CN105188030A - Geographic grid mapping method of mobile network data - Google Patents
Geographic grid mapping method of mobile network data Download PDFInfo
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- CN105188030A CN105188030A CN201510520048.XA CN201510520048A CN105188030A CN 105188030 A CN105188030 A CN 105188030A CN 201510520048 A CN201510520048 A CN 201510520048A CN 105188030 A CN105188030 A CN 105188030A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising 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
Abstract
The invention discloses a geographic grid mapping method of mobile network data, and belongs to the field of mobile communication network optimization and analysis. The steps are as follows: firstly, carrying out mathematical modeling based on the existing massive DT drive test data, forming a corresponding relationship between an actual geographic area grid and a base station cell identifier, and analyzing the system format to obtain the following comparison table: grid numbers, cells, the number of sampling points, a total number of the sampling points, a distribution ratio of the sampling points and a coverage weight; normalizing the obtained comparison table, and carrying out linear interpolation calculations on geographic grids having no data with reference to geographic grids having data to obtain a new comparison table having the same structure; carrying out statistical calculation from a whole network angle based on the new comparison table, and updating the coverage weights of the cells in the geographic grids to facilitate the geographic grid mapping calculation of the mobile network data in the next step. The mobile network data are concluded in several categories, and the mobile network data are mapped into the corresponding geographic grids according to the comparison table established in the last step.
Description
Technical field
The present invention relates to mobile communication network optimization analysis field, particularly a kind of mobile network data carries out the method for geographical grid mapping.
Background technology
Due to the reason of left over by history and technological evolvement, current operator is often facing to 2G, 3G, 4G, WLAN tetra-net the complex scene deposited, due to the difference of the system type of each net, frequency range, bearing capacity, load level, maturity, O&M cost, technical characterstic, the situation that operator needs Comprehensive consideration respectively to net when carrying out network operation and analyzing, this just requires to adopt multi-network cooperative analysis optimization method.Conventional analytic target and optimization unit are often a certain base station (bunch) or a certain community (bunch), although analytic target and optimization unit can extend as some grids or section sometimes, in fact the agent object of data subdividing remains network element dimension.And reality is, the agent object of network operation analysis should be that a certain geographical grid region is marketed section in other words, and user generally also can not pay close attention to the concrete network condition of base station cell.This just needs the technical scheme adopting certain feasible, and mobile network's related data (obtaining with network element dimension) is mapped to corresponding geographical grid region.These mobile network's related data packets includes network resource, performance, flow, business, user, terminal, consumption, complaint, WLAN hot spot, business halls etc.
The theoretical coverage region, each community that the mapping algorithm of traditional mobile network data and actual geographic grid is formed based on the coverage prediction simulation result of base station cell often maps, the theoretical boundary of community and minizone is generally the irregular curve of bar, the feature modeling result of curve depends on the underlying parameter data of each community as site, stand high, deflection, antenna type, antenna gain, transmitting power, frequency range etc., and relevant building, geographical environment data and the wireless signal propagation model etc. that adopts.Real network coverage condition can have bigger difference therewith, due to the factor of data accuracy, state modulator, capacity and communication environments, the coverage of community may not be continuous, and the border of minizone also can not be very regular full curve, so this method exists a lot of drawback.In the present invention, propose a kind of technical scheme of carrying out modeling based on the actual test data of mobile network and DT drive test data to have come.
Summary of the invention
The invention discloses a kind of method that mobile network data carries out geographical grid mapping, described method comprises the steps:
(1), first, mathematical modeling is carried out according to existing magnanimity DT drive test data, form the corresponding relation of actual geographic region grid and base station cell mark, subsystem standard, obtains the following table of comparisons: grid numbering, community, sampled point quantity, total number of sample points, sampling point distributions ratio, covering weights;
(2), to the table of comparisons obtained above be normalized, and according to there being the geographical grid of data to carry out linear interpolation calculating by there is no the geographical grid of data, obtain the new same structure table of comparisons;
(3), based on the new table of comparisons, carry out statistical computation from the angle of the whole network, and upgrade the covering weights of each geographical grid Nei Ge community;
(4) following large class, by mobile network data is generalized into: summation class counting item, polymerization duplicate removal class counting item, average class counting item, codomain maximum counting item, codomain minimum value counting item, percentages are several, and mobile network data are mapped in corresponding geographical grid according to the new table of comparisons that previous step is set up.
