CN111090642A - Method for cleaning signaling data of mobile phone - Google Patents
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Abstract
The invention relates to the field of big data analysis, and aims to provide a method for cleaning mobile phone signaling data. The method comprises the following steps: collecting various signaling events generated by a user mobile phone in a mobile phone communication network, and sequencing the signaling time of the user according to a timestamp; cutting a user trip chain, and taking the cut trip chain as a minimum research unit; cleaning each trip chain to remove invalid data based on the position change rule of the trip chain; and simplifying the acquired trip chain. The invention can effectively clear invalid signaling data in the trip chain, realizes the purpose of simplifying the trip chain and is convenient for subsequent data analysis. The speed factor is also considered while the time is considered, so that the divided user trip chain is more in line with the actual trip of the user and is more beneficial to cleaning of signaling data compared with the trip chain divided in the prior art. The method has high automation degree and strong applicability, and is suitable for cleaning the signaling data of the mobile phone with large sample size and a large range of various different characteristics.
Description
Technical Field
The invention belongs to the field of big data analysis, and particularly relates to a data cleaning technology based on a mobile communication signaling event.
Background
The mobile phone signaling data has large sample amount, objective and comprehensive data, no obvious tendency in sampling, and strong time-space continuity of the data, can observe the whole process of traffic travel, and cannot be compared by any other data source. However, due to reasons such as signal bouncing and drifting among base stations, the mobile phone signaling data has a lot of invalid and wrong data, so that the original data cannot truly reflect the travel track of the user. Therefore, it is the key to apply the signaling data of the mobile phone to quickly identify the invalid data and remove the invalid data.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides a method for cleaning mobile phone signaling data. The method aims to clean the disordered mobile phone signaling full data so as to acquire data which can be used for subsequent data analysis.
In order to solve the technical problem, the solution of the invention is as follows:
the method for cleaning the signaling data of the mobile phone comprises the following steps:
(1) collecting various signaling events generated by a user mobile phone in a mobile phone communication network, and sequencing the signaling time of the user according to a timestamp;
(2) cutting a user trip chain, and taking the cut trip chain as a minimum research unit;
(3) cleaning each trip chain to remove invalid data based on the position change rule of the trip chain;
(4) and (4) simplifying the trip chain acquired in the step (3).
In the present invention, the step (2) specifically includes:
calculating a division index S of the current signaling one by one from the 2 nd signaling of each trip chain, dividing the user trip chain into two trip chains by taking the current signaling as a division boundary when the division index is larger than 1, and re-executing the step (2) on the divided second trip chain until a new trip chain cannot be divided;
the segmentation index calculation formula is as follows:
wherein p is the number of the current trip chain signaling, t is the interval time of the current signaling, v is the instantaneous speed of the current signaling,
is the average speed of the current trip chain,
is the average interval time of the current trip chain,
a, b, c, d are fixed constants determined according to the elbow rule.
In the present invention, the step (3) specifically includes:
first, starting with the 2 nd signaling of each trip chain, until the second to last signaling: recording the current signaling as ith data, and deleting the ith data if j (j < i) exists so that D (j, m)/T (j, m) > K;
then, starting again with the 2 nd signaling of each trip chain, until the second to last signaling: recording the current signaling as the ith data, if j (j < i), m (m > i) exists, making D (j, m) < D1，∑q∈Q1＜L，∑q∈QT(q，i)＜T1If the three conditions are met simultaneously, the ith data is marked as data to be deleted;
after all data to be deleted of the whole trip chain are marked, deleting the marked data from the trip chain data;
finally, only the first signaling and the last signaling are reserved for the signaling data continuously at the same position, so that the calculation amount is saved to the maximum extent;
wherein, D (j, m) represents the distance between the jth signaling data and the mth signaling data, and T (j, m) represents the interval time between the jth signaling data and the mth signaling data; the definition of the point set Q is as follows: for the signaling data with subscript q in the trip chain, j < q < m and D (q, j) is more than or equal to D1On the basis of two conditions, if T (q, q +1) > T is also satisfied2，D(q，q+1)＞D2If any 1 of the two conditions is satisfied, Q is Q, K, L, D1，T1，D2，T2For six fixed constants determined according to the rules of the elbow.
