CN106792507A - A kind of WiFi localization methods and server based on network data - Google Patents
A kind of WiFi localization methods and server based on network data Download PDFInfo
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- CN106792507A CN106792507A CN201611046266.5A CN201611046266A CN106792507A CN 106792507 A CN106792507 A CN 106792507A CN 201611046266 A CN201611046266 A CN 201611046266A CN 106792507 A CN106792507 A CN 106792507A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S1/00—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
- G01S1/02—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
- G01S1/08—Systems for determining direction or position line
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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
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention discloses a kind of WiFi localization methods based on network data, methods described includes step：Each WAP receives the network data of the signal that client to be detected sends in S100, acquisition detection zone；The network data is multidimensional data；S200, the input data layer by the location model after network data input training；S300, the Internet based on the location model after training calculate the network data of client position to be detected, and the position of client to be detected is determined according to the output result of output layer.Location model in the present invention is trained by a large amount of training sample data using the deep neural network after training to deep neural network, lifts Position location accuracy and precision.
Description
Technical field
The present invention relates to wireless local area network technology field, more particularly to a kind of WiFi localization methods based on network data and
Server.
Background technology
Location technology worldwide mainly has GPS location, Wi-Fi positioning, bluetooth positioning etc., GPS location at present
Outdoor is mainly used in, Wi-Fi, bluetooth positioning can be not only used for interior, it can also be used to outdoor.Because Wi-Fi positions relative maturity,
Below particular content of the invention is introduced with Wi-Fi location technologies as background.With the popularization of wireless router, current big portion
Point public domain all has been carried out more than ten or even tens WiFi signals coverings, and these routers are propagated to surrounding
While WiFi signal, the information such as its physical address and signal intensity are also ceaselessly sent, as long as in its signal cover,
Even if not knowing the password of Wi-Fi, these information can be similarly obtained.
General WiFi indoor positioning technologies are mostly the WLANs (WLAN) based on IEEE802.11b/g agreements
Signal intensity location technology.Location technology general principle based on signal intensity is to calculate letter according to the intensity of the signal for receiving
The distance between number receiver and signal source, are largely divided into two classes：Triangle intensity algorithm and location fingerprint recognizer.Its
Intermediate cam shape intensity arithmetic accuracy is low, it is difficult to meet indoor positioning requirement；And there is receiving device in general fingerprint recognizer
It is different and cause to receive the defect that signal has error.
The content of the invention
In order to solve the above technical problems, the present invention provides a kind of WiFi localization methods and server based on network data,
By gathering the corresponding signal strength data of each WAP, realize that the WiFi based on deep neural network is positioned.
The technical scheme that the present invention is provided is as follows：
The invention discloses a kind of WiFi localization methods based on network data, methods described includes step：S100, acquisition
Each WAP receives the network data of the signal that client to be detected sends in detection zone；The network data is
Multidimensional data；S200, the input data layer by the location model after network data input training；S300, based on training after
The Internet of location model calculate the network data of client position to be detected, and according to the output knot of output layer
Fruit determines the position of client to be detected.
It is further preferred that the network data receives client to be detected in detection zone including each WAP
The input power of the signal strength data, channel number and correspondence WAP of the signal sent in domain.
It is further preferred that also including step before the step S100：S000, training in advance deep neural network, will
Deep neural network after training is used as the location model.
It is further preferred that the step S000 further includes step：S001, pre-set training location tags；
S002, each WAP is gathered successively receive training terminal in each training location tags institute in detection zone
The network data of the signal for sending；The network data includes that each WAP receives the training location tags in detection
The signal strength data of the signal that the training terminal on the correspondence position of region is sent out, channel number and correspondence WAP it is defeated
Enter power；S003, respectively by each it is described training location tags and its corresponding signal strength data, channel number and correspondence
The input power of WAP generates training dataset as one group of training sample data, and sends into deep neural network；
S004, the input data of deep neural network layer is defined as Three-channel data layer, the node of the Three-channel data layer with it is each
Individual WAP is corresponding, according to the node mode corresponding with WAP of Three-channel data layer respectively by every group of training
Each signal strength data combination channel number corresponding with WAP and corresponding WAP in sample data
Input power is input into three passages of the Three-channel data layer corresponding node；By the deep neural network output with it is described
The corresponding training result of training location tags；S005, the corresponding training position of training result that will be exported successively
Label is compared, and deep neural network is trained according to comparative result, using the deep neural network after training as institute
State location model.
