CN117062220A - Mobile network fingerprint positioning method, device and readable storage medium - Google Patents
Mobile network fingerprint positioning method, device and readable storage medium Download PDFInfo
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Abstract
The application provides a mobile network fingerprint positioning method, a mobile network fingerprint positioning device and a readable storage medium, wherein the mobile network fingerprint positioning method comprises the following steps: collecting MDT data of a mobile network and GIS map information of a geographic information system; dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions; training a fingerprint positioning model according to the segmented vector boundary region and the MDT data; the acquired measurement report MR data is input to a trained fingerprint positioning model to achieve fingerprint positioning. The method, the device and the readable storage medium can solve the problems that the existing fingerprint positioning technology based on equal-division rasterization cannot be suitable for service positioning requirements of functional attributes of user positions to be concerned, and the operation complexity of a fingerprint positioning model is easy to increase.
Description
Technical Field
The present application relates to the field of network technologies, and in particular, to a mobile network fingerprint positioning method, a mobile network fingerprint positioning device, and a readable storage medium.
Background
The mobile network terminal equipment and the position information of the user have high application value, so the industry develops a plurality of positioning technologies. Among these positioning technologies, the fingerprint positioning technology has the advantages of low cost, wide positioning range, no newly added hardware resources and the like, and has been widely applied to mobile networks such as 4G, 5G and the like in recent years.
However, the existing mobile network fingerprint positioning technology usually focuses on the accurate position of the user, but there are some service positioning requirements that focus on the functional attribute of the user position, and for this type of positioning requirement, the existing fingerprint positioning technology based on equal-division rasterization cannot be suitable for this type of positioning requirement, and the problem of easily increasing the operation complexity of the fingerprint positioning model is solved.
Disclosure of Invention
The technical problem to be solved by the application is to provide a mobile network fingerprint positioning method, a mobile network fingerprint positioning device and a mobile network fingerprint positioning readable storage medium aiming at the defects of the prior art, so as to solve the problems that the existing fingerprint positioning technology based on equal division rasterization cannot be suitable for service positioning requirements of functional attributes of user positions, and the operation complexity of a fingerprint positioning model is easy to increase.
In a first aspect, the present application provides a mobile network fingerprint positioning method, the method
The method comprises the following steps:
collecting MDT data of a mobile network and GIS map information of a geographic information system;
dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions;
training a fingerprint positioning model according to the segmented vector boundary region and the MDT data;
the acquired measurement report MR data is input to a trained fingerprint positioning model to achieve fingerprint positioning.
Further, the MDT data includes: and the Reference Signal Received Power (RSRP), all neighbor RSRP and position information of the serving cell reported by the terminal.
Further, the dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attribute of the geographic location specifically includes:
and dividing the GIS map into vector boundary areas with irregular vector boundaries according to the social function attribute of the geographic position and the geomorphic attribute affecting signal propagation.
Further, the dividing the GIS map into vector boundary areas with irregular vector boundaries according to the social function attribute of the geographic position and the geomorphic attribute affecting signal propagation specifically comprises the following steps:
dividing the geographic continuous positions with the same social function attribute in the GIS map into an area;
and dividing the geographical continuous landforms affecting the wireless signal transmission in the GIS map.
Further, the social functional attributes include at least one of: schools, roads, hospitals, parks;
the topography that affects the propagation of the wireless signal includes at least one of: open space, building, sparse tree, dense forest, water, bridge.
Further, after the geographic continuous landforms affecting the propagation of the wireless signal in the GIS map are divided into areas, the method further includes:
aiming at the areas with the social function attribute of schools, further dividing playgrounds, sidewalks, roadways and greenbelts;
for the area with the social function attribute of the road, the area is further divided according to the intersections, and the intersection area is divided into independent areas.
Further, the training fingerprint positioning model according to the segmented vector boundary region and the MDT data specifically includes:
numbering all the divided vector boundary areas;
adding a list of region number attributes into the MDT data;
adding a number corresponding to each sampling point in the region number attribute according to a vector boundary region in which each sampling point in the MDT data falls;
and training the fingerprint positioning model by taking the MDT data added with the numbers as a data set.
