CN105188030B - A kind of method that mobile network data carries out geographical grid mapping - Google Patents

A kind of method that mobile network data carries out geographical grid mapping Download PDF

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CN105188030B
CN105188030B CN201510520048.XA CN201510520048A CN105188030B CN 105188030 B CN105188030 B CN 105188030B CN 201510520048 A CN201510520048 A CN 201510520048A CN 105188030 B CN105188030 B CN 105188030B
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grid
data
geographical
cell
mobile network
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CN105188030A (en
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王广善
常青
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BEIJING TUOMING COMMUNICATION TECHNOLOGY Co Ltd
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BEIJING TUOMING COMMUNICATION TECHNOLOGY Co Ltd
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18Network planning tools
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/02Arrangements for optimising operational condition
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/08Testing, supervising or monitoring using real traffic

Abstract

The invention discloses a kind of methods that mobile network data carries out geographical grid mapping, belong to mobile communication network optimization analysis field.Its step are as follows: first, mathematical modeling is carried out according to existing magnanimity DT drive test data, form the corresponding relationship of actual geographic region grid and base station cell mark, subsystem standard obtains the following table of comparisons: grid number, cell, sampled point quantity, total number of sample points, sampling point distributions ratio, covering weight;The table of comparisons obtained above is normalized, and according to there is the geographical grid of data that the geographical grid of not data is carried out linear interpolation calculating, obtains the new identical structure table of comparisons;Based on the new table of comparisons, statistics calculating is carried out from the angle of the whole network, and updates the covering weight of each cell in each geographical grid, in favor of the geographical grid mapping calculation of the mobile network data of next step.Mobile network data is generalized into several major class, and mobile network data is mapped in corresponding geographical grid by the table of comparisons established according to previous step.

Description

A kind of method that mobile network data carries out geographical grid mapping
Technical field
The present invention relates to mobile communication network optimization analysis field, in particular to a kind of mobile network data carries out geographical grid The method of lattice mapping.
Background technique
Due to left over by history and technological evolvement, current operator often facing to tetra- net of 2G, 3G, 4G, WLAN simultaneously The complex scene deposited, since the system type of each net, frequency range, bearing capacity, load level, maturity, O&M cost, technology are special The case where difference of point, operator needs Comprehensive consideration respectively to net when carrying out network operation analysis, this requires using more net associations With analysis optimization method.Conventional analysis object and optimization unit is often a certain base station (cluster) or a certain cell (cluster), is use up Sometimes analysis object and optimization unit can extend as some grid or section pipe, but the main object of actually data subdividing is still It is so network element dimension.And reality is, the main object of network operation analysis should be a certain geographical grid region in other words Marketing section, user will not generally pay close attention to the specific Network status of base station cell.This is just needed using certain feasible technology Mobile network's related data (being obtained with network element dimension) is mapped to corresponding geographical grid region by scheme.These mobile networks Network related data includes Internet resources, performance, flow, business, user, terminal, consumption, complaint, WLAN hot spot, business hall etc..
The covering that the mapping algorithm of traditional mobile network data and actual geographic grid is often based on base station cell is pre- It surveys each cell theoretical coverage region that simulation result is formed to be mapped, the theoretical boundary of cell and minizone is usually that item is not advised Curve then, the feature modeling result of curve depend on the underlying parameter data such as site of each cell, height of standing, deflection, Antenna type, antenna gain, transmission power, frequency range etc. and relevant building, geographical data and used wireless Signal propagation model etc..Real network coverage condition can have bigger difference with this, due to data accuracy, state modulator, capacity And the factor of communication environments, the coverage area of cell may not be continuous, and the boundary of minizone will not be very regular full curve, So there are many drawbacks for this method.In the present invention, it proposes a kind of based on mobile network's actual test data i.e. DT drive test Technical solution that data are modeled is completed.
