CN109995884A - The method and apparatus for determining accurate geographic position - Google Patents
The method and apparatus for determining accurate geographic position Download PDFInfo
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- CN109995884A CN109995884A CN201711481337.9A CN201711481337A CN109995884A CN 109995884 A CN109995884 A CN 109995884A CN 201711481337 A CN201711481337 A CN 201711481337A CN 109995884 A CN109995884 A CN 109995884A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L61/00—Network arrangements, protocols or services for addressing or naming
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2101/00—Indexing scheme associated with group H04L61/00
- H04L2101/60—Types of network addresses
- H04L2101/668—Internet protocol [IP] address subnets
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2101/00—Indexing scheme associated with group H04L61/00
- H04L2101/60—Types of network addresses
- H04L2101/69—Types of network addresses using geographic information, e.g. room number
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Abstract
The invention discloses a kind of method and apparatus of determining accurate geographic position, are related to Internet technical field.One specific embodiment of this method include: obtain IP and with the associated multiple geographical locations the IP；Using clustering algorithm, the multiple geographical location is clustered to obtain the geographical location cluster result of the IP；Based on the geographical location cluster result, the corresponding optimal geographical location the IP is determined using optimization algorithm；According to the optimal geographical location and preset artificial nerve network model, the accurate geographic position of the IP is determined.The embodiment can determine the accurate geographic position of IP, improve positioning accuracy, and not need to be laid with a large amount of monitoring points, and then reduce costs while improving positioning accuracy.
Description
Technical field
The present invention relates to Internet technical field more particularly to a kind of method and apparatus of determining accurate geographic position.
Background technique
IP location technology is in brief the technology that its geographical location is determined by the IP address of equipment.IP positioning tool
Have and be extremely widely applied, mainly includes targeted ads, social networks, network security, performance optimization etc..In mobile Internet
Under overall background, mobile phone etc. includes the terminal device of GPS information module, and the street that can be easy to obtain user is reported by data
The geographical location of rank.But the terminal of GPS hardware device is free of if it is desktop computer, notebook etc., it can not just pass through GPS
Etc. technologies obtain user geographical location, at this time just need using high-precision IP location technology.And traditional IP positioning can only
City-level is navigated to, the accuracy of area's grade data is also worth discussion.
Traditional IP location algorithm estimates position according to the linear relationship between time delay and geographic distance, and by opening up
It flutters structure and reduces error.
Specifically, being based on BGP (Border Gateway Protocol Border Gateway Protocol)/ASN (Autonomous
System Number autonomous system number) data analysis and get, while in the self-built network monitor point in the whole world, according to be positioned
Network between IP and monitoring point returns to delay value and divides network topology structure, to further confirm that the geographical position of IP to be positioned
It sets, such mode positions more credible, but precision is not still high (area's class precision).
In realizing process of the present invention, at least there are the following problems in the prior art for inventor's discovery:
This technology needs to be laid with enough monitoring points, for confirming the physical address of IP, higher cost, and need compared with
For complicated step, and geographical location is pushed away Lai counter by then passing through network link delay, more may be used although such mode positions
Letter, but precision is not still high.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of method and apparatus of determining accurate geographic position, positioning is improved
Precision, and the present invention does not need to be laid with a large amount of monitoring points, and then reduces costs while improving positioning accuracy.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of determining accurate geographic position is provided
Method, comprising: obtain IP and with the associated multiple geographical locations the IP；Using clustering algorithm, to the multiple geography
Position is clustered to obtain the geographical location cluster result of the IP；Based on the geographical location cluster result, optimization is utilized
Algorithm determines the corresponding optimal geographical location the IP；According to the optimal geographical location and preset artificial neural network mould
Type determines the accurate geographic position of the IP.
Optionally, the clustering algorithm is k-means algorithm, and the optimization algorithm is weighted least-squares method.
Optionally, using clustering algorithm, the multiple geographical location is clustered to obtain the geographical location of the IP
The step of cluster result includes: that two geographical locations are chosen from the associated multiple geographical locations the IP as the first first prothyl
The heart and the second initial mass center；Calculate in the multiple geographical location between each geographical location and the first initial mass center
One spherical distance and the second spherical distance between the second initial mass center；According to first spherical distance and described second
Spherical distance, multiple geographical locations associated to the IP are clustered to obtain high density cluster, using the high density cluster as
The geographical location cluster result of the IP.
