CN110290536B - Method for establishing wireless base station and line segment type geographic terrain incidence matrix - Google Patents
Method for establishing wireless base station and line segment type geographic terrain incidence matrix Download PDFInfo
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Abstract
The invention belongs to the technical field of data analysis, and particularly relates to a method for establishing a wireless base station and a line segment type geographic terrain correlation matrix. Comprises the following steps of extracting training data; extracting and establishing ground feature information; segmenting the line segment type ground object; associating TrainData and PoiData through a geographic relation; establishing a relation matrix R based on training datacp_train、Rpc_train(ii) a Calculating the coverage strength from each wireless base station cell to each segmentation point by using a propagation model, and deducing the coverage probability; establishing a coverage probability matrix R from all cells to all segmentation pointsline_cov(ii) a Calculating adjoint matrix B of training matrixline(ii) a S9 general formula Rpc_train、Rline_cov、BlineThree matrixes establish a cell-ground object relation matrix R for line segment type ground objectsline(ii) a S10 Pair relationship matrix RlineAnd updating by adopting a periodic sliding window method. The invention provides basic data for providing socialized geographic information for the mobile phone user after accurately judging the staying place of the mobile phone user, and provides a basic model for personnel position-based applications of public safety, disaster prevention and reduction, smart cities, marketing and the like.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to a method for establishing a wireless base station and a line segment type geographic terrain correlation matrix.
Background
In the process of applying operator big data, base station information is often required to be mapped to specific geographic features, so that a data base is provided for further analyzing the social location attribute of a mobile phone user.
With the continuous development of short-range wireless communication and mobile network technologies, location-based services (LBS) are receiving more and more attention. A Global Positioning System (GPS) can provide position information outdoors, but cannot work normally in an indoor environment due to the characteristics of shielding of buildings, large floor density, and the like. Under the circumstances, an indoor positioning scheme using carrier technologies such as infrared rays, WiFi, Zigbee and the like appears, but no good effect is achieved. The WiFi technology has large energy consumption, is easily interfered by signals, and the coverage range of the signals is limited to a space within 90m, so the advantages of the technology are not obvious; although the Zigbee technology has the advantages of low power consumption, low cost, high communication efficiency, etc., the positioning result is unstable, and the system reliability is not strong.
A block feature fingerprinting method is proposed in application No. 201811550838.2. The method comprises the following steps: s1: acquiring base station engineering parameters provided by a communication operator and a geographical entity actual position coordinate point set provided by a map service provider; s2: calculating the coverage surface of the base station according to the base station engineering parameters; s3: and calculating the matching relation between the geographic entity and the base station according to the coverage surface of the base station and the actual position coordinate point set of the space block to form the characteristic fingerprint of the geographic entity. The invention matches the position relation of each geographical block entity and the base station by combining the base station engineering parameters with the actual position coordinate point set of the geographical entity to form the characteristic fingerprint of the geographical entity. However, this method has the following disadvantages: 1. the coverage of the radio frequency is complex, has influence factors such as refraction, scattering, diffraction and the like, adopts a geometric method to calculate the coverage area overlap, and cannot accurately calculate the relationship between the radio base station and the geographic ground object entity; 2. the method cannot establish the relationship between the wireless base station and the geographic feature entity for the line segment type geographic features such as roads.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for establishing a wireless base station and line segment type geographic feature association matrix.
The technical scheme for solving the technical problems is as follows: a method for establishing a wireless base station and line segment type geographic terrain correlation matrix specifically comprises the following steps:
s1, extracting training data TrainData from big data of a mobile operator;
s2, extracting surface feature information from the electronic map, and establishing PoiData;
s3, segmenting the line segment type ground object by using a linear difference method;
s4, associating TrainData and PoiData through a geographic relation;
s5, establishing a relation matrix R based on training datacp_train、Rpc_train；
S6, calculating the coverage intensity from each wireless base station cell to each segmentation point by using a propagation model, and deducing the coverage probability;
s7, establishing a coverage probability matrix R from all cells to all segmentation pointsline_cov；
S8, calculating a adjoint credibility matrix B of the training matrixline；
S9, comprehensive Rpc_train、Rline_cov、BlineThree matrixes establish a cell-ground object relation matrix R for line segment type ground objectsline；
S10, pairing relation matrix RlineAnd updating by adopting a periodic sliding window method.
