CN117479120A - Mobile phone signaling data processing method and device, terminal equipment and storage medium - Google Patents
Mobile phone signaling data processing method and device, terminal equipment and storage medium Download PDFInfo
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Abstract
The present disclosure provides a method and an apparatus for processing mobile phone signaling data, a terminal device, and a storage medium, where the method includes: extracting signaling data corresponding to each user according to the user identification in the mobile phone signaling data; determining time period stay point data of each user according to the signaling data corresponding to each user; determining daily stay point data of each user according to the time period stay point data of each user; determining month stay point data of each user according to the day stay point data of each user; the time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes. The mobile phone signaling data processing method and device, the terminal equipment and the storage medium can reduce the processing amount of mobile phone signaling data.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing signaling data of a mobile phone, a terminal device, and a storage medium.
Background
The mobile phone signaling data is communication data between a mobile phone user and a transmitting base station or a micro station, and the data field of the mobile phone signaling data contains information such as time, position and the like. Compared with the traditional data, the mobile phone signaling big data has the characteristics of continuous time, complete space, passive acquisition and real non-perception, becomes a natural collector of human activity big data after highly irreversible anonymous security treatment, and is widely applied to the fields of urban population space-time distribution, trip OD analysis, user portrait generation and the like.
At present, a large amount of invalid data exists in mobile phone signaling data, different processing needs to be carried out on the mobile phone signaling data according to the requirements of various application occasions, and the data processing amount is large.
Disclosure of Invention
The invention aims to provide a method and a device for processing mobile phone signaling data, terminal equipment and a storage medium, so as to reduce the processing amount of the mobile phone signaling data.
In a first aspect of an embodiment of the present disclosure, a method for processing signaling data of a mobile phone is provided, including: extracting signaling data corresponding to each user according to the user identification in the mobile phone signaling data;
determining time period stay point data of each user according to the signaling data corresponding to each user; the time period stay point data of any user is a set of stay points passed by the any user;
determining daily stay point data of each user according to the time period stay point data of each user; the daily stay point data of any user is a set of stay points of the any user on each day;
determining month stay point data of each user according to the day stay point data of each user; the month stay point data of any user is a set of stay points of the any user in each month;
the time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
In a second aspect of the embodiments of the present disclosure, there is provided a mobile phone signaling data processing apparatus, including:
the data extraction unit is used for extracting the signaling data corresponding to each user according to the user identification in the mobile phone signaling data;
the first processing unit is used for determining time period stay point data of each user according to the signaling data corresponding to each user; the time period stay point data of any user is a set of stay points passed by the any user;
the second processing unit is used for determining daily stay point data of each user according to the time period stay point data of each user; the daily stay point data of any user is a set of stay points of the any user on each day;
the third processing unit is used for determining the month stay point data of each user according to the day stay point data of each user; the month stay point data of any user is a set of stay points of the any user in each month;
the time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
In a third aspect of the embodiments of the present disclosure, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for processing mobile phone signaling data described above when the processor executes the computer program.
In a fourth aspect of the embodiments of the present disclosure, there is provided a computer storage medium storing a computer program, which when executed by a processor, implements the steps of the above-described method for processing signaling data of a mobile phone.
The mobile phone signaling data processing method and device, the terminal equipment and the storage medium provided by the embodiment of the disclosure have the beneficial effects that:
according to the embodiment of the disclosure, the time period stay point data of each user is firstly extracted according to the mobile phone signaling data, then the daily stay point data of each user is determined on the basis of the time period stay point data of each user, and further the month stay point data of each user is determined on the basis of the daily stay point data of each user. The time period stay point data, the day stay point data and the month stay point data are respectively used for different application occasions, and basic data are provided for data statistics of each application occasion. Meanwhile, the daily stay point data of each user is determined on the basis of the time period stay point data of each user, and the monthly stay point data of each user is determined on the basis of the daily stay point data of each user.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required for the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a flow chart of a mobile phone signaling data processing method according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of a method for extracting period stay point data according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of a method for extracting daily stay point data according to an embodiment of the disclosure;
FIG. 4 is a schematic diagram of a method for extracting month stay point data according to an embodiment of the present disclosure;
fig. 5 is a block diagram of a mobile phone signaling data processing device according to an embodiment of the present disclosure;
fig. 6 is a schematic block diagram of a terminal device according to an embodiment of the present disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings.