Optionally, (1) calculates the first ball between each geographical location and the first initial mass center according to the following formula
Identity distance is from and with the second spherical distance between the second initial mass center:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are ground
The latitude of position A is managed, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
Optionally, according to the geographical location cluster result, the corresponding optimal geography of each IP is determined using optimization algorithm
Position include: for each geographical location in high density cluster, according to the spherical surface of each geographical location and high density cluster mass center away from
From determining the weight in each geographical location；According to the weight, determine that each IP is corresponding using weighted least-squares method
Optimal geographical location.
Optionally, (2) determine the weight in each geographical location according to the following formula:
Wherein, λiIndicate the weight in i-th of geographical location, diIt indicates between i-th of geographical location and high density cluster mass center
Spherical distance, n are the integer more than or equal to 1；
(3) determine the corresponding optimal geographical location the IP according to the following formula:
Wherein, (xi,yi) indicate i-th of geographical location,Indicate optimal geographical location.
Optionally, according to the optimal geographical location and preset artificial nerve network model, determine that the IP's is accurate
Geographical location includes: that the optimal geographical location is inputted the preset artificial nerve network model, obtains output result；If
The output result is preset objective result, then the optimal geographical location is the accurate geographic position of the IP.
Optionally, the input layer of the preset artificial nerve network model has 3 neuron nodes, and hidden layer has
5 neuron nodes, output layer have 1 neuron node.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of determining accurate geographic position is provided
Device, comprising: obtain module, for obtain IP and with the associated multiple geographical locations the IP；Cluster module, for benefit
With clustering algorithm, the multiple geographical location is clustered to obtain the geographical location cluster result of the IP；Optimal geography
Position determination module determines that the IP is corresponding optimally using optimization algorithm for being based on the geographical location cluster result
Manage position；Accurate geographic position determining module is used for according to the optimal geographical location and preset artificial nerve network model,
Determine the accurate geographic position of the IP.
Optionally, the clustering algorithm is k-means algorithm, and the optimization algorithm is weighted least-squares method.
Optionally, the cluster module is also used to: two geographical positions are chosen from the associated multiple geographical locations the IP
It sets as the first initial mass center and the second initial mass center；Calculate each geographical location and described first in the multiple geographical location
The first spherical distance between initial mass center and the second spherical distance between the second initial mass center；According to first ball
Identity distance from second spherical distance, multiple geographical locations associated to the IP are clustered to obtain high density cluster, with
Geographical location cluster result of the high density cluster as the IP.
Optionally, the cluster module calculates each geographical location and the described first initial mass center according to the following formula (1)
Between the first spherical distance and the second spherical distance between the second initial mass center:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are ground
The latitude of position A is managed, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
Optionally, the optimal geolocation determination module is also used to: for each geographical location in high density cluster, root
According to the spherical distance in each geographical location and high density cluster mass center, the weight in each geographical location is determined；According to the power
Weight, determines the corresponding optimal geographical location each IP using weighted least-squares method.
Optionally, (2) determine the weight in each geographical location according to the following formula:
Wherein, λiIndicate the weight in i-th of geographical location, diIt indicates between i-th of geographical location and high density cluster mass center
Spherical distance, n are the integer more than or equal to 1；
(3) determine the corresponding optimal geographical location the IP according to the following formula:
Wherein, (xi,yi) indicate i-th of geographical location,Indicate optimal geographical location.
Optionally, the accurate geographic position determining module is also used to: the optimal geographical location input is described default
Artificial nerve network model, obtain output result；If the output result is preset objective result, the optimal geography
Position is the accurate geographic position of the IP.
Optionally, the input layer of the preset artificial nerve network model has 3 neuron nodes, and hidden layer has
5 neuron nodes, output layer have 1 neuron node.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of electronic equipment is provided, comprising: one
A or multiple processors；Storage device, for storing one or more programs, when one or more of programs are one
Or multiple processors execute, so that one or more of processors realize that position is accurately managed in determination described in the embodiment of the present invention
The method set.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of computer-readable medium is provided,
On be stored with computer program, when described program is executed by processor realize the embodiment of the present invention described in determination accurately manage position
The method set.
One embodiment in foregoing invention has the following advantages that or the utility model has the advantages that because using clustering algorithm, to described
Multiple geographical locations are clustered to obtain the geographical location cluster result of each IP；Based on the geographical location cluster result,
The corresponding optimal geographical location the IP is determined using optimization algorithm；According to the optimal geographical location and preset artificial neuron
Network model, determines the technological means of the accurate geographic position of the IP, so improving positioning accuracy, and does not need to be laid with
A large amount of monitoring points, reduce costs.Specifically, by k-means cluster, reduce redundant data, reduce due to weather, signal,
GPS positioning error caused by the factors such as surrounding enviroment；Then to different user (MAC) but the geographical location of same IP, utilize
Weighted least-squares method obtains optimal geographical location,；With the accumulation of data, ANN neural network training model is established, for same
The optimal solution that one IP is calculated is trained, and excludes (some movements of the dirty data as caused by certain factors in a period of time
End device can simulate GPS data, cause GPS data invalid), to improve the accuracy of positioning.