In a second aspect, the present application provides a mobile network fingerprint positioning device, comprising:
the data acquisition module is used for acquiring MDT data of the mobile network and GIS map information of the geographic information system;
the region segmentation module is connected with the data acquisition module and is used for segmenting the GIS map into vector boundary regions with irregular vector boundaries according to the functional attributes of the geographic positions;
the model training module is connected with the region segmentation module and is used for training a fingerprint positioning model according to the segmented vector boundary region and the MDT data;
and the fingerprint positioning module is connected with the model training module and used for inputting acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning.
In a third aspect, the present application provides a mobile network fingerprint positioning device comprising a memory and a processor, the memory storing a computer program, the processor being arranged to run the computer program to implement the mobile network fingerprint positioning method of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements the mobile network fingerprint positioning method according to the first aspect.
The application provides a mobile network fingerprint positioning method, a mobile network fingerprint positioning device and a readable storage medium. Firstly, collecting MDT data of a mobile network and GIS map information of a geographic information system; dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions; training a fingerprint positioning model according to the segmented vector boundary region and the MDT data; and finally, inputting the acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning. The application establishes the vector boundary area with the irregular vector boundary according to the functional attribute of the geographic position to replace the original equal-division rasterization method, is applied to fingerprint positioning model training and application, has the characteristics of low operation complexity, low operation cost and high model positioning accuracy, can more accurately distinguish the geographic type of the position, and is particularly suitable for service positioning requirements of the functional attribute of the user position which needs to be concerned, but not the accurate position of the user. The method solves the problems that the existing fingerprint positioning technology based on equal-division rasterization cannot be suitable for the service positioning requirement of focusing on the functional attribute of the user position and the operation complexity of a fingerprint positioning model is easy to increase.
Drawings
FIG. 1 is a schematic diagram of a prior art aliquoting grid;
fig. 2 is a flowchart of a mobile network fingerprint positioning method according to embodiment 1 of the present application;
FIG. 3 is a schematic diagram of a GIS map according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a segmented vector border region according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a mobile network fingerprint positioning device according to embodiment 2 of the present application;
fig. 6 is a schematic structural diagram of a mobile network fingerprint positioning device according to embodiment 3 of the present application.
Detailed Description
In order to make the technical scheme of the present application better understood by those skilled in the art, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the application, and are not limiting of the application.
It is to be understood that the various embodiments of the application and the features of the embodiments may be combined with each other without conflict.
It is to be understood that only the portions relevant to the present application are shown in the drawings for convenience of description, and the portions irrelevant to the present application are not shown in the drawings.
It should be understood that each unit and module in the embodiments of the present application may correspond to only one physical structure, may be formed by a plurality of physical structures, or may be integrated into one physical structure.
It will be appreciated that the terms "first," "second," and the like in embodiments of the present application are used to distinguish between different objects or to distinguish between different processes on the same object, and are not used to describe a particular order of objects.
It will be appreciated that, without conflict, the functions and steps noted in the flowcharts and block diagrams of the present application may occur out of the order noted in the figures.
It is to be understood that the flowcharts and block diagrams of the present application illustrate the architecture, functionality, and operation of possible implementations of systems, apparatuses, devices, methods according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a unit, module, segment, code, or the like, which comprises executable instructions for implementing the specified functions. Moreover, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by hardware-based systems that perform the specified functions, or by combinations of hardware and computer instructions.
It should be understood that the units and modules related in the embodiments of the present application may be implemented by software, or may be implemented by hardware, for example, the units and modules may be located in a processor.
In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, some technical terms related to the embodiments of the present application are briefly described below.
1) Model positioning accuracy: refers to the proximity of position information (typically coordinates) to its actual position.
2) Model positioning accuracy: refers to whether the grid in which the model is positioned is the correct grid.
