Surveying and mapping system for IP geographic positioning data

Abstract

The invention relates to the technical field of IP geographic positioning, and discloses an IP geographic positioning data surveying and mapping system, which comprises a data acquisition module, a scene division module, a clustering analysis module, a topology analysis module and a position output module. In the prior art, city-level positioning is realized only by depending on an IP distribution rule, static routing detection or a single algorithm, and particularly, datum point data cannot be fully utilized or a non-datum point IP address cannot be accurately positioned in a complex network environment. According to the method, meter-scale precision street-level geographic positioning of the datum point IP and the non-datum point IP is realized through the technical means of datum point screening based on geographic position entropy, scene-specific dynamic clustering analysis, network topology similarity calculation, shortest path correction and the like; therefore, the problems of low datum point data utilization rate and insufficient positioning precision in the prior art are avoided, and the accuracy and the application range of IP positioning are greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of IP geographic positioning, and in particular relates to a surveying and mapping system for IP geographic positioning data. Background Art

[0002] At present, IP geolocation technology has the problem of insufficient accuracy. For example, existing technologies mainly rely on IP address allocation rules, route detection and intelligence analysis methods. These technologies can only achieve city-level positioning accuracy and are difficult to meet the needs of high-precision geolocation. Especially in scenarios such as cyberspace asset supervision, map operations, asset exposure surface sorting and industry analysis, city-level accuracy cannot meet the precise needs of IP address positioning, resulting in incomplete data coverage and large positioning errors in actual applications. In addition, existing technologies have obvious deficiencies in benchmark data utilization, algorithm dynamic optimization, non-benchmark IP real-time positioning and adaptability to complex scenarios. For example, the geographic distribution data of benchmark IP has not been effectively mapped and utilized. Traditional methods are mostly based on static rules or simple algorithms and cannot cope with multi-dimensional data requirements in complex network environments. For non-benchmark IPs that are not bound to a clear geographic location, there is a lack of accurate dynamic positioning capabilities, resulting in accuracy far lower than the requirements of actual applications. In dynamic network scenarios or complex network topologies, the accuracy of existing technologies is significantly reduced, and street-level or even meter-level positioning cannot be achieved. Therefore, there is an urgent need for a high-precision IP positioning method that combines data mapping and network measurement technology and can integrate multiple data sources, so that when both benchmark point data and non-benchmark point data are applicable, street-level or even meter-level geographic positioning can still be achieved, thereby greatly improving the accuracy and applicability of IP positioning and meeting the high-precision positioning needs in multiple scenarios. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a surveying and mapping system for IP geographic positioning data, aiming to solve the technical problem that the prior art only relies on IP allocation rules, static routing detection or a single algorithm to achieve city-level positioning, especially in a complex network environment, and cannot fully utilize benchmark point data or accurately locate non-benchmark point IP addresses.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a mapping system for IP geographic positioning data, comprising:

[0005] A data collection module is used to obtain IP basic information from the World Wide Web and mobile platforms to form an open data set, which includes IP addresses, BGP routing information, Whois registration data, open WIFI hotspot information, host and domain name association information, user device records and geographical distribution statistics;

[0006] The obtained open data set is standardized, and the geographic location entropy value of the geographic data point i in the standardized open data set is calculated to obtain the geographic location entropy value H i , retain the geographic location entropy value H i Geographic data points with a value greater than the preset geographic location entropy threshold form a benchmark data set; retain the geographic location entropy value H i Geographic data points that are less than or equal to a preset geographic location entropy value threshold form a non-reference point data set;

[0007] The scenario division module is used to extract feature vectors F and IP addresses from open data sets. Feature vector F includes access frequency features, port distribution features, domain name association information features, and geographic distribution information features. Based on feature vector F, the K-means clustering algorithm is used to divide IP addresses into different application scenarios, including enterprise dedicated lines, residential users, and school institutions.

[0008] The division results of different application scenarios are used as classification labels, and the benchmark point datasets are marked to generate scenario-specific benchmark point datasets;

[0009] The clustering analysis module is used to set dynamic clustering parameters for scene-specific benchmark data sets. The dynamic clustering parameters include the distance threshold ∈ d and the minimum number of points N min The density clustering algorithm DBSCAN is used to perform dynamic clustering analysis on the scene-specific benchmark data set. The density clustering algorithm DBSCAN outputs the first-level positioning result P u , including the IP geographic location range of street-level benchmarks for different application scenarios;

[0010] The topology analysis module is used to obtain the network path information of the data points of the non-reference point data set through the network detection tool, and extract the first adjacent set E of the data points of the non-reference point data set. t , preset the target IP for the data points of the non-reference point dataset and construct the second adjacent set E b , according to the first adjacency set E t and the second adjacent set E b Calculate the network topology similarity S between the target IP and the data points of the non-reference point dataset t , based on the network topology similarity S t The calculation result binds the target IP to the data point of the non-reference point data set to obtain the non-reference point IP geographical location range, and corrects the non-reference point IP geographical location range through the shortest path algorithm to output the second-level positioning result Q t , including street-level non-reference point IP geographic location ranges;

[0011] The location output module is used to integrate the positioning results of the cluster analysis module and the topology analysis module, verify the consistency of the positioning results, and then generate and output the final IP geolocation data.