It is further preferred that also including step between the step S003 and the step S004：S035, respectively to each
The input work of the signal strength data for training the corresponding network data of location tags, channel number and correspondence WAP
Rate is normalized；Also include step between the step S100 and step S200：S150, the institute by client to be detected
The input power for stating the signal strength data in network data, channel number and correspondence WAP is normalized.
The invention also discloses a kind of WiFi location-servers based on network data, including：Data acquisition module, is used for
Each WAP receives the network data of the signal that client to be detected sends in acquisition detection zone；The network number
According to being multidimensional data；The network data receives client to be detected and is sent in detection zone including each WAP
The signal strength data of signal, channel number and correspondence WAP input power；Locating module, for that will collect
Network data input location model input data layer, Internet based on location model calculates the network data,
And the output result of the output layer for passing through location model determines the position of client to be detected.
It is further preferred that also including：Training module, for training in advance deep neural network, by the depth after training
Neutral net is used as the location model.
It is further preferred that the training module is further included：Label presets submodule, for pre-setting for instructing
Experienced training location tags；Training dataset generates submodule, and training is received eventually for gathering each WAP successively
The network data of the signal sent in detection zone in each training location tags is held, respectively by each training
Location tags and its corresponding network data generate training dataset, and send into depth god as one group of training sample data
Through in network；The network data includes that each WAP receives the training location tags in detection zone correspondence position
On the signal sent out of training terminal signal strength data, channel number and correspondence WAP input power；Input
Data Layer defines submodule, for the input data layer of deep neural network to be defined as into Three-channel data layer, the triple channel
The node of data Layer is corresponding with each WAP；Training prediction submodule, for the node according to Three-channel data layer
Mode corresponding with WAP is strong by each signal corresponding with WAP in every group of training sample data respectively
The input power of degrees of data combination channel number and correspondence WAP is input into the three of the Three-channel data layer corresponding node
Individual passage, by the deep neural network output training result corresponding with the training location tags, successively will output
The corresponding training location tags of training result be compared, deep neural network is instructed according to comparative result
Practice, using the deep neural network after training as the location model.
It is further preferred that also including：Data processing module, for corresponding to training location tags each described respectively
The input power of the signal strength data, channel number and correspondence WAP of network data is normalized, and
For the defeated of the signal strength data in the network data by client to be detected, channel number and correspondence WAP
Enter power to be normalized.
Compared with prior art, the present invention is provided a kind of WiFi localization methods and server based on network data, lead to
Cross the network data for collecting client position to be measured, the location model that input is trained, you can determine client institute to be measured
In position, trained by using the training data set pair deep neural network containing a large amount of training sample data, using depth god
Through network as location model, the lifting of positioning precision is not only lifted, while can be carried in the case where locating speed is not influenceed
The accuracy of positioning result is risen, successfully orientation problem is dissolved into the background of big data, and effectively using the excellent of big data
Gesture improves the performance of real-time location-server.
Brief description of the drawings
Below by clearly understandable mode, preferred embodiment is described with reference to the drawings, the present invention is given furtherly
It is bright.
Fig. 1 is a kind of key step schematic diagram of the WiFi localization methods based on network data of the present invention；
The step of Fig. 2 is a kind of training deep neural network of WiFi localization methods based on network data of the present invention is illustrated
Figure；
Fig. 3 is a kind of main composition schematic diagram of the WiFi location-servers based on network data of the present invention；
Fig. 4 is that a kind of present invention WiFi location-servers based on network data are fully composed schematic diagram.