In the present invention, the step (4) specifically includes:
starting from the second signalling data of the trip chain, up to the third last signalling data: recording the current signaling data as ith signaling data, and deleting the ith signaling data from the trip chain if any one of the following conditions is met:
(1) the distance from the position of the ith signaling data to a line segment formed by the positions of the (i-1) th and (i +1) th signaling data is less than s1(ii) a Or the like, or, alternatively,
(2) the area of a triangle formed by the position of the ith signaling data, the position of the (i-1) th signaling data and the position of the (i +1) th signaling data is less than s2(ii) a Or the like, or, alternatively,
(3) the included angle formed by the positions of the ith-1, ith and ith +1 signaling data and the included angle formed by the positions of the ith, ith +1 and ith +2 signaling data are all smaller than s3(ii) a Or the like, or, alternatively,
(4) a line segment formed by the positions of the ith-1 and ith signaling data is intersected with a line segment formed by the positions of the (i +1) th and (i + 2) th signaling data; or the like, or, alternatively,
(5) three line segments formed by the positions of the ith, the (i +1) th and the (i-1) th signaling data, wherein the ratio of the sum of the lengths of any two line segments to the length of the third line segment is greater than s4And the third line segment is small in lengthIn s5；
S is1、s2、s3、s4、s5Is a fixed constant determined according to the rule of the elbow.
The invention can be suitable for the mobile phone signaling data with different characteristics by modifying the given fixed constant, and the method has greater flexibility. The given way of fixing the constants is determined according to the rules of the elbow, which can be adjusted by the person skilled in the art according to his actual needs.
Compared with the prior art, the method has the beneficial effects that:
1. the trip chain refers to a set of a series of signaling data generated in the user trip process. In the invention, the purpose of simplifying the trip chain is realized by designing the method capable of effectively clearing the invalid signaling data in the trip chain, thereby facilitating the subsequent data analysis.
2. In the prior art, time factors are basically considered when the trip chain is divided, and speed factors are considered when time is considered, so that the divided user trip chain is more in line with actual trip of a user and is more favorable for cleaning signaling data compared with the trip chain divided in the prior art.
3. The existing data cleaning method has poor effect on cleaning the ABAB type circulation switching unique to the mobile phone signaling. For example, kalman filter algorithm, requires a large number of parameter tuning on the condition that the data set meets the severe conditions, so as to remove most of the ABAB type cycle switching data. The method can effectively remove almost all ABAB type circular switching data in the data set (through the operation of the step 3), and greatly improves the accuracy of the subsequent sequence comparison.
4. According to the invention, a large amount of signaling data irrelevant to the actual travel track can be cleaned (through the operation of the step 4), instead of only cleaning the signaling data staying in place or the signaling data with similar distance as in the prior art, so that a large amount of irrelevant data can be cleaned on the premise of ensuring the travel characteristics of the user, and the subsequent calculation amount is reduced.
5. The invention has high automation degree and strong applicability, and is suitable for cleaning the mobile phone signaling data with large sample size and large range and various characteristics.
Drawings
Fig. 1 is an exemplary diagram of a division result of a user trip chain in this embodiment.
Fig. 2 is a diagram illustrating an example of a cleansing result of trip chain data of a user in this embodiment.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings.
Step 1, collection of signaling data
The invention adopts partial mobile phone signaling data which is provided by an operator and contains 4 fields of user identification, time stamp, longitude of a base station and latitude of the base station as a data set used by the invention. The data set had 41654036 rows, 337686 traveling chains. Wherein some of the data are shown in table 1. Note that in this embodiment, the interval time unit is constant as seconds, the interval distance unit is constant as kilometers, and the interval speed unit is constant as kilometers per hour. Fig. 1 is a graph of the change of the longitude of the signaling data of the user 1 with time, where the abscissa is time and the ordinate is the longitude of the signaling data, and it can be seen that the period from 23 point 02 to 23 point 57 in the original trajectory of the user contains a large amount of abnormal data.