[0012] Preferably, in the data collection module, the step of standardizing the obtained open data set adopts a normalization formula:

[0013]

[0014] Among them, x' is the normalized open dataset, x is the open dataset, μ is the mean of the open dataset, and σ is the standard deviation of the open dataset.

[0015] Preferably, in the cluster analysis module, in the density clustering algorithm DBSCAN, the geographical distance between points in the scene-specific reference point data set is determined using the formula:

[0016]

[0017] Among them, φ m and λ m is the latitude and longitude of geographic data point m, φ n and λ n is the latitude and longitude of geographic data point n.

[0018] Preferably, in the cluster analysis module, the dynamic clustering parameters are adjusted according to the optimization objective function F, and the formula for the optimization objective function F is:

[0019] F(∈ d ,N min )=α@R+β·P

[0020] Among them, F(∈ d ,N min ) is the optimization objective function; ∈ d is the distance threshold in the dynamic clustering parameters; N min is the minimum number of points in the dynamic clustering parameters; α and β are weight coefficients; R is the benchmark recall rate, which is used to measure the coverage of benchmark points in the clustering results; P is the geographic positioning accuracy, which is used to measure the matching degree between the clustering range and the actual geographic location.

[0021] Preferably, in the network topology module, the network topology similarity S t The calculation formula is:

[0022]

[0023] Among them, |E t ∩E p | is the number of common adjacent nodes between the target IP and the data points of the non-benchmark point dataset.

[0024] Preferably, in the position output module, the consistency of the positioning result is verified using the formula:

[0025]

[0026] Among them, C is the consistency index.

[0027] Preferably, in the data collection module, the geographic location entropy value H i The calculation formula is:

[0028]

[0029] Among them, H i is the geographic location entropy value of geographic data point i, n is the number of geographic data points divided in the preset geographic area, p i is the probability of the IP address of a data point in the open dataset appearing in geographic data point i.

[0030] The beneficial effect of the present invention is that compared with the prior art that only relies on IP allocation rules, static route detection or a single algorithm to achieve city-level positioning, especially the technical problem of being unable to fully utilize benchmark point data or accurately locate non-benchmark point IP addresses in complex network environments, the present application achieves meter-level precision street-level geographic positioning of benchmark points and non-benchmark point IPs through benchmark point screening based on geographic location entropy, scene-specific dynamic clustering analysis, network topology similarity calculation and shortest path correction and other technical means, thereby avoiding the problems of low benchmark point data utilization and insufficient positioning accuracy in the prior art, and greatly improving the accuracy and scope of application of IP positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A schematic flow chart of a first embodiment of a system for surveying and mapping IP geographic positioning data provided by the present invention.

[0033] Figure 2 A schematic diagram of equipment of a system for surveying and mapping IP geographic positioning data provided by the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the system for surveying and mapping IP geographic positioning data of the present invention, provides the first embodiment of the system for surveying and mapping IP geographic positioning data of the present invention.

[0036] In a first embodiment, the system for mapping IP geolocation data includes:

[0037] A data collection module is used to obtain IP basic information from the World Wide Web and mobile platforms to form an open data set, which includes IP addresses, BGP routing information, Whois registration data, open WIFI hotspot information, host and domain name association information, user device records and geographical distribution statistics;

[0038] The obtained open data set is standardized, and the geographic location entropy value of the geographic data point i in the standardized open data set is calculated to obtain the geographic location entropy value H i , retain the geographic location entropy value H i Geographic data points with a value greater than the preset geographic location entropy threshold form a benchmark data set; retain the geographic location entropy value H i Geographic data points that are less than or equal to a preset geographic location entropy value threshold form a non-reference point data set;

[0039] It should be noted that the BGP routing information contained in the open data set in the data collection module is used to provide the approximate network path of the IP address, the Whois registration data is used to extract the ownership information and management agency information of the IP address, and the open WIFI hotspot information provides geographic annotation points of public places such as longitude and latitude and MAC addresses. The host and domain name association information can help further identify the usage environment of the IP address such as enterprise-specific IP or server IP. User device records and geographic distribution statistics provide auxiliary data support for subsequent positioning from the perspective of behavior and spatial distribution.