Further effect possessed by above-mentioned non-usual optional way adds hereinafter in conjunction with specific embodiment
With explanation.
Detailed description of the invention
Attached drawing for a better understanding of the present invention, does not constitute an undue limitation on the present invention.Wherein:
Fig. 1 is the schematic diagram of the main flow of the method for determining accurate geographic position according to an embodiment of the invention；
Fig. 2 is the schematic diagram of the main flow of the method for determining accurate geographic position according to another embodiment of the present invention；
Fig. 3 is the schematic diagram of the main modular of the device of determining accurate geographic position according to an embodiment of the present invention；
Fig. 4 is that the embodiment of the present invention can be applied to exemplary system architecture figure therein；
Fig. 5 is adapted for the structural representation of the computer system for the terminal device or server of realizing the embodiment of the present invention
Figure.
Specific embodiment
Below in conjunction with attached drawing, an exemplary embodiment of the present invention will be described, including the various of the embodiment of the present invention
Details should think them only exemplary to help understanding.Therefore, those of ordinary skill in the art should recognize
It arrives, it can be with various changes and modifications are made to the embodiments described herein, without departing from scope and spirit of the present invention.Together
Sample, for clarity and conciseness, descriptions of well-known functions and structures are omitted from the following description.
Fig. 1 is the main of the method that IP- geographic position data collection according to an embodiment of the present invention determines accurate geographic position
The schematic diagram of flow chart.As shown in Figure 1, this method comprises:
Step S101: obtain IP and with the associated multiple geographical locations the IP；
Step S102: clustering algorithm is utilized, the multiple geographical location is clustered to obtain the geographical position of the IP
Set cluster result；
Step S103: it is based on the geographical location cluster result, determines that the IP is corresponding optimally using optimization algorithm
Manage position；
Step S104: according to the optimal geographical location and preset artificial nerve network model, the essence of the IP is determined
True geographical location.
IP in the present embodiment and disclosed geographic information database can be passed through with the associated multiple geographical locations IP
It obtains.It can also obtain by receiving the IP that reports of data acquisition sources and with the associated multiple geographical locations IP, such as connect
Receive the IP address that reports of reporting device (such as the terminal devices such as smart phone, tablet computer) with GPS information module and
With the associated geographical location of the IP address.
With the development of mobile Internet science and technology, the terminal devices such as any mobile phone or tablet computer can be at book
Reporting device in embodiment all can serve as data acquisition sources, and therefore, the embodiment of the present invention does not need to be laid with a large amount of monitoring
Point, reduces costs.
In an alternate embodiment of the invention, when receiving the IP and geographical location associated with the IP that reporting device reports, also
The timestamp when device identification (such as MAC Address) of the available reporting device and reported data, thus by the equipment mark
Knowledge, timestamp, IP and the IP geographical location form a valid data, such as IP-MAC-GPS-TIMESTAMP, wherein
GPS is the latitude and longitude information reported, timestamp when TIMESTAMP is reported data.
Above-mentioned geographical location can show as the satellite positioning informations such as latitude and longitude information, altitude information, can also show as
The location informations such as city, street, trade company, office building.In the present embodiment, which is preferably latitude and longitude information.
Above-mentioned IP is one 32 unsigned int (unsigned int) data substantially, and range is from 0~232, it is
It is easy to use, the IP address of character string forms, that is, this form of 192.168.0.1 that usually uses generally are used, it is real
On border, every 8 binary digits are exactly converted into corresponding decimal integer, abbreviation numeric type IP.For example, 192.168.0.1
It is of equal value with 3232235521.192.168.0.1 mean 1*2560+0*2561+168*2562+192*2563=
3232235521.In embodiments of the present invention, to use simplicity, the IP is numeric type IP.
Since GPS is easy the shadow of the factors such as the signal by reporting device local environment, weather and reporting device itself
It rings, so the error in certain geographical locations may be larger in sample set, can not accept and believe completely.
Therefore, it for step S102, needs to be clustered using multiple geographical locations of the clustering algorithm to the IP to arrange
Except the biggish geographical location of error, to obtain the corresponding accurate geographical location the IP, and then the accurate of positioning is improved
Property.As specific example, which can be k-means clustering algorithm, further, can using device identification as
Dimension is clustered with timestamp, i.e., the data reported within certain a period of time to same reporting device cluster.
