CN114363800B - Triangle positioning method based on CDR data - Google Patents
Triangle positioning method based on CDR data Download PDFInfo
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Abstract
The invention discloses a triangle positioning method based on CDR data, which comprises the following steps: acquiring third MR data having a relationship of user information and a front-back switching chain by using the CDR data and the XDR data; constructing a user triangular distance positioning structure by using the third MR data; positioning the mobile user in real time by utilizing a triangular distance positioning structure; the third MR data is obtained through multi-level user information backfilling. The user information backfilling is carried out on the first MR data to obtain second MR data, and then the CDR data are associated with the second MR data according to the same user and time sequence to obtain third MR data; the problem that the triangle positioning cannot be performed under the condition that 2 or more than 2 non-co-sited neighbor cells exist in the existing MR data is solved by utilizing the user information, the front-back switching chain relation and the RSRP information contained in the third MR data.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a triangle positioning method based on CDR data.
Background
Based on the joint association analysis of signaling data and MR data of operators, the product scheme for providing real-time accurate positioning service for users is mature in the market, and the positioning accuracy of the product scheme is developed from the original industrial parameter positioning with the accuracy of only 400M through single signaling data to the high-accuracy fingerprint positioning based on MR data, and can reach the range of 100M.
However, fingerprint positioning has certain limitations, particularly in remote areas, the density of base stations is small, island stations without adjacent areas exist, agps fingerprint data are less, and the like, and the fingerprint positioning is not applicable and cannot be performed. The overall fingerprint positioning rate is only about 80%.
To more accurately locate the remaining 20% of the data that cannot be located with the fingerprint, a triangulation method is often used. In the actual calculation process, because the distance calculation error between the positioning point and the base station is larger, the positioning point cannot be accurately calculated, so that the overall accuracy of the triangular positioning is lower, and the triangular positioning cannot be performed under the condition that no neighbor area of the positioning point belongs to the isolated station.
As shown in fig. 1, three non-collinear base stations a, B, C and an unknown terminal D are arranged on the plane, and the distances from the three base stations to the terminal D are measured to be R1, R2 and R3 respectively, then three intersecting circles can be drawn by taking the coordinates of the three base stations as the circle centers and the distances from the three base stations to the unknown terminal as the radius, as shown in fig. 1, the coordinates of the unknown node are the intersecting points of the three circles. In actual measurement, there is often an error in measurement, so that three circles do not intersect at one point, but intersect in a region, as shown in fig. 2. In this case, the general solution is to solve using the least squares method.
Establishing an equation to obtain an equation set of the equation (1):
Subtracting the nth equation from the first n-1 equations in turn, the matrix representation of equation (2) can be obtained:
AX＝b (2)
Wherein:
Using least squares solution thereto
Solving the above equation by a least square method to obtain equation (3):
X＝(ATA)-1ATb (3)
However, the conventional triangulation method still has disadvantages and has more limiting conditions: two or more adjacent areas which are not co-sited are needed for MR recording with positioning; the TA value of the main area needs to be known for calculating the distance between the TA value and the main area; the neighbor rsrp value needs to be provided for calculating the distance between the main area and the neighbor; the longitude and latitude values of the main area need to be provided; the longitude and latitude values of the adjacent cells need to be provided; the accuracy is extremely dependent on the distance calculation of the second and third strips; the distance between the MR track point and the main area is calculated according to TA values, the TA values are integers, 1 TA is equal to 78 meters, the distance accuracy obtained by conversion according to the TA values fluctuates between 0 and 78 meters, the effective rate of the TA values in actual data is low, and 100% of each piece of MR data does not have the TA values. The distance between the MR track point and the adjacent region is estimated according to an ideal propagation attenuation model of the signal, and the influence factors such as waveguide effect, building blocking, weather change and the like have great influence on the estimation accuracy of the distance.
Therefore, the existing triangular positioning method has excessive limiting conditions, is difficult to realize, and cannot guarantee the positioning precision.