Reference：
100th, data acquisition module, 200, locating module, 300, training module, 311, the default submodule of label, 312, instruction
Practice data set generation submodule, 313, input data layer define submodule, 314, training prediction submodule, 400, data processing mould
Block.
Specific embodiment
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, control is illustrated below
Specific embodiment of the invention.It should be evident that drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing, and obtain other implementation methods.
To make simplified form, part related to the present invention is only schematically show in each figure, they are not represented
Its as product practical structures.In addition, so that simplified form is readily appreciated, there is identical structure or function in some figures
Part, only symbolically depicts one of those, or has only marked one of those.Herein, " one " is not only represented
" only this ", it is also possible to represent the situation of " more than one ".
Fig. 1 is a kind of key step schematic diagram of the WiFi localization methods based on network data of the present invention, as shown in figure 1,
A kind of WiFi localization methods based on network data, methods described includes step：Each wirelessly connects in S100, acquisition detection zone
Access point receives the network data of the signal that client to be detected sends；The network data is multidimensional data；S200, general are described
The input data layer of the location model after network data input training；S300, the Internet meter based on the location model after training
The network data of client position to be detected is calculated, and client to be detected is determined according to the output result of output layer
Position.
Specifically, above-mentioned client to be detected (hereinafter referred to as STA) is with smart mobile phone, notebook computer or personal flat board
The intelligent terminals such as computer are carrier.
Network data described in the present embodiment be multidimensional data, wherein specifically include each WAP receive it is to be checked
Survey the input work of the signal strength data, channel number and correspondence WAP of the signal that client sends in detection zone
Rate.Because channel number and Frequency point positive correlation, and frequency range is related to space attenuation, and channel number is bigger, decays in spatial
It is bigger, while the collection of the input power of AP can also influence progress, because the AP power outputs design objective of different model can be with
Different, even if identical AP is also entirely possible different in the output-index of 2.4G frequency ranges and 5G frequency ranges, therefore the present invention is not
RSSI is gathered on cochannel different frequency range, the input power and signal strength data channel number, AP in frequency range are gathered in the lump
Input deep neural network is trained, and greatly improves the accuracy rate of positioning.
Specifically, each WAP receives the signal of the signal that client to be detected is sent in detection zone
Intensity data is obtained in the following manner：STA sends detection frame in real time within a detection region, and WAP obtains institute after receiving
The signal intensity of detection frame is stated, each WAP reports signal intensity to home server or Cloud Server, server root
The RSSI field intensity message reported according to each WAP generates signal strength data.For example, the form of signal strength data is<
RSSI1, RSSI2, RSSI3, RSSI4, RSSI5>, the RSSI of the STA that wherein RSSI1 is received for AP1, RSSI2 receives for AP2
The RSSI of STA, by that analogy.By taking real data as an example, the form of the network data of present invention collection client to be detected is as follows,
<(- 30,6,20), (- 12,11,18), (- 14,1,20), (- 67,36,18), (- 54,149,23)>, wherein concrete meaning is such as
Under：
RSSI1=-30dBm, channel 6 exports 20dBm
RSSI2=-12dBm, channel 11 exports 18dBm
RSSI3=-14dBm, channel 1 exports 20dBm
RSSI4=-67dBm, channel 36 exports 18dBm
RSSI5=-54dBm, channel 149 exports 23dBm
Preferably, step is also included before the step S100：S000, training in advance deep neural network, after training
Deep neural network as the location model.
Specifically, the output result difference obtained according to positioning in the present invention, can include two kinds of specific implementations, side
Formula one is used as location model and exports belonging to client position to be detected certain and pre-sets by deep neural network
The probable value of classification, mode two is to be used as location model by deep neural network directly to export client position to be detected
Preset position coordinates.The present invention is not construed as limiting to specific training method.
