CN112541013A - Mobile signaling big data-based due graduate slot hopping frequency analysis method - Google Patents
Mobile signaling big data-based due graduate slot hopping frequency analysis method Download PDFInfo
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Abstract
The invention provides a graduate groove-skipping frequency analysis method based on mobile signaling big data, which is characterized in that by means of mobile signaling containing abundant information, a design caliber and an algorithm are used for dynamically and effectively mining and analyzing the living working condition after graduate leaving school, the data source is reliable, the reliability of an analysis result is high, and reliable basis can be provided for making decisions by related management departments.
Description
Technical Field
The invention belongs to the technical field of mobile big data mining and application, and particularly relates to an due graduate slot hopping frequency analysis method based on mobile signaling big data.
Background
College graduates are one of social employment groups with extreme particularity, the current situation of jumping of research graduates not only provides a theoretical framework for exploring how to 'call and reserve' university graduates from the management angle of enterprise employees, but also has important guiding significance for how to orderly realize the life value of the university graduates, but at present, the research on the graduates is limited to before leaving school, the dynamic information after leaving school is not fully mastered, the information after leaving school is mostly dependent on the traditional questionnaire survey technology, the reality of data is difficult to guarantee, and the obtained analysis conclusion is not accurate enough.
Disclosure of Invention
The invention aims to provide a frequency analysis method for the due graduate groove skipping based on mobile signaling big data, which utilizes the mobile big data to dig out mobile track information of a mobile phone user so as to analyze the working and living conditions of a graduate group after leaving school.
The technical scheme of the invention is as follows:
a should end graduate jump frequency analysis method based on mobile signaling big data is characterized in that:
(1) data acquisition: acquiring the position and the access time information of the base station sector where each IMSI identification number is located by utilizing signaling data of a telecom operator, and cleaning the data;
(2) data preprocessing: carrying out interpolation compensation on missing signaling of an in-out base station;
(3) and (3) judging the residence: reading the IMSI at 21:00 to the next day 7:00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations accessed in the residence time period corresponding to the IMSI, and further counting the stay time of each base station. And determining the geographic position corresponding to the base station with the longest residence time as the residence, wherein the residence is called a daily residence. And in a natural month, the position where the daily residence is accumulated most is determined as the monthly residence.
(4) The working area judges: reading the IMSI at 7: 00-19: 00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations accessed in all the working time periods corresponding to the IMSI, and further counting the stay time of each base station. And judging the geographic position corresponding to the base station with the longest stay time as the work place of the base station, wherein the work place is called a daily work place. And in a natural month, the position where the daily workplace is accumulated and stays most is judged as the monthly workplace.
(5) Identification of the due graduates: and identifying graduates according to the age of the mobile user and the change situation of the monthly residence. The changes in places of residence of graduates are characterised by the presence of: the graduate should stay in the school dormitory between february and june of the research year, but after 7 months of graduations, the school and school will clear the dormitory of the graduate, the place of residence of the graduate will not be in the school scope, and the specific identification method for the graduate according to the characteristics is as follows: the first step is to screen IMSI sets which accord with the graduate age range; secondly, screening IMSIs in the IMSI set, wherein the residence places of the IMSIs in any one month from February to June are within the range of a designated school; and thirdly, IMSI of which the residential areas from September to November are not in the school range is screened from the results of the first two steps, and the IMSI set screened in the third step is the due graduate of the year under study.
(6) Judging the working time length of the first part of the graduate work: and analyzing the change condition of the monthly workplace of the graduates according to the workplace judging method, wherein the time interval of the change of the monthly workplace is the working time length of the graduates in one work. Beginning in July of the study year, the time from the time of collection to the place of graduate to the time of change of place is the working time of the first work of graduate.
The invention implements dynamic and effective mining analysis on the living working condition after graduation leaving school by means of mobile signaling containing rich information and design caliber and algorithm, has reliable data source and high reliability of analysis result, and can provide reliable basis for making decisions by related management departments.