Disclosure of Invention
In order to solve the above problems, a triangle positioning method based on CDR data is provided, in which user information backfilling is performed on first MR data to obtain second MR data, and then CDR data is associated with the second MR data according to the same user and time sequence to obtain third MR data; the problem that the triangle positioning cannot be performed under the condition that 2 or more than 2 non-co-sited neighbor cells exist in the existing MR data is solved by utilizing the user information, the front-back switching chain relation and the RSRP information contained in the third MR data. By adopting the switching base station information and the RSRP information contained in the second CDR data to carry out mutual verification on the RSRP information of the adjacent region in the third MR data, the confidence coefficient of the RSRP value is improved, the coarse error of the RSRP value is eliminated, the RSRP value fluctuation caused by external environment factors is effectively identified, and therefore the triangle positioning precision is improved.
A method of triangulating based on CDR data, comprising:
Step 100, acquiring third MR data with user information and a front-back switching chain relationship by using CDR data and XDR data;
step 200, constructing a user triangular distance positioning structure by using the third MR data;
Step 300, utilizing the triangular distance positioning structure to position the mobile user in real time;
The third MR data is obtained through multi-level user information backfilling.
In combination with the CDR data-based triangulation method according to the present invention, in a first possible embodiment, the step 100 includes:
step 110, backfilling user information by associating the first MR data with XDR signaling data of a core network to obtain second MR data with the user information;
Step 120, backfilling user information by associating the first CDR data with XDR signaling data of a core network to obtain second CDR data with the user information;
Step 130, the second CDR data of the same user and the second MR data are associated according to a time sequence, and the switching chain information of the second CDR data is backfilled to the second MR data, so as to obtain third MR data with a relationship between the user information and the front-back switching chain.
In combination with the first possible embodiment of the present invention, in a second possible embodiment, the step 110 includes:
Step 111, adopting kafka or flink to access wireless first MR data and XDR signaling data of a core network;
Step 112, merging and parallel connecting the first MR data and the XDR signaling data of the core network.
In combination with the second possible embodiment of the present invention, in a third possible embodiment, the step 112 includes:
Step 1121, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first MR data to obtain second MR data with user information;
Step 1122, storing the user information fields imsi, mdn, imei in the second MR data in the form of an HIVE table.
In combination with the first possible embodiment of the present invention, in a fourth possible embodiment, the step 120 includes:
step 121, adopting kafka or flink to access wireless first CDR data and XDR signaling data of a core network;
and 122, merging and parallelly connecting the first CDR data with the XDR signaling data of the core network to obtain second CDR data with user information.
In combination with the fourth possible embodiment of the present invention, in a fifth possible embodiment, the step 122 includes:
Step 1221, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first CDR data to obtain second CDR data with user information;
step 1222, storing the user information fields imsi, mdn, imei in the second CDR data in the form of an HIVE table.
In combination with the first possible embodiment of the present invention, in a sixth possible embodiment, the step 130 includes:
Step 131, backfilling the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the second CDR data into the second MR data to obtain third MR data;
Step 132, storing the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the third MR data in the form of an HIVE table.
In combination with the CDR data-based triangulation method according to the present invention, in a seventh possible embodiment, the step 200 includes:
Step 210, acquiring the number of non-co-sited neighbor cells in the third MR data, wherein the number of non-co-sited neighbor cells is less than two, and then performing the next step;
step 220, acquiring CDR related information of second CDR data corresponding to the user information through the user information in the third MR data;
step 230, obtaining second CDR data non-co-sited with the main service cell in the latest time sequence through the CDR association information;
step 240, extracting longitude and latitude information and RSRP information of at least two pieces of second CDR data to perform non-common station distance calculation;
And 250, constructing a user triangular distance positioning structure by using the non-common station distance.
In combination with the CDR data-based triangulation method according to the present invention, in an eighth possible embodiment, the step 200 further includes:
Step 260, obtaining the number of non-co-sited neighbor cells in the third MR data, wherein the number of non-co-sited neighbor cells is greater than or equal to two, and then performing the next step;
Step 270, acquiring a plurality of base station information overlapped in the second CDR data and the third MR data;
Step 280, obtaining average RSRP values of the plurality of base stations;
step 290, calculating the triangulation distance by using the average RSRP value.