[0040] It can be understood that the standardization process of the open data set performed by the data collection module is to unify the data from different sources into a consistent format and value range.

[0041] It should be understood that the calculation of the geographic location entropy value is based on the probability of IP data distribution in each geographic area, and the geographic location entropy value is calculated by the following formula:

[0042]

[0043] Among them, H i is the geographic location entropy value of geographic data point i, n is the number of geographic data points divided in the preset geographic area, p i is the probability of the IP address of a data point in the open dataset appearing in the i-th geographic data point.

[0044] For example, an open data set contains access records of an IP address, which are distributed in five areas: Area A (50 times), Area B (30 times), Area C (20 times), Area D (0 times), and Area E (0 times). The corresponding probability distribution is: pA=0.5, pB=0.3, pC=0.2, pD=0, pE=0. The entropy formula is: H=-(0.5log(0.5)+0.3log(0.3)+0.2*log(0.2))≈1.485bits. If the entropy threshold is set to 1.2, the entropy value H=1.485 of this point exceeds the threshold and is retained as part of the benchmark point data set. Through the cooperation of the above modules and algorithms, data points with geographical location stability can be effectively screened out, providing reliable benchmark point data support for subsequent high-precision IP geolocation.

[0045] The scenario division module is used to extract feature vectors F and IP addresses from open data sets. Feature vector F includes access frequency features, port distribution features, domain name association information features, and geographic distribution information features. Based on feature vector F, the K-means clustering algorithm is used to divide IP addresses into different application scenarios, including enterprise dedicated lines, residential users, and school institutions.

[0046] The division results of different application scenarios are used as classification labels, and the benchmark point datasets are marked to generate scenario-specific benchmark point datasets;

[0047] It should be noted that the feature vector F is extracted to reflect the different usage patterns of IP addresses. For example, the access frequency can be measured by counting the average daily visits of the IP address, the port distribution can indicate the types of ports opened by the IP address and their proportions, the domain name association information can reflect the binding relationship between the IP address and the specific domain name, and the geographic distribution information reflects the usage scope and concentration of the IP address.

[0048] It can be understood that the core of the K-means clustering algorithm is to make each IP address belong to the application scenario class with the closest characteristics through iterative optimization.

[0049] It should be understood that after the application scenario division is completed, the reference point dataset needs to be associated with the classification results of different scenarios to generate a scene-specific reference point dataset. The purpose of this step is to refine the classification of the reference points so that more accurate positioning analysis can be performed in specific application scenarios.

[0050] For example, suppose the feature vector of an IP address is F = [access frequency = 100 times / day, port distribution = 80 / 443 / 22, domain name association = example.com, geographic distribution = concentrated in a certain office area]. After K-means clustering, the IP address is classified as an enterprise dedicated line scenario. Further combined with the geographic location entropy value and benchmark data set of the IP address, it is labeled as an enterprise dedicated line and included in the benchmark data set of the enterprise dedicated line scenario to provide support for subsequent positioning analysis. In this way, more accurate IP positioning results can be achieved in different application scenarios.

[0051] The clustering analysis module is used to set dynamic clustering parameters for scene-specific benchmark data sets. The dynamic clustering parameters include the distance threshold ∈ d and the minimum number of points N min The density clustering algorithm DBSCAN is used to perform dynamic clustering analysis on the scene-specific benchmark data set. The density clustering algorithm DBSCAN outputs the first-level positioning result P u , including the IP geographic location range of street-level benchmarks for different application scenarios;

[0052] It should be noted that the distance threshold in the dynamic clustering parameters is used to limit the maximum geographical distance between benchmark points. Benchmark points that exceed this threshold will not belong to the same cluster; the minimum number of points is used to define the minimum number of benchmark points required to form a valid cluster to avoid misclustering caused by interference from isolated points or sparse areas; the density clustering algorithm DBSCAN analyzes the scene-specific benchmark point dataset through these two parameters and can automatically identify the number of clusters and their distribution.

[0053] It can be understood that the working principle of DBSCAN is to calculate the number of neighborhood points of each point with the distance threshold as the radius. If the number of neighborhood points of a point is greater than or equal to the minimum number of points, the point is regarded as a core point, and all points in its neighborhood are classified into the same cluster; for points that do not reach the minimum number of points, they are regarded as noise points or isolated points and do not participate in clustering.