Further, mobile network data as above carries out the method for geographical grid mapping, and described mobile network data comprises resource data, business datum, qualitative data, user data, terminal data etc.
Further, mobile network data as above carries out the method for geographical grid mapping, and described concrete steps of carrying out mathematical modeling according to existing magnanimity DT drive test data are as follows:
(1-1) rasterizing is carried out in the geographic area at mobile network place, set up reference frame;
(1-2) existing magnanimity DT drive test data result is processed, and under its each sampling number certificate is mapped to the Grid Coordinate System identical with (1-1); (1-2) process of this step needs subsystem standard to carry out, and obtains the table of comparisons under different system standard.
Further, mobile network data as above carries out the method for geographical grid mapping, and described step (2) specifically comprises the following steps:
(2-1) unified normalized is carried out to the table of comparisons obtained above, specific as follows:
1) for each system type, the total number of sample points maximum of all geographical grids is got respectively;
2) by above-mentioned total number of sample points maximum, equal proportion increase is carried out to the sampled point quantity of the different districts of all geographical grids, make the total number of sample points of each geographical grid equal above-mentioned total number of sample points maximum, thus obtain the geographical grid after new normalization;
(2-2) after previous step process, lack the geographical grid of data for those, adopt linear interpolation method to process further, specific as follows:
1) first, for the geographical grid after new normalization, the geographical grid quantity all lacking data is added up;
2) the plan range definition of geographical grid is introduced;
3) iterative computation is carried out to the geographical grid all lacking data;
4) previous step is obtained described in lack data geographical grid result-reverse-checking be updated in the geographical grid after normalization;
5) so far, obtained the sampling point distributions ratio table of each community of whole geographical grid or the covering weight table result of each community, and all had data;
6) obtain the geographical grid ownership statistics of 2G, 3G, 4G community, and the geographical grid of WLANAP/ focus is belonged to, then the positional information of WLANAP/ focus directly can be utilized to find corresponding geographical grid ownership, and set up the new table of comparisons.
Further, mobile network data as above carries out the method for geographical grid mapping, and described step (3) specifically comprises the following steps:
(3-1) based on the geographical grid sampling point distributions ratio table generated or covering weight table, iterative computation is carried out for each geographical grid above, to obtain the total number of sample points of each community involved in the new table of comparisons;
(3-2) to the new table of comparisons, the covering weights of its each community are recalculated.
Further, mobile network data as above carries out the method for geographical grid mapping, and described step (4) specifically comprises the following steps:
(4-1) first, mobile network data storehouse is built;
(4-2) secondly, geographical raster data storehouse is built, Lattice encoding wherein and new table of comparisons one_to_one corresponding;
(4-3) based on MPS process weight table each in geographical grid newly-generated in step (3), calculate every mapping every to geographical raster data storehouse in mobile network data storehouse, and its result is presented on 2D map.
Further, mobile network data as above carries out the method for geographical grid mapping, and the geographical raster data storehouse that step (4) is formed needs regularly to calculate once, to keep renewal real network topological sum being covered to change, the cycle upgraded can establish, and is defaulted as one month.
After establishing such data-mapping algorithm and model, no matter from the network optimization, the network planning or the angle of the marketing, all treat the problems referred to above with geographical grid visual angle, user place, based on large data mining analysis technological means, Operation Decision scheme proposals is exported with suiting measures to local conditions by scene, comprise cooperate optimization, collaborative planning, co-marketing etc., analyze problems and solve them and just can reach better effect, just really can realize the transformation from network O&M to network operation, thus open up a new self-break through direction for operator walks out current predicament.