In combination with the eighth possible embodiment of the present invention, in a ninth possible embodiment, the step 280 further includes:
Step 281, checking the RSRP value of the second CDR data and the RSRP value in the third MR data;
Step 282, if the difference between the RSRP value of the second CDR data and the RSRP value in the third MR data is smaller than the threshold range, obtaining the average RSRP value by averaging.
By implementing the triangle positioning method based on the CDR data, the second MR data is acquired by backfilling the user information of the first MR data, and then the CDR data is associated with the second MR data according to the same user and time sequence to acquire the third MR data; the problem that the triangle positioning cannot be performed under the condition that 2 or more than 2 non-co-sited neighbor cells exist in the existing MR data is solved by utilizing the user information, the front-back switching chain relation and the RSRP information contained in the third MR data. By adopting the switching base station information and the RSRP information contained in the second CDR data to carry out mutual verification on the RSRP information of the adjacent region in the third MR data, the confidence coefficient of the RSRP value is improved, the coarse error of the RSRP value is eliminated, the RSRP value fluctuation caused by external environment factors is effectively identified, and therefore the triangle positioning precision is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic representation of a first embodiment of prior art triangulation;
FIG. 2 is a schematic diagram of a second embodiment of prior art triangulation;
FIG. 3 is a first schematic diagram of the present invention in a triangulated position;
FIG. 4 is a second schematic diagram of the triangular positioning of the present invention;
FIG. 5 is a schematic diagram of a first embodiment of a triangulation method based on CDR data according to the present invention;
FIG. 6 is a schematic diagram of a second embodiment of a triangulation method based on CDR data in the present invention;
FIG. 7 is a schematic diagram of a third embodiment of a triangulation method based on CDR data according to the present invention;
FIG. 8 is a schematic diagram of a third embodiment of a triangulation method based on CDR data according to the present invention;
FIG. 9 is a schematic diagram of a fifth embodiment of a triangulation method based on CDR data according to the present invention;
FIG. 10 is a diagram of a sixth embodiment of a method for triangulating based on CDR data in the present invention;
FIG. 11 is a schematic diagram of a seventh embodiment of a triangulation method based on CDR data according to the present invention;
FIG. 12 is a schematic diagram of an eighth embodiment of a triangulation method based on CDR data in the present invention;
FIG. 13 is a schematic diagram of a ninth embodiment of a triangulation method based on CDR data in the present invention;
FIG. 14 is a schematic diagram of a tenth embodiment of a triangulation method based on CDR data in the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Based on the embodiments of the present invention, other embodiments that may be obtained by those of ordinary skill in the art without undue burden are within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The existing triangular positioning method has excessive limiting conditions, is difficult to realize, and cannot guarantee positioning accuracy.
In order to solve the above problems, a triangulation method based on CDR data is proposed.
By adopting the switching base station information and the RSRP information contained in the CDR data to carry out mutual verification on the RSRP information of the neighbor cells in the MR data, the confidence coefficient of the RSRP value is improved, the coarse error of the RSRP value is eliminated, the RSRP value fluctuation caused by external environment factors is effectively identified, and the triangular positioning precision is improved.
Fig. 5, fig. 5 is a schematic diagram showing a first embodiment of a triangle positioning method based on CDR data in the present invention, which includes:
Step 100, acquiring third MR data with user information and a front-back switching chain relationship by using CDR data and XDR data; step 200, constructing a user triangular distance positioning structure by using third MR data; step 300, positioning the mobile user in real time by utilizing a triangular distance positioning structure; the third MR data is obtained through multi-level user information backfilling.
The user information backfilling is carried out on the first MR data to obtain second MR data, and then the CDR data are associated with the second MR data according to the same user and time sequence to obtain third MR data; the problem that the triangle positioning cannot be performed under the condition that 2 or more than 2 non-co-sited neighbor cells exist in the existing MR data is solved by utilizing the user information, the front-back switching chain relation and the RSRP information contained in the third MR data.