[0054] It should be understood that by dynamically adjusting the distance threshold and the minimum number of points, the clustering effect can be optimized for different application scenarios. For example, in the enterprise dedicated line scenario, since the IP distribution is more concentrated, the distance threshold can be set smaller (such as 50 meters), while in the residential user scenario, the IP distribution is more dispersed, and the distance threshold can be appropriately relaxed (such as 200 meters) to improve the clustering coverage.

[0055] For example, assuming that the benchmark point data set of an enterprise dedicated line scenario contains 10 benchmark points, whose geographical locations are relatively concentrated, the distance threshold is set to 50 meters, and the minimum number of points is 3. The DBSCAN algorithm finds through analysis that the maximum distance between three points (A, B, C) is less than 50 meters, and the number of points in the neighborhood of each point is greater than or equal to 3. In this case, A, B, and C are classified into the same cluster, and the clustering results of the benchmark points with their street-level geographic scope are output; in the residential user scenario, if the distance threshold is set to 200 meters, the same method can cover a larger range of benchmark point clustering results.

[0056] Through the above cluster analysis module, the IP geographic location range of street-level benchmark points can be generated for different application scenarios, providing key input data support for the subsequent positioning of non-benchmark points.

[0057] The topology analysis module is used to obtain the network path information of the data points of the non-reference point data set through the network detection tool, and extract the first adjacent set E of the data points of the non-reference point data set. t , preset the target IP for the data points of the non-reference point dataset and construct the second adjacent set E b , according to the first adjacency set E t and the second adjacent set E b Calculate the network topology similarity S between the target IP and the data points of the non-reference point dataset t , based on the network topology similarity S t The calculation result binds the target IP to the data point of the non-reference point data set to obtain the non-reference point IP geographical location range, and corrects the non-reference point IP geographical location range through the shortest path algorithm to output the second-level positioning result Q t , including street-level non-reference point IP geographic location ranges;

[0058] It should be noted that the network topology similarity is used to measure the similarity between the target IP and the data points in the non-reference point data set in the network path structure, which is defined as the ratio of common adjacent points between the target IP and the non-reference point. The network detection tool can provide the network path information of the target IP, including the number of path hops and intermediate nodes. By extracting the adjacent set, the topological similarity between the target IP and the non-reference point data points can be calculated.

[0059] It should be understood that by calculating the network topology similarity, the target IP can be bound to the non-reference point data point that is most similar to its path. After the binding is completed, the positioning error of the target IP can be further corrected by combining the street-level reference point IP geographic location range and the shortest path algorithm to ensure that the positioning accuracy of the non-reference point IP meets the street-level requirements.

[0060] For example, assuming that the adjacency set of a target IP is {A, B, C, D}, the adjacency sets of non-reference points b1 and b2 are E_b1 = {B, C, D, E} and E_b2 = {A, C, F, G} respectively. The topological similarity calculated according to the formula is S_t, b1 = 2*3 / (4+4) = 0.75, S_t, b2 = 2*2 / (4+4) = 0.5; because S_t, b1>S_t, b2, the target IP is bound to the non-reference point b1, and then the positioning range of b1 is corrected by the shortest path algorithm, and finally the street-level positioning result of the target IP is output.

[0061] The topology analysis module can effectively solve the positioning problem of non-reference point IPs and provide accurate input data for subsequent result fusion.

[0062] The location output module is used to integrate the positioning results of the cluster analysis module and the topology analysis module, verify the consistency of the positioning results, and then generate and output the final IP geolocation data.

[0063] It should be noted that the cluster analysis module and the topology analysis module process the geolocation of the benchmark IP and non-benchmark IP respectively. The results generated by the two may overlap or deviate in some areas. The role of the location output module is to merge and verify the consistency of the two types of results to ensure that the final output geolocation data has high accuracy and reliability. The core method of verifying consistency is to calculate the overlap between the positioning results from different sources to quantify their consistency level.

[0064] It should be understood that when the consistency verification result is lower than the preset threshold, it is necessary to readjust the parameters of the clustering analysis and topology analysis modules, such as the distance threshold, the minimum number of points or the topological similarity threshold, to improve the matching degree of the positioning results. When the consistency verification passes, the two types of results are fused to output the final positioning data.

[0065] In addition, the present invention also provides a surveying and mapping device for IP geographic positioning data, please refer to Figure 2, a device for mapping IP geolocation data includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the system for mapping IP geolocation data in the first embodiment. The device for mapping IP geolocation data in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The device for mapping IP geolocation data is merely an example and should not limit the functions and scope of use of the embodiments of the present invention. An IP geolocation data surveying and mapping device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of an IP geolocation data surveying and mapping device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow a surveying and mapping device for IP geolocation data to communicate wirelessly or by wire with other devices to exchange data. Although the figure shows a surveying and mapping device for IP geolocation data with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0066] The present invention also provides a computer program product, including a computer program, which implements the steps of the above-mentioned system for mapping IP geographic positioning data when executed by a processor. The computer program product provided by the present invention can solve the technical problem of mapping IP geographic positioning data. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the system for mapping IP geographic positioning data provided by the above-mentioned embodiment, which will not be repeated here.