Summary of the invention
The invention discloses a kind of methods that mobile network data carries out geographical grid mapping, and the method includes walking as follows It is rapid:
(1), firstly, carrying out mathematical modeling according to existing magnanimity DT drive test data, actual geographic region grid and base are formed It stands the corresponding relationship of cell ID, subsystem standard obtains the following table of comparisons: grid number, cell, sampled point quantity, sampling Point sum, sampling point distributions ratio, covering weight;
(2), the table of comparisons obtained above is normalized, and foundation has the geographical grid of data that will not have data Geographical grid carry out linear interpolation calculating, obtain the new identical structure table of comparisons;
(3), based on the new table of comparisons, statistics calculating is carried out from the angle of the whole network, and updates each cell in each geographical grid Covering weight;
(4), mobile network data is generalized into following major class: summation class counts item, polymerization duplicate removal class counts item, average class Count item, that codomain maximum value counts item, codomain minimum value counts item, percentages is several etc., and establish according to previous step new Mobile network data is mapped in corresponding geographical grid by the table of comparisons.
Further, the method that mobile network data as described above carries out geographical grid mapping, mobile network's number According to including resource data, business datum, qualitative data, user data, terminal data etc..
Further, the method that mobile network data as described above carries out geographical grid mapping, it is described according to existing sea Measuring DT drive test data progress mathematical modeling, specific step is as follows:
Geographic area where mobile network is carried out rasterizing by (1-1), establishes reference frame;
(1-2) handles existing magnanimity DT drive test data result, and its each sample point data is mapped to and (1-1) Under identical Grid Coordinate System;The processing of (1-2) this step needs subsystem standard to carry out, and obtains pair under different system types According to table.
Further, the method that mobile network data as described above carries out geographical grid mapping, the step (2) are specific The following steps are included:
(2-1) carries out unified normalized to the table of comparisons obtained above, specific as follows:
1) it is directed to each system type, takes the total number of sample points maximum value of all geographical grids respectively;
2) ratio is carried out etc. by above-mentioned total number of sample points maximum value to the sampled point quantity of the different community of all geographical grids Example increases, and so that the total number of sample points of each geographical grid is equal to above-mentioned total number of sample points maximum value, thus after obtaining new normalization Geographical grid;
(2-2) lacks the geographical grid of data for those after previous step is handled, and further uses linear interpolation method It is handled, specific as follows:
1) it firstly, for the geographical grid after new normalization, unites to the geographical grid quantity for all lacking data Meter;
2) the plan range definition of geographical grid is introduced;
3) calculating is iterated to the geographical grid for all lacking data;
4) the geographical grid result-reverse-checking for lacking data described in acquired in previous step is updated to the geography after normalization In grid;
5) the covering power of all sampling point distributions ratio tables of each cell of geographical grid or each cell so far, has been obtained Value table is as a result, and all have data;
6) the geographical grid for obtaining 2G, 3G, 4G cell belongs to statistical result, and for the geographical grid of WLAN AP/ hot spot Ownership then directly can find corresponding geographical grid using the location information of WLAN AP/ hot spot and belong to, and foundation is new right According to table.
Further, the method that mobile network data as described above carries out geographical grid mapping, the step (3) are specific The following steps are included:
(3-1) is based on generated geographical grid sampling point distributions ratio table or covering weight table above, for each Geographical grid is iterated calculating, to obtain the total number of sample points of each cell involved in the new table of comparisons;
(3-2) recalculates the covering weight of its each cell to the new table of comparisons.
Further, the method that mobile network data as described above carries out geographical grid mapping, the step (4) are specific The following steps are included:
(4-1) is firstly, building mobile network data library;
(4-2) is secondly, building geographical raster data library, Lattice encoding therein and the new table of comparisons correspond;
(4-3) is based on each MPS process weight table in geographical grid newly-generated in step (3), calculates mobile network data The items in library and show its result to the mapping of geographical raster data library items on 2D map.
Further, the method that mobile network data as described above carries out geographical grid mapping, the ground that step (4) is formed It manages raster database to need periodically to calculate once, to be kept for the update to real network topology and covering variation, the period of update It can set, be defaulted as one month.