The method that the present invention is trained using the global parameter for having supervision：The physical location of the known network data for receiving,
Cause that the output of the Internet of deep neural network is identical with real result by constantly adjustment network parameter.
The step of Fig. 2 is a kind of training deep neural network of WiFi localization methods based on network data of the present invention is illustrated
Figure.Preferably, as shown in Fig. 2 the step S000 further includes step：S001, pre-set training location tags；
S002, each WAP is gathered successively receive training terminal in each training location tags institute in detection zone
The network data of the signal for sending；The network data includes that each WAP receives the training location tags in detection
The signal strength data of the signal that the training terminal on the correspondence position of region is sent out, channel number and correspondence WAP it is defeated
Enter power；S003, respectively by each it is described training location tags and its corresponding signal strength data, channel number and correspondence
The input power of WAP generates training dataset as one group of training sample data, and sends into deep neural network；
S004, the input data of deep neural network layer is defined as Three-channel data layer, the node of the Three-channel data layer with it is each
Individual WAP is corresponding, according to the node mode corresponding with WAP of Three-channel data layer respectively by every group of training
Each signal strength data combination channel number corresponding with WAP and corresponding WAP in sample data
Input power is input into three passages of the Three-channel data layer corresponding node；By the deep neural network output with it is described
The corresponding training result of training location tags；S005, the corresponding training position of training result that will be exported successively
Label is compared, and deep neural network is trained according to comparative result, using the deep neural network after training as institute
State location model.
Specifically, because the present embodiment in network data be multidimensional data, and specially signal data intensity, channel number with
And correspondence WAP input power, therefore definition as location model deep neural network input data layer be also
Three-channel data layer.
Exported belonging to client position to be detected as location model by deep neural network in mode one below
As a example by the probable value of certain classification for pre-setting, the specific process for introducing the present invention to deep neural network training.
1st, in training, each WAP is gathered first and receives training terminal in default training location tags in inspection
Survey the network data signaled on correspondence position in region.It is self-defining, tool that training location tags are preset in the present embodiment
Body detection zone can be divided into the grid classification of predetermined number, by the classification point of each grid by mesh generation detection zone
With corresponding default training location tags, also plane right-angle coordinate can be set up by detection zone, respectively in a coordinate system
It is default training location tags to set corresponding position coordinates.Illustrated in mode one in the present embodiment, below with actual number
According to the network data for explaining each the default training location tags correspondence position gathered in the present invention.
Assuming that a default network data for training location tags to be collected is as follows wherein：
<(- 30,6,20), (- 12,11,18), (- 14,1,20), (- 67,36,18), (- 54,149,23), 34>
Represent：
RSSI1=-30dBm, channel 6 exports 20dBm
RSSI2=-12dBm, channel 11 exports 18dBm
RSSI3=-14dBm, channel 1 exports 20dBm
RSSI4=-67dBm, channel 36 exports 18dBm
RSSI5=-54dBm, channel 149 exports 23dBm
Label=34, represents that this default training location tags is designated 34, represents the net that 34 are designated in monitored area
Lattice position.
2nd, training sample data training deep neural network is passed sequentially through.
The original network data of collection is three-dimensional data, therefore, deep neural network of the definition as location model
Input data layer is also Three-channel data layer, and the node of Three-channel data layer is corresponding with each WAP.According to threeway
The node mode corresponding with WAP of track data layer is respectively by each in every group of training sample data and wireless access
The input power of the corresponding signal strength data combination channel number of point and correspondence WAP is input into the Three-channel data
Three passages of layer corresponding node.
Table one
| Passage 1 | Passage 2 | Passage 3 |
| -30 | 6 | 20 |
| -12 | 11 | 18 |
| -14 | 1 | 20 |
| -67 | 36 | 18 |
| -54 | 149 | 23 |
As shown in Table 1, passage 1 represents the signal strength data corresponding with each WAP of collection in table one, leads to
Road 2 represents corresponding channel number, and passage 3 represents the incoming frequency of each WAP of correspondence.Successively by each default training
Three passages of the Three-channel data layer of the corresponding training sample data feeding deep neural network of location tags, finally output is instructed
Practice the error of result and training location tags, the Loss of whole network is caused finally by the parameter in percentage regulation neutral net
I.e. error is minimum.