Further, the feature information in step S2 is: highway, urban road, provincial road, urban road, county road etc. access & exit, parking area etc. to establish the ground feature information and specifically do:
PoiData＝{(Poiname,Poitype,Centerx,Centery,Points)}
wherein: poinameRepresenting the name of the ground object; poitypeRepresenting a ground object type; centerxAnd CenteryRespectively representing the coordinates of the central points of the line segments; points describe a set of ordered Points of a multi-line segment.
Further, the step S3 is specifically: the urban environment is divided according to 20-50 meters by adopting a linear difference method, and the suburban environment is divided according to 100-5000 meters.
Further, the method for associating the TrainData and the PoiData in step S4 specifically includes: for the line segment type PoiData, the line segment is extended to be a rectangle along the normal direction, if the longitude and latitude in the TrainData falls into the Points corresponding to the PoiData, the TrainData record can be associated with the line segment type PoiData.
Further, the relation matrix R in the step S5cp_train、Rpc_trainThe specific establishment method comprises the following steps:
all the TrainData are calculated, and then the geographic features are merged to obtain the following data
Where Cnt is the number of training data satisfying the association condition,
let CnttrainNormalizing according to rows to obtain a relation matrix R of the cell and the ground objectcp_train：
R is a handlecp_trainTransposing and normalizing the transpose according to the rows to obtain a ground feature-cell relation matrix Rpc_train：
Further, the calculation method of step S6 is specifically: calculating the wireless receiving power from the cell to the central point of the line segment by adopting the following propagation model:
Rx＝Tx-(K1+K2log(d)+K3log(hcell)+K4Diff+K5log(d)*log(hcell)+K6(hms)+K7f(clutter))
wherein, K1The value of the factor is 23.5; k2A distance-related factor, with a value of 44.9, K3The value of the antenna height correlation factor is 5.83; k4Is a factor related to diffraction, and takes the value of 0; k5Is a transmitting antenna anddistance-related factors, the value of which is-6.55; k6Is a highly relevant factor of the receiver and takes the value of 0, K7Is a ground object correlation factor, and the value is 0;
tx is the transmission power of a cell and is obtained from the engineering parameters of a base station;
the hcell is the height of the cell antenna and is obtained from the engineering parameters of the base station;
d is the distance from the cell to the center point of the line segment;
coverage probability matrix Rline_covThe establishing method comprises the following steps:
further, the step S8 accompanies the confidence matrix BlineThe calculation method comprises the following steps: suppose the number of users passing through the feature is usercntExceeds N1, and averages the number of samples per user avg _ samplecntIf N2 is exceeded, the sample space is considered to be statistically significant; the confidence of the training matrix is highest, and the maximum weight of 10x is obtained; x is 1, N1 is 100, N2 is 1000;
when the sample space is lower than the above space, the confidence level is calculated as follows:
performing confidence level calculation on each element in the training matrix to obtain a confidence level matrix Bline：
Bline＝[bi,j]n*m。
Further, the step S9 is specifically: according to Rcp_train、Rline_cov、BlineThe three matrices are jointly calculated, and the final combination formula is as follows:
R'line＝Rpc_train*Bline+Rline_cov
to Rl'ineNormalized by row toTo the final Rline
Further, the periodic sliding window method in step S10 is specifically:
updating mode of sliding calculation window for relation matrix R by taking T as period and nT as datalineUpdating is carried out, wherein T is 1 month, n is 3, the relational matrix is recalculated once every month, and the data adopted in each calculation is the data of the last 3 months.
The invention relates to a GPS-cell-geographical ground object relation method based on the accurate GPS reported by the mobile phone user and the base station number, which is adopted by the invention, the GPS is accurately related to a geographical entity, the GPS-cell-geographical ground object relation is established, and a more accurate base station-geographical ground object relation probability matrix can be established through mass data.
The invention also classifies the geographic features and applies different calculation methods to different features.
The invention also provides a probability weighting and combining method, which combines the relation matrix obtained by training and the relation matrix calculated according to the base station and the geographic entity, thereby obtaining a more accurate relation matrix.