After establishing such data mapping algorithm and model, no matter sought from the network optimization, the network planning or market The angle of pin is all to treat the above problem with geographical grid visual angle where user, is based on big data mining analysis technological means, presses Export to scene adaptation to local conditions Operation Decision scheme proposals, including collaboration optimization, collaborative planning, co-marketing etc., problem analysis With solve the problems, such as to can be only achieved better effect, the transformation from network O&M to network operation just can be really realized, to be Operator walks out current predicament and opens up self new break through direction.
Detailed description of the invention
Fig. 1 is the schematic diagram that mobile network data is carried out geographical rasterizing by the present invention.
Fig. 2 is that each DT drive test data record is used as a sampled point, projects it onto the longitude and latitude institute of the sampled point Schematic diagram in corresponding geography grid.
Fig. 3 is the schematic diagram for introducing the plan range of geographical grid and defining.
Specific embodiment
The present invention is described in detail below with reference to the accompanying drawings and embodiments.
The present invention provides a kind of method that mobile network data carries out geographical grid mapping, including following committed step:
(1) firstly, carrying out mathematical modeling according to existing magnanimity DT drive test data, (the marketing of actual geographic region grid is formed Section) and base station cell mark LAC/CI corresponding relationship, subsystem standard obtains following table of comparisons Grid (n): grid is compiled Number, cell, sampled point quantity, total number of sample points, sampling point distributions ratio, covering weight;
It is described that according to existing magnanimity DT drive test data progress mathematical modeling, specific step is as follows:
Geographic area where mobile network is carried out rasterizing by (1-1), establishes reference frame.It is first depending on mobile network The base station range of network delimit a rectangle geographic area, and the size of rectangle geographic area should make analyzed Network element object All it is included in this region.The boundary in region is defined by the longitude and latitude extreme value of four points: LatitudeTop, LatitudeBottom, LongitudeLeft, LongitudeRight, their combination have respectively corresponded rectangle geographic area Four vertex up and down.For this rectangle geographic area, we first stamp a series of n*n on longitude and latitude direction The grid lines of rice, as shown in Fig. 1.Note: the value of n be decided by think when real network case study granularity to be achieved to it is related Cost-effectiveness between compromise.General common value is 10,50,100m etc..The square area formed by the above grid lines We are defined as geographical grid G rid_Property (), then the geographical grid quantity divided is GirdNumber=Int (every latitude distance (the m)/n of (LatitudeTop-LatitudeBottom) *) * int ((LongitudeRight- LongitudeLeft) every longitude distance (the m)/n of *).The table structure of Grid_Property () defines and sample data is as follows:
It is formed by grid region for these grid lines, we are numbered to each geographical grid.By one or We term it marketing sections in region composed by multiple geography grids, and the geographical grid quantity that a marketing section is included is not Fixed, involved home cell quantity (generally with geographic correlation to judge) is also not necessarily.As above it is shown in FIG. 1 by The marketing section MA1 of polygon line segment institute's frame choosing just includes 39 geographical grids (when marketing section and geographical grid faying surface For product when being more than 1/3 geographical grid, then defining the marketing section includes this geography grid) and 8 cells (A, B, C, D, E, F, G, H)。
(1-2) handles existing magnanimity DT drive test data result, and its each sample point data is mapped to and (1-1) Under identical Grid Coordinate System.So-called DT drive test refers to Drive Test wireless network test, can generally be obtained by DT drive test Relevant radio network signaling event and metrical information are obtained, in order to carry out the network optimization and customer complaint processing.Pass through the road DT The obtained drive test file of survey tool can generally export sample data as follows:
Wherein each field is defined as follows:
√ test file: refer to the file name of DT drive test data storage
√ timestamp (TimeStamp): refer to the specific time point that the sampling of some drive test data occurs
√ mobile phone logo: the measurement result of same portion's mobile phone is identified
√ S_LAC: the position area coding of the current service cell when sampling of some drive test data occurs
√ S_CellID: the cell coding of the current service cell when sampling of some drive test data occurs
√ S_Level: the received signal strength of current service cell when the sampling of some drive test data occurs
√ event type: occur some drive test data sampling when mobile phone signaling event type, generally comprise caller, Be called, send short messages, receiving short message, location area updating, switching, booting, shutdown and measurement report etc..