TABLE 1
| User identification | Time stamp | Base station longitude | Base station latitude |
| User 1 | 20180919110759 | 120.26731 | 30.88472 |
| User 2 | 20180919105619 | 120.26731 | 30.88472 |
| User 3 | 20180919185212 | 120.26731 | 30.88472 |
| User 4 | 20180919193046 | 120.26731 | 30.88472 |
| User 5 | 20180919175801 | 120.26731 | 30.88472 |
| User 6 | 20180919155420 | 120.26731 | 30.88472 |
| User 7 | 20180919160746 | 120.26731 | 30.88472 |
Step 2, dividing user trip chain
The above-described trip chain partitioning method is employed for each trip chain, where the values of the fixed parameters are selected using the elbow rule. The values of the determined parameters were calculated as follows: a is 0.16, b is 5, c is 2400, and d is 0.62. The total number of the divided trip chains is 346247, and 8561 trip chains are added compared with the trip chains before the division. Taking the trip chain of the user 1 as an example, the trip chain has 123 pieces of signaling data, the starting time is 18 o 'clock 0 minutes, the ending time is 23 o' clock 57 minutes, the whole day trip process of the user is covered, and 2 sub trip chains are obtained after the trip chain division. As shown in fig. 2, after division of the trip chain, the long silence period from 18 o 'clock 21 o' clock to 21 o 'clock 14 o' clock of the user is identified as a division point of two sub-trip chains, and both the two sub-trip chains have practical significance, so that the trip characteristics of the user can be conveniently researched.
Step 3, cleaning and simplifying signaling data
And then cleaning each trip chain data by using the data cleaning method, simplifying the data, and determining each parameter value as follows by using the elbow rule: 282, 5, D1＝1，T1＝1200，D2＝2，T2＝30，s1＝0.1，s2＝0.01，s3＝15，s4＝3.5，s50.2. The simplified data set comprises 5845106 rows of data, and compared with the data set, 35808930 rows of data are reduced, and the reduction amplitude reaches 86%. Taking the trip chain of the user 1 as an example, after the trip chain is processed, the trip chain only contains 59 pieces of signaling data, 64 pieces of signaling are reduced compared with the trip chain before the processing, the reduction amplitude reaches 52%, and all the characteristics of the trip chain before the processing are still kept, and a change diagram of the longitude with time is shown in fig. 2, so that it can be seen that the data processing algorithm can effectively delete the bounce point in the signaling data and extract the user dwell point on the basis of keeping the characteristic attribute of the user trip chain: a plurality of wave-shaped bounce data in a time period from 23 points 02 to 23 points 57 in a traveler chain of the actual travel track of the user are effectively eliminated, and only 2 pieces of data representing the stay characteristics of the user are reserved. The removal of the wave-shaped bounce data (ABAB type cycle switching data) can effectively reduce the burden of subsequent calculation and is convenient forVisualization of the user trajectory.
Claims (5)
1. A method for cleaning mobile phone signaling data is characterized by comprising the following steps:
(1) collecting various signaling events generated by a user mobile phone in a mobile phone communication network, and sequencing the signaling time of the user according to a timestamp;
(2) cutting a user trip chain, and taking the cut trip chain as a minimum research unit;
(3) cleaning each trip chain to remove invalid data based on the position change rule of the trip chain;
(4) and (4) simplifying the trip chain acquired in the step (3).
2. The method according to claim 1, wherein the step (2) comprises in particular:
calculating a division index S of the current signaling one by one from the 2 nd signaling of each trip chain, dividing the user trip chain into two trip chains by taking the current signaling as a division boundary when the division index is larger than 1, and re-executing the step (2) on the divided second trip chain until a new trip chain cannot be divided;
the segmentation index calculation formula is as follows:
wherein p is the number of the current trip chain signaling, t is the interval time of the current signaling, v is the instantaneous speed of the current signaling,
is the average speed of the current trip chain,
is the average interval time of the current trip chain,
a, b, c, d are fixed constants determined according to the elbow rule.