Above-mentioned k-means algorithm is the very typically evaluation based on the clustering algorithm of distance, using distance as similitude
Index thinks that the distance of two objects is closer, similarity is bigger.The core of the algorithm is to arrive by resolved data point
Certain function of distance as optimization aim of mass center, takes the continuous iteration of extreme value using function, therefore compact and independent obtaining
Cluster as final goal.
Further, as shown in Fig. 2, using k-means clustering algorithm, multiple geographical locations associated to the IP are carried out
The step of clustering the geographical location cluster result to obtain the IP includes the following steps:
Step S201: two geographical locations are chosen from the associated multiple geographical locations the IP as the first initial mass center
With the second initial mass center；
Step S202: in the multiple geographical location between each geographical location and the first initial mass center is calculated
One spherical distance and the second spherical distance between the second initial mass center；
Step S203: according to first spherical distance and second spherical distance, geography position associated to the IP
It sets and is clustered to obtain high density cluster and low-density cluster, clustered and tied as the geographical location of the IP using the high density cluster
Fruit.
For step S201, the longitude and latitude data acquired whithin a period of time for same reporting device (i.e. same IP) exist
It all being hashed in distribution in the true geographic vicinity of the IP, such dot density is larger, but due to by extraneous factor
It influences, base point and actual position deviation are larger, and density is sparse.Therefore, it is by density regions that the embodiment of the present invention, which defines cluster,
Separated high-density region selects two classes when choosing initialization mass center on the cluster based on density.
Specifically, 2 longitudes and latitudes can be randomly selected as the first initial mass center and the second initial mass center, can also choose
The average value of all longitudes and latitudes is as the first initial mass center, with the maximum longitude and latitude of mean deviation as the second initial mass center.
For step S202, since longitude and latitude is the coordinate of ellipsoid, cannot simply use Euclidean distance as
The compact index for measuring cluster uses spherical distance as the compact index for measuring cluster in this embodiment of the present invention.It can be by such as
Lower formula calculates the spherical distance between two geographical locations:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are ground
The latitude of position A is managed, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
For step S203, after the first spherical distance and the second spherical distance is calculated according to formula (1), distance
The close geographical location of one initial mass center is cluster, and the close geographical location of the initial mass center of distance second is another cluster.Then, it counts again
The mass center of every cluster, iteration are calculated, until final mass center is constant or varies less.Choose geography of the high density cluster as the IP
Position cluster result, low-density cluster are abandoned as error cluster, avoid data contamination.
For step S103, after the biggish geographical location of error is tentatively eliminated by clustering algorithm, in order to further
Positioning accuracy is improved, needs to determine the corresponding optimal geographical location each IP using optimization algorithm.Specifically, can use excellent
Change algorithm and optimal solution is sought to the high density cluster of same IP.As specific example, which can be weighting minimum two
Multiplication.
Above-mentioned weighted least-squares method is a kind of mathematical optimization techniques, it finds data by minimizing the quadratic sum of error
Optimal function matching.Weighted least-squares method has a wide range of applications in field of engineering technology, utilizes weighted least-squares method
Unknown parameter can be easily acquired, and these is made to acquire the quadratic sum of error between data and real data minimum.
Specifically, being based on geographical location cluster result, determine that the IP is corresponding optimally using weighted least-squares method
The process of reason position may include steps of:
1. for each geographical location in high density cluster, according to the spherical surface of each geographical location and high density cluster mass center away from
From determining the weight in each geographical location；
Formula is as follows:
λiIndicate the weight of i-th of longitude and latitude, diIndicate that the distance between i-th of longitude and latitude and mass center, n are to be greater than or wait
In 1 integer.
2. determining the corresponding optimal geographical location each IP using weighted least-squares method according to the weight.It crosses herein
Cheng Zhong needs the longitude and latitude to same IP to establish non-linear curve fitting function, keeps its variance minimum, specific formula such as following formula
(3):
Wherein, (xi,yi) indicate i-th of geographical location,For the corresponding optimal geographical location the IP.In reality
When calculating, (xi,yi) it is that longitude and latitude is converted to the plane coordinates after geodetic coordinates by gauss projection by i-th of geographical location.
In embodiments of the present invention, nonlinear regression model (NLRM) is established to the longitude and latitude data of same IP:
WhereinFor central coordinate of circle, r is radius.Ask the IP corresponding optimally
Position is managed to solveMake its satisfactionIt is minimum.