In a preferred embodiment, as shown in fig. 6, fig. 6 is a schematic diagram of a second embodiment of a triangulation method based on CDR data according to the present invention, and step 100 includes:
Step 110, backfilling user information by associating the first MR data with XDR signaling data of a core network to obtain second MR data with the user information; step 120, backfilling user information by associating the first CDR data with XDR signaling data of a core network to obtain second CDR data with the user information; and 130, backfilling switching chain information of the second CDR data to the second MR data by associating the second CDR data of the same user with the second MR data according to a time sequence, and obtaining third MR data with a relation between the user information and the front-back switching chain.
Generally:
the wireless MR data is measurement data reported by a cell period, and the period is generally set to 10s. The measurement data comprises:
Base station information: enodbid, cellid;
User equipment information: enbs1apid, mmegroupid, mmecode, mmes 1.1. 1apid.
Wireless CDR data, call detail record, describes the overall process of call continuation. The parameters recorded in the CDR data are derived from the original signaling message data. In LTE, the end user counts as a call every time a service occurs. The inside contains:
the accurate time of each occurrence of business by the user;
Base station information of the occurrence of the service: enodbid, cellid the process of the preparation of the pharmaceutical composition,
User equipment information: enbs1apid, mmegroupid, mmecode, mmes1apid;
Handover chain information ：cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp.
The XDR data of the core network is also called signaling data, which includes:
Base station information: enodbid, cellid;
user equipment information: enbs1apid, mmegroupid, mmecode, mmes1apid;
user information: imsi, mdn, imei.
Preferably, as shown in fig. 7, fig. 7 is a schematic diagram of a third embodiment of a triangulation method based on CDR data in the present invention, and step 110 includes:
step 111, adopting kafka or flink to access wireless first MR data and XDR signaling data of a core network; step 112, merging and parallel connection is performed on the first MR data and the XDR signaling data of the core network.
In a preferred embodiment, as shown in fig. 8, fig. 8 is a schematic diagram of a fourth embodiment of a triangulation method based on CDR data according to the present invention, and step 112 includes:
Step 1121, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first MR data to obtain second MR data with user information; step 1122, the user information fields imsi, mdn, imei are stored in the second MR data in the form of HIVE tables.
Preferably, step 120 comprises: as shown in fig. 9, fig. 9 is a schematic diagram of a fifth embodiment of a triangulation method based on CDR data according to the present invention, step 121, using kafka or flink to access wireless first CDR data and core network XDR signaling data; step 122, merging and parallel connection is performed on the first CDR data and the XDR signaling data of the core network, so as to obtain second CDR data with user information.
In a preferred embodiment, as shown in fig. 10, fig. 10 is a schematic diagram of a sixth embodiment of a triangulation method based on CDR data according to the present invention, and step 122 includes: step 1221, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first CDR data to obtain second CDR data with user information; step 1222, storing the user information fields imsi, mdn, imei in the second CDR data in the form of HIVE table.
Preferably, as shown in fig. 11, fig. 11 is a schematic diagram of a seventh embodiment of a triangulation method based on CDR data in the present invention, and step 130 includes: step 131, backfilling the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the second CDR data into the second MR data to obtain third MR data; step 132, storing the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the form of HIVE table in the third MR data.
In a preferred embodiment, as shown in fig. 12, fig. 12 is a schematic diagram of an eighth embodiment of a triangulation method based on CDR data according to the present invention, and step 200 includes: step 210, acquiring the number of non-co-sited neighbor cells in the third MR data, and if the number of non-co-sited neighbor cells is less than two, performing the next step; step 220, acquiring CDR related information of second CDR data corresponding to the user information through the user information in the third MR data; step 230, obtaining second CDR data which is not co-sited with the main service cell in the latest time sequence through CDR associated information; step 240, extracting longitude and latitude information and RSRP information of at least two pieces of second CDR data to perform non-common station distance calculation; step 250, constructing a user triangular distance positioning structure by using the non-co-station distances.
