CN113015092A - Network positioning method and device - Google Patents
Network positioning method and device Download PDFInfo
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- CN113015092A CN113015092A CN201911327915.2A CN201911327915A CN113015092A CN 113015092 A CN113015092 A CN 113015092A CN 201911327915 A CN201911327915 A CN 201911327915A CN 113015092 A CN113015092 A CN 113015092A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
The invention discloses a method and a device for network positioning, which comprises the following steps: acquiring at least one grid covered by a wireless access point and offline fingerprint characteristics corresponding to the grid from a preset fingerprint characteristic library according to fingerprint information of at least one wireless access point carried by a positioning request of terminal equipment, acquiring real-time fingerprint characteristics of the wireless access point according to the fingerprint information, inputting the offline fingerprint characteristics and the real-time fingerprint characteristics of the wireless access point covering the same grid into a first sequencing model, scoring and sequencing the grids, and selecting a preset number of grids as candidate grids from the sequenced grids; inputting the fingerprint characteristic data of the candidate grids into a second sorting model, scoring and sorting the candidate grids, and selecting the grids where the terminal equipment is located from the sorted candidate grids. According to the invention, by establishing the sequencing model, the application range of the positioning service is expanded from indoor to indoor and outdoor integration, so that the positioning framework is easy to maintain and expand, and different scene requirements are met.
Description
Technical Field
The present invention belongs to the field of positioning technology, and more particularly, to a method and an apparatus for network positioning.
Background
With the development of hardware devices such as mobile communication technology and mobile terminals, network positioning services play an increasingly important role in the production and life of people. The existing positioning technology introduces a deep learning algorithm, but the following defects exist at present: (1) the application range is limited, the positioning algorithm based on deep learning disclosed by the prior art is generally suitable for positioning in an indoor environment, and the depth features are extracted from the signal feature RSSI big data, and a fingerprint database is established according to the features, so that the positioning of a user is realized, and the requirement of outdoor positioning is not considered. (2) The fingerprint database is established by adopting single characteristics, only the RSSI characteristics are used, and the fingerprint database is easily influenced by environmental changes. (3) Most of the training algorithms use algorithms such as RBM or BP, and for positioning problems, the generalization capability of classification or regression models is insufficient. In view of the above-mentioned drawbacks, it is an urgent need in the industry to provide network positioning that can be applied in engineering and also provides both indoor and outdoor positioning.
Disclosure of Invention
One of the technical problems to be solved by the present invention is to provide a method and an apparatus for network positioning.
According to an embodiment of an aspect of the present invention, there is provided a method for network positioning, including:
acquiring at least one geographical grid covered by the wireless access point and offline fingerprint characteristics corresponding to the geographical grid from a preset fingerprint characteristic library according to fingerprint information of at least one wireless access point carried by a positioning request of terminal equipment; acquiring real-time fingerprint characteristics of the wireless access point according to the fingerprint information; the fingerprint compression characteristics and the real-time fingerprint characteristics of wireless access points covering the same geographic grid are organized together to serve as the fingerprint characteristic data of the geographic grid; inputting the fingerprint characteristic data of all the geographic grids into a first sequencing model, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids; inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting model, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
According to an embodiment of another aspect of the present invention, there is provided an apparatus for network positioning, the apparatus including: the device comprises an offline feature calculation module, a real-time feature calculation module, a first sequencing module and a second sequencing module; the off-line feature calculation module acquires at least one geographic grid covered by the wireless access point and off-line fingerprint features corresponding to the geographic grid from a preset fingerprint feature library according to fingerprint information of at least one wireless access point carried by a positioning request of the terminal equipment; the real-time characteristic calculation module acquires real-time fingerprint characteristics of the wireless access point according to the fingerprint information; the method comprises the steps that offline fingerprint features and real-time fingerprint features of wireless access points covering the same geographic grid are organized together to serve as fingerprint feature data of the geographic grid; inputting the fingerprint feature data of all the geographic grids into the first sequencing module, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids; inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting module, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
According to an embodiment of an aspect of the present invention, there is provided a server including: a storage device; one or more processors; wherein the storage is configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the aforementioned method of network location.
According to an embodiment of an aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed, implements the aforementioned method of network positioning.