The invention has the beneficial effects that: the invention provides a joint use method for comprehensively utilizing signaling data, APP data, base station engineering parameters and surface feature data in an electronic map in a mobile communication network. And extracting data with a certain historical duration, and establishing a basic relationship between a base station and a geographical ground object by adopting relatively accurate GPS data reported by a mobile phone. And establishing the relationship between the base station and the geographic ground object by adopting a wireless coverage simulation model in a place without GPS data or GSP data deficiency. A fusion method is also provided, and the two relation matrixes are fused into a unified relation matrix. The invention provides basic data for providing socialized geographic information for the mobile phone user after accurately judging the staying place of the mobile phone user, and provides a basic model for personnel position-based applications of public safety, disaster prevention and reduction, smart cities, marketing and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a diagram illustrating a relationship matrix R aligned by a periodic sliding window method in step S10 according to an embodiment of the present inventionlineSchematic diagram of the update.
Detailed Description
The invention will be further illustrated by means of specific examples. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
In the description of the present application, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
In the following description, specific details are provided to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Examples
The invention provides a method for establishing a wireless base station and line segment type geographic terrain correlation matrix, which comprises the following specific steps:
s1, extracting training data TrainData from big data of a mobile operator;
by using the mobile operator pipeline data, signaling data can be collected from control plane interfaces such as A/Iu/S1-MME of the mobile operator 2G/3G/4G network, and location data, namely the location, time and duration of a base station where each user has a communication event can be obtained through data conversion.
The network data flow can be collected from the Gb/IuPS/Gn/S1-u user interface of the mobile operator 2G/3G/4G network, and the network traffic data of the user can be obtained through data conversion. With the development of mobile internet, a large number of LBS-based APPs report the longitude and latitude, the GPS position of the user can be obtained by decoding the longitude and latitude, and the longitude and latitude can be converted into a standard GPS by using coordinate conversion services of various map service providers such as Baidu and Goodand the like.
By means of a control plane and user plane association method for mobile data collection, such as TMSI, M-TMSI, GTP-ID, APID and the like, the following record sets can be obtained finally through data sorting. This record set is the training data for the relationship matrix, as follows:
TrainData＝{(userid,timestart,timeend,cellid,gpsx,gpsy)}
wherein, useridRepresents a user ID; timestartThe starting time of the base station indicating the communication event of the user; timeendIndicating the end time of the base station where the communication event occurs; cellidRepresents a mobile phone ID; gpsxAnd gpsyRespectively representing the longitude and latitude of the user in the occurrence of a communication event.
S2, extracting feature information from the electronic map, classifying the feature information, and establishing PoiData;
geographic land features of a region are obtained through a GIS system, and land feature information such as expressways, urban roads, provincial roads, urban roads, county roads and the like, entrances and exits, parking lots and the like is extracted from the land features. The ground feature information is arranged into data with the following format:
PoiData＝{(Poiname,Poitype,Centerx,Centery,Points)}
wherein: poinameRepresenting the names of features, such as: xx mansions; poitypeRepresenting a type of terrain, such as a building; centerxAnd CenteryRespectively representing the coordinates of the central points of the line segments; points describe a set of ordered Points of a multi-line segment.
S3, segmenting the line segment type ground object by using the current difference method:
expressway and urban roads are often line segment type ground objects. And often over relatively long distances. The long-distance ground object geographical span is too large, and is often related to a large number of base station cells. Therefore, simply establishing a relation matrix of line segment type ground objects and cells is inevitably inaccurate and unreasonable. In order to improve the accuracy, the line segments need to be divided according to n meters, the urban environment can be divided according to 20-50 meters, and the suburban environment can be divided according to 100-5000 meters. The division method may employ a linear difference method.
S4, associating TrainData and PoiData through a geographic relation;
for the line segment type PoiData, the line segment is expanded into a rectangle along the normal direction, and the correlation method of TrainData and PoiData is as follows: if the latitude and longitude in TrainData falls within the Points corresponding to the PoiData, the TrainData record can be associated with the regional PoiData.
S5, establishing a relation matrix R based on training datacp_train、Rpc_train；
All the TrainData are calculated, and then the geographic features are merged to obtain the following data
Cnt is the number of training data that satisfy the association condition.