Summary of the application
The mobile network terminal equipment and the position information of the user have high application value, so the industry develops a plurality of positioning technologies. Among these positioning technologies, the fingerprint positioning technology has the advantages of low cost, wide positioning range, no newly added hardware resources and the like, and has been widely applied to mobile networks such as 4G, 5G and the like in recent years.
In the existing mobile network fingerprint positioning technology, data information such as coverage, interference and the like of a wireless network in a specific geographic position is generally collected, fingerprint library modeling is performed through algorithms such as machine learning and the like, finally, the similarity between MR (Measurement Report ) data newly reported by a terminal/user and the fingerprint library is compared, and the nearest fingerprint position is selected as the terminal position.
The mobile network fingerprint positioning technology can be divided into two types, namely indoor positioning and outdoor positioning. Because of the wide outdoor positioning space area, the existing outdoor fingerprint positioning technology generally performs grid processing on a GIS (Geographic Information System ) map, and divides the map into equal grids (in square, hexagonal and other forms) according to longitude and latitude, taking account of sparsity of terminal data, uncertainty of wireless signal propagation and cost of hardware computing capability. The grid size is customized according to fingerprint modeling algorithm capability, model training data precision and other factors. After geographic rasterization, fingerprint models are built for each grid through model training, and finally a complete fingerprint library is formed. The fingerprint positioning method based on rasterization has the positioning precision of about 20 meters, and the side length of a common rectangular grid is 30 meters to 100 meters. The rasterization method is a method of balancing positioning accuracy with system operation costs, and it positions a terminal or user to a geographic grid. This can meet the positioning requirements of some specific locations of interest to the user.
However, there are also some business positioning requirements that focus more on the functional attributes of the user's location (i.e., environmental attributes) than the user's precise location, typical business requirements include: mobile network operation and maintenance, network optimization, advertisement pushing, and the like. For example, in the wireless network optimization work, for a problem area with user complaints, an optimization engineer more hopes to know whether the network has problems outdoors or indoors, in an open square or a park with dense forest by fingerprint positioning, because the wireless signal propagation is directly affected by different environments, and the network analysis is directly affected; for another example, the operator's marketing of advertisements, it is desirable to know that the user is in the stadium and thus push sporting goods advertisements for him, rather than pushing users who merely pass by the stadium.
Therefore, aiming at the positioning requirement, the prior fingerprint positioning method based on rasterization has the following defects:
1) There may be multiple geographic functional attributes within a single grid area, possibly several distinct functional areas (e.g., a portion of a street, a portion of a stadium, a portion of a building);
2) The equally divided geographic grids have no more information value, but rather increase the operation complexity of the fingerprint positioning model and reduce the accuracy of the model.
3) The equal grid joint has no data difference of any dimension, time-varying wireless propagation environment and instability of wireless channels are overlapped, and various wireless environment scenes possibly exist in the grids, so that the generalization and convergence difficulty of the model is greatly increased, and the research and development difficulty and the realization cost are increased. Fig. 1 shows a schematic diagram of a conventional aliquoting grid, and as can be seen from fig. 1, the learning of a fingerprint positioning model is disturbed based on the aliquoting grid boundary due to the instability of a wireless channel.
Aiming at the technical problems, the application provides a mobile network fingerprint positioning method, a mobile network fingerprint positioning device and a readable storage medium, which are used for establishing a vector boundary area with an irregular vector boundary according to the functional attribute of a geographic position to replace the original equal-division rasterization method, are applied to fingerprint positioning model training and application, have the characteristics of low operation complexity, low operation cost and high model positioning accuracy, can more accurately distinguish the geographic position types, are particularly suitable for service positioning requirements of the functional attribute of a user needing to be concerned instead of the accurate position of the user, and at least solve the problems that the traditional equal-division rasterization-based fingerprint positioning technology cannot be suitable for service positioning requirements of the functional attribute of the user needing to be concerned and the operation complexity of a fingerprint positioning model is easy to increase.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1:
the present embodiment provides a mobile network fingerprint positioning method, as shown in fig. 2, including:
step S101: MDT (Minimization Drive Test) data of the mobile network and geographic information system GIS map information are collected.
