CN112561759A - Graduate going dynamic monitoring method based on mobile signaling big data - Google Patents
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Abstract
The invention provides a graduate going-to-dynamic monitoring 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 the graduate departs from school, the data source is reliable, the reliability of an analysis result is high, and a 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 a mobile signaling big data-based graduation-oriented analysis method.
Background
College graduates as a group with advanced knowledge storage directly influence the economic development, the safety and stability of society and graduates, but at present, researches on graduates are mostly limited before leaving school, the dynamic information after leaving school is not fully mastered, the information after leaving school is mastered by traditional questionnaire survey technologies, 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 university graduate going direction analysis method based on mobile signaling big data, which is used for analyzing the working and living conditions of a graduate group after leaving the school by utilizing the mobile track information of a mobile phone user contained in the mobile big data.
The technical scheme of the invention is as follows:
a graduate going 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) Graduate identification: the graduates are identified according to the age of the mobile user and the change of the monthly residence. The changes in places of residence of graduates are characterised by the presence of: the students should stay in school dormitories between February and June, but after 7 months of graduation, the school will clear the dormitories of graduates, the residence places of the graduates will not belong to the school, and the specific identification method for the graduates 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, selecting IMSIs of which the residential areas from September to November are not in the school range from the results of the first two steps, wherein the IMSI set selected in the third step is a graduate.
(6) Graduate residence identification and residence migration changes: according to the method for judging the monthly residence, the monthly residence of the graduates is identified, and the migration change of the residence of the graduates is analyzed and judged.
(7) Graduate job identification and job migration changes: according to the judgment method of the monthly workplace, the workplace of the graduate in each month is identified, and the migration change condition of the workplace of the graduate is analyzed and judged.
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, cleaning the data, deleting repeated data and ensuring the consistency of 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 a certain 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 a certain IMSI is 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: graduate identification: 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 students should stay in school dormitories between February and June, but after 7 months of graduation, the school will clear the dormitories of graduates, the residence places of the graduates will not be in the scope of the school, and the graduates are identified according to the characteristics:
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 area of the IMSI set is within the range of a designated school in at least one month from February to June; thirdly, selecting IMSIs in the residential areas from September to November which are not in the school range from September to November from the results of the first two steps; the IMSI set screened by the three steps is a graduate.
One specific identification method is as follows:
first, a set of IMSIs meeting a graduate age range between 21 and 30 years old is screened, the age range covers substantially all graduates from the home graduate to the doctor graduate, and graduates of different scholars can be subdivided, e.g., 21-24 years old are home graduates, 25-30 years old are graduates of the research and past scholars.
Secondly, the IMSIs in the specified school range in the residence areas of any two months from February to June are screened from the IMSI set, so that the calculation amount is reduced as much as possible on the basis of ensuring the accuracy.
And finally, selecting IMSIs with residence areas which are not in the school range for at least two consecutive months from September to November in the results of the first two steps, wherein the IMSI set selected in the three steps is a graduate. The aim of this is also to minimize the number of calculations while maintaining accuracy.
Step 6: graduate residence identification and residence migration changes: according to the judgment method of the monthly residence, the residence and migration conditions of graduates with different sexes and different school calendars are analyzed.
Besides analyzing the living situation and living migration situation of the graduate population, the graduate population can be divided into different school calendars according to the division of the school calendars according to the age groups, and then the living and living migration situations of different populations are analyzed.
And 7: graduate job identification and job migration changes: according to the judgment method of the monthly workplace, the workplaces and the migration situations of graduates with different genders and different school calendars are analyzed.
Besides analyzing the work place distribution and work place migration conditions of the graduate population, the work place distribution and migration conditions of different groups can be further analyzed according to the division of the academic calendar according to the age groups;
furthermore, in the identification of graduate workplaces and the migration analysis of workplaces, the employment and industry analysis of graduates of colleges and universities can be carried out according to the industry gathering characteristics of the graduate workplaces. If a certain area is a gathering place for the IT industry, graduates working at the place can be considered to be engaged in the IT industry work.
Claims (8)
1. A university graduate going 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 each 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, 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 of the base station, wherein the residence place is called a daily residence place, and the position where the daily residence place is accumulated and stays most in a natural month is determined as a monthly residence place;
(4) the working area judges: reading each IMSI is 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 geographic 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, and the position with the largest accumulated stay time in the daily working place in a natural month is determined as a monthly working place;
(5) graduate identification: 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 area of the IMSI set is within the range of a designated school in at least one month from February to June; thirdly, selecting IMSIs in the residential areas from September to November which are not in the school range from September to November from the results of the first two steps; the IMSI set screened out by the three steps is a graduate;
(6) graduate residence identification and residence migration changes: identifying the monthly residence of the graduates according to the judgment method of the monthly residence, and further analyzing and judging the migration change condition of the residence of the graduates;
(7) graduate job identification and job migration changes: according to the judgment method of the monthly workplace, the workplace of the graduate in each month is identified, and the migration change condition of the workplace of the graduate is analyzed and judged.
2. The mobile signaling big data based graduate going analysis method of claim 1, 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 mobile signaling big data based graduate going analysis method of claim 1, characterized in that: the graduate age range in the first screening of step (5) is between 21 and 30 years of age.
4. The mobile signaling big data based graduate going analysis method of claim 1, characterized in that: and (5) selecting IMSIs with residence areas within a designated school range in any two months from February to June in the second screening step, wherein the purpose of the IMSIs is to reduce the calculated amount as much as possible on the basis of ensuring the accuracy.
5. The mobile signaling big data based graduate going analysis method of claim 1, characterized in that: and (5) selecting IMSIs with residence places which are not within the school range from September to November for at least two months from the September to the November in the third screening step, wherein the selection time is continuous, and the purpose of reducing the calculated amount as much as possible on the basis of ensuring the accuracy is achieved.
6. The mobile signaling big data based graduate going analysis method of claim 1, characterized in that: in the recognition of the graduate residence and the residence migration analysis in the step (6), besides the residence situation and the residence migration situation of the graduate population, the graduates are divided into groups with different academic calendars according to the age groups, and then the residence and the residence migration situations of different groups are analyzed.
7. The mobile signaling big data based graduate going analysis method of claim 1, characterized in that: in the graduate workplace identification and workplace migration analysis in the step (7), besides analyzing the general workplace distribution and workplace migration conditions of the graduates, the graduates are divided into groups with different scholars according to age groups, and then the workplace distribution and migration conditions of different groups are analyzed.
8. The mobile signaling big data based graduate going analysis method of claim 1, characterized in that: and (4) in the graduate workplace identification and workplace migration analysis in the step (7), performing employment and industry analysis on graduates of colleges and universities according to industry gathering characteristics of the graduate workplace.
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