CN108093423A - A kind of discovery method of base station location exception in user bill big data based on Ransac algorithms - Google Patents
A kind of discovery method of base station location exception in user bill big data based on Ransac algorithms Download PDFInfo
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- CN108093423A CN108093423A CN201711369019.3A CN201711369019A CN108093423A CN 108093423 A CN108093423 A CN 108093423A CN 201711369019 A CN201711369019 A CN 201711369019A CN 108093423 A CN108093423 A CN 108093423A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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Abstract
The present invention discloses a kind of discovery method of base station location exception in user bill big data based on Ransac algorithms, including：For call bill data to be detected, the information with being connected other two base stations afterwards before connecting the base station in preset time is counted；Predetermined quantity base station is chosen, the center of these base stations and center is calculated and arrives the distance of these base stations, the parameter model as the use of Ransac algorithms；Utilize the parameter model; remaining base station is counted; according to Ransac algorithm principles, when the base station position information that the calculating Center Parameter or centre-to-centre spacing parameter such as newly obtained makes to meet the parameter model is less than a certain threshold value, then judge this base station for malposition base station.The present invention is by excavation, analysis and the calculating with being connected other two base stations relevant informations afterwards before the base station is connected in user bill, finding out the current base station information that leaves a question open.
Description
Technical field
The present invention relates to mode identification method, more particularly in a kind of user bill big data based on Ransac algorithms
The discovery method of base station location exception.
Background technology
Include the access base station information of current talking in telecom operators' ticket big data, and these base station informations be by
Base station installation, maintenance personnel is artificially manually entered in advance, tabulating is stored in server end.These data are usually because input people
Member's the reasons such as neglects, is tired out and causing the typing of wrong data, therefore inevitably usually containing a certain amount of base station position
The error message put.
The presence of these error messages, the usually base station construction to telecom operators, maintenance, user's value-added service service etc.
Many problems are brought, seriously affect the usage experience of end user, therefore it is necessary to by appropriate technological means, to above-mentioned base
The errors present information stood is investigated, found, and then the location information for base station of leaving a question open is modified.Obviously, it is artificial to go
Search, corrigendum above- mentioned information be it is heavy, it is unpractical.
Based on this, set forth herein a kind of discoveries of base station location exception in user bill big data based on Ransac algorithms
Method and system is excavated by the research of the user bill big data information to magnanimity, using the outer point deletion based on Ransac
Algorithm is investigated the base station that position is left a question open, is found.
The content of the invention
The middle error message that may be introduced is manually entered for base station data, proposes a kind of user based on Ransac algorithms
The discovery method of base station location exception in ticket big data, according in call bill data the characteristics of base station information and this method it is specific
Purposes, the present invention are innovatively calculated according to Ransac algorithms using statistical informations such as center, the centre-to-centre spacing between base station as Ransac
The relevant parameter of model to be established in method proposes that base station location is abnormal in a kind of user bill big data based on Ransac algorithms
Discovery method, by connected in user bill before the base station be connected afterwards other two base stations relevant informations excavation,
Analysis and calculating, find out the base station information currently to leave a question open.
A kind of base station location anomaly method in user bill big data based on Ransac algorithms, including walking as follows
Suddenly：
(1) for call bill data to be detected, count in certain number of days (such as 1 month), calculate (example in the range of certain time
Such as, in maximum half an hour), connect the information with being connected other two base stations afterwards before the base station；
(2) for these base stations, choose certain amount base station (such as 5 base stations), calculate these base stations center and
The center to these above-mentioned base stations distance (abbreviation centre-to-centre spacing here), as Ransac exterior points remove algorithm used by parameter
Model；
(3) using the parameter model, remaining base station is counted, according to Ransac algorithm principles, the meter such as newly obtained
When the base station position information that calculation Center Parameter or centre-to-centre spacing parameter make to meet the parameter model is less than a certain threshold value, then this is judged
Base station is malposition base station.
Compared with prior art, the present invention has following apparent advantage and advantageous effect：
1st, the present invention is according to Ransac algorithms, innovatively using statistical informations such as center, the centre-to-centre spacing between base station as desire
The parameter (to improve precision, can further increase statistic as requested, such as second order is away from, High Order Moment) of model is established, is proposed
A kind of discovery method of base station location exception in user bill big data based on Ransac algorithms.By to the big number of user bill
According to excavation, analysis and the calculating with being connected other two base stations relevant informations afterwards before middle connection base station, present bit is investigated out
Put the base station information to leave a question open.
2nd, the present invention is based on Ransac algorithm principles to be designed, and can accurately find out the base station that location information leaves a question open,
The exterior point (base station location error caused by artificial input) in base station data with large error is fundamentally overcome for position
Put the influence that the position for base station of leaving a question open accurately is estimated.
Description of the drawings
Fig. 1 is that base station location is sent out extremely in a kind of user bill big data based on Ransac algorithms proposed by the invention
Existing methodological function block diagram；
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and detailed description.
