CN106231588B - A kind of mobile network cell identification information correction method - Google Patents
A kind of mobile network cell identification information correction method Download PDFInfo
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- H—ELECTRICITY
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Abstract
A kind of mobile network cell identification information correction method belongs to mobile communication field, to solve how cell identity information missing or invalid in terminal sampled data to be supplemented and be corrected by way of post-processing.The present invention is based on minimum time-space matrix criterion to carry out supplement and modified method to cell identity information missing or invalid in terminal sampled data.The present invention can effectively improve the sample number of available terminal sampled data, be conducive to carry out more accurate data analysis mining by the way that missing or invalid cell identity information is supplemented and corrected.
Description
Technical field
The invention belongs to mobile network fields.
Background technique
For mobile network, cell (also referred to as sector, cell) is the basic unit that network's coverage area divides.Work as mobile phone
When terminal is switched on when being resident network or carries out business, the cell ID of place cell is important parameter.Cell ID is only
One determines the combination of the identification parameter of a cell, is generally formed by several parameter combinations, for different network formats, is made
Number of parameters, title and meaning are different.For example, to GSM, WCDMA, TD-SCDMA network, cell ID is
(LAC, CI), is then (SID, NID, BID) to CDMA 1x/EVDO network, and LTE network is (TAC, eNodeBID, CI, PCI).
In the application scenarios much based on terminal, require to acquire information from terminal side.A kind of important application scenarios
It is customer service Quality of experience (also referred to as service-aware) information for utilizing the software monitors run in terminal and acquisition terminal, network
Quality information, business usage behavior etc., the server of the information back of acquisition to cloud, and on the server based on collected
Relevant information carries out the evaluation and the data minings such as optimization, the evaluation of network quality and optimization, user's portrait analysis of service-aware
Work.In the information of above-mentioned acquisition, a critically important data are the cell IDs of cell locating for sampling instant user terminal,
The information be used to determine for the terminal provide service base station equipment, to terminal carry out position positioning, as unit of cell into
The statistical analysis of row data and excavation etc..
But when carrying out terminal side data acquisition, since the individual sex differernce of terminal, different brands terminal are to Android OS
Customized development, iOS the reasons such as closure, it is complete, accurate to collect in many cases from the api interface of terminal OS
The cell identity information that is currently located (for example may only to collect partial cell identification information or collected cell ID super
Normal range (NR) out), therefore serving cell when customer service can not occur does accurate judgement, causes not can be carried out targetedly
Service-aware evaluation with optimization, network quality evaluation with optimization etc. work.
At present for the sampled data of this key message missing, general processing mode is in data cleansing as nothing
Effect data discard.
Summary of the invention
The present invention is to solve how by way of post-processing by cell mark missing or invalid in terminal sampled data
Know information to be supplemented and corrected.
A kind of mobile network cell identification information correction method, which is characterized in that steps are as follows:
Step 1: terminal data acquisition
In the service-aware evaluation, network quality evaluation, business conduct analysis for carrying out acquiring data based on terminal side,
The service-aware data, network quality data or business conduct data, data of acquisition terminal side include: the date under monitoring pattern,
Time, longitude and latitude, positioning method, positioning accuracy are currently located province and districts and cities' title, operator, network formats, cell ID,
Terminal iidentification, user identifier, { index set }, { wireless parameter collection }；
Wherein cell ID is the combination for uniquely determining the identification parameter of a cell；
Wherein { index set } includes evaluating service-aware or evaluating network quality；
The wherein parameter that { wireless parameter collection } refers to wireless environment locating for sampling instant terminal, such as signal strength, signal-to-noise ratio
(SNR), Ec/Io, RSRQ etc. (for different network formats, parameter used is also different).
Above-mentioned sampled data is passed back to the server in cloud by the data acquisition software that runs in terminal by data channel,
And corresponding terminal sampling data table in database is stored in after parsing；Table is generally built as unit of a local network, i.e., it is same
Whole sample datas under city under the same particular network of the same operator are stored on a table, can with the date and when
Between for sequence arrange.
Step 2: exceptional sample calibration
To terminal sampling data table, " cell ID " field in each sample data is scanned one by one, if the field exists
Effective value range of the value of part or all of loss of learning or field beyond setting, then demarcating the sample is exceptional sample,
It otherwise is normal sample；
The setting of effective value range: for the different network formats of different operators, the range of virtual value sets difference,
The definition of the identification parameter and operator should determine the allocation rule of cell ID according in international standard.