The invention not only considers the access point of the wireless local area network, but also comprises a cellular base station, the application range of positioning is expanded from indoor to indoor and outdoor integration, the input offline neural network not only comprises the characteristics of signal intensity or channel parameters, and the like, but also inputs various other characteristics of acquisition, association, ip, and the like, various information is fused and refined through an offline characteristic compression layer (using a deep neural network model), the positioning precision is improved, the use scene is enlarged, in addition, an online network is introduced, the offline characteristic and the online characteristic are fused, the positioning precision and the robustness are improved, meanwhile, calculation and storage are considered, finally, the frame of a sequencing algorithm LTR (lower ranking rank) is used for positioning, and the positioning precision is improved and the calculation amount is reduced through the series connection of rough rank and fine rank. And the framework of the overall positioning algorithm is easy to maintain and expand, and parameters are flexible and adjustable in different scenes.
It will be appreciated by those of ordinary skill in the art that although the following detailed description will proceed with reference being made to illustrative embodiments, the drawings, the present invention is not intended to be limited to these embodiments, but rather the scope of the present invention is to be broadly construed, and is intended to be limited only by the appended claims.
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, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a system framework diagram according to the present invention.
Fig. 2 is a flow chart of a method of network location according to the present invention.
Fig. 3 is a schematic diagram of a network-locating apparatus according to the present invention.
Detailed Description
To facilitate understanding and implementing the present invention for those skilled in the art, the following technical solutions of the present invention are described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
To facilitate understanding and implementing the present invention for those skilled in the art, the following technical solutions of the present invention are described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a system framework diagram for network localization using a neural network and a ranking algorithm is provided according to an embodiment of the present invention.
For ease of understanding, a description will first be made of the overall structure of the system:
fig. 1 is a system structure diagram of the present invention, and the system is composed of a server and a mobile terminal, wherein the server is responsible for storing offline feature fingerprint information and calculating a positioning result, and feeding the positioning result back to the mobile terminal of a user. The server comprises an offline calculation layer and an online calculation layer, the offline compression characteristics of the wireless access points are calculated by inputting the original characteristics of the wireless access points, such as W WIFI access points and C base stations, into an offline characteristic compression model in the offline calculation layer, and the offline compression characteristics of all the wireless access points are stored in a fingerprint characteristic library preset in the offline calculation layer. The online computing layer comprises a rough arrangement model and a fine arrangement model, when a server receives a positioning request containing access point fingerprint information sent by a mobile terminal, real-time fingerprint characteristics of wireless access points are obtained according to the fingerprint information, offline fingerprint characteristics and real-time fingerprint characteristics of wireless access points covering the same geographic grid are organized together to serve as fingerprint characteristic data of the geographic grid, the fingerprint characteristic data are input into the rough arrangement model to obtain a preset number of candidate geographic grids, then the preset number of candidate geographic grids are input into the fine arrangement model to finally determine the geographic grid where the mobile terminal is located. The mobile terminal comprises a display module, a processor module, a communication module and a signal acquisition module, wherein the display module is used for displaying a positioning result to a user, the processor module is used for controlling wireless signal acquisition and interactive communication with a server end, the communication module is used for communicating with the server end and a wireless access point, the signal acquisition module is used for acquiring related information of the wireless access point, the mobile terminal equipment is usually carried by the user needing positioning service, when the user needs the positioning service, the mobile terminal is used for sending the positioning request to the server, and then receiving positioning estimation information returned by the server end and displaying the positioning estimation information to the user in a visual mode. At present, a popular smart phone, a tablet computer, a vehicle-mounted terminal or a personal digital assistant and the like can be used as a mobile terminal.
Example two
Referring to fig. 2, a second embodiment of the present invention provides a network positioning method using a neural network and a ranking algorithm, including the following steps:
and step S10, acquiring at least one geographical grid covered by the wireless access point and an offline fingerprint characteristic corresponding to the geographical grid from a preset fingerprint characteristic library according to the fingerprint information of at least one wireless access point carried by the positioning request of the terminal equipment.
Before this step, the original features of the wireless access points need to be input into an offline feature compression layer model to calculate the offline compression features of the wireless access points, and the offline compression features of all the wireless access points are stored in a preset fingerprint feature library.