Referring to fig. 1, fig. 1 is a flowchart of a mobile phone signaling data processing method according to an embodiment of the disclosure, where the method includes:
s101: and extracting the signaling data corresponding to each user according to the user identification in the signaling data of the mobile phone.
In this embodiment, the mobile phone signaling data includes a plurality of pieces, and each piece of mobile phone signaling data includes a unique user identifier. And extracting a plurality of pieces of mobile phone signaling data with the same user identification as the signaling data of the corresponding user so as to analyze the stay points of the corresponding user.
S102: and determining the time period stay point data of each user according to the signaling data corresponding to each user. The time period stay point data of any user is a set of stay points passed by the any user.
In this embodiment, each piece of mobile phone signaling data further includes a time stamp and location information, a location change track of a corresponding user can be determined according to the location information, for any location, a time when the user arrives at the location and a time when the user leaves the location can be determined according to the time stamp, so as to determine a residence time of the user at the location, if the residence time of the user at the location is greater than a preset duration, the location is determined to be a residence point, and correspondingly, a period corresponding to the residence point is determined according to the time when the user arrives at the location and the time when the user leaves the location.
The time slot stay point data of each user can be used for analyzing population distribution of different time slots, for example, the time slot stay point data of each user is input into the urban planning management system as basic data, and can be used for analyzing daytime population distribution, nighttime population distribution, residence population distribution, population density and the like, so that reference data is provided for urban planning.
Specific values of the preset duration, for example 10min,20min,30 min, etc., can be flexibly designed by a person skilled in the art.
S103: and determining the daily stay point data of each user according to the time period stay point data of each user. The daily stay point data of any user is a set of stay points of the any user on each day.
In this embodiment, feature data of time period stop point data of each user may be extracted, and the feature data is used as day stop point data of each user, so that a change of day stop points of the user can be reflected. Meanwhile, compared with the original time period stay point data, the characteristic data has the advantages that the data quantity is greatly reduced, and the burden of subsequent data processing is reduced.
The daily stay point data of each user can be used for analyzing population distribution of different dates, for example, the daily stay point data is used as basic data to be input into a travel planning management system, daily people flow of each scenic spot can be counted, the travel population distribution is analyzed, and reference data are provided for travel planning.
S104: and determining the month stay point data of each user according to the day stay point data of each user. The month stay point data of any user is a set of the month stay points of the any user. The time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
In this embodiment, a method similar to step S103 may be used to extract feature data of day stop data of each user, and the feature data of day stop data is used as month stop data of each user, so as to reflect a change of month stop of the user.
The month stay point data can be used for analyzing the population distribution of quarters or years, for example, the month stay point data is used as basic data to be input into a travel planning management system, and the month stay point data can be used for analyzing the change of annual travel population distribution so as to provide reference data for travel planning.
As can be seen from the above, in the embodiment of the present disclosure, the time period stop point data of each user is first extracted according to the mobile phone signaling data, then the day stop point data of each user is determined based on the time period stop point data of each user, and further the month stop point data of each user is determined based on the day stop point data of each user. The time period stay point data, the day stay point data and the month stay point data are respectively used for different application occasions, and basic data are provided for data statistics of each application occasion. Meanwhile, the daily stay point data of each user is determined on the basis of the time period stay point data of each user, and the monthly stay point data of each user is determined on the basis of the daily stay point data of each user.
In one embodiment of the present disclosure, the signaling data corresponding to each user includes a plurality of pieces, and determining the time period stay point data of each user according to the signaling data corresponding to each user includes:
for any one of the users,
and dividing the signaling data corresponding to any user into a plurality of groups according to the date, wherein each group corresponds to the signaling data of one date.
And drawing a position scatter diagram of the corresponding date according to the time stamp and the position information in each group of signaling data.
And determining a target area according to the position scatter diagram. The target area is an area through which the corresponding user passes in the corresponding date.