As shown in fig. 3, fig. 3 is a first schematic diagram of triangulation location according to the present invention, where the triangulation location is implemented by using multiple pieces of second CDR data in adjacent time sequences of MR data of the same third user, and using base station and RSRP information included in the second CDR data to construct two or more non-co-sited neighbor conditions for the triangulation location.
Preferably, as shown in fig. 13, fig. 13 is a schematic diagram of a ninth embodiment of a triangulation method based on CDR data in the present invention, and step 200 further includes: step 260, acquiring the number of non-co-sited neighbor cells in the third MR data, and if the number of non-co-sited neighbor cells is greater than or equal to two, performing the next step; step 270, acquiring a plurality of base station information overlapped in the second CDR data and the third MR data; step 280, obtaining average RSRP values of a plurality of base stations; step 290, calculating the triangulation distance using the average RSRP value.
In a preferred embodiment, as shown in fig. 14, fig. 14 is a schematic diagram of a tenth embodiment of a triangulation method based on CDR data in the present invention, and step 280 further includes: step 281, checking the RSRP value of the second CDR data and the RSRP value in the third MR data; step 282, if the difference between the RSRP value of the second CDR data and the RSRP value in the third MR data is smaller than the threshold range, obtaining an average RSRP value by averaging.
If the third MR data includes a plurality of non-co-sited neighbor cells, as shown in fig. 4, fig. 4 is a second schematic diagram of the triangulation of the present invention, and the base stations included in the plurality of second CDR data in a shorter time of the user coincide with a plurality of neighbor base stations of the third MR data.
And extracting the RSRP information of the part of base stations from the second CDR data, checking the RSRP information of the part of base stations and the RSRP value of the adjacent cells in the third MR data, if the phase difference range is within the threshold range, considering that the checking is qualified, obtaining the new RSRP value of the adjacent cells by averaging, and then carrying out propagation model calculation according to the new RSRP, otherwise, discarding the information of the adjacent cells and not participating in the triangular positioning.
The method has the advantages that the influence of external environment factors on the RSRP value can be effectively reduced, coarse errors of the RSRP value are eliminated, and the accuracy of distance calculation is improved.
By implementing the triangle positioning method based on the CDR data, the second MR data is acquired by backfilling the user information of the first MR data, and then the CDR data is associated with the second MR data according to the same user and time sequence to acquire the third MR data; the problem that the triangle positioning cannot be performed under the condition that 2 or more than 2 non-co-sited neighbor cells exist in the existing MR data is solved by utilizing the user information, the front-back switching chain relation and the RSRP information contained in the third MR data. By adopting the switching base station information and the RSRP information contained in the second CDR data to carry out mutual verification on the RSRP information of the adjacent region in the third MR data, the confidence coefficient of the RSRP value is improved, the coarse error of the RSRP value is eliminated, the RSRP value fluctuation caused by external environment factors is effectively identified, and therefore the triangle positioning precision is improved.
The foregoing is only illustrative of the present invention and is not to be construed as limiting thereof, but rather as various modifications, equivalent arrangements, improvements, etc., within the spirit and principles of the present invention.
Claims (8)
1. A method for triangulating based on CDR data, comprising:
Step 100, acquiring third MR data with user information and a front-back switching chain relationship by using CDR data and XDR data;
step 200, constructing a user triangular distance positioning structure by using the third MR data;
wherein, the step 200 includes:
Step 210, acquiring the number of non-co-sited neighbor cells in the third MR data, wherein the number of non-co-sited neighbor cells is less than two, and then performing the next step;
step 220, acquiring CDR related information of second CDR data corresponding to the user information through the user information in the third MR data;
step 230, obtaining second CDR data non-co-sited with the main service cell in the latest time sequence through the CDR association information;
step 240, extracting longitude and latitude information and RSRP information of at least two pieces of second CDR data to perform non-common station distance calculation;
step 250, constructing a user triangular distance positioning structure by utilizing the non-common station distance;
Step 260, obtaining the number of non-co-sited neighbor cells in the third MR data, wherein the number of non-co-sited neighbor cells is greater than or equal to two, and then performing the next step;
Step 270, acquiring a plurality of base station information overlapped in the second CDR data and the third MR data;
Step 280, obtaining average RSRP values of the plurality of base stations;
step 290, calculating a triangular positioning distance by using the average RSRP value;
Step 300, utilizing the triangular distance positioning structure to position the mobile user in real time;
The third MR data is obtained through multi-level user information backfilling.
