CN109995884B - Method and apparatus for determining precise geographic location - Google Patents

Method and apparatus for determining precise geographic location Download PDF

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CN109995884B
CN109995884B CN201711481337.9A CN201711481337A CN109995884B CN 109995884 B CN109995884 B CN 109995884B CN 201711481337 A CN201711481337 A CN 201711481337A CN 109995884 B CN109995884 B CN 109995884B
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CN109995884A (en
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肖明科
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Shangke Information Technology Co Ltd
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    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L2101/60—Types of network addresses
    • H04L2101/668—Internet protocol [IP] address subnets
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract

The invention discloses a method and a device for determining an accurate geographical position, and relates to the technical field of internet. One embodiment of the method comprises: acquiring an IP and a plurality of geographic positions associated with the IP; clustering the plurality of geographic positions by using a clustering algorithm to obtain a geographic position clustering result of the IP; determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result; and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model. The embodiment can determine the accurate geographic position of the IP, improves the positioning accuracy, does not need to lay a large number of monitoring points, and further reduces the cost while improving the positioning accuracy.

Description

Method and apparatus for determining precise geographic location
Technical Field
The invention relates to the technical field of internet, in particular to a method and a device for determining an accurate geographical position.
Background
IP location technology, in short, is a technology that determines the geographic location of a device by its IP address. IP positioning has extremely wide applications, mainly including targeted advertising, social networking, network security, performance optimization, and the like. Under the large background of the mobile internet, the geographic position of the street level of the user can be easily acquired by the mobile phone and other terminal equipment containing a GPS information module through data reporting. However, if the terminal does not include GPS hardware devices, such as a desktop computer and a notebook computer, the geographical location of the user cannot be obtained by using techniques such as GPS, and a high-precision IP positioning technique is required. Traditional IP positioning can only be localized to urban level, and the accuracy of regional level data is questionable.
Traditional IP positioning algorithms estimate position based on a linear relationship between time delay and geographic distance and reduce errors through topological structures.
Specifically, the positioning method is obtained based on BGP (Border Gateway Protocol)/ASN (Autonomous System Number) data analysis, and at the same time, network topology is divided according to a network return delay value between an IP to be positioned and a monitoring point at a global self-established network monitoring point, so as to further confirm the geographic position of the IP to be positioned.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the technology needs to lay enough monitoring points for confirming the physical address of the IP, is high in cost and complex in steps, and due to the fact that the geographic position is reversely deduced through network link delay, the positioning in the mode is reliable, but the accuracy is still low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for determining an accurate geographic location, so as to improve positioning accuracy, and the method and the apparatus do not need to lay a large number of monitoring points, thereby improving positioning accuracy and reducing cost.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of determining a precise geographical position, including: acquiring an IP and a plurality of geographic positions associated with the IP; clustering the plurality of geographic positions by using a clustering algorithm to obtain a geographic position clustering result of the IP; determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result; and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
Optionally, the clustering algorithm is a k-means algorithm, and the optimization algorithm is a weighted least squares method.
Optionally, the clustering the plurality of geographic locations by using a clustering algorithm to obtain a geographic location clustering result of the IP includes: selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid; calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid; and clustering the plurality of geographic positions associated with the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
Optionally, a first spherical distance between each geographic location and the first initial centroid and a second spherical distance between each geographic location and a second initial centroid are calculated according to the following equation (1):
S＝R·ar cos(cosβ1·cosβ2·cos(α1-α2)+sinβ1·sinβ2) (1)
wherein, R represents the radius of the major axis of the earth, S represents the spherical distance between the geographic position a and the geographic position B, β 1 is the latitude of the geographic position a, α 1 is the longitude of the geographic position a, β 2 is the latitude of the geographic position B, and α 2 is the longitude of the geographic position B.
Optionally, determining the optimal geographic location corresponding to each IP by using an optimization algorithm according to the geographic location clustering result includes: for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster; and determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight.
Optionally, the weight of each geographical location is determined according to the following equation (2):
Figure BDA0001533883460000031
wherein λ isiWeight representing the ith geographical position, diRepresenting a spherical distance between the ith geographic location and the centroid of the high-density cluster, n being an integer greater than or equal to 1;
determining an optimal geographic location corresponding to the IP according to the following formula (3):
Figure BDA0001533883460000032
wherein (x)i,yi) Which represents the (i) th geographical location,
Figure BDA0001533883460000033
representing an optimal geographical location.