Accompanying drawing explanation
Fig. 1 is that mobile network data is carried out the schematic diagram of geographical rasterizing by the present invention.
Fig. 2 be each DT drive test data record as a sampled point, projected the schematic diagram in the geographical grid corresponding to longitude and latitude of this sampled point.
Fig. 3 is the schematic diagram of the plan range definition introducing geographical grid.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in detail.
The invention provides a kind of method that mobile network data carries out geographical grid mapping, comprise following committed step:
(1) first, mathematical modeling is carried out according to existing magnanimity DT drive test data, form the corresponding relation of actual geographic region grid (marketing section) and base station cell mark LAC/CI, subsystem standard, obtains following table of comparisons Grid (n): grid numbering, community, sampled point quantity, total number of sample points, sampling point distributions ratio, covering weights;
Described concrete steps of carrying out mathematical modeling according to existing magnanimity DT drive test data are as follows:
(1-1) rasterizing is carried out in the geographic area at mobile network place, set up reference frame.First the base station range according to mobile network delimit a rectangle geographic area, and the size of rectangle geographic area should make analyzed Network element object all include in this region.The border in region is defined by the longitude and latitude extreme value of four points: LatitudeTop, LatitudeBottom, LongitudeLeft, LongitudeRight, and correspondence four summits up and down of rectangle geographic area are distinguished in their combination.For this rectangle geographic area, we first stamp the grid line of a series of n*n rice on longitude and latitude direction, as shown in Figure 1.The granularity that reaches and the compromise between relevant cost benefit is wanted when the value of note: n is decided by real network case study.General conventional value is 10,50,100m etc.The square area formed by above grid line we be defined as geographical grid G rid_Property (), then divided geographical grid quantity is GirdNumber=Int ((LatitudeTop-LatitudeBottom) * every latitude distance (m)/n) * int (the every longitude distance of (LongitudeRight-LongitudeLeft) * (m)/n).Grid_Property () list structure definition and sample data as follows:
For the grid region that these grid line are formed, we are numbered to each geographical grid.The region be made up of one or more geographical grid we be referred to as section of marketing, the geographical grid quantity that comprises of marketing section is indefinite, and involved home cell quantity (generally judging with geographic correlation) is also not necessarily.As above the marketing section MA1 selected by polygon line segment institute frame shown in Fig. 1, just include 39 geographical grids (when geographical grid more than 1/3 of market section and geographical grid overlapping area, then define this marketing section and comprise this geographical grid) and 8 community (A, B, C, D, E, F, G, H).
(1-2) existing magnanimity DT drive test data result is processed, and under its each sampling number certificate is mapped to the Grid Coordinate System identical with (1-1).So-called DT drive test refers to DriveTest wireless network test, generally can obtain relevant radio network signaling event and metrical information, so that carry out the network optimization and customer complaint process by DT drive test.By DT drive test instrument obtain drive test file and generally can derive sample data as follows:
Wherein being defined as follows of each field:
√ test file: refer to the file name that DT drive test data stores
√ timestamp (TimeStamp): refer to the concrete time point that the sampling of some drive test datas occurs
√ mobile phone logo: the measurement result identifying same portion mobile phone
√ S_LAC: the lane place coding that current service cell when some drive test datas are sampled occurs
√ S_CellID: the community coding that current service cell when some drive test datas are sampled occurs
√ S_Level: the received signal strength that current service cell during the sampling of some drive test datas occurs
√ event type: the type of mobile phone signaling event during the sampling of some drive test datas occurs, generally comprise caller, called, send short messages, receive note, location area updating, switching, start, shutdown and measurement report etc.
√ system type: the system type that current service cell belongs to, comprises GSM, TD-SCDMA, CDMA, WCDMA, CDMA2000, FDD-LTE, TDD-LTE etc.