The wireless access point comprises an access point and a base station, and the original characteristics of the access point comprise at least one of the following: the base station comprises the following raw characteristics of the number of acquisition points, the type of access points, the number of grids covered by the acquisition points, the proportion of pv in each grid to the total pv of the access points, the proportion of pv in each grid to the total number of the acquisition points of the current grid, and a signal strength RSSI (received signal strength indicator) distribution vector, wherein the raw characteristics of the base station comprise at least one of the following characteristics: the method comprises the following steps of acquiring point number, base station type, grid number covered by the acquiring points, proportion of pv in each grid to total pv of the base station, proportion of pv in each grid to total number of the acquiring points of the current grid, and signal strength RSSI distribution vector. The acquisition of the original features of the access point and the base station does not need to be performed in the mobile device, the server side can store the original features after offline calculation, and the grid division mode can be a geo-hash or mercator projection or other geographical division modes, which is specifically shown in table 1:
| grid 1 | AP 1 | AP 2 | .. | BS 1 | BS 2 | BS 3 |
| Grid 2 | AP 3 | AP 4 | .. | BS 4 | BS 5 | BS 6 |
| … | ||||||
| Grid N | AP N | AP N+1 | .. | BS N | BS N+1 | BS N+2 |
TABLE 1
The offline feature compression layer model adopts a deep neural network model, and during calculation, the original features in each grid are firstly extracted, and then the offline compression features of the wireless access points in a certain grid are obtained through the offline feature compression layer model. If the grids covered by the wireless access point have grids which do not store the offline fingerprint features in the preset fingerprint feature library, the default value is used as the original features of the grids, and the default offline compression features of the grids are obtained through the offline feature compression layer model. The resulting off-line compression feature format is shown in table 2:
TABLE 2
Step S20, acquiring real-time fingerprint characteristics of the wireless access point according to the fingerprint information;
in the step, the server receives a positioning request sent by the mobile terminal, wherein the positioning request comprises a wireless access point list, IP information and context information of user positioning. The list of wireless access points includes: and acquiring the real-time fingerprint characteristics of the wireless access point according to the fingerprint information.
And step S30, the offline fingerprint characteristics and the real-time fingerprint characteristics of the wireless access points covering the same geographic grid are organized together to be used as the fingerprint characteristic data of the geographic grid.
Step S40, inputting the fingerprint characteristic data of all the geographic grids into a first sequencing model, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids; inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting model, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
In this step, the first ranking model and the second ranking model are both neural network models, and the number of neural network layers of the second ranking model is greater than that of the first ranking model. The output of the first ordering model is the score of each candidate grid, the ordering is carried out according to the score, namely the coarse ordering is completed, the coarse screening (or sea selection) is a process of selecting partial grids from all candidate grids according to the result of the coarse ordering, the partial grids selected by the coarse screening are calculated through the second ordering model, the best grids are selected according to the calculation scores of the second ordering model during final positioning, and in order to further improve the positioning accuracy, accurate position point coordinates can be calculated through a clustering algorithm, a weighted average centroid algorithm or a Fermat point algorithm after the best grids are obtained.
EXAMPLE III
Referring to fig. 3, a third embodiment of the present invention discloses a network positioning apparatus, which is characterized in that the apparatus includes: the device comprises an offline feature calculation module, a real-time feature calculation module, a first sequencing module and a second sequencing module; wherein the content of the first and second substances,
the off-line feature calculation module acquires at least one geographic grid covered by the wireless access point and off-line fingerprint features corresponding to the geographic grid from a preset fingerprint feature library according to fingerprint information of at least one wireless access point carried by a positioning request of the terminal equipment;
the real-time characteristic calculation module acquires real-time fingerprint characteristics of the wireless access point according to the fingerprint information;
the method comprises the steps that offline fingerprint features and real-time fingerprint features of wireless access points covering the same geographic grid are organized together to serve as fingerprint feature data of the geographic grid;
inputting the fingerprint feature data of all the geographic grids into the first sequencing module, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids;
inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting module, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatus and system may refer to the corresponding processes of the foregoing method embodiments, and are not described herein again.
In addition, the embodiment of the present invention also discloses a server, which includes a storage device and one or more processors, where the storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to the first embodiment.
The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed, the method of the first embodiment is realized.
According to the technical scheme disclosed by the invention, the acquired original information simultaneously considers the indoor access point and the outdoor communication base station, so that the application range of positioning is expanded from indoor to indoor and outdoor integration, compared with the prior art that a model is trained by relying on signal intensity parameters which are easily influenced by environmental changes, the method introduces characteristics such as pv proportion and access point type which are not easily influenced by the environmental changes when the model is trained, and a plurality of information is fused and refined by using an offline feature compression layer of a deep neural network model, so that the robustness of a positioning system is improved.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart and block diagrams may represent a module, segment, or portion of code, which comprises one or more computer-executable instructions for implementing the logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. It will also be noted that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention, and is provided by way of illustration only and not limitation. It will be apparent to those skilled in the art from this disclosure that various other changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (14)
1. A method of network positioning, comprising:
acquiring at least one geographical grid covered by a wireless access point and an offline fingerprint characteristic corresponding to the geographical grid from a preset fingerprint characteristic library according to fingerprint information of the wireless access point carried by a positioning request of terminal equipment;
acquiring real-time fingerprint characteristics of the wireless access point according to the fingerprint information;
the method comprises the steps that offline fingerprint features and real-time fingerprint features of wireless access points covering the same geographic grid are organized together to serve as fingerprint feature data of the geographic grid;
inputting the fingerprint characteristic data of all the geographic grids into a first sequencing model, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids;
inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting model, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
2. The method of claim 1,
and obtaining the offline fingerprint characteristics of the wireless access point through the offline characteristic compression layer model, and storing the offline compression characteristics of the wireless access point into an offline characteristic fingerprint library.