√ system type: the system type that current service cell is belonged to, including GSM, TD-SCDMA, CDMA, WCDMA, CDMA2000, FDD-LTE, TDD-LTE etc..
√ longitude (Long): the absolute fix longitude when sampling of some drive test data occurs.
√ latitude (Lat): the absolute fix latitude when sampling of some drive test data occurs.
We are recorded using each DT drive test data as a sampled point, project it onto the longitude and latitude institute of the sampled point (using the identical reference frame with (1-1)) in corresponding geography grid.As shown in Fig. 2, most of geography grid has phase The data of pass are presented.But there is fraction geography grid there is no any data, this is mainly due to the limitations of DT drive test itself to cause 's.DT drive test can only carry out on the main roads of city, and belong to sampling test, many secondary streets, residential block, in office building The regions such as portion, enterprises and institutions' closing garden, river, park, can not often carry out drive test, thus also just without any test data.
Note: convenient for comparison, marketing section MA1 recited above has also been drawn in corresponding region by we, as shown in Figure 2.
From Figure 2 it can be seen that the geographical grid having has data, the geographical grid having in the region that marketing section MA1 is confined There is no data.For the geographical grid for having data, the sampling point distributions ratio of each cell in the geography grid is can be generated in we The covering weight table Grid (n) of rate table or each cell can use following formulation:
Grid (n)=
{
GridID,
Cell(i),
CountALL
}
Wherein, Cell (i)=
{
CellID,
CellName,
Count,
Ratio, //=Count/CountALL
Weight//initial value is set as=Ratio, subsequent to modify
}
Its data sample is as shown in following two table:
Note: the processing of (1-2) this step needs subsystem standard to carry out, i.e., we can obtain pair under different system types According to table Grid (n).
(2) table of comparisons Grid (n) obtained above is normalized, and foundation has the geographical grid of data that will not have There is the geographical grid of data to carry out linear interpolation calculating, obtains new identical structure table of comparisons Grid ' (n);
The case where two geographical grids are only simply listed more than (2-1), it is however generally that, in different geographical grids Total number of sample points is different, and for sampled point quantity variance between balanced different geographical grids, we use unified normalization Processing, as follows:
1) it is directed to each system type, the total number of sample points maximum value of all geographical grids is taken respectively, is denoted as Grid_ Max
2) to the difference of all geography grid G rid (n) (n=1,2 ..., Grid_Count (all geography grid quantity)) Sampled point quantity Grid (n) .Cell (i) .Count of cell Grid (n) .Cell (i) .Cellname is carried out etc. by Grid_Max Ratio increases, and so that the total number of sample points of each geographical grid is equal to Grid_Max, to obtain the geographical grid after new normalization Grid ' (n), set define, satisfaction identical as Grid (n):
Grid ' (n) .Cell (i) .Count=Grid (n) .Cell (i) .Count*Grid_Max/Grid (n) .CountALL Grid ' (n) other each thresholdings remain unchanged
(2-2) lacks the geographical grid of data for those after previous step is handled, we may further use line Property interpolation method is handled, specific as follows:
1) firstly, for geographical grid G rid ' (n) after new normalization, to the geographical grid number for all lacking data Amount is counted, and sum is denoted as Grid_NULL_Count, constructs new set Grid_NULL (k) (k=1,2 ..., Grid_ NULL_Count), set defines identical as Grid (n), and meets:
Condition one: Grid_NULL (k) .CountALL=0 or NULL and
Condition two: Grid_NULL (k) ∈ Grid ' (n) (n=1,2 ..., Grid_Count) and
Condition three: Grid_NULL (k) .GridID=Grid ' (n) .GridID (n=1,2 ..., Grid_Count)
2) plan range for introducing geographical grid defines Grid_Distance, as shown in Figure 3:
Plan range Grid_Distance between any two geography grid is equal to centered on one of geographical grid With the step number that a circle is when overlapping when step-length is spread around with another geographical grid.Geography grid A and geography in such as figure The plan range of grid B is 4, and the plan range of geography grid A and geographical grid C are 6 in figure, geography grid B and geography in figure The plan range of grid C is 2.