√ longitude (Long): the absolute fix longitude during sampling of some drive test datas occurs.
√ latitude (Lat): the absolute fix latitude during sampling of some drive test datas occurs.
We are using each DT drive test data record as a sampled point, are projected in the geographical grid corresponding to longitude and latitude of this sampled point and (adopt the reference frame identical with (1-1)).As shown in Figure 2, most of geographical grid has relevant data to present.But have the geographical grid of fraction without any data, this is mainly because the restriction of DT drive test itself causes.DT drive test can only carry out on the main roads of city, and belongs to sampling test, and a lot of secondary street, residential block, office building inside, enterprises and institutions close the regions such as garden, river, park, often cannot carry out drive test, thus also just without any test data.
Note: for contrasting conveniently, we have also been drawn in corresponding region marketing section MA1 recited above, as shown in Figure 2.
As seen from Figure 2, in the region that marketing section MA1 confines, the geographical grid had has data, and the geographical grid had does not have data.For the geographical grid having data, we can generate this geographical sampling point distributions ratio table of grid Nei Ge community or covering weight table Grid (n) of each community, available following formulation:
Grid(n)＝
{
GridID,
Cell(i),
CountALL
}
Wherein, Cell (i)=
{
CellID,
CellName,
Count,
Ratio,//＝Count/CountALL
Weight//initial value is set to=Ratio, follow-uply modifies
}
Its data sample is as shown in two tables below:
Note: the process of (1-2) this step needs subsystem standard to carry out, namely we can obtain table of comparisons Grid (n) under different system standard.
(2) table of comparisons Grid (n) obtained above is normalized, and according to there being the geographical grid of data to carry out linear interpolation calculating by there is no the geographical grid of data, obtains new same structure table of comparisons Grid ' (n);
(2-1) situation of two geographical grids is just simply listed more than, generally speaking, the total number of sample points in different geographical grids is different, in order to sampled point quantity variance between equilibrium different geographical grid, we adopt unified normalized, as follows:
1) for each system type, get the total number of sample points maximum of all geographical grids respectively, be designated as Grid_Max
2) to all geographical grid G rid (n) (n=1,2, Grid_Count (whole geographical grid quantity)) sampled point quantity Grid (n) .Cell (i) .Count of different districts Grid (n) .Cell (i) .Cellname carry out equal proportion increase by Grid_Max, the total number of sample points of each geographical grid is made to equal Grid_Max, thus obtain the geographical grid G rid ' (n) after new normalization, its sets definition is identical with Grid (n), meets:
Other each thresholdings of Grid ' (n) .Cell (i) .Count=Grid (n) .Cell (i) .Count*Grid_Max/Grid (n) .CountALLGrid ' (n) remain unchanged
(2-2) after previous step process, lack the geographical grid of data for those, we can adopt linear interpolation method to process further, specific as follows:
1) first, for the geographical grid G rid ' (n) after new normalization, the geographical grid quantity all lacking data is added up, sum is designated as Grid_NULL_Count, builds new set Grid_NULL (k) (k=1,2, Grid_NULL_Count), its sets definition is identical with 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) the plan range definition Grid_Distance of geographical grid is introduced, as shown in Figure 3:
Plan range Grid_Distance between any two geographical grids equals to enclose as step number when step-length spreads to surrounding and when another geographical grid overlaps with one centered by one of them geographical grid.The plan range of to be the plan range of geographical grid A and geographical grid C in 4, figure be geographical grid B and the geographical grid C in 6, figure of the plan range as grid A geographical in figure and geographical grid B is 2.