It should be noted that do not indicate design parameter in entire depth neutral net, because these parameters and specific
The number of space and AP is relevant, not in the range of this patent.
Preferably, step is also included between the step S003 and the step S004：S035, respectively to instruction each described
The input power for practicing the signal strength data, channel number and correspondence WAP of the corresponding network data of location tags is carried out
Normalized；Also include step between the step S100 and step S200：S150, the network by client to be detected
The input power of signal strength data, channel number and correspondence WAP in data is normalized.
Specifically, in order that the network data of the multidimensional of collection can be efficiently entering deep neural network treatment, it is necessary to every
Individual initial data is all normalized.Following normalization formula manipulation is used in the present embodiment：
X '=(X-u)/σ, u are average, and σ is standard deviation
Normalization is only normalized in respective passage in the present embodiment.
Fig. 3 is a kind of main composition schematic diagram of the WiFi location-servers based on network data of the present invention, such as Fig. 3 institutes
Show, a kind of WiFi location-servers based on network data, including：Data acquisition module 100, it is each in detection zone for obtaining
Individual WAP receives the network data of the signal that client to be detected sends；The network data is multidimensional data；Institute
State network data including each WAP receive the signal that client to be detected sends in detection zone signal it is strong
The input power of degrees of data, channel number and correspondence WAP；Locating module 200, for the network that will be collected
The input data layer of data input location model, the Internet based on location model calculates the network data, and by positioning
The output result of the output layer of model determines the position of client to be detected.
Specifically, above-mentioned client to be detected (hereinafter referred to as STA) is with smart mobile phone, notebook computer or personal flat board
The intelligent terminals such as computer are carrier.
Network data described in the present embodiment be multidimensional data, wherein specifically include each WAP receive it is to be checked
Survey the input work of the signal strength data, channel number and correspondence WAP of the signal that client sends in detection zone
Rate.Because channel number and Frequency point positive correlation, and frequency range is related to space attenuation, and channel number is bigger, decays in spatial
It is bigger, while the collection of the input power of AP can also influence progress, because the AP power outputs design objective of different model can be with
Different, even if identical AP is also entirely possible different in the output-index of 2.4G frequency ranges and 5G frequency ranges, therefore the present invention is not
RSSI is gathered on cochannel different frequency range, the input power and signal strength data channel number, AP in frequency range are gathered in the lump
Input deep neural network is trained, and greatly improves the accuracy rate of positioning.
Specifically, each WAP receives the signal of the signal that client to be detected is sent in detection zone
Intensity data is obtained in the following manner：STA sends detection frame in real time within a detection region, and WAP obtains institute after receiving
The signal intensity of detection frame is stated, each WAP reports signal intensity to home server or Cloud Server, server root
The RSSI field intensity message reported according to each WAP generates signal strength data.For example, the form of signal strength data is<
RSSI1, RSSI2, RSSI3, RSSI4, RSSI5>, the RSSI of the STA that wherein RSSI1 is received for AP1, RSSI2 receives for AP2
The RSSI of STA, by that analogy.By taking real data as an example, the form of the network data of present invention collection client to be detected is as follows,
<(- 30,6,20), (- 12,11,18), (- 14,1,20), (- 67,36,18), (- 54,149,23)>, wherein concrete meaning is such as
Under：
RSSI1=-30dBm, channel 6 exports 20dBm
RSSI2=-12dBm, channel 11 exports 18dBm
RSSI3=-14dBm, channel 1 exports 20dBm
RSSI4=-67dBm, channel 36 exports 18dBm
RSSI5=-54dBm, channel 149 exports 23dBm
Fig. 4 is that a kind of present invention WiFi location-servers based on network data are fully composed schematic diagram.Such as Fig. 4 institutes
Show, it is preferred that also include：Training module 300, for training in advance deep neural network, by the deep neural network after training
As the location model.