3) calculating is iterated to the geographical grid G rid_NULL (k) for all lacking data, as follows:
A) k=1 is set
B) to Grid_NULL (k), centered on it, step-length Step=1 is spread around
C) geographical grid G rid ' (n) that its whole spread within the scope of step-length around there are data is searched for
I. if this step has found at least one geographical grid for there are data, these geography grids are defined as temporarily collecting T_Grid (m) (the value sum of m=1,2 ..., tGrid_Count, m are tGrid_Count) is closed, is met:
T_Grid (m) ∈ Grid ' (n) (n=1,2 ..., Grid_Count), and t_Grid (m) .CountALL<>0 or NULL.Then:
Grid_NULL (k) .GridID=Grid_NULL (k) .GridID, (k is defined as above)
Grid_NULL (k) .CountALL=Average (t_Grid (m) .CountALL)=Grid_Max, (k, m definition Ibid)
Grid_NULL (k) .Cell (i) .cellid=t_Grid (m) .Cell (i) .cellid (i=1,2 ..., t_ Whole different community quantity included in Grid (m)), (k, m are defined as above)
Grid_NULL (k) .Cell (i) .cellname=t_Grid (m) .Cell (i) .cellname (i=1,2 ..., Whole different community quantity included in t_Grid (m)), (k, m are defined as above)
Grid_NULL (k) .Cell (i) .Count=sum (t_Grid (m) .Cell (i) .Count)/tGrid_Count (whole different community quantity included in i=1,2 ..., t_Grid (m)), (k, m are defined as above)
Grid_NULL (k) .Cell (i) .ratio=Grid_NULL (k) .Cell (i) .Count/Grid_Max (i=1, Whole different community quantity included in 2 ..., t_Grid (m)), (k, m are defined as above)
Grid_NULL (k) .Cell (i) .weight=Grid_NULL (k) .Cell (i) .Count/Grid_Max (i= Whole different community quantity included in 1,2 ..., t_Grid (m)), (k, m are defined as above)
Ii. if previous step does not find any one geographical grid G rid ' (n) for having data:
Step-length Step=Step+1 is spread around, is returned to and c) is continued to execute
D) k=k+1
E) b), c) step is exited until k > Grid_NULL_Count for continuation
4) Grid_NULL (k) result acquired in previous step is looked into update and arrived by Grid_NULL (k) .GridID is counter In Grid ' (n), steps are as follows:
A) the array index n of Grid ' (n) is searched, condition is: Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
B) Grid ' (n) .CountALL=Grid_NULL (k) .CountALL works as Grid ' (n) .GridID=Grid_ NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
C) Grid ' (n) .Cell (i) .cellid=Grid_NULL (k) .Cell (i) .cellid works as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
D) Grid ' (n) .Cell (i) .cellname=Grid_NULL (k) .Cell (i) .cellname works as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
E) Grid ' (n) .Cell (i) .count=Grid_NULL (k) .Cell (i) .count works as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
F) Grid ' (n) .Cell (i) .ratio=Grid_NULL (k) .Cell (i) .ratio works as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
G) Grid ' (n) .Cell (i) .weight=Grid_NULL (k) .Cell (i) .weight works as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
H) it returns to a), until the array element whole iteration of Grid_NULL (k) finishes
5) so far, we have obtained all the sampling point distributions ratio table of each cell of geographical grid or covering for each cell Lid weight table is as a result, and all have data.
6) by algorithmic procedure above, we can get the geographical grid ownership statistical result of 2G, 3G, 4G cell, and right Belong in the geographical grid of WLAN AP/ hot spot, then can directly be found using the location information of WLAN AP/ hot spot corresponding Geographical grid ownership, and it is as follows to establish the table of comparisons:
(3) based on new table of comparisons Grid ' (n), statistics calculating is carried out from the angle of the whole network, and is updated in each geographical grid Covering weight Grid ' (n) .Cell (i) .weight of each cell, in favor of the mobile network data mapping calculation of next step.