Step 3: establishing temporal and spatial correlations data set
Since first exceptional sample data, corresponding temporal and spatial correlations data set is established to each exceptional sample data；
Specific method is: for selected exceptional sample, from the terminal sampling data table where the sample, access time interval is less than
The time correlation thresholding T of settingtWhole samples；If the sampling instant of the exceptional sample is Ta, the time correlation thresholding that sets as
TtMinute, then choosing sampling instant is (Ta-Tt~Ta+Tt) whole normal samples；
According to the latitude and longitude information in each sample, calculates separately and record the exceptional sample and selected each normal sample
Euclidean distance, if distance be less than setting space correlation thresholding Ts, then the normal sample is put into temporal and spatial correlations data set；Ts
Default value is 500 meters desirable；
If the longitude and latitude of an exceptional sample A is (Xa,Ya), positioning accuracy Pa, PaUnit is rice；Sampling instant is Ta；
Its corresponding temporal and spatial correlations sample set { Oi, i=1~M } in normal sample number be M, by these normal samples according to cell mark
Knowledge is divided into N group and is arranged, and cell ID is respectively { Cj, j=1~N }, sample number is respectively M in every groupj, j=1~N, then
If the longitude and latitude of each sample is { (X in sample seti,Yi), i=1~M }, positioning accuracy is { Pi, i=1~M }；Then
Exceptional sample A and sample set { OiIn Euclidean distance between each point be respectively as follows:
If the sampling instant of each sample is { T in sample seti, i=1~M }, then it is calculated as follows and saves exceptional sample A
With sample set { OiIn time interval between each point:
Gi=| Ta-Ti|, i=1~M；
Step 4: similarity calculation
According to the principle that similarity and distance and time interval are inversely proportional, while considering to calculate exceptional sample and temporal and spatial correlations
Similarity { the S of each cell ID group in sample setj, j=1~N }, method particularly includes:
First according to the respective positioning confidence level of the precision calculation of each normal sample:
Secondly, calculating the similarity of exceptional sample and each group sample according to the following formula:
Take SjCell ID of the cell ID corresponding to the maximum as the exceptional sample；
Step 5: cell ID completion
Choose cell ID of the cell ID of the highest cell ID group of similarity as the exceptional sample；
When all exceptional samples are disposed all in accordance with step 3~5, then this process terminates.
In addition, when the affiliated terminal of exceptional sample is dual-network and dual-standby mobile phone, double nets are set as A net and B net；Its A cell mark off the net
Knowing is missing or invalid value, but the cell ID C that its B is off the netBFor virtual value, then according to the B net cell ID C adoptedBAt other
It is searched in sample, other samples are that collected, B net cell ID is in the terminal of dual-network and dual-standby function having the same
CBAnd its A net cell ID is also effective normal sample；If will match it is multiple when, be subject to apart from nearest person, will
A net cell ID of the A net cell ID for the normal sample allotted as the exceptional sample.
By the way that missing or invalid cell identity information is supplemented and corrected, it can effectively improve available terminal and adopt
The sample number of sample data is conducive to carry out more accurate data analysis mining.
Detailed description of the invention
Fig. 1 is complete algorithm flow chart of the invention.
Specific embodiment
The present invention provides a kind of based on minimum time-space matrix criterion to cell missing or invalid in terminal sampled data
Identification information carries out supplement and modified method.
Specific steps are described in detail as follows:
Step 1: terminal data acquisition
In the service-aware evaluation, network quality evaluation, business conduct analysis for carrying out acquiring data based on terminal side, lead to
Cross the forms such as the App being deployed on mass users intelligent terminal service-aware data of acquisition terminal side, net under monitoring pattern
Network qualitative data or business conduct data, these sampled datas generally comprise: the date, the time, longitude and latitude, positioning method (GPS,
Network assistance positioning, base station coordinates etc.), positioning accuracy is currently located province and districts and cities' title, operator, network formats, MCC,
MNC, cell ID, terminal iidentification (IMEI), user identifier (IMSI), { index set }, { wireless parameter collection } etc..
Wherein cell ID is the combination for uniquely determining the identification parameter of a cell, generally by several parameter combinations and
At for different network formats, used number of parameters, title and meaning are different.For example, to GSM, WCDMA,
TD-SCDMA network, cell ID are (LAC, CI), are then (SID, NID, BID) to CDMA 1x/EVDO network, LTE network is
(TAC, eNodeBID, CI, PCI).
Wherein { index set } is different for different concrete application scenes, for example evaluates service-aware, including net
Page opens the KQI indexs such as time delay, video download rate, then includes service rate to network quality evaluation, calls and connect time delay, number
According to the KPI index such as connection set-up delay.
The wherein parameter that { wireless parameter collection } refers to wireless environment locating for sampling instant terminal, such as signal strength, signal-to-noise ratio
(SNR), Ec/Io, RSRQ etc. (for different network formats, parameter used is also different).