For step S103, by above-mentioned k-means algorithm and weighted least-squares method, it can be assumed that a certain is adopted
Sampling device reported data has had been properly processed, but in the actual process, due to the IP there are factors such as simulators, reported
With longitude and latitude data there may be relatively large deviation, this partial data may be considered abnormal data.Therefore, in the present embodiment may be used
To utilize artificial nerve network model, the optimal geographical location that same IP is calculated is screened, to exclude exception
Data.Specifically, artificial nerve network model can be introduced to the optimal geography after determining the optimal geographical location of the IP
Position carries out one simple ' classification ', i.e., all optimal geographical locations is divided into two classes, normal and abnormal two classes.
Therefore, further, this method further includes: according to the optimal geographical location and preset artificial neural network mould
Type determines the accurate geographic position of the IP.
Specifically, may include steps of:
The optimal geographical location is inputted into the preset artificial nerve network model, obtains output result；
If the output result is preset objective result, the accurate geographical position that the optimal geographical location is the IP
It sets.
Before being screened using preset artificial nerve network model to optimal geographical location, this method further include:
The training artificial nerve network model, i.e., adjust the weight of each neurode by training data, so that normal optimal geography
The desired output of position is 1, and the desired output in abnormal optimal geographical location is 0.
Specifically, the IP data that selection is largely associated with correct geographical location (are greater than 20000 numbers as normal data
According to), and Artificial Anomalies data are added to same IP, artificial neural network mould is carried out using the normal data and Artificial Anomalies data
Type hidden layer weight training, guarantees final function convergence, and hidden layer weight parameter at this time is as initiation parameter.
As specific example, the input layer of the preset artificial nerve network model has 3 neuron nodes, point
IP (numeric type IP), longitude and latitude are not corresponded to；Hidden layer has 5 neuron nodes, which is led to by developer
It crosses training data convergence time and method determines；Output layer has 1 neuron node, by exporting the result judgement longitude and latitude
Whether degree is abnormal data, and it is normal data that output result, which is the 1 expression longitude and latitude, and output result is that the 0 expression longitude and latitude is
Abnormal data.
Therefore, above-mentioned preset objective result can be 1, if output result is 1, which is described
The accurate geographic position of IP.
In an alternate embodiment of the invention, the accurate geographic position of the IP of the acquisition and IP can be saved.
In the present embodiment, artificial nerve network model (Artificial Neural Network, ANN) are as follows: from information
Process angle is abstracted human brain neuroid, establishes certain naive model, forms by different connection types different
Network.Neural network or neural network are also often directly referred to as in engineering and academia.Neural network is a kind of operational model,
It is constituted by being coupled to each other between a large amount of node (or neuron).A kind of each specific output function of node on behalf, referred to as
Excitation function (activation function).Connection between every two node all represents one for by the connection signal
Weighted value, referred to as weight, this is equivalent to the memory of artificial neural network.The output of network then according to the connection type of network,
The difference of weighted value and excitation function and it is different.And network itself is forced certain algorithm of nature or function
Closely, it is also possible to the expression to a kind of logic strategy.
The method of the determination accurate geographic position of the embodiment of the present invention improves positioning accuracy, and does not need to be laid with a large amount of
Monitoring point reduces costs.Specifically, clustering by k-means, redundant data is reduced, is reduced due to weather, signal, periphery
GPS positioning error caused by the factors such as environment；Then to different user (MAC) but the geographical location of same IP, utilize weighting
Least square method obtains optimal geographical location,；With the accumulation of data, ANN neural network training model is established, for same IP
The optimal solution being calculated is trained, and excludes (some mobile terminal dresses of the dirty data as caused by certain factors in a period of time
GPS data can be simulated by setting, and cause GPS data invalid), to improve the accuracy of positioning.
The method of the embodiment of the present invention can also obtain variance according to formula (3), and the accuracy for IP positioning provides quantization
Index, variance is smaller, and accuracy is higher.
Fig. 3 is the schematic diagram of the main modular of the IP positioning device of another embodiment according to the present invention.As shown in figure 3, should
Device 300 include: obtain module 301, for obtain IP and with the associated multiple geographical locations the IP；Cluster module 302,
For utilizing clustering algorithm, the multiple geographical location is clustered to obtain the geographical location cluster result of the IP；Most
Excellent geolocation determination module 303 determines that the IP is corresponding using optimization algorithm for being based on the geographical location cluster result
Optimal geographical location；Accurate geographic position determining module 304, for according to the optimal geographical location and preset artificial mind
Through network model, the accurate geographic position of the IP is determined.
Optionally, the clustering algorithm is k-means algorithm, and the optimization algorithm is weighted least-squares method.
Optionally, the cluster module 302 is also used to: two geography are chosen from the associated multiple geographical locations the IP
Position is as the first initial mass center and the second initial mass center；Calculate in the multiple geographical location each geographical location and described the
The first spherical distance between one initial mass center and the second spherical distance between the second initial mass center；According to described first
Spherical distance and second spherical distance, multiple geographical locations associated to the IP are clustered to obtain high density cluster,
Using the high density cluster as the geographical location cluster result of the IP.