Screening preset mark points in a target area, determining the preset mark points as initial bird nest positions of a cuckoo search algorithm, performing global search in the target area to obtain optimal bird nest positions, determining the optimal bird nest positions as initial cluster centers, and performing cluster analysis on the position scatter diagrams to obtain a plurality of cluster clusters in the position scatter diagrams.
The position information corresponding to the clustering center of each cluster is determined to be the position information corresponding to the stay point of one time period, the earliest time stamp in the corresponding cluster is determined to be the starting time corresponding to the stay point of the time period, and the latest time stamp in the corresponding cluster is determined to be the stop time corresponding to the stay point of the time period.
Position information, start time and stop time corresponding to the plurality of time period stay points are determined as time period stay point data of each user.
In the embodiment of the present disclosure, the preset mark point is typically a landmark building of a city, such as a building, a school, etc., and has corresponding position information.
When the time period stay point data of each user is extracted, the signaling data corresponding to each user can be divided into a plurality of groups according to the time stamp and the position information in each signaling data, each group corresponds to the signaling data of one date, and then a position scatter diagram of each date is drawn. And performing cluster analysis on the position scatter diagram, taking the cluster centers of a plurality of clusters obtained by the cluster analysis as a plurality of stay points, and further saving the position information, the starting time and the stopping time of the plurality of stay points as time period stay point data.
The clustering algorithm in the embodiment of the disclosure adopts a K-means clustering algorithm, which is simple and quick, but is easily influenced by an initial clustering center to be in local optimum. To solve this problem, in determining an initial cluster center, first, a target area is determined from each position scatter diagram, and then cluster analysis is performed in each target area. The method comprises the following steps: as shown in fig. 2, a target area is first determined according to a position scatter diagram of a certain day, if a preset mark point (for example, a point A, B, C, D in fig. 2) falls into the target area, the preset mark point is used as an initial bird nest position of a cuckoo search algorithm, global search is performed in the target area to obtain an optimal bird nest position, and the optimal bird nest position is determined as an initial cluster center. The initial clustering center obtained by the method can utilize the global searching capability of the cuckoo searching algorithm to avoid the problem that the K-means clustering algorithm falls into local optimum, and on the other hand, the preset mark point is usually the center of the travel activity of people, and the preset mark point is used as the initial bird nest position of the cuckoo searching algorithm, so that the optimum bird nest position can be found quickly.
In one embodiment of the disclosure, the determining the target area according to the location scatter diagram includes:
and connecting the points at the outer edge of the position scatter diagram to obtain a closed area, and determining the closed area as the target area.
In the disclosed embodiment, the target area is determined along the point lines of the outer edge of each position scatter plot to ensure that the target area contains all the data points in the corresponding day.
In one embodiment of the present disclosure, determining daily stay point data for each user from time period stay point data for each user includes:
and obtaining a plurality of time period stay points of each user according to the time period stay point data of each user.
The method comprises the steps of dividing a plurality of time period stay points of each user into a plurality of groups according to dates, wherein each group corresponds to a plurality of time period stay points of one day.
Dividing the time period stopping points of each day into a plurality of groups of first stopping points and a plurality of second stopping points according to the distances among the time period stopping points of each day; the distance between any period stop point in each group of the first stop points and the minimum distance point is smaller than or equal to a first preset threshold value, and the second stop points are period stop points except the first stop points in a plurality of period stop points every day; the minimum distance point of any period dwell point is the period dwell point nearest to the any period dwell point.
And combining the plurality of first stay points of each group to obtain a third stay point. The position information of any third dwell point is an average value of the position information corresponding to a plurality of first dwell points in the corresponding group, the starting time of the third dwell point is the earliest starting time in the corresponding group, and the stopping time of the third dwell point is the latest stopping time in the corresponding group.
And determining the data of the second stop point and the data of the third stop point as daily stop point data of each user.