Optionally, determining the accurate geographic location of the IP according to the optimal geographic location and a preset artificial neural network model includes: inputting the optimal geographical position into the preset artificial neural network model to obtain an output result; and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
Optionally, the input layer of the preset artificial neural network model has 3 neuron nodes, the hidden layer has 5 neuron nodes, and the output layer has 1 neuron node.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an apparatus for determining a precise geographical position, including: an acquisition module for acquiring an IP and a plurality of geographic locations associated with the IP; the clustering module is used for clustering the plurality of geographic positions by utilizing a clustering algorithm to obtain a geographic position clustering result of the IP; the optimal geographic position determining module is used for determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result; and the accurate geographic position determining module is used for determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
Optionally, the clustering algorithm is a k-means algorithm, and the optimization algorithm is a weighted least squares method.
Optionally, the clustering module is further configured to: selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid; calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid; and clustering the plurality of geographic positions associated with the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
Optionally, the clustering module calculates a first spherical distance between each geographic location and the first initial centroid and a second spherical distance between each geographic location and a second initial centroid according to the following formula (1):
S＝R·ar cos(cosβ1·cosβ2·cos(α1-α2)+sinβ1·sinβ2) (1)
wherein, R represents the radius of the major axis of the earth, S represents the spherical distance between the geographic position a and the geographic position B, β 1 is the latitude of the geographic position a, α 1 is the longitude of the geographic position a, β 2 is the latitude of the geographic position B, and α 2 is the longitude of the geographic position B.
Optionally, the optimal geographic location determination module is further configured to: for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster; and determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight.
Optionally, the weight of each geographical location is determined according to the following equation (2):
Figure BDA0001533883460000051
wherein λ isiWeight representing the ith geographical position, diRepresenting a spherical distance between the ith geographic location and the centroid of the high-density cluster, n being an integer greater than or equal to 1;
determining an optimal geographic location corresponding to the IP according to the following formula (3):
Figure BDA0001533883460000052
wherein (x)i,yi) Which represents the (i) th geographical location,
Figure BDA0001533883460000053
representing an optimal geographical location.
Optionally, the precise geographic location determination module is further configured to: inputting the optimal geographical position into the preset artificial neural network model to obtain an output result; and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
Optionally, the input layer of the preset artificial neural network model has 3 neuron nodes, the hidden layer has 5 neuron nodes, and the output layer has 1 neuron node.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for determining a precise geographic location according to embodiments of the present invention.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of determining a precise geographical position according to the embodiments of the present invention.
One embodiment of the above invention has the following advantages or benefits: because a clustering algorithm is adopted, the plurality of geographic positions are clustered to obtain a geographic position clustering result of each IP; determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result; and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model, so that the positioning precision is improved, a large number of monitoring points do not need to be laid, and the cost is reduced. Specifically, redundant data is reduced through k-means clustering, and GPS positioning errors caused by factors such as weather, signals and surrounding environment are reduced; then, acquiring the optimal geographical position of different users (MAC) but the geographical position of the same IP by using a weighted least square method; along with the accumulation of data, an ANN neural network training model is established, the optimal solution obtained by the same IP calculation is trained, dirty data caused by certain factors in a period of time is eliminated (some mobile terminal devices can simulate GPS data to cause the GPS data to be invalid), and therefore the positioning accuracy is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a method of determining a precise geographic location according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of the main flow of a method of determining a precise geographic location according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of the main modules of an apparatus for determining a precise geographic location according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow chart of a method for determining an accurate geographical position by an IP-geographical position data set according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step S101: acquiring an IP and a plurality of geographic positions associated with the IP;
step S102: clustering the plurality of geographic positions by using a clustering algorithm to obtain a geographic position clustering result of the IP;
step S103: determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result;
step S104: and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
The IP and the plurality of geographic locations associated with the IP in this embodiment may be obtained through a public geographic information database. The information processing method can also be obtained by receiving the IP reported by the data acquisition source and a plurality of geographic locations associated with the IP, for example, receiving an IP address reported by a reporting device (e.g., a terminal device such as a smart phone or a tablet computer) having a GPS information module and a geographic location associated with the IP address.
