the user to be detected based on the communication information;
the track point generating module is used for generating one or more track points of the user to be detected based on the position information and the reporting time;
the clustering module is used for carrying out clustering analysis on one or more track points to generate one or more track point aggregation areas, and the track point aggregation areas are used as appearance areas of the user to be detected;
and the judging module is used for carrying out cluster analysis on one or more track points, taking the region of track point aggregation as the appearance region of the user to be detected, and if the appearance region is larger than or equal to the appearance region, taking the appearance region as the regular station of the user to be detected.
8. The customer premises census system to be tested according to claim 7, wherein the reporting time includes a working day and a non-working day, and the working day time includes a working time period and an accommodation time period, then the clustering module is further configured to:
if the reporting time is the working day, clustering the track points appearing in the working time period, and taking the obtained area as the working area of the user to be tested;
meanwhile, clustering track points appearing in the lodging time period, and taking the obtained area as the lodging area of the user to be detected;
and if the reporting time is a non-working day, clustering the track points, and taking the clustered area as the living area of the user to be tested.
9. A server comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program implements the customer premises screening method according to any of claims 1-6.
10. A terminal readable storage medium having a program stored thereon, wherein the program, when executed by a processor, is capable of implementing the customer premises screening method of any of claims 1-6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569978A (en) * | 2021-08-05 | 2021-10-29 | 北京红山信息科技研究院有限公司 | Travel track identification method and device, computer equipment and storage medium |
| CN114363823A (en) * | 2021-05-26 | 2022-04-15 | 科大国创云网科技有限公司 | Population density monitoring method and system based on MR (magnetic resonance) permanent station and building outline |
| CN116033354A (en) * | 2022-12-16 | 2023-04-28 | 中科世通亨奇(北京)科技有限公司 | Analysis method and system for user position attribute information |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103116696A (en) * | 2013-01-16 | 2013-05-22 | 上海美慧软件有限公司 | Personnel resident site recognizing method based on sparsely sampled mobile phone locating data |
| US20140100900A1 (en) * | 2006-03-17 | 2014-04-10 | Raj V. Abhyanker | Short-term residential spaces in a geo-spatial environment |
| CN104252527A (en) * | 2014-09-02 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Method and device for determining resident point information of mobile subscriber |
| CN106407277A (en) * | 2016-08-26 | 2017-02-15 | 北京车网互联科技有限公司 | Internet of vehicles data-based attribute analysis method for vehicle owner parking point after being clustered |
| CN107547633A (en) * | 2017-07-27 | 2018-01-05 | 腾讯科技（深圳）有限公司 | Processing method, device and the storage medium of a kind of resident point of user |
| CN108966265A (en) * | 2018-06-01 | 2018-12-07 | 北京万相融通科技股份有限公司 | A kind of method and its system of station passenger flow forecast and statistical analysis |
| CN110493706A (en) * | 2019-06-27 | 2019-11-22 | 中国移动通信集团海南有限公司 | The permanent residence of mobile subscriber determines method, apparatus and computer equipment |
| CN111510859A (en) * | 2020-05-25 | 2020-08-07 | 北京红山信息科技研究院有限公司 | User track positioning method, system, server and storage medium |
| CN111694914A (en) * | 2020-06-08 | 2020-09-22 | 北京百度网讯科技有限公司 | User resident area determining method and device |
-
2020
- 2020-11-03 CN CN202011211124.6A patent/CN112364907A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140100900A1 (en) * | 2006-03-17 | 2014-04-10 | Raj V. Abhyanker | Short-term residential spaces in a geo-spatial environment |
| CN103116696A (en) * | 2013-01-16 | 2013-05-22 | 上海美慧软件有限公司 | Personnel resident site recognizing method based on sparsely sampled mobile phone locating data |
| CN104252527A (en) * | 2014-09-02 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Method and device for determining resident point information of mobile subscriber |
| CN106407277A (en) * | 2016-08-26 | 2017-02-15 | 北京车网互联科技有限公司 | Internet of vehicles data-based attribute analysis method for vehicle owner parking point after being clustered |
| CN107547633A (en) * | 2017-07-27 | 2018-01-05 | 腾讯科技（深圳）有限公司 | Processing method, device and the storage medium of a kind of resident point of user |
| CN108966265A (en) * | 2018-06-01 | 2018-12-07 | 北京万相融通科技股份有限公司 | A kind of method and its system of station passenger flow forecast and statistical analysis |
| CN110493706A (en) * | 2019-06-27 | 2019-11-22 | 中国移动通信集团海南有限公司 | The permanent residence of mobile subscriber determines method, apparatus and computer equipment |
| CN111510859A (en) * | 2020-05-25 | 2020-08-07 | 北京红山信息科技研究院有限公司 | User track positioning method, system, server and storage medium |
| CN111694914A (en) * | 2020-06-08 | 2020-09-22 | 北京百度网讯科技有限公司 | User resident area determining method and device |
Non-Patent Citations (2)
| Title |
|---|
| HAN SU 等: "Personalized Route Description Based On Historical Trajectories", 《CONFERENCE: THE 28TH ACM INTERNATIONAL CONFERENCE》, pages 1 - 10 * |
| 韩卓 等: "基于时空聚类的职住分析研究", 《计算机与数字工程》, vol. 48, no. 3, pages 596 - 602 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114363823A (en) * | 2021-05-26 | 2022-04-15 | 科大国创云网科技有限公司 | Population density monitoring method and system based on MR (magnetic resonance) permanent station and building outline |
| CN114363823B (en) * | 2021-05-26 | 2023-09-19 | 科大国创云网科技有限公司 | Population density monitoring method and system based on MR (magnetic resonance) resident places and building outlines |
| CN113569978A (en) * | 2021-08-05 | 2021-10-29 | 北京红山信息科技研究院有限公司 | Travel track identification method and device, computer equipment and storage medium |
| CN116033354A (en) * | 2022-12-16 | 2023-04-28 | 中科世通亨奇(北京)科技有限公司 | Analysis method and system for user position attribute information |
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