In the embodiment of the disclosure, the date stop point data of the corresponding date is obtained by combining the time period stop point data of each date. The specific process is as follows: as shown in fig. 3, a distance between each time period stop point and an adjacent time period stop point is calculated, and for any time period stop point, if one or more time period stop points (hereinafter referred to as adjacent stop points) exist in the adjacent time period stop points, the distance between the time period stop point and any time period stop point is smaller than or equal to a first preset threshold value, any time period stop point and the adjacent stop points are used as a group of first stop points. The time period stop points in each group of the first stop points are distributed relatively densely, the average value of the position information of all the first stop points in the same group is calculated and used as the position information of a third stop point (such as point E in fig. 3), the earliest starting time in the group is used as the starting time of the third stop point, and the latest stopping time in the group is used as the stopping time of the third stop point. The data of the third stopover point (including the position information, the start time, and the stop time) is used as the feature data of all the first stopover point data in the group for generating the daily stopover point data. The earliest start time in the group is the earliest start time in the start time of all the first stopping points in the group, and the latest stop time in the group is the latest stop time in the stop time of all the first stopping points in the group.
Otherwise, if there is a period stop point, the distance between the period stop point and the adjacent period stop point is greater than the first preset threshold, the period stop point is taken as a second stop point (such as a point F in fig. 3), and the second stop point is a period stop point with relatively sparse distribution, so that the second stop point is not merged any more.
And taking the data of the second stop point and the data of the third stop point as the combined daily stop point data.
In one embodiment of the present disclosure, day stop data includes data for a plurality of day stops, the data for each day stop including location information, a start time and an end time,
determining month stay point data of each user according to the day stay point data of each user, including:
and determining the geographic area of each daily stay point according to the position information of each daily stay point.
The center of each geographic area is determined as a month stop point, the earliest starting time in the corresponding geographic area is determined as the starting time of each month stop point, and the latest stopping time in the corresponding geographic area is determined as the stopping time of each month stop point.
In the embodiment of the disclosure, the month stay point data is obtained by merging the day stay point data according to the geographic area. The geographic area may be an administrative division of each province or city, for example, the positions of a plurality of daily stay points of a certain user belong to the same geographic area, and the center of the geographic area is used as characteristic data of the plurality of daily stay points of the user to generate the monthly stay points of the user.
In some application occasions, for example, when the annual track curve of the user is counted, the stay point data only need to be accurate to a specific city, so that the annual track curve of the user can be generated according to the month stay point, namely the center of the city, and the user does not need to be positioned in a certain area of the city.
As shown in fig. 4, if the point G1 and the point G2 belong to the same geographic area, the center G of the geographic area is taken as a month stop point; the point H1 and the point H2 belong to the same geographical area, and the center H of the geographical area serves as a month stay point.
In one embodiment of the present disclosure, determining a geographic area to which each day stop belongs according to location information of each day stop includes:
for any one of the stay points of the day,
comparing the position information of any one of the stopover points with a plurality of preset geographic areas, and if the position information of any one of the stopover points is located in a certain preset geographic area, determining the geographic area as the geographic area to which the any one of the stopover points belongs.
In the embodiment of the present disclosure, the location information of a plurality of geographic areas may be preset, and if a certain day stop falls within a certain preset geographic area, the preset geographic area is determined as the geographic area to which the day stop belongs by comparing the location information of each day stop with the location information of the preset plurality of geographic areas.
In one embodiment of the disclosure, the preset marker point is determined as an initial bird nest position of a cuckoo search algorithm, and when global search is performed in a target area, an adaptive function is adopted as follows:
=/>
wherein f (x) represents an fitness function,for clustering, ->K is the number of cluster centers, < ->Data points, d is the number of data points in the cluster, and a is a preset constant.
In the embodiment of the disclosure, k-means division of mobile phone signaling data is realized by adopting a Euclidean distance measurement method, so as to obtain a fitness function, wherein a is a parameter, and a specific value is determined by repeated debugging. From the calculation formula, the greater the adaptability, the better the dividing effect.
Corresponding to the method for processing mobile phone signaling data in the above embodiment, fig. 5 is a block diagram of a mobile phone signaling data processing device according to an embodiment of the present disclosure. For ease of illustration, only portions relevant to embodiments of the present disclosure are shown. Referring to fig. 5, the mobile phone signaling data processing device 20 includes: a data extraction unit 21, a first processing unit 22, a second processing unit 23, and a third processing unit 24.
The data extraction unit 21 is configured to extract signaling data corresponding to each user according to a user identifier in the signaling data of the mobile phone.