With the development of mobile internet technology, any terminal device such as a mobile phone or a tablet computer can be a reporting device in the embodiment and can be used as a data acquisition source, so that a large number of monitoring points do not need to be laid in the embodiment of the invention, and the cost is reduced.
In an optional embodiment, when receiving the IP reported by the reporting device and the geographic location associated with the IP, the device identifier (e.g., MAC address) of the reporting device and a timestamp of reporting data may also be obtained, so that the device identifier, the timestamp, the IP and the geographic location of the IP form a piece of valid data, such as IP-MAC-GPS-TIMESTAMP, where GPS is longitude and latitude information reported, and TIMESTAMP is a timestamp of reporting data.
The geographical position can be represented as satellite positioning information such as longitude and latitude information, altitude information and the like, and can also be represented as position information such as a city, a street, a merchant, an office building and the like. In this embodiment, the geographic location is preferably latitude and longitude information.
The IP is essentially 32-bit unsigned int data ranging from 0 to 232For convenience of use, the IP address is generally in the form of a character string, that is, 192.168.0.1 which is usually used, and in fact, every 8 binary bits are converted into a corresponding decimal integer, which is called numeric IP for short. For example, 192.168.0.1 and 3232235521 are equivalent. 192.168.0.1 means 1 x 2560+0*2561+168*2562+192*25633232235521. In the embodiment of the invention, for the convenience of use, the IP is numerical IP.
Because the GPS is easily affected by factors such as the environment where the reporting device is located, the weather, and the signal of the reporting device itself, the error of some geographical locations in the sample set may be large, and the GPS cannot be completely informed.
Therefore, in step S102, the clustering algorithm is required to cluster the plurality of geographic positions of the IP to eliminate the geographic position with a large error, so as to obtain a more accurate geographic position corresponding to the IP, thereby improving the accuracy of positioning. As a specific example, the clustering algorithm may be a k-means clustering algorithm, and further, the device identifiers may be used as dimensions, and clustering is performed with timestamps, that is, clustering is performed on data reported by the same reporting apparatus within a certain period of time.
The k-means algorithm is a typical clustering algorithm based on distance, and the distance is used as an evaluation index of similarity, that is, the closer the distance between two objects is, the greater the similarity of the two objects is. The core of the algorithm is that a certain distance from a data point to a centroid is solved to serve as a function of an optimization target, and extreme values are continuously iterated by using the function, so that a compact and independent cluster is obtained to serve as a final target.
Further, as shown in fig. 2, the step of clustering the plurality of geographic locations associated with the IP by using a k-means clustering algorithm to obtain a geographic location clustering result of the IP comprises the following steps:
step S201: selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid;
step S202: calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid;
step S203: and clustering the geographic positions related to the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster and a low-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
In step S201, longitude and latitude data collected by the same reporting device (i.e., the same IP) within a period of time are all hashed in distribution near the real geographic location of the IP, and the density of such points is high, but due to the influence of external factors, a few points have a large deviation from the real location, and the density is sparse. Thus, embodiments of the present invention define clusters as high density regions separated by low density regions, and when selecting an initial centroid, two categories are selected on the density-based clusters.
Specifically, 2 longitudes and latitudes can be randomly selected as a first initial centroid and a second initial centroid, or an average value of all the longitudes and latitudes can be selected as the first initial centroid, and the longitude and latitude with the largest deviation from the average value can be selected as the second initial centroid.
In step S202, since the longitude and latitude are coordinates of an ellipsoid, the euclidean distance cannot be simply used as a compact index for measuring a cluster, and the embodiment of the present invention uses the spherical distance as the compact index for measuring a cluster. The spherical distance between two geographic locations can be calculated by the following formula:
S＝R·ar cos(cosβ1·cosβ2·cos(α1-α2)+sinβ1·sinβ2) (1)
wherein, R represents the radius of the major axis of the earth, S represents the spherical distance between the geographic position a and the geographic position B, β 1 is the latitude of the geographic position a, α 1 is the longitude of the geographic position a, β 2 is the latitude of the geographic position B, and α 2 is the longitude of the geographic position B.