Specifically, the output result difference obtained according to positioning in the present invention, can include two kinds of specific implementations, side
Formula one is used as location model and exports belonging to client position to be detected certain and pre-sets by deep neural network
The probable value of classification, mode two is to be used as location model by deep neural network directly to export client position to be detected
Preset position coordinates.The present invention is not construed as limiting to specific training method.
The method that the present invention is trained using the global parameter for having supervision：The network data of known certain training location tags,
Cause that the output of the Internet of deep neural network is identical with real result by constantly adjustment network parameter.
Preferably, the training module 300 is further included：Label presets submodule 311, for pre-setting for instructing
Experienced training location tags；Training dataset generates submodule 312, and training is received for gathering each WAP successively
The network data of the signal that terminal is sent in each described training location tags in detection zone, respectively by each instruction
Practice location tags and its corresponding network data as one group of training sample data, generate training dataset, and send into depth
In neutral net；The network data includes that each WAP receives the training location tags in detection zone correspondence position
The input power of the signal strength data, channel number and correspondence WAP of the signal that the training terminal put is sent out；It is defeated
Enter data Layer and define submodule 313, for the input data layer of deep neural network to be defined as into Three-channel data layer, described three
The node of channel data layer is corresponding with each WAP；Training prediction submodule 314, for according to Three-channel data layer
Node mode corresponding with WAP it is respectively that each in every group of training sample data is corresponding with WAP
The input power of signal strength data combination channel number and correspondence WAP is input into the Three-channel data layer correspondence section
Three passages of point, export and the training result for training location tags corresponding, successively by the deep neural network
The corresponding training location tags of the training result of output are compared, according to comparative result to deep neural network
It is trained, using the deep neural network after training as the location model.
It should be noted that referring to the inventive method part for training for the training process of above-mentioned training module 300
The explanation of deep neural network, no longer repeats herein.The contents such as information exchange, implementation procedure in book server between each module
Same design is based on above method embodiment, particular content can be found in the narration in the inventive method embodiment, herein no longer
Repeat.
Preferably, also include：Data processing module 400, for respectively to training the corresponding net of location tags each described
The input power of the signal strength data, channel number and correspondence WAP of network data is normalized, Yi Jiyong
The input of signal strength data, channel number and correspondence WAP in the network data by client to be detected
Power is normalized.
Specifically, in order that the network data of the multidimensional of collection can be efficiently entering deep neural network treatment, it is necessary to every
Individual initial data is all normalized.Following normalization formula manipulation is used in the present embodiment：
X '=(X-u)/σ, u are average, and σ is standard deviation
Normalization is only normalized in respective passage in the present embodiment.
The contents such as information exchange, implementation procedure in book server between each module and above method embodiment are based on same
Design, particular content can be found in the narration in the inventive method embodiment, and here is omitted.
It should be noted that above-described embodiment can independent assortment as needed.The above is only of the invention preferred
Implementation method, it is noted that for those skilled in the art, is not departing from the premise of the principle of the invention
Under, some improvements and modifications can also be made, these improvements and modifications also should be regarded as protection scope of the present invention.
Claims (9)
1. a kind of WiFi localization methods based on network data, it is characterised in that methods described includes step：
Each WAP receives the network data of the signal that client to be detected sends in S100, acquisition detection zone；
The network data is multidimensional data；
S200, the input data layer by the location model after network data input training；
S300, the Internet based on the location model after training calculate the network data of client position to be detected,
And the position of client to be detected is determined according to the output result of output layer.
2. the WiFi localization methods of network data are based on as claimed in claim 1, it is characterised in that the network data includes
Each WAP receive the signal strength data of the signal that client to be detected sends in detection zone, channel number with
And the input power of correspondence WAP.