The first processing unit 22 is configured to determine the period stay point data of each user according to the signaling data corresponding to each user. The time period stay point data of any user is a set of stay points passed by the any user.
The second processing unit 23 is configured to determine day stop point data of each user according to the time period stop point data of each user. The daily stay point data of any user is a set of stay points of the any user on each day.
And a third processing unit 24 for determining month stay point data of each user according to the day stay point data of each user. The month stay point data of any user is a set of the month stay points of the any user.
The time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
In one embodiment of the present disclosure, the signaling data corresponding to each user includes a plurality of pieces, and the first processing unit 22 is specifically configured to:
for any one of the users,
and dividing the signaling data corresponding to any user into a plurality of groups according to the date, wherein each group corresponds to the signaling data of one date.
And drawing a position scatter diagram of the corresponding date according to the time stamp and the position information in each group of signaling data.
And determining a target area according to the position scatter diagram. The target area is an area through which the corresponding user passes in the corresponding date.
Screening preset mark points in a target area, determining the preset mark points as initial bird nest positions of a cuckoo search algorithm, performing global search in the target area to obtain optimal bird nest positions, determining the optimal bird nest positions as initial cluster centers, and performing cluster analysis on the position scatter diagrams to obtain a plurality of cluster clusters in the position scatter diagrams.
The position information corresponding to the clustering center of each cluster is determined to be the position information corresponding to the stay point of one time period, the earliest time stamp in the corresponding cluster is determined to be the starting time corresponding to the stay point of the time period, and the latest time stamp in the corresponding cluster is determined to be the stop time corresponding to the stay point of the time period.
Position information, start time and stop time corresponding to the plurality of time period stay points are determined as time period stay point data of each user.
In one embodiment of the present disclosure, the first processing unit 22 is specifically configured to:
and connecting the points at the outer edge of the position scatter diagram to obtain a closed area, and determining the closed area as the target area.
In one embodiment of the present disclosure, the first processing unit 22 is further configured to:
and obtaining a plurality of time period stay points of each user according to the time period stay point data of each user. The data for any time period dwell point includes location information, start time, and end time.
The method comprises the steps of dividing a plurality of time period stay points of each user into a plurality of groups according to dates, wherein each group corresponds to a plurality of time period stay points of one day.
Dividing the time period stopping points of each day into a plurality of groups of first stopping points and a plurality of second stopping points according to the distances among the time period stopping points of each day; the distance between any period stop point in each group of the first stop points and the minimum distance point is smaller than or equal to a first preset threshold value, and the second stop points are period stop points except the first stop points in a plurality of period stop points every day; the minimum distance point of any period dwell point is the period dwell point nearest to the any period dwell point.
And combining the plurality of first stay points of each group to obtain a third stay point. The position information of any third dwell point is an average value of the position information of a plurality of first dwell points in the corresponding group, the starting time of the third dwell point is the earliest starting time in the corresponding group, and the stopping time of the third dwell point is the latest stopping time in the corresponding group.
And determining the data of the second stop point and the data of the third stop point as daily stop point data of each user.
In one embodiment of the present disclosure, the data of the day stop includes data of a plurality of day stops, and the data of each day stop includes position information, a start time, and an end time, and the second processing unit 23 is specifically configured to:
and determining the geographic area of each daily stay point according to the position information of each daily stay point.
The center of each geographic area is determined as a month stop point, the earliest starting time in the corresponding geographic area is determined as the starting time of each month stop point, and the latest stopping time in the corresponding geographic area is determined as the stopping time of each month stop point.
In one embodiment of the present disclosure, the second processing unit 23 is further configured to:
for any one of the stay points of the day,
comparing the position information of any one of the stopover points with a plurality of preset geographic areas, and if the position information of any one of the stopover points is located in a certain preset geographic area, determining the geographic area as the geographic area to which the any one of the stopover points belongs.
In one embodiment of the disclosure, the preset marker point is determined as an initial bird nest position of a cuckoo search algorithm, and when global search is performed in a target area, an adaptive function is adopted as follows:
=/>
wherein f (x) represents an fitness function,for clustering, ->K is the number of cluster centers, < ->Data points, d is the number of data points in the cluster, and a is a preset constant.