3. The method of claim 1,
the first sequencing model and the second sequencing model are neural network models, and the number of neural network layers of the second sequencing model is more than that of the first sequencing model.
4. The method of claim 2, wherein: the calculating the offline compression characteristics of the wireless access point through the offline characteristic compression layer model comprises the following steps: inputting the original characteristics of the wireless access point into an offline characteristic compression layer model to obtain the offline compression characteristics of the wireless access point.
5. The method of claim 4, wherein: obtaining raw characteristics of a wireless access point includes: extracting the original features of each mesh in all meshes covered by the wireless access point.
6. The method of claim 5, wherein: the mesh division is performed using a GeoHash algorithm or a mercator projection algorithm.
7. The method of claim 6, wherein: if the grids covered by the wireless access point have grids which do not store the offline fingerprint features in a preset fingerprint feature library, using a default value as the original features of the grids, and obtaining the default offline compression features of the grids through an offline feature compression layer model.
8. The method of claim 4, wherein: the original characteristics of the wireless access point include at least one of: the method comprises the steps of acquiring point number, wireless access point types, the number of grids covered by the acquiring points, the proportion of pv in each grid to the whole pv of the wireless access points, the proportion of pv in each grid to the total number of the acquiring points of the current grid, and a signal strength RSSI distribution vector.
9. The method of claim 1, wherein: the positioning request comprises at least one of: a list of wireless access points, IP information, context information for user positioning.
10. The method of claim 9, wherein: the list of wireless access points includes: the MAC address of the access point, the KEY of the base station, the names of the access point and the base station, and the signal strengths of the access point and the base station.
11. The method of claim 1, wherein: inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting model, scoring and sorting the candidate geographic grids, and calculating accurate position point coordinates through a clustering algorithm, a weighted average centroid algorithm or a Fermat point algorithm according to the selected geographic grids after selecting the geographic grids where the terminal equipment is located from the sorted candidate geographic grids.
12. An apparatus for network positioning, the apparatus comprising: the device comprises an offline feature calculation module, a real-time feature calculation module, a first sequencing module and a second sequencing module; wherein the content of the first and second substances,
the off-line feature calculation module acquires at least one geographic grid covered by the wireless access point and off-line fingerprint features corresponding to the geographic grid from a preset fingerprint feature library according to fingerprint information of at least one wireless access point carried by a positioning request of the terminal equipment;
the real-time characteristic calculation module acquires real-time fingerprint characteristics of the wireless access point according to the fingerprint information;
the method comprises the steps that offline fingerprint features and real-time fingerprint features of wireless access points covering the same geographic grid are organized together to serve as fingerprint feature data of the geographic grid;
inputting the fingerprint feature data of all the geographic grids into the first sequencing module, scoring and sequencing the geographic grids, and selecting a preset number of geographic grids from the sequenced geographic grids as candidate geographic grids;
inputting the fingerprint characteristic data of the candidate geographic grids into a second sorting module, scoring and sorting the candidate geographic grids, and selecting the geographic grid where the terminal equipment is located from the sorted candidate geographic grids.
13. A server, characterized in that the server comprises:
a storage device;
one or more processors;
wherein the storage is configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method of network positioning as recited in any of claims 1-11.
14. A computer-readable storage medium, on which a computer program is stored which, when executed, implements a method of network positioning as claimed in any of claims 1-11.
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| CN116801383A (en) * | 2023-07-05 | 2023-09-22 | 广州市梦享网络技术有限公司 | Positioning method, device and equipment of wireless access point and storage medium |
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| CN116801383A (en) * | 2023-07-05 | 2023-09-22 | 广州市梦享网络技术有限公司 | Positioning method, device and equipment of wireless access point and storage medium |
| CN116801383B (en) * | 2023-07-05 | 2024-01-26 | 广州市梦享网络技术有限公司 | Positioning method, device and equipment of wireless access point and storage medium |
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