Let CnttrainNormalizing according to rows to obtain a relation matrix R of the cell and the ground objectcp_train：
R is a handlecp_trainTransposing and normalizing the transpose according to the rows to obtain a ground feature-cell relation matrix Rpc_train：
S6, calculating the coverage strength from each wireless base station cell to each segmentation point by using the propagation model, and deducing the coverage probability:
firstly, calculating the wireless path loss from a cell to the center point of a line segment;
calculating the wireless receiving power from the cell to the central point of the line segment by adopting the following propagation model
Rx＝Tx-(K1+K2log(d)+K3log(hcell)+K4Diff+K5log(d)*log(hcell)+K6(hms)+K7f(clutter))。
Wherein, K1A frequency-dependent factor, here 23.5, K2A distance-dependent factor, here 44.9, K3For the antenna highly dependent factor, here the value is 5.83, K4For the diffraction-related factor, 0, K can be taken here5A factor related to the transmitting antenna and the distance, here taken to be-6.55, K6Receiver high correlation factor, which can be taken to be 0, K7For the feature correlation factor, 0 may be assumed here.
Tx is the transmit power of the cell, obtained from engineering parameters of the base station.
hcell is the cell antenna height, obtained from the engineering parameters of the base station.
d is the distance from the cell to the center point of the line segment.
Secondly, calculating the coverage probability of the cell to the line segment type ground object:
by the above formula, the wireless receiving power from each cell to the center point of the line segment can be calculated.
In order to increase the calculation speed, it can be defined that when the distance from the cell to the center point of the line segment exceeds 3 times of the radius of the cell, the calculation of the path loss is not involved.
Thus, the received power vectors (Rx1, Rx2, Rx3 … Rxn) from each cell to the center point of the line segment can be obtained, the vectors are normalized, and the normalized vector is obtained, namely the coverage probability p of the cell to the ground feature is obtainedline_cov(poii,cellj)；
S7, establishing a coverage probability matrix R of coverage-based cell-line segment type land featuresline_cov；
Traversing each line segment type ground object, namely obtaining a coverage probability matrix of the cell-ground object:
s8, calculating a adjoint credibility matrix B of the training matrixline；
Since not every feature has sufficient training data, and even the training data may be 0, the coverage model needs to be used to supplement and correct the training model at this time. Generally, training models based on real data are more reliable than coverage models. Therefore, when two matrices are merged, weights are set, and different credibility is generated due to different data volumes, so different weight distribution needs to be adopted for different credibility. Since different cells have different training data characteristics, the fusion weight needs to be calculated for each feature corresponding to each cell. The fusion weight based on different regions of different cells is an adjoint matrix. And establishing a adjoint matrix B aiming at the training matrix by comprehensively considering the factors. For line segment type ground features, we denote this matrix as Bline。
According to the data analysis experience, the user number of the users passing through the ground feature is assumedcntExceeds N1, and averages the number of samples per user avg _ samplecntBeyond N2, the sample space is considered statistically significant. The confidence of the training matrix is highest, resulting in the largest weight of 10 x. x may be 1, N1 may be 100, and N2 may be 1000.
When the sample space is lower than the above space, the confidence level is calculated as follows:
performing confidence level calculation on each element in the training matrix to obtain a confidence level matrix Bline：
Bline＝[bi,j]n*m。
S9, according to Rcp_train、Rline_cov、BlineThe three matrices are jointly calculated, and the final combination formula is as follows:
R'line＝Rpc_train*Bline+Rline_cov
to Rl'ineNormalizing by row to obtain the final Rline
S10, aligning the relation matrix R by adopting a periodic sliding window methodlineUpdating is carried out;
since the engineering parameters and radio environment of a cell are constantly changed and the geographical features are also constantly changed along with the change of cities, the R is required to be adjustedlineThe matrix is continuously updated. The invention comprehensively considers the change frequency of the wireless environment and the speed of city construction, and provides an updating mode of sliding a calculation window by taking T as a period and nT as data. As shown in fig. 1, for example, T is 1 month, and n is 3, it means that the relationship matrix will be recalculated once every month, and the data used in each calculation is the data of the last 3 months.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for establishing a wireless base station and a line segment type geographic terrain correlation matrix is characterized in that: the method specifically comprises the following steps:
s1, extracting training data TrainData from big data of a mobile operator;
s2, extracting surface feature information from the electronic map, and establishing PoiData;
s3, segmenting the line segment type ground object by using a linear difference method;
s4, associating TrainData and PoiData through a geographic relation;
s5, establishing a cell-ground feature relation matrix R based on training datacp_trainLand object-cell relation matrix Rpc_train；
S6, calculating the coverage intensity from each wireless base station cell to each segmentation point by using a propagation model, and deducing the coverage probability;
s7, establishing a coverage probability matrix R from all cells to all segmentation pointsline_cov；
S8, calculating a adjoint credibility matrix B of the training matrixline；
S9, according to Rpc_train、Rline_cov、BlineThe three matrices are jointly calculated, and the final combination formula is as follows:
R′line＝Rpc_train*Bline+Rline_cov
to R'lineNormalizing by row to obtain the final Rline
S10, pairing relation matrix RlineAnd updating by adopting a periodic sliding window method.