The flow chart of method involved in the present invention is as shown in Figure 1, comprise the following steps：
(1) base station information counts in ticket big data
For call bill data to be detected, count in certain number of days (such as 1 month), calculate in the range of certain time (for example,
In maximum half an hour), connect the information with being connected other two base stations afterwards before the base station；
For each telephone number in C, using the base station of leaving a question open as reference base station, to connect reference base station time point to
For the reference time, the two base station (b for connecting and being connected recently before and after the reference base station are searchedi, bj), and establish base station set
B is closed, extracts warp, latitude information lo, la of these base stations respectively；
(2) base station center calculates
For these base stations, choosing certain amount base station, (such as 5 base stations are set to b1, b2, b3, b4, b5), calculate these
The center of base station：
o1=(b1+b2+b3+b4+b5)/5
(3) base station center is away from calculating
These base stations are to the distance of the base station center：ob1, ob2, ob3, ob4, ob5, the average value of this 5 distances is simple here
Claim centre-to-centre spacing, as the parameter model that Ransac algorithms use, wherein, the Ransac algorithms include abnormal data according to one group
Sample data set, calculate the mathematical model parameter of data, obtain the algorithm of effective sample data, Ransac basic thoughts are retouched
It states as follows：
Consider model (n is the smallest sample number needed for initialization model parameter) that a minimum sampling cardinality is n and
The sample number # (P) of one sample set P, set P>N randomly selects the subset S initialization models of the P comprising n sample from P
M, set P herein are equal to B, and n takes 5, M to be defined herein as the centre-to-centre spacing model of base station, the ginseng as the use of Ransac algorithms
Exponential model；
(4) the abnormal data removal based on Ransac algorithms
Herein, complementary set SC=P formed with sample set of the error of model M less than a certain given threshold t and S in S
S*.S* is considered interior point set, they form the consistent collection (Consensus Set) of S.If # (S*) >=N, it is believed that obtain correctly
Model parameter, N herein take INT (90%*B) (INT is bracket function).
And new model M * is recalculated using the methods of least square using S* (interior point inliers) is collected；Again it is random
New S is extracted, repeats above procedure；Finally, according to Ransac algorithm principles, the base station number for meeting the centre-to-centre spacing threshold value is judged,
Calculating Center Parameter or centre-to-centre spacing parameter as newly obtained make the base station position information for meeting the parameter model be less than a certain threshold value
When, then judge that this base station for malposition base station, is deposited into abnormal station list, for subsequent processing.
Claims (3)
1. a kind of discovery method of base station location exception in user bill big data based on Ransac algorithms, which is characterized in that
Including：
Step 1, for call bill data to be detected, connected in statistics preset time before the base station with being connected other two base stations afterwards
Information；
Step 2 chooses predetermined quantity base station, calculate these base stations center and center to these base stations distance, as
The parameter model that Ransac algorithms use；
Step 3, using the parameter model, remaining base station is counted, according to Ransac algorithm principles, the calculating such as newly obtained
When the base station position information that Center Parameter or centre-to-centre spacing parameter make to meet the parameter model is less than a certain threshold value, then this base is judged
It stands as malposition base station, abnormal station list is deposited into, for subsequent processing.
2. as described in claim 1 in the user bill big data based on Ransac algorithms base station location exception discovery side
Method, preset time is in daily half an hour in 1 middle of the month in step 1.
3. as described in claim 1 in the user bill big data based on Ransac algorithms base station location exception discovery side
Method, it is 5 that quantity is preset in step 2.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201711369019.3A CN108093423A (en) | 2017-12-18 | 2017-12-18 | A kind of discovery method of base station location exception in user bill big data based on Ransac algorithms |
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| CN201711369019.3A CN108093423A (en) | 2017-12-18 | 2017-12-18 | A kind of discovery method of base station location exception in user bill big data based on Ransac algorithms |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113490143A (en) * | 2021-07-19 | 2021-10-08 | 北京工业大学 | Method for screening and correcting error base station and repeated base station |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295402A (en) * | 2007-04-25 | 2008-10-29 | 佳能株式会社 | Information processing apparatus and information processing method |
| US20150358781A1 (en) * | 2013-03-28 | 2015-12-10 | Fujitsu Limited | Terminal apparatus, position estimating method, position estimating system, and computer-readable storage medium |
-
2017
- 2017-12-18 CN CN201711369019.3A patent/CN108093423A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295402A (en) * | 2007-04-25 | 2008-10-29 | 佳能株式会社 | Information processing apparatus and information processing method |
| US20150358781A1 (en) * | 2013-03-28 | 2015-12-10 | Fujitsu Limited | Terminal apparatus, position estimating method, position estimating system, and computer-readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 孙伟: "《嵌入式图像处理系统工程案例精选——DaVinci系统设计与实现》", 30 April 2017 * |
| 梁春民: "基于大数据与图形分析的无线网络基站配置核查的研究与实现", 《应用技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113490143A (en) * | 2021-07-19 | 2021-10-08 | 北京工业大学 | Method for screening and correcting error base station and repeated base station |
| CN113490143B (en) * | 2021-07-19 | 2022-11-29 | 北京工业大学 | Method for screening and correcting error base station and repeated base station |
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