Optionally, the cluster module 302 calculates each geographical location and the described first first prothyl according to the following formula (1)
The first spherical distance between the heart and the second spherical distance between the second initial mass center:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are ground
The latitude of position A is managed, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
Optionally, the optimal geolocation determination module 303 is also used to: position geographical for each of high density cluster
It sets, according to the spherical distance in each geographical location and high density cluster mass center, determines the weight in each geographical location；According to institute
Weight is stated, determines the corresponding optimal geographical location each IP using weighted least-squares method.
Optionally, (2) determine the weight in each geographical location according to the following formula:
Wherein, λiIndicate the weight in i-th of geographical location, diIt indicates between i-th of geographical location and high density cluster mass center
Spherical distance, n are the integer more than or equal to 1；
(3) determine the corresponding optimal geographical location the IP according to the following formula:
Wherein, (xi,yi) indicate i-th of geographical location,Indicate optimal geographical location.
Optionally, the accurate geographic position determining module 304 is also used to: the optimal geographical location input is described pre-
If artificial nerve network model, obtain output result；If the output result is preset objective result, it is described optimally
Manage the accurate geographic position that position is the IP.
Optionally, the input layer of the preset artificial nerve network model has 3 neuron nodes, and hidden layer has
5 neuron nodes, output layer have 1 neuron node.
The device of the determination accurate geographic position of the embodiment of the present invention improves positioning accuracy, and does not need to be laid with a large amount of
Monitoring point reduces costs.Specifically, clustering by k-means, redundant data is reduced, is reduced due to weather, signal, periphery
GPS positioning error caused by the factors such as environment；Then to different user (MAC) but the geographical location of same IP, utilize weighting
Least square method obtains optimal geographical location,；With the accumulation of data, ANN neural network training model is established, for same IP
The optimal solution being calculated is trained, and excludes (some mobile terminal dresses of the dirty data as caused by certain factors in a period of time
GPS data can be simulated by setting, and cause GPS data invalid), to improve the accuracy of positioning.
Fig. 4 shows IP- geographic position data collection construction method or the geographical location IP- that can apply the embodiment of the present invention
The exemplary system architecture 400 of data set construction device.
As shown in figure 4, system architecture 400 may include terminal device 401,402,403, network 404 and server 405.
Network 404 between terminal device 401,402,403 and server 405 to provide the medium of communication link.Network 404 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 401,402,403 and be interacted by network 404 with server 405, to receive or send out
Send message etc..Various telecommunication customer end applications, such as the application of shopping class, net can be installed on terminal device 401,402,403
The application of page browsing device, searching class application, instant messaging tools, mailbox client, social platform software etc..
Terminal device 401,402,403 can be the various electronic equipments with display screen and supported web page browsing, packet
Include but be not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 405 can be to provide the server of various services, such as utilize terminal device 401,402,403 to user
The shopping class website browsed provides the back-stage management server supported.Back-stage management server can believe the product received
The data such as breath inquiry request carry out the processing such as analyzing, and processing result (such as target push information, product information) is fed back to
Terminal device.
It should be noted that determining the method for accurate geographic position generally by server provided by the embodiment of the present invention
405 execute, and correspondingly, IP positioning device is generally positioned in server 405.
It should be understood that the number of terminal device, network and server in Fig. 4 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
Below with reference to Fig. 5, it illustrates the computer systems 500 for the terminal device for being suitable for being used to realize the embodiment of the present invention
Structural schematic diagram.Terminal device shown in Fig. 5 is only an example, function to the embodiment of the present invention and should not use model
Shroud carrys out any restrictions.
As shown in figure 5, computer system 500 includes central processing unit (CPU) 501, it can be read-only according to being stored in
Program in memory (ROM) 502 or be loaded into the program in random access storage device (RAM) 503 from storage section 508 and
Execute various movements appropriate and processing.In RAM 503, also it is stored with system 500 and operates required various programs and data.
CPU 501, ROM 502 and RAM 503 are connected with each other by bus 504.Input/output (I/O) interface 505 is also connected to always
Line 504.
I/O interface 505 is connected to lower component: the importation 506 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 507 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 508 including hard disk etc.；
And the communications portion 509 of the network interface card including LAN card, modem etc..Communications portion 509 via such as because
The network of spy's net executes communication process.Driver 510 is also connected to I/O interface 505 as needed.Detachable media 511, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 510, in order to read from thereon
Computer program be mounted into storage section 508 as needed.