It should be noted that, since the MDT data is a data acquisition mode of measurement information such as network coverage of a user terminal supported by 3Gpp protocol, the method has the advantages of wide acquisition range, large data volume and no additional hardware input cost, so that machine learning based on the MDT data is a technical method for training and constructing a fingerprint library with the lowest cost and the widest application of operators.
Optionally, the MDT data includes: the serving cell RSRP (Reference Signal Receiving Power, reference signal received power), all neighbor RSRP and location information reported by the terminal.
In this embodiment, the acquired MDT data of the mobile network at least includes the serving cell RSRP, all neighbor cell RSRP and location information reported by the terminal, and may further include information such as serving cell RSQR (Reference Signal Received Quality, reference signal reception quality), TA (Tracking Area), PCI (Physical Cell Identities physical cell ID), ul SINR (UpLink SINR), and the like. Server cells PCI, RSRP, RSRQ, TA and ul sinr for each sample point in the MDT data and PCI, RSRP information for up to 7 neighbors may be stored in one csv file.
In this embodiment, the open source GIS map opentreetmap may be used to collect GIS map information of the region to be modeled, for example, the collected GIS map may be as shown in fig. 3.
Step S102: and dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions.
In this embodiment, the functional attributes of the geographic location include social functional attributes and topographical attributes that affect signal propagation. Specifically, the GIS map may be partitioned into vector boundary regions having irregular vector boundaries according to the socially functional attributes of the geographic location and the geomorphic attributes affecting signal propagation.
Optionally, the dividing the GIS map into vector boundary areas with irregular vector boundaries according to the social function attribute of the geographic location and the geomorphic attribute affecting signal propagation specifically includes:
dividing the geographic continuous positions with the same social function attribute in the GIS map into an area;
and dividing the geographical continuous landforms affecting the wireless signal transmission in the GIS map.
In this embodiment, the social function attribute includes at least one of: schools, roads, hospitals, parks; the topography that affects the propagation of the wireless signal includes at least one of: open space, building, sparse tree, dense forest, water, bridge.
Optionally, after the geographic continuous land feature affecting the propagation of the wireless signal in the GIS map is divided into areas, the method further includes:
aiming at the areas with the social function attribute of schools, further dividing playgrounds, sidewalks, roadways and greenbelts;
for the area with the social function attribute of the road, the area is further divided according to the intersections, and the intersection area is divided into independent areas.
In this embodiment, for a GIS map in which a fingerprint positioning model needs to be established, a vector boundary method is adopted to divide all regions into a plurality of vector boundary regions, which specifically includes:
(1) Dividing geographically continuous locations having the same social functional attributes into an area; the method can be divided according to the actual positioning accuracy requirement, for example, large-area scenes such as schools, hospitals, parks and the like are distinguished.
(2) Finer division is performed according to social function attributes, for example, for school scenes, scenes such as playgrounds, sidewalks, roadways, greenbelts and the like can be further divided.
(3) Aiming at a road scene, carrying out region segmentation according to an intersection, and dividing the intersection region into independent regions;
(4) The area division is carried out according to the geographic continuous landforms which have obvious influence on the wireless signal propagation, and the area division can be generally divided into: open spaces, buildings, sparse trees, dense forests, bodies of water, bridges, and the like.
In this embodiment, taking the region shown in fig. 3 as an example, after the GIS map of fig. 3 is segmented, a segmented vector boundary region as shown in fig. 4 may be obtained, and the vector boundary region may be stored in a GeoJSON format.
Step S103: and training a fingerprint positioning model according to the segmented vector boundary region and the MDT data.
In this embodiment, the fingerprint positioning model is trained through the vector boundary area corresponding to each sampling point in the MDT data, so that a trained fingerprint positioning model can be obtained.