3. The method according to claim 1, wherein the step (3) comprises in particular:
first, starting with the 2 nd signaling of each trip chain, until the second to last signaling: recording the current signaling as ith data, and deleting the ith data if j is greater than i and j is less than i, so that D (j, m)/T (j, m) > K;
then, starting again with the 2 nd signaling of each trip chain, until the second to last signaling: recording the current signaling as the ith data, if j is less than i, m is more than i, and making D (j, m) < D1，∑q∈Q1＜L，∑q∈QT(q，i)＜T1If the three conditions are met simultaneously, the ith data is marked as data to be deleted;
after all data to be deleted of the whole trip chain are marked, deleting the marked data from the trip chain data;
finally, only the first signaling and the last signaling are reserved for the signaling data continuously at the same position, so that the calculation amount is saved to the maximum extent;
wherein, D (j, m) represents the distance between the jth signaling data and the mth signaling data, and T (j, m) represents the interval time between the jth signaling data and the mth signaling data; the definition of the point set Q is as follows: for the signaling data with subscript q in the trip chain, j < q < m and D (q, j) is more than or equal to D1On the basis of two conditions, if T (q, q +1) > T is also satisfied2，D(q，q+1)＞D2If any 1 of the two conditions is satisfied, Q is Q, K, L, D1，T1，D2，T2For six fixed constants determined according to the rules of the elbow.
4. The method according to claim 1, characterized in that said step (4) comprises in particular:
starting from the second signalling data of the trip chain, up to the third last signalling data: recording the current signaling data as ith signaling data, and deleting the ith signaling data from the trip chain if any one of the following conditions is met:
(1) the distance from the position of the ith signaling data to a line segment formed by the positions of the (i-1) th and (i +1) th signaling data is less than s1(ii) a Or the like, or, alternatively,
(2) the area of a triangle formed by the position of the ith signaling data, the position of the (i-1) th signaling data and the position of the (i +1) th signaling data is less than s2(ii) a Or the like, or, alternatively,
(3) the included angle formed by the positions of the ith-1, ith and ith +1 signaling data and the included angle formed by the positions of the ith, ith +1 and ith +2 signaling data are all smaller than s3(ii) a Or the like, or, alternatively,
(4) a line segment formed by the positions of the ith-1 and ith signaling data is intersected with a line segment formed by the positions of the (i +1) th and (i + 2) th signaling data; or the like, or, alternatively,
(5) three line segments formed by the positions of the ith, the (i +1) th and the (i-1) th signaling data, wherein the ratio of the sum of the lengths of any two line segments to the length of the third line segment is greater than s4And the length of the third line segment is less than s5；
S is1、s2、s3、s4、s5Is a fixed constant determined according to the rule of the elbow.