In one embodiment of the present disclosure, the discovery probability and step size in the cuckoo search algorithm are specifically:
=/>
wherein T is the maximum iteration number, T is the current iteration,for the discovery probability of this iteration, +.>For the step length of this iteration, +.>To find the maximum value of probability +.>To find the minimum value of probability, +.>Maximum value of step length, +.>Is the minimum of the step size. />、/>、/>、/>All are preset initial parameters.
In the disclosed embodiment, if a fixed discovery probability and step size are used, there are two problems: firstly, when the discovery probability is smaller and the step length is larger, the search speed is slower, and more iteration times are needed to obtain the optimal solution; second, when the discovery probability is large and the step size is small, the convergence speed is high, but the optimal solution cannot be found.
In order to solve the above problems, the embodiment adopts the variable discovery probability and step length, that is, from the larger discovery probability and step length, the discovery probability and step length are adjusted along with the increase of iteration times, so that the improved algorithm can keep good global searching capability in the early operation stage, meanwhile, the local searching is considered, the local searching is gradually enhanced in the later operation stage, the global searching is considered, the algorithm convergence speed is improved, and the situation of sinking local optimum is avoided.
Referring to fig. 6, fig. 6 is a schematic block diagram of a terminal device according to an embodiment of the present disclosure. The terminal 300 in the present embodiment as shown in fig. 6 may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processor 301, the input device 302, the output device 303, and the memory 304 communicate with each other via a communication bus 305. The memory 304 is used to store a computer program comprising program instructions. The processor 301 is configured to execute program instructions stored in the memory 304. Wherein the processor 301 is configured to invoke program instructions to perform the following functions of the modules/units in the above described device embodiments, such as the functions of the modules 21 to 22 shown in fig. 5.
It should be appreciated that in the disclosed embodiments, the processor 301 may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include read only memory and random access memory and provides instructions and data to the processor 301. A portion of memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information of device type.
In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may perform the implementation manners described in the first embodiment and the second embodiment of the method for processing mobile phone signaling data provided in the embodiments of the present disclosure, and may also perform the implementation manner of the terminal described in the embodiments of the present disclosure, which is not described herein again.
In another embodiment of the present disclosure, a computer storage medium is provided, where the computer storage medium stores a computer program, where the computer program includes program instructions, where the program instructions, when executed by a processor, implement all or part of the procedures in the method embodiments described above, or may be implemented by instructing related hardware by the computer program, where the computer program may be stored in a computer storage medium, where the computer program, when executed by the processor, implements the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The computer storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, for example, a hard disk or a memory of the terminal. The computer storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer storage medium is used for storing computer programs and other programs and data required by the terminal. The computer storage medium may also be used for temporarily storing data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working procedures of the terminal and the unit described above may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In several embodiments provided in the present application, it should be understood that the disclosed terminal and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via some interfaces or units, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purposes of the embodiments of the present disclosure.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and any equivalent modifications or substitutions will be apparent to those skilled in the art within the scope of the present disclosure, and these modifications or substitutions should be covered in the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (10)
1. The mobile phone signaling data processing method is characterized by comprising the following steps:
extracting signaling data corresponding to each user according to the user identification in the mobile phone signaling data;
determining time period stay point data of each user according to the signaling data corresponding to each user; the time period stay point data of any user is a set of stay points passed by the any user;
determining daily stay point data of each user according to the time period stay point data of each user; the daily stay point data of any user is a set of stay points of the any user on each day;
determining month stay point data of each user according to the day stay point data of each user; the month stay point data of any user is a set of stay points of the any user in each month;
the time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
2. The method for processing mobile phone signaling data according to claim 1, wherein the signaling data corresponding to each user includes a plurality of pieces, and the determining the time slot dwell point data of each user according to the signaling data corresponding to each user includes:
for any one of the users,
dividing the signaling data corresponding to any user into a plurality of groups according to the date, wherein each group corresponds to the signaling data of one date;
drawing a position scatter diagram of a corresponding date according to the time stamp and the position information in each group of signaling data;
determining a target area according to the position scatter diagram; the target area is an area which the corresponding user passes through in the corresponding date;
screening a preset mark point in the target area, determining the preset mark point as an initial bird nest position of a cuckoo search algorithm, performing global search in the target area to obtain an optimal bird nest position, determining the optimal bird nest position as an initial cluster center, and performing cluster analysis on the position scatter diagram to obtain a plurality of cluster clusters in the position scatter diagram;
determining position information corresponding to a cluster center of each cluster as position information corresponding to a time period stop point, determining the earliest time stamp in the corresponding cluster as the starting time corresponding to the time period stop point, and determining the latest time stamp in the corresponding cluster as the stopping time corresponding to the time period stop point;
position information, start time and stop time corresponding to the plurality of time period stay points are determined as time period stay point data of each user.