3) iterative computation is carried out to the geographical grid G rid_NULL (k) all lacking data, as follows:
A) k=1 is established
B) to Grid_NULL (k), centered by it, to surrounding diffusion step-length Step=1
C) it is searched for the whole geographical grid G rid ' (n) having data within the scope of surrounding diffusion step-length
If i. this step have found the geographical grid that at least one has data, these geographical grids be defined as gather temporarily t_Grid (m) (m=1,2 ..., the value of tGrid_Count, m adds up to tGrid_Count), meet:
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 definition is the same)
Grid_NULL (k) .CountALL=Average (t_Grid (m) .CountALL)=Grid_Max, (k, m definition is the same)
Grid_NULL (k) .Cell (i) .cellid=t_Grid (m) .Cell (i) .cellid (i=1,2 ..., the whole different districts quantity comprised in t_Grid (m)), (k, m definition is the same)
Grid_NULL (k) .Cell (i) .cellname=t_Grid (m) .Cell (i) .cellname (i=1,2 ..., the whole different districts quantity comprised in t_Grid (m)), (k, m definition is the same)
Grid_NULL (k) .Cell (i) .Count=sum (t_Grid (m) .Cell (i) .Count)/tGrid_Count (i=1,2, the whole different districts quantity comprised in t_Grid (m)), (k, m definition is the same)
Grid_NULL (k) .Cell (i) .ratio=Grid_NULL (k) .Cell (i) .Count/Grid_Max (i=1,2, the whole different districts quantity comprised in t_Grid (m)), (k, m definition is the same)
Grid_NULL (k) .Cell (i) .weight=Grid_NULL (k) .Cell (i) .Count/Grid_Max (i=1,2, the whole different districts quantity comprised in t_Grid (m)), (k, m definition is the same)
If ii. previous step does not find any one to have the geographical grid G rid ' (n) of data, then:
To surrounding diffusion step-length Step=Step+1, get back to and c) continue to perform
d)k＝k+1
E) continue b), c) step until k>Grid_NULL_Count, exit
4) Grid_NULL (k) result previous step obtained is updated in Grid ' (n) by counter the looking into of Grid_NULL (k) .GridID, and step is as follows:
A) search the array index n of Grid ' (n), condition is: Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2 ..., Grid_NULL_Count)
B) Grid ' (n) .CountALL=Grid_NULL (k) .CountALL, work 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, work 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, work 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, work 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, work 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, work as Grid ' (n) .GridID=Grid_NULL (k) .GridID (k=1,2,, Grid_NULL_Count)
H) get back to a), until the whole iteration of the array element of Grid_NULL (k) is complete
5) so far, we have obtained the sampling point distributions ratio table of each community of whole geographical grid or the covering weight table result of each community, and all have data.
6) algorithmic procedure is above passed through, we can obtain the geographical grid ownership statistics of 2G, 3G, 4G community, and the geographical grid of WLANAP/ focus is belonged to, then can directly utilize the positional information of WLANAP/ focus to find corresponding geographical grid ownership, and it is as follows to set up the table of comparisons:
(3) based on new table of comparisons Grid ' (n), statistical computation is carried out from the angle of the whole network, and upgrade covering weights Grid ' (n) .Cell (i) .weight of each geographical grid Nei Ge community, be beneficial to next step mobile network data mapping calculation.
(3-1) based on the geographical grid sampling point distributions ratio table generated above or covering weight table Grid ' (n), iterative computation is carried out for each geographical grid, to obtain total number of sample points Cell (i) .CounAllGrid of each community involved in Grid ' (n), be defined as set CellCounAllGrid (l), (l=1,2, CellCount), the sum of the community of CellCount 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=1,2 ..., the whole different districts quantity comprised in Grid ' (n)), and guarantee that CellCounAllGrid (l) .Cellname does not repeat in set
CellCounAllGrid (l) .CounAllGrid=sum (Grid ' (n) .cell (i) .count), (n=1,2 ..., Grid_Count; I=1,2 ..., the whole different districts quantity comprised in Grid ' (n)), as CellCounAllGrid (l) .Cellname=Grid ' (n) .cell (i) .cellname
(3-2) to Grid ' (n), covering weights Cell (i) the .weight algorithm recalculating its each community is as follows:
a)n＝1
B) Grid ' (n) .Cell (i) .weight=Grid ' (n) .Cell (i) .Count/CellCounAllGrid (l) .CounAllGrid, (i=1,2, the whole different districts quantity comprised in Grid ' (n)), as CellCounAllGrid (l) .Cellname=Grid ' (n) .cell (i) .cellname.