Particularly, disclosed embodiment, the process described above with reference to flow chart may be implemented as counting according to the present invention
Calculation machine software program.For example, embodiment disclosed by the invention includes a kind of computer program product comprising be carried on computer
Computer program on readable medium, the computer program include the program code for method shown in execution flow chart.?
In such embodiment, which can be downloaded and installed from network by communications portion 509, and/or from can
Medium 511 is dismantled to be mounted.When the computer program is executed by central processing unit (CPU) 501, system of the invention is executed
The above-mentioned function of middle restriction.
It should be noted that computer-readable medium shown in the present invention can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the present invention, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In invention, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc. are above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Being described in module involved in the embodiment of the present invention can be realized by way of software, can also be by hard
The mode of part is realized.Described module also can be set in the processor, for example, can be described as: a kind of processor packet
It includes sending module, obtain module, determining module and first processing module.Wherein, the title of these modules is under certain conditions simultaneously
The restriction to the unit itself is not constituted, for example, sending module is also described as " sending picture to the server-side connected
The module of acquisition request ".
As on the other hand, the present invention also provides a kind of computer-readable medium, which be can be
Included in equipment described in above-described embodiment；It is also possible to individualism, and without in the supplying equipment.Above-mentioned calculating
Machine readable medium carries one or more program, when said one or multiple programs are executed by the equipment, makes
Obtaining the equipment includes:
Obtain IP and with the associated multiple geographical locations the IP；
Using clustering algorithm, the multiple geographical location is clustered to obtain the geographical location of IP cluster knot
Fruit；
Based on the geographical location cluster result, the corresponding optimal geographical location the IP is determined using optimization algorithm；
According to the optimal geographical location and preset artificial nerve network model, the accurate geographical position of the IP is determined
It sets.
The technical solution of the embodiment of the present invention
Because being clustered to the multiple geographical location using clustering algorithm to obtain the geographical location of each IP cluster
As a result；Based on the geographical location cluster result, the corresponding optimal geographical location the IP is determined using optimization algorithm；According to institute
Optimal geographical location and preset artificial nerve network model are stated, determines the technological means of the accurate geographic position of the IP, institute
It to improve positioning accuracy, and does not need to be laid with a large amount of monitoring points, reduce costs.Specifically, clustered by k-means,
Redundant data is reduced, the GPS positioning error as caused by the factors such as weather, signal, surrounding enviroment is reduced；Then to different user
(MAC) but the geographical location of same IP, obtain optimal geographical location using weighted least-squares method,；With the accumulation of data,
Establish ANN neural network training model, the same IP optimal solution being calculated be trained, exclude a period of time in due to
Dirty data caused by certain factors (some mobile end devices can simulate GPS data, cause GPS data invalid), to improve
The accuracy of positioning.
Above-mentioned specific embodiment, does not constitute a limitation on the scope of protection of the present invention.Those skilled in the art should be bright
It is white, design requirement and other factors are depended on, various modifications, combination, sub-portfolio and substitution can occur.It is any
Made modifications, equivalent substitutions and improvements etc. within the spirit and principles in the present invention, should be included in the scope of the present invention
Within.
Claims (18)
1. a kind of method of determining accurate geographic position characterized by comprising
Obtain IP and with the associated multiple geographical locations the IP；
Using clustering algorithm, the multiple geographical location is clustered to obtain the geographical location cluster result of the IP；
Based on the geographical location cluster result, the corresponding optimal geographical location the IP is determined using optimization algorithm；
According to the optimal geographical location and preset artificial nerve network model, the accurate geographic position of the IP is determined.
2. the optimization is calculated the method according to claim 1, wherein the clustering algorithm is k-means algorithm
Method is weighted least-squares method.
3. according to the method described in claim 2, it is characterized in that, being carried out using clustering algorithm to the multiple geographical location
The step of clustering geographical location cluster result to obtain the IP include:
Two geographical locations are chosen from the associated multiple geographical locations the IP as the first initial mass center and the second first prothyl
The heart；
Calculate the first spherical distance in the multiple geographical location between each geographical location and the first initial mass center with
And the second spherical distance between the second initial mass center；
According to first spherical distance and second spherical distance, multiple geographical locations associated to the IP are clustered
To obtain high density cluster, using the high density cluster as the geographical location cluster result of the IP.