3. the WiFi localization methods of network data are based on as claimed in claim 2, it is characterised in that before the step S100
Also include step：S000, training in advance deep neural network, using the deep neural network after training as the location model.
4. the WiFi localization methods of network data are based on as claimed in claim 3, it is characterised in that the step S000 enters one
Step includes step：
S001, pre-set training location tags；
S002, gather successively each WAP receive training terminal each it is described training location tags in detection zone
The network data of interior sent signal；The network data includes that each WAP receives the training location tags and exists
The signal strength data of the signal that the training terminal on detection zone correspondence position is sent out, channel number and correspondence WAP
Input power；
S003, respectively by each it is described training location tags and its corresponding signal strength data, channel number and correspondence nothing
The input power of line access point generates training dataset as one group of training sample data, and sends into deep neural network；
S004, the input data of deep neural network layer is defined as Three-channel data layer, the node of the Three-channel data layer
It is corresponding with each WAP, according to the node mode corresponding with WAP of Three-channel data layer respectively by every group
Each signal strength data combination channel number corresponding with WAP and corresponding wireless access in training sample data
The input power of point is input into three passages of the Three-channel data layer corresponding node；By the deep neural network output with
The corresponding training result of the training location tags；
S005, the corresponding training location tags of the training result of output are compared successively, according to comparative result
Deep neural network is trained, using the deep neural network after training as the location model.
5. the WiFi localization methods of network data are based on as claimed in claim 4, it is characterised in that：
Also include step between the step S003 and the step S004：
S035, respectively to training the signal strength data of the corresponding network data of location tags, channel number and right each described
The input power of WAP is answered to be normalized；
Also include step between the step S100 and step S200：
S150, the signal strength data by the network data of client to be detected, channel number and correspondence wireless access
The input power of point is normalized.
6. a kind of WiFi location-servers based on network data, it is characterised in that including：
Data acquisition module, for obtaining detection zone in each WAP receive the signal that client to be detected sends
Network data；The network data is multidimensional data；The network data receives to be detected including each WAP
The input work of the signal strength data, channel number and correspondence WAP of the signal that client sends in detection zone
Rate；
Locating module, the input data layer of the network data input location model for that will collect, based on location model
Internet calculate the network data, and the output result of the output layer for passing through location model determines the position of client to be detected
Put.
7. the WiFi location-servers of network data are based on as claimed in claim 6, it is characterised in that also included：
Training module, for training in advance deep neural network, using the deep neural network after training as the location model.
8. the WiFi location-servers of network data are based on as claimed in claim 7, it is characterised in that the training module enters
One step includes：
Label presets submodule, for pre-setting the training location tags for training；
Training dataset generates submodule, and training terminal is received in each instruction for gathering each WAP successively
Practice the network data of signal that location tags are sent in detection zone, respectively by each described training location tags and its
Corresponding network data generates training dataset as one group of training sample data, and sends into deep neural network；The net
Network data include that each WAP receives training terminal institute of the training location tags on detection zone correspondence position
The input power of the signal strength data, channel number and correspondence WAP of the signal of hair；
Input data layer defines submodule, for the input data layer of deep neural network to be defined as into Three-channel data layer, institute
The node for stating Three-channel data layer is corresponding with each WAP；
Training prediction submodule, for the node mode corresponding with WAP according to Three-channel data layer respectively by every group
Each signal strength data combination channel number corresponding with WAP and corresponding wireless access in training sample data
The input power of point is input into three passages of Three-channel data layer corresponding node, by deep neural network output with
The corresponding training result of the training location tags, successively marks the corresponding training position of the training result for exporting
Label are compared, and deep neural network is trained according to comparative result, using the deep neural network after training as described
Location model.
9. the WiFi location-servers of network data are based on as claimed in claim 8, it is characterised in that also included：
Data processing module, for respectively to trained each described the corresponding network data of location tags signal strength data,
The input power of channel number and correspondence WAP is normalized, and for by described in client to be detected
The input power of signal strength data, channel number and correspondence WAP in network data is normalized.
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