(3-1) is based on generated geographical grid sampling point distributions ratio table or covering weight table Grid ' (n) above, needle Calculating is iterated to each geography grid, to obtain the total number of sample points Cell of each cell involved in Grid ' (n) (i) .CounAllGrid is defined as set CellCounAllGrid (l), (l=1,2 ..., CellCount), CellCount For the sum of cell involved by Grid ' (n).The formulation of set CellCounAllGrid (l) is as follows:
CellCounAllGrid (l)=
{
Cellname
CounAllGrid
}
And meet:
CellCounAllGrid (l) .Cellname=Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_Count;I=1, whole different community quantity included in 2 ..., Grid ' (n)), and ensure CellCounAllGrid (l) .Cellname does not repeat in set
CellCounAllGrid (l) .CounAllGrid=sum (Grid ' (n) .cell (i) .count), (n=1, 2,…,Grid_Count;I=1, whole different community quantity included in 2 ..., Grid ' (n)), when CellCounAllGrid (l) .Cellname=Grid ' (n) .cell (i) .cellname
(3-2) to Grid ' (n), covering weight Cell (i) the .weight algorithm for recalculating its each cell is as follows:
A) n=1
B) Grid ' (n) .Cell (i) .weight=Grid ' (n) .Cell (i) .Count/CellCounAllGrid (l) .CounAllGrid, (i=1, whole different community quantity included in 2 ..., Grid ' (n)), works as CellCounAllGrid (l) .Cellname=Grid ' (n) .cell (i) .cellname.
C) n=n+1
D) b), c) step is exited until n > Grid_Count for continuation
(4) mobile network data such as resource data, business datum, qualitative data, user data, terminal data etc. is concluded At several major class: summation class counts item, polymerization duplicate removal class counts item, average class counts item, codomain maximum value counts item, codomain most It is several etc. that small value counts item, percentages, and maps mobile network data according to table of comparisons Grid ' (n) that previous step is established Into corresponding geographical grid.
(4-1) firstly, building mobile network data library NetworkDatabase (m), (m=1,2 ..., CellALLCount (cell total quantity in network)), the table structure of sample data is as follows:
CellName LAC CI Period SUM_Count Distinct_Count Average_Count MAX_Count MIN_Count Percent_Count
Cell_A 13752 34081 17 1169 149 1.6 2.2 1.5 74%
Cell_B 13752 34082 17 25235 3497 2.8 3.8 2.5 48%
Cell_C 13752 34083 17 52819 2970 0.6 0.8 0.5 38%
Cell_D 13599 62652 17 17769 2997 1.8 2.5 1.7 44%
Cell_E 13752 62653 17 23637 3407 2.2 3.0 2.0 54%
Cell_F 13752 32051 17 19153 2793 3.7 5.1 3.4 40%
Cell_G 13599 32052 17 26573 1319 3.6 5.0 3.3 48%
Cell_H 13594 57193 17 7984 786 2.7 3.7 2.4 53%
Cell_I 13594 57192 17 4788 1107 4.2 5.8 3.8 32%
Cell_J 13748 47673 17 154 2.6 3.6 2.4 47%
Cell_K 13748 47671 17 18310 3180 4.8 6.6 4.4 39%
Cell_L 13748 47672 17 48210 3857 2.0 2.8 1.8 48%
Cell_M 13752 33253 17 21329 1965 1.8 2.4 1.6 67%
Wherein,
CellName: the title of mobile network cell is counted
LAC: the position area coding of mobile network cell is counted
CI: the cell coding of mobile network cell is counted
Period: the period of mobile network cell is counted
SUM_Count: summation class counts item
Distinct_Count: polymerization duplicate removal class counts item
Average_Count: average class counts item
MAX_Count: codomain maximum value counts item
MIN_Count: codomain minimum value counts item
Percent_Count: percentages are several
(4-2) is secondly, building geographical raster data library GridDataBase (n), n=1,2 ..., Grid_Count are (geographical Grid total quantity), Lattice encoding GridID and Grid ' (n) therein are corresponded, and the table structure of sample data is as follows:
Wherein,