5. The method of claim 1, wherein the signaling event of step (1) comprises handset signaling data of at least 4 fields of a subscriber identity, a timestamp, a base station longitude and a base station latitude.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114979995A (en) * | 2022-05-23 | 2022-08-30 | 智慧足迹数据科技有限公司 | Mobile phone signaling data simplifying method and device, electronic equipment and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104244314A (en) * | 2014-07-30 | 2014-12-24 | 北京拓明科技有限公司 | Potential group client identification method based on Mc interface signaling |
| CN105117789A (en) * | 2015-07-29 | 2015-12-02 | 西南交通大学 | Resident trip mode comprehensive judging method based on handset signaling data |
| CN105142106A (en) * | 2015-07-29 | 2015-12-09 | 西南交通大学 | Traveler home-work location identification and trip chain depicting method based on mobile phone signaling data |
| CN105761190A (en) * | 2016-02-01 | 2016-07-13 | 东南大学 | Urban community vacancy rate dynamic monitoring method based on mobile phone location data |
| CN106897420A (en) * | 2017-02-24 | 2017-06-27 | 东南大学 | A kind of resident Activity recognition method of user's trip based on mobile phone signaling data |
| CN106953858A (en) * | 2017-03-17 | 2017-07-14 | 贵州省煤炭产品质量监督检验院 | A kind of clean energy resource information acquisition control system based on internet communication |
| CN107133318A (en) * | 2017-05-03 | 2017-09-05 | 北京市交通信息中心 | A kind of population recognition methods based on mobile phone signaling data |
| CN107305590A (en) * | 2017-06-14 | 2017-10-31 | 北京市交通信息中心 | A kind of urban transportation trip characteristicses based on mobile phone signaling data determine method |
| CN108171974A (en) * | 2017-12-27 | 2018-06-15 | 东南大学 | A kind of traffic trip mode discrimination method based on cellular triangulation location data |
| CN108955693A (en) * | 2018-08-02 | 2018-12-07 | 吉林大学 | A kind of method and system of road network |
| CN109104694A (en) * | 2018-06-26 | 2018-12-28 | 重庆市交通规划研究院 | A kind of user stop place discovery method and system based on mobile phone signaling |
| CN109902930A (en) * | 2019-01-28 | 2019-06-18 | 同济大学 | A kind of auxiliary facility planning auxiliary system based on real population index |
-
2019
- 2019-12-02 CN CN201911217881.1A patent/CN111090642B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104244314A (en) * | 2014-07-30 | 2014-12-24 | 北京拓明科技有限公司 | Potential group client identification method based on Mc interface signaling |
| CN105117789A (en) * | 2015-07-29 | 2015-12-02 | 西南交通大学 | Resident trip mode comprehensive judging method based on handset signaling data |
| CN105142106A (en) * | 2015-07-29 | 2015-12-09 | 西南交通大学 | Traveler home-work location identification and trip chain depicting method based on mobile phone signaling data |
| CN105761190A (en) * | 2016-02-01 | 2016-07-13 | 东南大学 | Urban community vacancy rate dynamic monitoring method based on mobile phone location data |
| CN106897420A (en) * | 2017-02-24 | 2017-06-27 | 东南大学 | A kind of resident Activity recognition method of user's trip based on mobile phone signaling data |
| CN106953858A (en) * | 2017-03-17 | 2017-07-14 | 贵州省煤炭产品质量监督检验院 | A kind of clean energy resource information acquisition control system based on internet communication |
| CN107133318A (en) * | 2017-05-03 | 2017-09-05 | 北京市交通信息中心 | A kind of population recognition methods based on mobile phone signaling data |
| CN107305590A (en) * | 2017-06-14 | 2017-10-31 | 北京市交通信息中心 | A kind of urban transportation trip characteristicses based on mobile phone signaling data determine method |
| CN108171974A (en) * | 2017-12-27 | 2018-06-15 | 东南大学 | A kind of traffic trip mode discrimination method based on cellular triangulation location data |
| CN109104694A (en) * | 2018-06-26 | 2018-12-28 | 重庆市交通规划研究院 | A kind of user stop place discovery method and system based on mobile phone signaling |
| CN108955693A (en) * | 2018-08-02 | 2018-12-07 | 吉林大学 | A kind of method and system of road network |
| CN109902930A (en) * | 2019-01-28 | 2019-06-18 | 同济大学 | A kind of auxiliary facility planning auxiliary system based on real population index |
Non-Patent Citations (3)
| Title |
|---|
| 余锦斌: ""基于手机信令的数据分析引擎设计与实现"", 《中国优秀硕士学位论文全文数据库（信息科技辑）》, no. 2019, pages 138 - 1071 * |
| 廖锟: ""基于手机信令数据的区域住房价格与居民出行特性相关性研究"", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑)》, pages 034 - 298 * |
| 项译: "基于手机信令数据的旅游交通客流特征分析研究", no. 4, pages 153 - 105 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114979995A (en) * | 2022-05-23 | 2022-08-30 | 智慧足迹数据科技有限公司 | Mobile phone signaling data simplifying method and device, electronic equipment and storage medium |
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