For step S203, after the first spherical distance and the second spherical distance are calculated according to formula (1), the geographic position close to the first initial centroid is a cluster, and the geographic position close to the second initial centroid is another cluster. Then, the centroid of each cluster is recalculated, and the iteration is repeated until the final centroid is unchanged or slightly changed. And selecting the high-density cluster as a geographical position clustering result of the IP, and discarding the low-density cluster as an error cluster to avoid causing data pollution.
In step S103, after the geographical location with the large error is preliminarily excluded by the clustering algorithm, in order to further improve the positioning accuracy, the optimal geographical location corresponding to each IP needs to be determined by using the optimization algorithm. Specifically, an optimization algorithm can be used to find the optimal solution for the high-density clusters of the same IP. As a specific example, the optimization algorithm may be a weighted least squares method.
The weighted least squares method described above is a mathematical optimization technique that finds the best functional match of the data by minimizing the sum of the squares of the errors. The weighted least square method is widely applied in the technical field of engineering, and unknown parameters can be simply obtained by the weighted least square method, and the sum of squares of errors between obtained data and actual data is minimum.
Specifically, the process of determining the optimal geographic location corresponding to the IP by using a weighted least square method based on the geographic location clustering result may include the following steps:
1. for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster;
the formula is as follows:
Figure BDA0001533883460000101
λiweight representing the ith longitude and latitude, diRepresents the distance between the ith longitude and latitude and the centroid, and n is an integer greater than or equal to 1.
2. And determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight. In the process, a nonlinear curve fitting function needs to be established for the latitude and longitude of the same IP, so that the variance of the function is minimum, and a specific formula is as follows (3):
Figure BDA0001533883460000111
wherein (x)i,yi) Which represents the (i) th geographical location,
Figure BDA0001533883460000112
and the optimal geographic position corresponding to the IP is obtained. In actual calculation, (x)i,yi) And converting the longitude and latitude into a plane coordinate after geodetic coordinates for the ith geographical position through Gaussian projection.
In the embodiment of the invention, a nonlinear regression model is established for longitude and latitude data of the same IP:
Figure BDA0001533883460000113
wherein
Figure BDA0001533883460000114
As the coordinate of the center of the circle, r is the radius. Solving for the optimal geographic location corresponding to the IP
Figure BDA0001533883460000115
Make it satisfy
Figure BDA0001533883460000116
And minimum.
In step S103, it can be determined that the data reported by a certain sampling device has been correctly processed through the k-means algorithm and the weighted least square method, but in an actual process, due to factors such as a simulator, the reported IP and the latitude and longitude data may have a large deviation, and this part of data may be considered as abnormal data. Therefore, in this embodiment, the artificial neural network model may be used to screen the optimal geographic location obtained by the same IP calculation, so as to exclude abnormal data. Specifically, after the optimal geographic location of the IP is determined, an artificial neural network model is introduced to perform a simple 'classification' of the optimal geographic location, that is, all the optimal geographic locations are classified into two types, namely, a normal type and an abnormal type.
Therefore, further, the method further comprises: and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
Specifically, the method can comprise the following steps:
inputting the optimal geographical position into the preset artificial neural network model to obtain an output result;
and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
Before the optimal geographical position is screened by using the preset artificial neural network model, the method further comprises the following steps: training the artificial neural network model, namely adjusting the weight of each neural node through training data to enable the expected output of the normal optimal geographical position to be 1 and the expected output of the abnormal optimal geographical position to be 0.
Specifically, a large amount of IP data related to the correct geographic position are selected as normal data (for example, more than 20000 pieces of data), artificial abnormal data are added into the same IP, the normal data and the artificial abnormal data are used for carrying out artificial neural network model hidden layer weight training, the final function convergence is ensured, and the hidden layer weight parameter at the moment is used as an initialization parameter.
As a specific example, the input layer of the preset artificial neural network model has 3 neuron nodes corresponding to an IP (numerical IP), a longitude, and a latitude; the hidden layer is provided with 5 neuron nodes, and the number of the neuron nodes is determined by a developer through training data convergence time and a method; the output layer is provided with 1 neuron node, whether the longitude and latitude are abnormal data or not is judged through an output result, the output result is 1 to indicate that the longitude and latitude are normal data, and the output result is 0 to indicate that the longitude and latitude are abnormal data.