c)n＝n+1
D) continue b), c) step until n>Grid_Count, exit
(4) by mobile network data as resource data, business datum, qualitative data, user data, terminal data etc. are generalized into several large class: summation class counting item, polymerization duplicate removal class counting item, average class counting item, codomain maximum counting item, codomain minimum value counting item, percentages are several, and mobile network data are mapped in corresponding geographical grid according to table of comparisons Grid ' (n) that previous step is set up.
(4-1) first, mobile network data storehouse NetworkDatabase (m) is built, (m=1,2 ..., CellALLCount (network small area total quantity)), the list structure of its 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 counting mobile network cell
LAC: the lane place coding of counting mobile network cell
CI: the community coding of counting mobile network cell
Period: the period of counting mobile network cell
SUM_Count: summation class counting item
Distinct_Count: polymerization duplicate removal class counting item
Average_Count: average class counting item
MAX_Count: codomain maximum counting item
MIN_Count: codomain minimum value counting item
Percent_Count: percentages is several
(4-2) secondly, geographical raster data storehouse GridDataBase (n) is built, n=1,2 ..., Grid_Count (geographical grid total quantity), Lattice encoding GridID and Grid ' (n) one_to_one corresponding wherein, the list structure of its sample data is as follows:
Wherein,
GridID: the Lattice encoding counting geographical grid, with the GridID one_to_one corresponding in Grid ' (n)
Period: the period counting geographical grid
SUM_Count: summation class counting item
Distinct_Count: polymerization duplicate removal class counting item
Average_Count: average class counting item
MAX_Count: codomain maximum counting item
MIN_Count: codomain minimum value counting item
Percent_Count: percentages is several
(4-3) based on each MPS process weight table Grid ' (n) in geographical grid newly-generated above, calculate NetworkDatabase (m) (m=1,2, CellALLCount (network small area total quantity)) every to GridDataBase (n) (n=1,2 ... Grid_Count (geographical grid total quantity)) every mapping, and its result is presented on 2D map.Concrete mapping algorithm is as follows:
Note: compute sign sum represents summation, average represents and asks arithmetic mean
A) GridDataBase (n) .GridID=Grid ' (n) .GridID (n=1,2 ..., Grid_Count (geographical grid total quantity))
B) GridDataBase (n) .Period=NetworkDatabase (m) .Period (m=1,2 ..., CellALLCount (network small area total quantity))
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 ..., the whole different districts quantity comprised in Grid ' (n))
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 ..., the whole different districts quantity comprised 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 ..., the whole different districts quantity comprised 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 ..., the whole different districts quantity comprised in Grid ' (n)), 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 ..., the whole different districts quantity comprised in Grid ' (n)), 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 ..., the whole different districts quantity comprised in Grid ' (n)), p_Percent_Count is adjustment factor.
Geographical raster data storehouse GridDataBase (n) more than formed needs regularly to calculate once, and to keep renewal real network topological sum being covered to change, the cycle of renewal can establish, and is defaulted as one month.
Obviously, those skilled in the art can carry out various change and modification to the present invention and not depart from the spirit and scope of the present invention.Like this, if belong within the scope of the claims in the present invention and equivalent technology thereof to these amendments of the present invention and modification, then the present invention is also intended to comprise these change and modification.