Detailed Description
The specific implementation process of the invention is as follows:
step 1: data acquisition: and acquiring the position and the access time information of the base station sector where each IMSI identification number is located by utilizing signaling data of a telecom operator, and cleaning the data.
Step 2: data preprocessing: carrying out interpolation compensation on missing signaling entering and exiting a base station, and if a user only enters a certain base station time and does not leave the base station time or only leaves the certain base station time and does not enter the base station time in a statistical time period, carrying out interpolation on missing data, wherein interpolation time points are the starting time and the ending time of the statistical time period;
for example, a T user enters sector X at 21:00:00 on day 1 of 6 month, leaves sector X at 7:00:00 on day 2 of 6 month, and the T user enters sector Y at 22:00:00 on day 2 of 6 month, and leaves at 7:00: 00:00 on day 3 of 6 month, and when the information of the T user on day 2 of 6 month is collected, the time point of entering sector X and the time point of leaving sector Y are missing, so that it is necessary to interpolate the time point of entering sector X at 00:00: 00:00 on day 2 of 6 month, and the time point of leaving sector Y at 23:59:59 on day 2 of 6 month.
And step 3: and (3) judging the residence: reading the IMSI at 21:00 to the next day 7:00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations accessed in the residence time period corresponding to the IMSI, and further counting the stay time of each base station. And determining the geographic position corresponding to the base station with the longest residence time as the residence, wherein the residence is called a daily residence. And in a natural month, the position where the daily residence is accumulated most is determined as the monthly residence.
And 4, step 4: the working area judges: reading the IMSI at 7: 00-19: 00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations accessed in all the working time periods corresponding to the IMSI, and further counting the stay time of each base station. And judging the geographic position corresponding to the base station with the longest stay time as the work place of the base station, wherein the work place is called a daily work place. And in a natural month, the position where the daily workplace is accumulated and stays most is judged as the monthly workplace.
And 5: identification of the due graduates: according to the age of the mobile user and the change situation of the monthly residence, the current graduate is identified.
The changes in places of residence of graduates are characterised by the presence of: the graduates should remain in school dormitories between february and june of the research year, but after 7 months of graduations, the school students will clear their dormitories of the graduates, the places of residence of the graduates will not be within the school, and the graduates are identified according to this feature:
the first step is to screen IMSI sets which accord with the graduate age range; secondly, screening IMSIs in the IMSI set, wherein the residence places of the IMSIs in any one month from February to June are within the range of a designated school; and thirdly, IMSI of which the residential areas from September to November are not in the school range is screened from the results of the first two steps, and the IMSI set screened in the third step is the due graduate of the year under study.
One specific identification method is as follows:
firstly, screening IMSI sets which are in line with the graduation age range between 21 and 30 years, wherein the age range basically covers all graduates from the graduation of the subject to the doctor graduation, and the IMSI sets can be subdivided for distinguishing graduates with different scholars, such as 21 to 24 years of the graduates of the subject, and 25 to 30 years of the graduates of the research students and the graduates of the school students;
secondly, screening the IMSI in the IMSI set, wherein the residence place of the IMSI in any month of April or May is within the range of a designated school;
and finally, IMSI of which the residence areas in September and October are not within the school range is screened in the results of the first two steps, and the IMSI set screened in the three steps is the due graduate of the year.
Step 6: judging the working time length of the first part of the graduate work: and analyzing the change condition of the monthly workplace of the graduates according to the workplace judging method, wherein the time interval of the change of the monthly workplace is the working time length of the graduates in one work. Starting from the seventh month of the research year, the time from the first collection to the first change of the working place of the graduates is the working time of the first work of the graduates, and the time interval is the working time of the first work of the graduates, so that whether the working frequency of the graduate group is frequent or not can be judged according to the screened average working time of the graduates.