Therefore, the preset target result may be 1, and if the output result is 1, the optimal geographic location is the precise geographic location of the IP.
In an alternative embodiment, the obtained IP and the precise geographic location of the IP may be saved.
In this embodiment, an Artificial Neural Network (ANN) model is: abstracting the human brain neuron network from the information processing angle, establishing a certain simple model, and forming different networks according to different connection modes. It is also often directly referred to in engineering and academia as neural networks or neural-like networks. A neural network is an operational model, which is formed by connecting a large number of nodes (or neurons). Each node represents a particular output function, called the excitation function. Every connection between two nodes represents a weighted value, called weight, for the signal passing through the connection, which is equivalent to the memory of the artificial neural network. The output of the network is different according to the connection mode of the network, the weight value and the excitation function. The network itself is usually an approximation to some algorithm or function in nature, and may also be an expression of a logic strategy.
The method for determining the accurate geographic position improves the positioning accuracy, does not need to lay a large number of monitoring points, and reduces the cost. Specifically, redundant data is reduced through k-means clustering, and GPS positioning errors caused by factors such as weather, signals and surrounding environment are reduced; then, acquiring the optimal geographical position of different users (MAC) but the geographical position of the same IP by using a weighted least square method; along with the accumulation of data, an ANN neural network training model is established, the optimal solution obtained by the same IP calculation is trained, dirty data caused by certain factors in a period of time is eliminated (some mobile terminal devices can simulate GPS data to cause the GPS data to be invalid), and therefore the positioning accuracy is improved.
The method of the embodiment of the invention can also obtain the variance according to the formula (3) to provide a quantitative index for the accuracy of IP positioning, and the smaller the variance is, the higher the accuracy is.
Fig. 3 is a schematic diagram of main blocks of an IP positioning apparatus according to still another embodiment of the present invention. As shown in fig. 3, the apparatus 300 includes: an obtaining module 301, configured to obtain an IP and a plurality of geographic locations associated with the IP; a clustering module 302, configured to cluster the multiple geographic locations by using a clustering algorithm to obtain a geographic location clustering result of the IP; an optimal geographic location determining module 303, configured to determine, based on the geographic location clustering result, an optimal geographic location corresponding to the IP by using an optimization algorithm; and the precise geographic position determining module 304 is configured to determine the precise geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
Optionally, the clustering algorithm is a k-means algorithm, and the optimization algorithm is a weighted least squares method.
Optionally, the clustering module 302 is further configured to: selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid; calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid; and clustering the plurality of geographic positions associated with the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
Optionally, the clustering module 302 calculates a first spherical distance between each geographic location and the first initial centroid and a second spherical distance between each geographic location and a second initial centroid according to the following formula (1):
S＝R·ar cos(cosβ1·cosβ2·cos(α1-α2)+sinβ1·sinβ2) (1)
wherein, R represents the radius of the major axis of the earth, S represents the spherical distance between the geographic position a and the geographic position B, β 1 is the latitude of the geographic position a, α 1 is the longitude of the geographic position a, β 2 is the latitude of the geographic position B, and α 2 is the longitude of the geographic position B.
Optionally, the optimal geographic location determining module 303 is further configured to: for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster; and determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight.
Optionally, the weight of each geographical location is determined according to the following equation (2):
Figure BDA0001533883460000141
wherein λ isiWeight representing the ith geographical position, diIndicating the ith geographic location and high densityThe spherical distance between the centers of mass of the clusters, n is an integer greater than or equal to 1;
determining an optimal geographic location corresponding to the IP according to the following formula (3):
Figure BDA0001533883460000142
wherein (x)i,yi) Which represents the (i) th geographical location,
Figure BDA0001533883460000143
representing an optimal geographical location.
Optionally, the precise geographic position determination module 304 is further configured to: inputting the optimal geographical position into the preset artificial neural network model to obtain an output result; and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
Optionally, the input layer of the preset artificial neural network model has 3 neuron nodes, the hidden layer has 5 neuron nodes, and the output layer has 1 neuron node.