Above-mentioned sampled data is passed back to the server in cloud by the data acquisition software that runs in terminal by data channel,
And corresponding terminal sampling data table in database is stored in after parsing.Table is generally built as unit of a local network, i.e., it is same
Whole sample datas under city under the same particular network of the same operator are stored on a table, can with the date and when
Between for sequence arrange.
Step 2: exceptional sample calibration
To terminal sampling data table, " cell ID " field in each sample data is scanned one by one, if the field exists
Effective value range of the value of part or all of loss of learning or field beyond setting, then demarcating the sample is exceptional sample,
It otherwise is normal sample.
The setting of effective value range: for the different network formats of different operators, the range of virtual value sets difference,
The definition of the identification parameter and operator should determine the allocation rule of cell ID according in international standard.Such as: it is right
In LTE network, TAC 16bit, corresponding value range is 0~65535；ENodeBID is 20bit, and corresponding value range is 0
~1048575；CI is 8bit, and corresponding value range is 0~255；PCI value range is 0~503.Specific to some operator
Some districts and cities some network under, then effective value range of parameter can according to the operator each province field distribution rule
It further reduces, for example, the LTE network in Chengdu mobile for Sichuan, effective value range of eNodeBID are 0x80000
~0x80FFF (16 system).
Step 3: establishing temporal and spatial correlations data set
Since first exceptional sample data, corresponding temporal and spatial correlations data set is established to each exceptional sample data.
Specific method is: for selected exceptional sample, from the terminal sampling data table where the sample, access time interval is less than
The time correlation thresholding T of settingt(default value takes Tt=15 minutes) whole samples (set the sampling instant of the exceptional sample as Ta,
The time correlation thresholding set is Tt=15 minutes, then choosing sampling instant is (Ta- 15 '~Ta+ 15 ' whole normal samples).
Further, according to the latitude and longitude information in each sample, calculate separately and record the exceptional sample with it is selected each
The Euclidean distance of normal sample, if distance is less than the space correlation thresholding T of settings(default value takes Ts=500 meters), then this is being just
Normal sample is put into temporal and spatial correlations data set.
If the longitude and latitude of an exceptional sample A is (Xa,Ya), positioning accuracy Pa(unit is rice), sampling instant Ta。
Its corresponding temporal and spatial correlations sample set { Oi, i=1~M } in normal sample number be M, by these normal samples according to cell mark
Knowledge is divided into N group and is arranged, and cell ID is respectively { Cj, j=1~N }, sample number is respectively M in every groupj, j=1~N, then
If the longitude and latitude of each sample is { (X in sample seti,Yi), i=1~M }, positioning accuracy is { Pi, i=1~M }.Then
Exceptional sample A and sample set { OiIn Euclidean distance between each point be respectively as follows:
If the sampling instant of each sample is { T in sample seti, i=1~M }, then it is calculated as follows and saves exceptional sample A
With sample set { OiIn time interval between each point:
Gi=| Ta-Ti|, i=1~M.
Step 4: similarity calculation
According to the principle that similarity and distance and time interval are inversely proportional, while considering to calculate exceptional sample and temporal and spatial correlations
Similarity { the S of each cell ID group in sample setj, j=1~N }, method particularly includes:
First according to the respective positioning confidence level of the precision calculation of each normal sample:
Secondly, calculating the similarity of exceptional sample and each group sample according to the following formula:
Take SjCell ID of the cell ID corresponding to the maximum as the exceptional sample.
Step 5: cell ID completion
Choose cell ID of the cell ID of the highest cell ID group of similarity as the exceptional sample.
In addition, if the affiliated terminal of exceptional sample is dual-network and dual-standby (being set as A net and B net) mobile phone, A cell ID off the net
For missing or invalid value, but the cell ID C that its B is off the netBIt, then can be according to the B net cell ID C adopted for virtual valueBAt other
(collected in the terminal of dual-network and dual-standby function having the same, B net cell ID is C to sampleBAnd its A net cell ID
For effective normal sample) in searched, if will match it is multiple when, be subject to apart from nearest person, should by what is matched
A net cell ID of the A net cell ID of normal sample as the exceptional sample.
When all exceptional samples are disposed all in accordance with step 3~5, then this process terminates.
Experimental data: based on the true service-aware number acquired in the commercial 4G network of China Mobile North City of China
According to we are tested and verify using the above method.Sample data collected totally 312038, it is related to 12085 altogether
A 4G cell, for the ease of verifying to algorithm validity, we select totally at random from normal sample data (250439)
Select total 12521, sample of 5%, will cell identity information therein delete after be used as exceptional sample, using above-mentioned algorithm into
The correction and backfill of row cell ID, and be compared with the true cell mark before deletion, final accuracy is 89.3%, table
Bright this method is with good performance.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the present invention.