Optionally, the training fingerprint positioning model according to the segmented vector boundary area and the MDT data specifically includes:
numbering all the divided vector boundary areas;
adding a list of region number attributes into the MDT data;
adding a number corresponding to each sampling point in the region number attribute according to a vector boundary region in which each sampling point in the MDT data falls;
and training the fingerprint positioning model by taking the MDT data added with the numbers as a data set.
In this embodiment, each vector border area has a unique number, a column of "area number" attribute is added to the MDT data, for example, a column of "polygon_id" is added, and according to the vector border area in which the sampling point falls, the area number of each sampling point is added, that is, the vector border area id to which each sampling point belongs is filled.
And inputting the MDT data added with the area numbers as a training data set into a machine learning model, and training a fingerprint positioning model. For example, the Xgboost model may be used for fingerprint positioning model training:
model＝xgb.XGBClassifier(max_depth＝4,min_child_weight＝1,gamma＝0.5,subsample＝0.8,colsample_bytree＝0.3,learning_rate＝0.1,n_estimators＝100)
model.fit(X_train,y_train)
wherein the newly added polygon_id is the target data y_train, and the other data is X_train.
Step S104: the acquired measurement report MR data is input to a trained fingerprint positioning model to achieve fingerprint positioning.
In this embodiment, MR data without longitude and latitude in the region is acquired, and the data is input into a trained fingerprint positioning model, so that polyon_id information can be added to each MR sampling point data, thereby realizing fingerprint position positioning.
Specifically, through experimental tests, compared with the model training time of the rasterization method, the mobile network fingerprint positioning method provided by the embodiment of the application is reduced by about 74%, the model execution speed is improved by about 32%, and the model positioning accuracy is improved by 35%. However, the model positioning accuracy is affected by the size of the vector boundary region separation, and may be degraded. The vector boundary area divided in this embodiment averages 1200 square meters, and the positioning accuracy is slightly lower than 900 square meters of a square grid of 30 by 30 meters.
The method can sacrifice part of positioning accuracy, but has low complexity and low operation cost, can improve the positioning accuracy of the model so as to more accurately distinguish the geographic type of the position, and is particularly suitable for paying attention to the functional attribute of the position of the user rather than the service positioning requirement of the accurate position of the user.
In a specific embodiment, the mobile network fingerprint positioning method may include the steps of:
the first step: data acquisition
1) Collecting MDT data of a mobile network: at least comprises a serving cell RSRP, all neighbor cell RSRP and position information reported by a terminal. Preferably, information such as serving cell RSQR, TA and the like can be further included.
2) And collecting GIS map information.
And a second step of: dividing the GIS map into areas with irregular vector boundaries according to the functional attributes of the geographic positions, and numbering all the divided areas.
Specifically, for a map area in which a fingerprint positioning model needs to be established, a vector boundary method is adopted to divide all the area into a plurality of vector boundary areas, and the method can comprise the following steps:
1) Dividing geographically continuous locations having the same social functional attributes into an area; the method can be divided according to the actual positioning accuracy requirement, for example, large-area scenes such as schools, roads, hospitals, parks and the like are distinguished.
2) Finer division can be performed according to social functional attributes, for example, for school scenes, scenes such as playgrounds, sidewalks, roadways, greenbelts and the like can be further divided.
3) Aiming at a road scene, carrying out region segmentation according to an intersection, and dividing the intersection region into independent regions;
4) The area division is carried out according to the geographic continuous landforms which have obvious influence on the wireless signal propagation, and the area division can be generally divided into: open spaces, buildings, sparse trees, dense forests, bodies of water, bridges, and the like.
And a third step of: fingerprint positioning model learning is carried out according to the segmented vector boundary region
1) And adding a column of 'region number' attribute to the MDT data, and adding the region number of each sampling point according to the region in which the sampling point falls.
2) The collated data set is input into a machine learning model for fingerprint positioning model training (the application is not limited to which machine learning model is adopted for model training).
Fourth step: application of fingerprint positioning model
And acquiring MR data of the mobile network, and applying a trained fingerprint positioning model to realize fingerprint positioning.