GridID: the Lattice encoding of geographical grid is counted, is corresponded with the GridID in Grid ' (n)
Period: the period of geographical grid is counted
SUM_Count: summation class counts item
Distinct_Count: polymerization duplicate removal class counts item
Average_Count: average class counts item
MAX_Count: codomain maximum value counts item
MIN_Count: codomain minimum value counts item
Percent_Count: percentages are several
(4-3) is calculated based on each MPS process weight table Grid ' (n) in geographical grid newly-generated above The items of NetworkDatabase (m) (m=1,2 ..., CellALLCount (cell total quantity in network)) arrive The mapping of GridDataBase (n) (n=1,2 ..., Grid_Count (geographical grid total quantity)) items, and its result is existed It is showed on 2D map.Specific mapping algorithm is as follows:
Note: calculating symbol sum indicates that arithmetic mean is sought in summation, average expression
A) GridDataBase (n) .GridID=Grid ' (n) .GridID (n=1,2 ..., Grid_Count (geographical grid Lattice total quantity))
B) GridDataBase (n) .Period=NetworkDatabase (m) .Period (m=1,2 ..., CellALLCount (cell total quantity in network))
C) GridDataBase (n) .SUM_Count=sum (NetworkDatabase (m) .SUM_Count*Grid ' (n) .cell (i) .weight), as NetworkDatabase (m) .cellname=Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I=1,2 ..., included in Grid ' (n) all Different community quantity)
D) GridDataBase (n) .Distinct_Count=sum (NetworkDatabase (m) .Distinct_ Count*Grid ' (n) .cell (i) .weight) * p_Distinct_Count, as NetworkDatabase (m) .cellname =Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I= 1,2 ..., whole different community quantity included in Grid ' (n)), p_Distinct_Count is adjustment factor.
E) GridDataBase (n) .Average_Count=average (NetworkDatabase (m) .Average_ Count*Grid ' (n) .cell (i) .weight) * p_Average_Count, as NetworkDatabase (m) .cellname =Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I= 1,2 ..., whole different community quantity included in Grid ' (n)), p_Average_Count is adjustment factor.
F) GridDataBase (n) .MAX_Count=sum (NetworkDatabase (m) .MAX_Count*Grid ' (n) .cell (i) .weight) * p_MAX_Count, as NetworkDatabase (m) .cellname=Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I=1,2 ..., Grid ' (n) Included in whole different community quantity), p_MAX_Count is adjustment factor.
G) GridDataBase (n) .MIN_Count=sum (NetworkDatabase (m) .MIN_Count*Grid ' (n) .cell (i) .weight) * p_MIN_Count, as NetworkDatabase (m) .cellname=Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I=1,2 ..., Grid ' (n) Included in whole different community quantity), p_MIN_Count is adjustment factor.
H) GridDataBase (n) .Percent_Count=average (NetworkDatabase (m) .Percent_ Count*Grid ' (n) .cell (i) .weight) * p_Percent_Count, as NetworkDatabase (m) .cellname =Grid ' (n) .cell (i) .cellname, (n=1,2 ..., Grid_count;M=1,2 ..., CellALLCount;I= 1,2 ..., whole different community quantity included in Grid ' (n)), p_Percent_Count is adjustment factor.
Geographical raster data library GridDataBase (n) formed above needs periodically to calculate once, to keep to reality The update of network topology and covering variation, the period of update can set, be defaulted as one month.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art Mind and range.If in this way, belonging to the model of the claims in the present invention and its equivalent technology to these modifications and changes of the present invention Within enclosing, then the present invention is also intended to include these modifications and variations.