The content being not described in detail in the present patent application book belongs to the prior art well known to professional and technical personnel in the field.
Claims (2)
1. a kind of mobile network cell identification information correction method, which is characterized in that steps are as follows:
Step 1: terminal data acquisition
In the service-aware evaluation, network quality evaluation, business conduct analysis for carrying out acquiring data based on terminal side, monitoring
The service-aware data, network quality data or business conduct data of acquisition terminal side under mode, data include: the date, the time,
Longitude and latitude, positioning method, positioning accuracy are currently located province and districts and cities' title, operator, network formats, cell ID, terminal mark
Know, user identifier, { index set }, { wireless parameter collection }；
Wherein cell ID is the combination for uniquely determining the identification parameter of a cell；
Wherein { index set } includes evaluating service-aware or evaluating network quality；
Above-mentioned sampled data is passed back to the server in cloud by the data acquisition software that runs in terminal by data channel, and is solved
Corresponding terminal sampling data table in database is stored in after analysis；
Step 2: exceptional sample calibration
To terminal sampling data table, " cell ID " field in each sample data is scanned one by one, if there are parts for the field
Or all information lacks or effective value range of the value of field beyond setting, then demarcating the sample is exceptional sample, otherwise
For normal sample；
Step 3: establishing temporal and spatial correlations data set
Since first exceptional sample data, corresponding temporal and spatial correlations data set is established to each exceptional sample data；Specifically
Method is: for selected exceptional sample, from the terminal sampling data table where the sample, access time interval is less than setting
Time correlation thresholding TtWhole samples；If the sampling instant of the exceptional sample is Ta, the time correlation thresholding set is TtPoint
Clock, then choosing sampling instant is (Ta-Tt~Ta+Tt) whole normal samples；
According to the latitude and longitude information in each sample, the Europe of the exceptional sample Yu selected each normal sample is calculated separately and recorded
Family name's distance, if distance is less than the space correlation thresholding T of settings, then the normal sample is put into temporal and spatial correlations data set；Ts default
Value takes 500 meters；
If the longitude and latitude of an exceptional sample A is (Xa,Ya), positioning accuracy Pa, PaUnit is rice；Sampling instant is Ta；Its is right
Temporal and spatial correlations sample set { the O answeredi, i=1~M } in normal sample number be M, by these normal samples according to cell ID point
It is arranged for N group, cell ID is respectively { Cj, j=1~N }, sample number is respectively M in every groupj, j=1~N, then
If the longitude and latitude of each sample is { (X in sample seti,Yi), i=1~M }, positioning accuracy is { Pi, i=1~M }；It is then abnormal
Sample A and sample set { OiIn Euclidean distance between each point be respectively as follows:
If the sampling instant of each sample is { T in sample seti, i=1~M }, then it is calculated as follows and saves exceptional sample A and sample
Collect { OiIn time interval between each point:
Gi=| Ta-Ti|, i=1~M；
Step 4: similarity calculation
According to the principle that similarity and distance and time interval are inversely proportional, while considering to calculate exceptional sample and temporal and spatial correlations sample
Concentrate the similarity { S of each cell ID groupj, j=1~N }, method particularly includes:
First according to the respective positioning confidence level of the precision calculation of each normal sample:
Secondly, calculating the similarity of exceptional sample and each group sample according to the following formula:
Take SjCell ID of the cell ID corresponding to the maximum as the exceptional sample；TsFor the space correlation thresholding of setting；
Step 5: cell ID completion
Choose cell ID of the cell ID of the highest cell ID group of similarity as the exceptional sample；
When all exceptional samples are disposed all in accordance with step 3~5, then this process terminates.
2. method as described in claim 1, it is characterised in that:
When the affiliated terminal of exceptional sample is dual-network and dual-standby mobile phone, double nets are set as A net and B net；Its A cell ID off the net be missing or
Invalid value, but the cell ID C that its B is off the netBFor virtual value, then according to the B net cell ID C adoptedBIt is carried out in other samples
It searches, other samples are that collected, B net cell ID is C in the terminal of dual-network and dual-standby function having the sameBAnd its A net
Cell ID is also effective normal sample；If will match it is multiple when, be subject to apart from nearest person, by match this just
A net cell ID of the A net cell ID of normal sample as the exceptional sample.
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| CN105678046A (en) * | 2014-11-18 | 2016-06-15 | 日本电气株式会社 | Missing data repairing method and device in time-space sequence data |
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| CN103971520A (en) * | 2014-04-17 | 2014-08-06 | 浙江大学 | Traffic flow data recovery method based on space-time correlation |
| CN105678046A (en) * | 2014-11-18 | 2016-06-15 | 日本电气株式会社 | Missing data repairing method and device in time-space sequence data |
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