The device for determining the accurate geographical position improves the positioning accuracy, does not need to lay a large number of monitoring points, and reduces the cost. Specifically, redundant data is reduced through k-means clustering, and GPS positioning errors caused by factors such as weather, signals and surrounding environment are reduced; then, acquiring the optimal geographical position of different users (MAC) but the geographical position of the same IP by using a weighted least square method; along with the accumulation of data, an ANN neural network training model is established, the optimal solution obtained by the same IP calculation is trained, dirty data caused by certain factors in a period of time is eliminated (some mobile terminal devices can simulate GPS data to cause the GPS data to be invalid), and therefore the positioning accuracy is improved.
Fig. 4 shows an exemplary system architecture 400 of an IP-geolocation data set building method or IP-geolocation data set building apparatus to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have various communication client applications installed thereon, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 401, 402, and 403. The background management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (e.g., target push information and product information) to the terminal device.
It should be noted that the method for determining the precise geographic location provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the IP positioning device is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not in some cases constitute a limitation on the unit itself, and for example, the sending module may also be described as a "module that sends a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
acquiring an IP and a plurality of geographic positions associated with the IP;
clustering the plurality of geographic positions by using a clustering algorithm to obtain a geographic position clustering result of the IP;
determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result;
and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model.
Technical scheme of embodiment of the invention
Because a clustering algorithm is adopted, the plurality of geographic positions are clustered to obtain a geographic position clustering result of each IP; determining the optimal geographic position corresponding to the IP by utilizing an optimization algorithm based on the geographic position clustering result; and determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model, so that the positioning precision is improved, a large number of monitoring points do not need to be laid, and the cost is reduced. Specifically, redundant data is reduced through k-means clustering, and GPS positioning errors caused by factors such as weather, signals and surrounding environment are reduced; then, acquiring the optimal geographical position of different users (MAC) but the geographical position of the same IP by using a weighted least square method; along with the accumulation of data, an ANN neural network training model is established, the optimal solution obtained by the same IP calculation is trained, dirty data caused by certain factors in a period of time is eliminated (some mobile terminal devices can simulate GPS data to cause the GPS data to be invalid), and therefore the positioning accuracy is improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (16)

1. A method of determining a precise geographic location, comprising:
acquiring an IP and a plurality of geographic positions associated with the IP;
clustering the plurality of geographic positions by using a clustering algorithm to obtain a high-density cluster as a geographic position clustering result of the IP;
based on the geographical position clustering result, an optimal solution is obtained for the high-density clusters of the IP by utilizing an optimization algorithm, and the optimal geographical position corresponding to the IP is determined;
determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model;
wherein, determining the accurate geographical position of the IP according to the optimal geographical position and a preset artificial neural network model comprises:
inputting the optimal geographical position into the preset artificial neural network model to obtain an output result;
and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
2. The method of claim 1, wherein the clustering algorithm is a k-means algorithm and the optimization algorithm is a weighted least squares method.
3. The method of claim 2, wherein the clustering the plurality of geographic locations using a clustering algorithm to obtain a high density cluster, the step of clustering the geographic locations as the geographic location of the IP comprises:
selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid;
calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid;
and clustering the plurality of geographic positions associated with the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
4. The method of claim 3, wherein a first spherical distance between each geographic location and the first initial centroid and a second spherical distance between each geographic location and a second initial centroid are calculated according to the following equation (1):
Figure 593718DEST_PATH_IMAGE001
（1）
wherein R represents the radius of the earth' S major axis, S represents the spherical distance between the geographic position A and the geographic position B,
Figure 208239DEST_PATH_IMAGE002
is the latitude of the geographic location a,
Figure 286047DEST_PATH_IMAGE003
is the longitude of the geographic location a and,
Figure 459146DEST_PATH_IMAGE004
is the latitude of the geographic location B,
Figure 996307DEST_PATH_IMAGE005
the longitude of geographic location B.
5. The method of claim 3, wherein determining the optimal geographical location corresponding to each IP by using an optimization algorithm according to the geographical location clustering result comprises:
for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster;
and determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight.