3. The method for processing mobile phone signaling data according to claim 2, wherein said determining a target area according to the location scatter diagram comprises:
and connecting the points at the outer edge of the position scatter diagram to obtain a closed area, and determining the closed area as the target area.
4. The method for processing mobile phone signaling data according to claim 1, wherein said determining daily stay point data of each user according to the time slot stay point data of each user comprises:
obtaining a plurality of time period stay points of each user according to the time period stay point data of each user;
dividing a plurality of time period stay points of each user into a plurality of groups according to dates, wherein each group corresponds to a plurality of time period stay points of one day;
dividing the time period stopping points of each day into a plurality of groups of first stopping points and a plurality of second stopping points according to the distances among the time period stopping points of each day; the distance between any period stop point in each group of the first stop points and the minimum distance point is smaller than or equal to a first preset threshold value, and the second stop points are period stop points except the first stop points in a plurality of period stop points every day; the minimum distance point of the stop point of any period is the stop point of the period closest to the stop point of any period;
combining the first stay points of each group to obtain a third stay point; the position information of any one of the third dwell points is an average value of the position information corresponding to the plurality of first dwell points in the corresponding group, the starting time of the third dwell point is the earliest starting time in the corresponding group, and the stopping time of the third dwell point is the latest stopping time in the corresponding group;
and determining the data of the second stop point and the data of the third stop point as daily stop point data of each user.
5. The method of claim 1, wherein the daily dwell point data includes data of a plurality of daily dwell points, the data of each daily dwell point including position information, a start time and an end time,
the determining the month stay point data of each user according to the day stay point data of each user comprises the following steps:
determining the geographic area of each day stop point according to the position information of each day stop point;
the center of each geographic area is determined as a month stop point, the earliest starting time in the corresponding geographic area is determined as the starting time of each month stop point, and the latest stopping time in the corresponding geographic area is determined as the stopping time of each month stop point.
6. The method for processing mobile phone signaling data according to claim 5, wherein determining the geographic area to which each of the stopover points belongs according to the location information of each of the stopover points comprises:
for any one of the stay points of the day,
comparing the position information of any one of the stopover points with a plurality of preset geographic areas, and if the position information of any one of the stopover points is located in a certain preset geographic area, determining the geographic area as the geographic area to which the any one of the stopover points belongs.
7. The method for processing mobile phone signaling data according to claim 2, wherein the predetermined marker point is determined as an initial bird nest position of a cuckoo search algorithm, and the fitness function adopted when global search is performed in the target area is:
=/>
wherein f (x) represents an fitness function,for clustering, ->K is the number of cluster centers, < ->Data points, d is the number of data points in the cluster, and a is a preset constant.
8. A device for processing signaling data of a mobile phone, comprising:
the data extraction unit is used for extracting the signaling data corresponding to each user according to the user identification in the mobile phone signaling data;
the first processing unit is used for determining time period stay point data of each user according to the signaling data corresponding to each user; the time period stay point data of any user is a set of stay points passed by the any user;
the second processing unit is used for determining daily stay point data of each user according to the time period stay point data of each user; the daily stay point data of any user is a set of stay points of the any user on each day;
the third processing unit is used for determining the month stay point data of each user according to the day stay point data of each user; the month stay point data of any user is a set of stay points of the any user in each month;
the time period stay point data, the day stay point data and the month stay point data are respectively used for generating target stay point data corresponding to data purposes.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A computer storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
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