Claims (7)

1. a kind of method that mobile network data carries out geographical grid mapping, it is characterised in that:
Described method includes following steps:
(1), it firstly, carrying out mathematical modeling according to existing magnanimity DT drive test data, forms actual geographic region grid and base station is small The corresponding relationship of area's mark, subsystem standard, obtain the following table of comparisons: grid number, cell, sampled point quantity, sampled point are total Number, sampling point distributions ratio, covering weight;
(2), the table of comparisons obtained above is normalized, and foundation has the geographical grid of data by the ground of not data It manages grid and carries out linear interpolation calculating, obtain the new identical structure table of comparisons;
(3), based on the new table of comparisons, statistics calculating is carried out from the angle of the whole network, and updates covering for each cell in each geographical grid Lid weight;
(4), mobile network data is generalized into following major class: summation class counts item, polymerization duplicate removal class counts item, average class counts Item, codomain maximum value count item, codomain minimum value counting item, the percentages new tables of comparisons that are several, and establishing according to previous step Mobile network data is mapped in corresponding geographical grid.
2. the method that mobile network data as described in claim 1 carries out geographical grid mapping, it is characterised in that:
The mobile network data includes resource data, business datum, qualitative data, user data, terminal data.
3. the method that mobile network data as described in claim 1 carries out geographical grid mapping, it is characterised in that:
It is described that according to existing magnanimity DT drive test data progress mathematical modeling, specific step is as follows:
Geographic area where mobile network is carried out rasterizing by (1-1), establishes reference frame;
(1-2) handles existing magnanimity DT drive test data result, and its each sample point data is mapped to identical as (1-1) Grid Coordinate System under;The processing of (1-2) this step needs subsystem standard to carry out, and obtains the control under different system types Table.
4. the method that mobile network data as claimed in claim 3 carries out geographical grid mapping, it is characterised in that:
The step (2) specifically includes the following steps:
(2-1) carries out unified normalized to the table of comparisons obtained above, specific as follows:
1) it is directed to each system type, takes the total number of sample points maximum value of all geographical grids respectively;
2) equal proportion increasing is carried out by above-mentioned total number of sample points maximum value to the sampled point quantity of the different community of all geographical grids Greatly, the total number of sample points of each geographical grid is made to be equal to above-mentioned total number of sample points maximum value, to obtain the ground after new normalization Manage grid;
(2-2) lacks the geographical grid of data for those after previous step is handled, and is further carried out using linear interpolation method Processing, specific as follows:
1) firstly, for the geographical grid after new normalization, the geographical grid quantity for all lacking data is counted;
2) the plan range definition of geographical grid is introduced, the plan range between any two geography grid is equal to one of ground Step number when overlapping when being spread around with a circle for step-length centered on reason grid with another geographical grid;
3) calculating is iterated to the geographical grid for all lacking data;
4) the geographical grid result-reverse-checking for lacking data described in acquired in previous step is updated to the geographical grid after normalization In;
5) all sampling point distributions ratio tables of each cell of geographical grid or the covering weight table of each cell so far, have been obtained As a result, and all having data;
6) the geographical grid for obtaining 2G, 3G, 4G cell belongs to statistical result, and the geographical grid of WLAN AP/ hot spot is returned Belong to, then directly can find corresponding geographical grid using the location information of WLAN AP/ hot spot and belong to, and establish new control Table.
5. the method that mobile network data as claimed in claim 4 carries out geographical grid mapping, it is characterised in that:
The step (3) specifically includes the following steps:
(3-1) is based on generated geographical grid sampling point distributions ratio table or covering weight table above, for each geography Grid is iterated calculating, to obtain the total number of sample points of each cell involved in the new table of comparisons;
(3-2) recalculates the covering weight of its each cell to the new table of comparisons.
6. the method that mobile network data as claimed in claim 5 carries out geographical grid mapping, it is characterised in that:
The step (4) specifically includes the following steps:
(4-1) is firstly, building mobile network data library;
(4-2) is secondly, building geographical raster data library, Lattice encoding therein and the new table of comparisons correspond;
(4-3) is based on each MPS process weight table in geographical grid newly-generated in step (3), calculates mobile network data library Items arrive the mapping of geographical raster data library items, and its result is showed on 2D map.
7. the method that mobile network data as claimed in claim 6 carries out geographical grid mapping, it is characterised in that:
The geographical raster data library that step (4) is formed needs periodically to calculate once, to keep becoming real network topology and covering The update of change.
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