The mobile network fingerprint positioning method provided by the embodiment of the application comprises the steps of firstly collecting MDT data of a mobile network and GIS map information of a geographic information system; dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions; training a fingerprint positioning model according to the segmented vector boundary region and the MDT data; and finally, inputting the acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning. The application establishes the vector boundary area with the irregular vector boundary according to the functional attribute of the geographic position to replace the original equal-division rasterization method, is applied to fingerprint positioning model training and application, has the characteristics of low operation complexity, low operation cost and high model positioning accuracy, can more accurately distinguish the geographic type of the position, and is particularly suitable for service positioning requirements of the functional attribute of the user position which needs to be concerned, but not the accurate position of the user. The method solves the problems that the existing fingerprint positioning technology based on equal-division rasterization cannot be suitable for the service positioning requirement of focusing on the functional attribute of the user position and the operation complexity of a fingerprint positioning model is easy to increase.
Example 2:
as shown in fig. 5, the present embodiment provides a mobile network fingerprint positioning device, configured to perform the above mobile network fingerprint positioning method, including:
the data acquisition module 11 is used for acquiring MDT data of a mobile network and GIS map information of a geographic information system;
the region segmentation module 12 is connected with the data acquisition module 11 and is used for segmenting the GIS map into vector boundary regions with irregular vector boundaries according to the functional attribute of the geographic position;
the model training module 13 is connected with the region segmentation module 12 and is used for training a fingerprint positioning model according to the segmented vector boundary region and the MDT data;
and the fingerprint positioning module 14 is connected with the model training module 13 and is used for inputting acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning.
Optionally, the MDT data includes: and the Reference Signal Received Power (RSRP), all neighbor RSRP and position information of the serving cell reported by the terminal.
Optionally, the region segmentation module 12 includes:
and the attribute segmentation unit is used for segmenting the GIS map into vector boundary areas with irregular vector boundaries according to the social function attribute of the geographic position and the landform attribute affecting signal propagation.
Optionally, the attribute segmentation unit specifically includes:
the first dividing unit is used for dividing the geographic continuous positions with the same social function attribute in the GIS map into an area;
the second dividing unit is used for dividing the geographic continuous landforms affecting the propagation of the wireless signal signals in the GIS map into areas.
Optionally, the social functional attribute includes at least one of: schools, roads, hospitals, parks;
the topography that affects the propagation of the wireless signal includes at least one of: open space, building, sparse tree, dense forest, water, bridge.
Optionally, the apparatus further comprises:
the school subdivision unit is used for further dividing playgrounds, sidewalks, roadways and greenbelts aiming at areas with social functional attributes of schools;
and the road subdivision unit is used for further dividing the region of the road according to the road junction aiming at the region with the social function attribute and dividing the road junction region into independent regions.
Optionally, the model training module 13 includes:
a number definition unit, configured to number all the segmented vector boundary regions;
a column adding unit, configured to add a column of region number attribute to the MDT data;
a number adding unit, configured to add a number corresponding to each sampling point in the region number attribute according to a vector boundary region in which each sampling point in the MDT data falls;
and the training unit is used for training the fingerprint positioning model by taking the MDT data added with the numbers as a data set.
Example 3:
referring to fig. 6, the present embodiment provides a mobile network fingerprint positioning device comprising a memory 21 and a processor 22, the memory 21 storing a computer program, the processor 22 being arranged to run the computer program to perform the mobile network fingerprint positioning method of embodiment 1.
The memory 21 is connected to the processor 22, the memory 21 may be a flash memory, a read-only memory, or other memories, and the processor 22 may be a central processing unit or a single chip microcomputer.
Example 4:
the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the mobile network fingerprint positioning method in embodiment 1 described above.