4. according to the method described in claim 3, it is characterized in that, (1) calculates each geographical location and institute according to the following formula
State the first spherical distance between the first initial mass center and the second spherical distance between the second initial mass center:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are geographical position
The latitude of A is set, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
5. according to the method described in claim 3, it is characterized in that, being calculated according to the geographical location cluster result using optimization
Method determines that the corresponding optimal geographical location each IP includes:
For each geographical location in high density cluster, according to the spherical distance in each geographical location and high density cluster mass center, really
The weight in fixed each geographical location；
According to the weight, the corresponding optimal geographical location each IP is determined using weighted least-squares method.
6. according to the method described in claim 5, it is characterized in that, (2) determine the power in each geographical location according to the following formula
Weight:
Wherein, λiIndicate the weight in i-th of geographical location, diIndicate the spherical surface between i-th of geographical location and high density cluster mass center
Distance, n are the integer more than or equal to 1；
(3) determine the corresponding optimal geographical location the IP according to the following formula:
Wherein, (xi,yi) indicate i-th of geographical location,Indicate optimal geographical location.
7. the method according to claim 1, wherein according to the optimal geographical location and preset artificial neuron
Network model determines that the accurate geographic position of the IP includes:
The optimal geographical location is inputted into the preset artificial nerve network model, obtains output result；
If the output result is preset objective result, the optimal geographical location is the accurate geographic position of the IP.
8. the method according to the description of claim 7 is characterized in that the input layer of the preset artificial nerve network model has
There are 3 neuron nodes, hidden layer has 5 neuron nodes, and output layer has 1 neuron node.
9. a kind of device of determining accurate geographic position characterized by comprising
Obtain module, for obtain IP and with the associated multiple geographical locations the IP；
Cluster module clusters the multiple geographical location for utilizing clustering algorithm to obtain the geographical position of the IP
Set cluster result；
Optimal geolocation determination module determines the IP using optimization algorithm for being based on the geographical location cluster result
Corresponding optimal geographical location；
Accurate geographic position determining module is used for according to the optimal geographical location and preset artificial nerve network model, really
The accurate geographic position of the fixed IP.
10. device according to claim 9, which is characterized in that the clustering algorithm is k-means algorithm, the optimization
Algorithm is weighted least-squares method.
11. device according to claim 10, which is characterized in that the cluster module is also used to:
Two geographical locations are chosen from the associated multiple geographical locations the IP as the first initial mass center and the second first prothyl
The heart；
Calculate the first spherical distance in the multiple geographical location between each geographical location and the first initial mass center with
And the second spherical distance between the second initial mass center；
According to first spherical distance and second spherical distance, multiple geographical locations associated to the IP are clustered
To obtain high density cluster, using the high density cluster as the geographical location cluster result of the IP.
12. device according to claim 11, which is characterized in that the cluster module calculates often according to the following formula (1)
The first spherical distance between a geographical location and the first initial mass center and the second ball between the second initial mass center
Identity distance from:
S=Rar cos (cos β 1cos β 2cos (α 1- α 2)+sin β 1sin β 2) (1)
Wherein, R indicates earth major axis radius, and S indicates that the spherical distance between geographical location A and geographical location B, β 1 are geographical position
The latitude of A is set, α 1 is the longitude of geographical location A, and β 2 is the latitude of geographical location B, and α 2 is the longitude of geographical location B.
13. device according to claim 10, which is characterized in that the optimal geolocation determination module is also used to:
For each geographical location in high density cluster, according to the spherical distance in each geographical location and high density cluster mass center, really
The weight in fixed each geographical location；
According to the weight, the corresponding optimal geographical location each IP is determined using weighted least-squares method.
14. device according to claim 13, which is characterized in that (2) determine each geographical location according to the following formula
Weight:
Wherein, λiIndicate the weight in i-th of geographical location, diIndicate the spherical surface between i-th of geographical location and high density cluster mass center
Distance, n are the integer more than or equal to 1；
(3) determine the corresponding optimal geographical location the IP according to the following formula:
Wherein, (xi,yi) indicate i-th of geographical location,Indicate optimal geographical location.
15. device according to claim 8, which is characterized in that the accurate geographic position determining module is also used to:
The optimal geographical location is inputted into the preset artificial nerve network model, obtains output result；
If the output result is preset objective result, the optimal geographical location is the accurate geographic position of the IP.
16. device according to claim 15, which is characterized in that the input layer of the preset artificial nerve network model
With 3 neuron nodes, hidden layer has 5 neuron nodes, and output layer has 1 neuron node.
17. a kind of electronic equipment characterized by comprising
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Now such as method described in any one of claims 1-8.
18. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is held by processor
Such as method described in any one of claims 1-8 is realized when row.
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| PCT/CN2018/108635 WO2019128355A1 (en) | 2017-12-29 | 2018-09-29 | Method and device for determining accurate geographic location |
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| WO2019128355A1 (en) | 2019-07-04 |
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