2. The CDR data-based triangulation method as claimed in claim 1, wherein said step 100 comprises:
step 110, backfilling user information by associating the first MR data with XDR signaling data of a core network to obtain second MR data with the user information;
Step 120, backfilling user information by associating the first CDR data with XDR signaling data of a core network to obtain second CDR data with the user information;
Step 130, the second CDR data of the same user and the second MR data are associated according to a time sequence, and the switching chain information of the second CDR data is backfilled to the second MR data, so as to obtain third MR data with a relationship between the user information and the front-back switching chain.
3. The CDR data-based triangulation method as claimed in claim 2, wherein said step 110 comprises:
Step 111, adopting kafka or flink to access wireless first MR data and XDR signaling data of a core network;
Step 112, merging and parallel connecting the first MR data and the XDR signaling data of the core network.
4. The CDR data-based triangulation method as claimed in claim 3, wherein said step 112 comprises:
Step 1121, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first MR data to obtain second MR data with user information;
Step 1122, storing the user information fields imsi, mdn, imei in the second MR data in the form of an HIVE table.
5. The CDR data-based triangulation method as claimed in claim 2, wherein said step 120 comprises:
step 121, adopting kafka or flink to access wireless first CDR data and XDR signaling data of a core network;
and 122, merging and parallelly connecting the first CDR data with the XDR signaling data of the core network to obtain second CDR data with user information.
6. The CDR data-based triangulation method as claimed in claim 5, wherein said step 122 comprises:
Step 1221, backfilling user information fields imsi, mdn, imei in the XDR signaling data of the core network into the first CDR data to obtain second CDR data with user information;
step 1222, storing the user information fields imsi, mdn, imei in the second CDR data in the form of an HIVE table.
7. The CDR data-based triangulation method as claimed in claim 2, wherein said step 130 comprises:
Step 131, backfilling the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the second CDR data into the second MR data to obtain third MR data;
Step 132, storing the switching chain information cdr_starttime、ho_first_srcenbid、ho_first_srccellid、ho_last_srcenbid、ho_last_srccellid,ho_first_rsrp,ho_last_rsrp in the third MR data in the form of an HIVE table.
8. The CDR data-based triangulation method of claim 1, wherein the step 280 further comprises:
Step 281, checking the RSRP value of the second CDR data and the RSRP value in the third MR data;
Step 282, if the difference between the RSRP value of the second CDR data and the RSRP value in the third MR data is smaller than the threshold range, obtaining the average RSRP value by averaging.
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| CN109005514A (en) * | 2018-08-15 | 2018-12-14 | 广东宜通世纪科技股份有限公司 | Earth-filling method, device, terminal device and the storage medium of customer position information |
| CN110933601A (en) * | 2019-12-25 | 2020-03-27 | 中国移动通信集团内蒙古有限公司 | Target area determination method, device, equipment and medium |
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| US9277431B1 (en) * | 2014-05-01 | 2016-03-01 | Amdocs Software Systems Limited | System, method, and computer program for generating mobile subscriber network experience indicators based on geo-located events |
| CN109246592B (en) * | 2017-06-15 | 2020-09-08 | 中国移动通信集团浙江有限公司 | Method and device for acquiring position information of user terminal |
| CN110475252B (en) * | 2018-05-10 | 2022-05-24 | 上海大唐移动通信设备有限公司 | Indoor sub-cell MR weak coverage optimization method and device based on user behavior judgment |
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| CN109005514A (en) * | 2018-08-15 | 2018-12-14 | 广东宜通世纪科技股份有限公司 | Earth-filling method, device, terminal device and the storage medium of customer position information |
| CN110933601A (en) * | 2019-12-25 | 2020-03-27 | 中国移动通信集团内蒙古有限公司 | Target area determination method, device, equipment and medium |
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