Computer-readable storage media include volatile or nonvolatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, computer program modules or other data. Computer-readable storage media includes, but is not limited to, RAM (Random Access Memory ), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory, charged erasable programmable Read-Only Memory), flash Memory or other Memory technology, CD-ROM (Compact Disc Read-Only Memory), digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
In summary, the mobile network fingerprint positioning method, device and readable storage medium provided by the embodiments of the present application collect the MDT data of the mobile network and the GIS map information of the geographic information system; dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions; training a fingerprint positioning model according to the segmented vector boundary region and the MDT data; and finally, inputting the acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning. The application establishes the vector boundary area with the irregular vector boundary according to the functional attribute of the geographic position to replace the original equal-division rasterization method, is applied to fingerprint positioning model training and application, has the characteristics of low operation complexity, low operation cost and high model positioning accuracy, can more accurately distinguish the geographic type of the position, and is particularly suitable for service positioning requirements of the functional attribute of the user position which needs to be concerned, but not the accurate position of the user. The method solves the problems that the existing fingerprint positioning technology based on equal-division rasterization cannot be suitable for the service positioning requirement of focusing on the functional attribute of the user position and the operation complexity of a fingerprint positioning model is easy to increase.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present application, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the application, and are also considered to be within the scope of the application.
Claims (10)
1. A mobile network fingerprint positioning method, the method comprising:
collecting MDT data of a mobile network and GIS map information of a geographic information system;
dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic positions;
training a fingerprint positioning model according to the segmented vector boundary region and the MDT data;
the acquired measurement report MR data is input to a trained fingerprint positioning model to achieve fingerprint positioning.
2. The method of claim 1, wherein the MDT data comprises: and the Reference Signal Received Power (RSRP), all neighbor RSRP and position information of the serving cell reported by the terminal.
3. The method according to claim 1, wherein the dividing the GIS map into vector boundary areas with irregular vector boundaries according to the functional attributes of the geographic location, specifically comprises:
and dividing the GIS map into vector boundary areas with irregular vector boundaries according to the social function attribute of the geographic position and the geomorphic attribute affecting signal propagation.
4. A method according to claim 3, wherein the dividing the GIS map into vector border areas with irregular vector borders according to the social function attribute of the geographic location and the geomorphic attribute affecting signal propagation, specifically comprises:
dividing the geographic continuous positions with the same social function attribute in the GIS map into an area;
and dividing the geographical continuous landforms affecting the wireless signal transmission in the GIS map.
5. The method of claim 4, wherein the social function attribute comprises at least one of: schools, roads, hospitals, parks;
the topography that affects the propagation of the wireless signal includes at least one of: open space, building, sparse tree, dense forest, water, bridge.
6. The method of claim 5, wherein after the dividing the geographic continuous topography of the GIS map that affects the propagation of the wireless signal, the method further comprises:
aiming at the areas with the social function attribute of schools, further dividing playgrounds, sidewalks, roadways and greenbelts;
for the area with the social function attribute of the road, the area is further divided according to the intersections, and the intersection area is divided into independent areas.
7. The method according to claim 1, wherein the training of the fingerprint positioning model from the segmented vector border region and the MDT data comprises:
numbering all the divided vector boundary areas;
adding a list of region number attributes into the MDT data;
adding a number corresponding to each sampling point in the region number attribute according to a vector boundary region in which each sampling point in the MDT data falls;
and training the fingerprint positioning model by taking the MDT data added with the numbers as a data set.
8. A mobile network fingerprint positioning device, comprising:
the data acquisition module is used for acquiring MDT data of the mobile network and GIS map information of the geographic information system;
the region segmentation module is connected with the data acquisition module and is used for segmenting the GIS map into vector boundary regions with irregular vector boundaries according to the functional attributes of the geographic positions;
the model training module is connected with the region segmentation module and is used for training a fingerprint positioning model according to the segmented vector boundary region and the MDT data;
and the fingerprint positioning module is connected with the model training module and used for inputting acquired measurement report MR data into a trained fingerprint positioning model to realize fingerprint positioning.
9. A mobile network fingerprint positioning device, comprising a memory and a processor, the memory having stored therein a computer program, the processor being arranged to run the computer program to implement a mobile network fingerprint positioning method according to any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the mobile network fingerprint positioning method according to any of claims 1-7.
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