By the method, not only the working time of the first part of the graduate can be calculated, but also the working time of each part of the graduate can be calculated, and the working time of the individual graduate is not recommended to be researched when the method is applied, because if the graduate goes on a business trip or has relatively long holiday time (such as more than 3 days), the working time is influenced by the caliber in the judgment method of the monthly working place or the working place is changed, so that the judgment result is influenced, and therefore the average working time of the first part of the graduate which should be ended is recommended to be calculated from the group of the graduates. In addition, according to the method, graduate groups can be subdivided according to the gender and the school calendar and used for researching whether the frequency of jumping the groove is related to the school calendar and the gender.
Claims (6)
1. A graduate slot hopping frequency analysis method based on mobile signaling big data is characterized in that:
(1) data acquisition: acquiring the position and the access time information of the base station sector where each IMSI identification number is located by utilizing signaling data of a telecom operator, and cleaning the data;
(2) data preprocessing: carrying out interpolation compensation on missing signaling of an in-out base station;
(3) and (3) judging the residence: reading the IMSI at 21:00 to the next day 7:00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations visited in the residence time period corresponding to the IMSI, further counting the residence time of each base station, and determining the geographic position corresponding to the base station with the longest residence time as the residence place, wherein the residence place is called a daily residence place; in a natural month, the position where the daily residence is accumulated and stays most is judged as the monthly residence;
(4) the working area judges: reading the IMSI at 7: 00-19: 00, according to the track data obtained in the data acquisition step, establishing a statistical table of the information of the base stations accessed in all the working time periods corresponding to the IMSI, further counting the stay time of each base station, and determining the geographical position corresponding to the base station with the longest stay time as the working place of the base station, wherein the working place is called a daily working place; in a natural month, the position where the daily workplace is accumulated and stays most is judged as the monthly workplace;
(5) identification of the due graduates: the first step is to screen IMSI sets which accord with the graduate age range; secondly, screening IMSIs in the IMSI set, wherein the residence places of the IMSIs in any one month from February to June are within the range of a designated school; thirdly, IMSI of which the residential areas from September to November are not in the school range is screened from the results of the first two steps, and the IMSI set screened in the third step is the due graduate of the year under study;
(6) judging the working time length of the first part of the graduate work: according to the working place judging method, the change situation of the graduate monthly working place is analyzed, starting from the seventh month of the research year, from the time of first collection to the working place of the graduate to the time of first change of the working place, and the time interval is the working time of the first work of the graduate.
2. The method of claim 1 for frequency analysis of due graduate slot hopping based on big data of mobile signaling, characterized in that: the interpolation compensation in the step (3) means that if the user only enters a certain base station time and does not leave the base station time or only leaves the certain base station time and does not enter the base station time in the statistical time period, missing data is interpolated, and the interpolation time points are the starting time and the ending time of the statistical time period.
3. The method of claim 1 for frequency analysis of due graduate slot hopping based on big data of mobile signaling, characterized in that: the graduate age range in the first screening of step (5) is between 21 and 30 years of age.
4. The method of claim 1 for frequency analysis of due graduate slot hopping based on big data of mobile signaling, characterized in that: the time selection in the second step of screening in the step (5) can be two months from February to June, and the purpose of doing so is to reduce the calculated amount as much as possible on the basis of ensuring the accuracy.
5. The method of claim 1 for frequency analysis of due graduate slot hopping based on big data of mobile signaling, characterized in that: the time selection in the third step of the step (5) can be at least two months from September to November, and the selection time is continuous in time, so that the calculation amount is reduced as much as possible on the basis of ensuring the accuracy.
6. The method of claim 1 for frequency analysis of due graduate slot hopping based on big data of mobile signaling, characterized in that: the specific implementation method of the time interval of the monthly workplace change in the step (6) is that the working places of all graduates are identified from July, if the working place of the next month is not changed, the working time is added by one month in an accumulated mode, if the working place is changed, the work is considered to be changed, and the accumulated month number before the work is changed is the working time of one work.
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