2. The method of claim 1, wherein the method comprises the steps of: the creating PoiData of the feature information in the step S2 specifically includes:
PoiData＝{(Poiname,Poitype,Centerx,Centery,Points)}
wherein: poinameRepresenting the name of the ground object; poitypeRepresenting a ground object type; centerxAnd CenteryRespectively representing the coordinates of the central points of the line segments; points describe a set of ordered Points of a multi-line segment.
3. The method of claim 2, wherein the method comprises the steps of: the step S3 specifically includes: the urban environment is divided according to 20-50 meters by adopting a linear difference method, and the suburban environment is divided according to 100-5000 meters.
4. The method of claim 3, wherein the method comprises the steps of: the method for associating the TrainData and the PoiData in the step S4 specifically includes: for the line segment type PoiData, the line segment is extended to be a rectangle along the normal direction, if the longitude and latitude in the TrainData falls into the Points corresponding to the PoiData, the TrainData record can be associated with the line segment type PoiData.
5. The method of claim 4, wherein the method comprises the steps of: the relation matrix R in the step S5cp_train、Rpc_trainThe specific establishment method comprises the following steps:
all the TrainData are calculated, and then the geographic features are merged to obtain the following data
Where Cnt is the number of training data satisfying the association condition,
let CnttrainNormalizing according to rows to obtain a relation matrix R of the cell and the ground objectcp_train：
R is a handlecp_trainTransposing and normalizing the transpose according to the rows to obtain a ground feature-cell relation matrix Rpc_train：
6. The method of claim 5, wherein the method comprises the steps of: the calculation method of step S6 specifically includes: calculating the wireless receiving power from the cell to the central point of the line segment by adopting the following propagation model:
Rx＝Tx-(K1+K2log(d)+K3log(hcell)+K4Diff+K5log(d)*log(hcell)+K6(hms)+K7f(clutter))
wherein, K1The value of the factor is 23.5; k2A distance-related factor, with a value of 44.9, K3The value of the antenna height correlation factor is 5.83; k4Is a factor related to diffraction, and takes the value of 0; k5The value is-6.55 for factors related to the transmitting antenna and the distance; k6Is a highly relevant factor of the receiver and takes the value of 0, K7Is a ground object correlation factor, and the value is 0;
tx is the transmission power of a cell and is obtained from the engineering parameters of a base station;
the hcell is the height of the cell antenna and is obtained from the engineering parameters of the base station;
d is the distance from the cell to the center point of the line segment;
coverage probability matrix Rline_covThe establishing method comprises the following steps:
7. the method of claim 6, wherein the method comprises the steps of: the step S8 is accompanied by the confidence matrix BlineThe calculation method comprises the following steps: suppose the number of users passing through the feature is usercntExceeds N1, andaverage number of samples generated per user avg samplecntIf N2 is exceeded, the sample space is considered to be statistically significant; the confidence of the training matrix is highest, and the maximum weight of 10x is obtained; x is 1, N1 is 100, N2 is 1000;
when the sample space is lower than the above space, the confidence level is calculated as follows:
performing confidence level calculation on each element in the training matrix to obtain a confidence level matrix Bline：
Bline＝[bi,j]n*m。
8. The method of claim 7, wherein the method comprises the steps of: the periodic sliding window method in step S10 specifically includes:
updating mode of sliding calculation window for relation matrix R by taking T as period and nT as datalineUpdating is carried out, wherein T is 1 month, n is 3, the relational matrix is recalculated once every month, and the data adopted in each calculation is the data of the last 3 months.
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