6. The method of claim 5, wherein the weight for each geographic location is determined according to equation (2) below:
Figure 650404DEST_PATH_IMAGE006
（2）
wherein λ isiWeight representing the ith geographical position, diRepresenting a spherical distance between the ith geographic location and the centroid of the high-density cluster, n being an integer greater than or equal to 1;
determining an optimal geographic location corresponding to the IP according to the following formula (3):
Figure 956621DEST_PATH_IMAGE007
（3）
wherein (x)i,yi) Represents the ith geographical location (
Figure 634376DEST_PATH_IMAGE008
，
Figure 425877DEST_PATH_IMAGE009
) Representing an optimal geographical location.
7. The method of claim 1, wherein the input layer of the preset artificial neural network model has 3 neuron nodes, the hidden layer has 5 neuron nodes, and the output layer has 1 neuron node.
8. An apparatus for determining a precise geographic location, comprising:
an acquisition module for acquiring an IP and a plurality of geographic locations associated with the IP;
the clustering module is used for clustering the plurality of geographic positions by utilizing a clustering algorithm to obtain a high-density cluster as a geographic position clustering result of the IP;
the optimal geographic position determining module is used for solving an optimal solution for the high-density clusters of the IP by utilizing an optimization algorithm based on the geographic position clustering result and determining the optimal geographic position corresponding to the IP;
the accurate geographic position determining module is used for determining the accurate geographic position of the IP according to the optimal geographic position and a preset artificial neural network model;
wherein the precise geographic location determination module is further to:
inputting the optimal geographical position into the preset artificial neural network model to obtain an output result;
and if the output result is a preset target result, the optimal geographic position is the accurate geographic position of the IP.
9. The apparatus of claim 8, wherein the clustering algorithm is a k-means algorithm and the optimization algorithm is a weighted least squares method.
10. The apparatus of claim 9, wherein the clustering module is further configured to:
selecting two geographic locations from the plurality of geographic locations associated with the IP as a first initial centroid and a second initial centroid;
calculating a first spherical distance between each geographic location of the plurality of geographic locations and the first initial centroid and a second spherical distance between each geographic location of the plurality of geographic locations and a second initial centroid;
and clustering the plurality of geographic positions associated with the IP according to the first spherical distance and the second spherical distance to obtain a high-density cluster, and taking the high-density cluster as a geographic position clustering result of the IP.
11. The apparatus of claim 10, wherein the clustering module calculates a first spherical distance between each geographic location and the first initial centroid and a second spherical distance between each geographic location and a second initial centroid according to the following equation (1):
Figure 116621DEST_PATH_IMAGE001
（1）
wherein R represents the radius of the earth' S major axis, S represents the spherical distance between the geographic position A and the geographic position B,
Figure 887131DEST_PATH_IMAGE002
is the latitude of the geographic location a,
Figure 995508DEST_PATH_IMAGE003
is the longitude of the geographic location a and,
Figure 477567DEST_PATH_IMAGE004
is the latitude of the geographic location B,
Figure 706424DEST_PATH_IMAGE005
the longitude of geographic location B.
12. The apparatus of claim 9, wherein the optimal geographic location determination module is further configured to:
for each geographic position in the high-density cluster, determining the weight of each geographic position according to the spherical distance between each geographic position and the centroid of the high-density cluster;
and determining the optimal geographic position corresponding to each IP by using a weighted least square method according to the weight.
13. The apparatus of claim 12, wherein the weight for each geographic location is determined according to equation (2) below:
Figure 479908DEST_PATH_IMAGE010
（2）
wherein λ isiWeight representing the ith geographical position, diRepresenting a spherical distance between the ith geographic location and the centroid of the high-density cluster, n being an integer greater than or equal to 1;
determining an optimal geographic location corresponding to the IP according to the following formula (3):
Figure 824433DEST_PATH_IMAGE007
（3）
wherein (x)i,yi) Represents the ith geographical location (
Figure 167689DEST_PATH_IMAGE008
，
Figure 465816DEST_PATH_IMAGE009
) Representing an optimal geographical location.
14. The apparatus of claim 8, wherein the input layer of the preset artificial neural network model has 3 neuron nodes, the hidden layer has 5 neuron nodes, and the output layer has 1 neuron node.
15. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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