Claims (7)
1. mobile network data carries out a method for geographical grid mapping, it is characterized in that:
Described method comprises the steps:
(1), first, mathematical modeling is carried out according to existing magnanimity DT drive test data, form the corresponding relation of actual geographic region grid and base station cell mark, subsystem standard, obtains the following table of comparisons: grid numbering, community, sampled point quantity, total number of sample points, sampling point distributions ratio, covering weights;
(2), to the table of comparisons obtained above be normalized, and according to there being the geographical grid of data to carry out linear interpolation calculating by there is no the geographical grid of data, obtain the new same structure table of comparisons;
(3), based on the new table of comparisons, carry out statistical computation from the angle of the whole network, and upgrade the covering weights of each geographical grid Nei Ge community;
(4) following large class, by mobile network data is generalized into: summation class counting item, polymerization duplicate removal class counting item, average class counting item, codomain maximum counting item, codomain minimum value counting item, percentages are several, and mobile network data are mapped in corresponding geographical grid according to the new table of comparisons that previous step is set up.
2. mobile network data as claimed in claim 1 carries out the method for geographical grid mapping, it is characterized in that:
Described mobile network data comprises resource data, business datum, qualitative data, user data, terminal data etc.
3. mobile network data as claimed in claim 1 carries out the method for geographical grid mapping, it is characterized in that:
Described concrete steps of carrying out mathematical modeling according to existing magnanimity DT drive test data are as follows:
(1-1) rasterizing is carried out in the geographic area at mobile network place, set up reference frame;
(1-2) existing magnanimity DT drive test data result is processed, and under its each sampling number certificate is mapped to the Grid Coordinate System identical with (1-1); (1-2) process of this step needs subsystem standard to carry out, and obtains the table of comparisons under different system standard.
4. mobile network data as claimed in claim 3 carries out the method for geographical grid mapping, it is characterized in that:
Described step (2) specifically comprises the following steps:
(2-1) unified normalized is carried out to the table of comparisons obtained above, specific as follows:
1) for each system type, the total number of sample points maximum of all geographical grids is got respectively;
2) by above-mentioned total number of sample points maximum, equal proportion increase is carried out to the sampled point quantity of the different districts of all geographical grids, make the total number of sample points of each geographical grid equal above-mentioned total number of sample points maximum, thus obtain the geographical grid after new normalization;
(2-2) after previous step process, lack the geographical grid of data for those, adopt linear interpolation method to process further, specific as follows:
1) first, for the geographical grid after new normalization, the geographical grid quantity all lacking data is added up;
2) the plan range definition of geographical grid is introduced;
3) iterative computation is carried out to the geographical grid all lacking data;
4) previous step is obtained described in lack data geographical grid result-reverse-checking be updated in the geographical grid after normalization;
5) so far, obtained the sampling point distributions ratio table of each community of whole geographical grid or the covering weight table result of each community, and all had data;
6) obtain the geographical grid ownership statistics of 2G, 3G, 4G community, and the geographical grid of WLANAP/ focus is belonged to, then the positional information of WLANAP/ focus directly can be utilized to find corresponding geographical grid ownership, and set up the new table of comparisons.
5. mobile network data as claimed in claim 4 carries out the method for geographical grid mapping, it is characterized in that:
Described step (3) specifically comprises the following steps:
(3-1) based on the geographical grid sampling point distributions ratio table generated or covering weight table, iterative computation is carried out for each geographical grid above, to obtain the total number of sample points of each community involved in the new table of comparisons;
(3-2) to the new table of comparisons, the covering weights of its each community are recalculated.
6. mobile network data as claimed in claim 5 carries out the method for geographical grid mapping, it is characterized in that:
Described step (4) specifically comprises the following steps:
(4-1) first, mobile network data storehouse is built;
(4-2) secondly, geographical raster data storehouse is built, Lattice encoding wherein and new table of comparisons one_to_one corresponding;
(4-3) based on MPS process weight table each in geographical grid newly-generated in step (3), calculate every mapping every to geographical raster data storehouse in mobile network data storehouse, and its result is presented on 2D map.
7. mobile network data as claimed in claim 6 carries out the method for geographical grid mapping, it is characterized in that:
The geographical raster data storehouse that step (4) is formed needs regularly to calculate once, and to keep renewal real network topological sum being covered to change, the cycle of renewal can establish, and is defaulted as one month.
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