[0067] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the system of the embodiments disclosed in the present invention are executed.

[0068] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A system for mapping IP geolocation data, characterized in that: The system includes: A data collection module is used to obtain IP basic information from the World Wide Web and mobile platforms to form an open data set, which includes IP addresses, BGP routing information, Whois registration data, open WIFI hotspot information, host and domain name association information, user device records and geographical distribution statistics; The obtained open data set is standardized, and the geographic location entropy value of the geographic data point i in the standardized open data set is calculated to obtain the geographic location entropy value H i , retain the geographic location entropy value H i Geographic data points with entropy values ​​greater than a preset geographic location threshold form a benchmark point data set; Keep the geographic location entropy value H i Geographic data points that are less than or equal to a preset geographic location entropy value threshold form a non-reference point data set; The scenario division module is used to extract feature vectors F and IP addresses from open data sets. Feature vector F includes access frequency features, port distribution features, domain name association information features, and geographic distribution information features. Based on feature vector F, the K-means clustering algorithm is used to divide IP addresses into different application scenarios, including enterprise dedicated lines, residential users, and school institutions. The division results of different application scenarios are used as classification labels, and the benchmark point datasets are marked to generate scenario-specific benchmark point datasets; The clustering analysis module is used to set dynamic clustering parameters for scene-specific benchmark data sets. The dynamic clustering parameters include the distance threshold ∈ d and the minimum number of points N min , the density clustering algorithm DBSCAN is used to perform dynamic clustering analysis on the scene-specific benchmark data set, and the first-level positioning result P is output. u , including the IP geographic location range of street-level benchmarks for different application scenarios; The topology analysis module is used to obtain the network path information of the data points of the non-reference point data set through the network detection tool, and extract the first adjacent set E of the data points of the non-reference point data set. t , preset the target IP for the data points of the non-reference point dataset and construct the second adjacent set E b , according to the first adjacency set E t and the second adjacent set E b Calculate the network topology similarity S between the target IP and the data points of the non-reference point dataset t , based on the network topology similarity S t The calculation result binds the target IP to the data point of the non-reference point data set to obtain the non-reference point IP geographical location range, and corrects the non-reference point IP geographical location range through the shortest path algorithm to output the second-level positioning result Q t , including street-level non-reference point IP geographic location ranges; The location output module is used to integrate the positioning results of the cluster analysis module and the topology analysis module, verify the consistency of the positioning results, and then generate and output the final IP geolocation data.

2. A system for mapping IP geographic location data as claimed in claim 1, characterized in that: In the data collection module, the step of standardizing the obtained open data set adopts the normalization formula: Among them, x' is the normalized open dataset, x is the open dataset, μ is the mean of the open dataset, and σ is the standard deviation of the open dataset.

3. The system for mapping IP geographic location data according to claim 1, characterized in that: In the cluster analysis module, in the density clustering algorithm DBSCAN, the formula used to determine the geographic distance between points in the scene-specific benchmark data set is: Among them, φ m and λ m is the latitude and longitude of geographic data point m, φ n and λ n is the latitude and longitude of geographic data point n.

4. The system for mapping IP geographic location data according to claim 1, characterized in that: In the cluster analysis module, the dynamic clustering parameters are adjusted according to the optimization objective function F. The formula of the optimization objective function F is: F(∈ d ,N min )=α·R+β@P Among them, F(∈ d ,N min ) is the optimization objective function; ∈ d is the distance threshold in the dynamic clustering parameters; N min is the minimum number of points in the dynamic clustering parameters; α and β are weight coefficients; R is the benchmark recall rate, which is used to measure the coverage of benchmark points in the clustering results; P is the geographic positioning accuracy, which is used to measure the matching degree between the clustering range and the actual geographic location.

5. The system for mapping IP geographic location data according to claim 1, characterized in that: In the network topology module, the network topology similarity S t The calculation formula is: Among them, |E t ∩E p | is the number of common adjacent nodes between the target IP and the data points of the non-benchmark point dataset.

6. The system for mapping IP geographic location data according to claim 1, characterized in that: In the position output module, the consistency of the positioning results is verified using the formula: Among them, C is the consistency index.

7. The system for mapping IP geographic location data according to claim 1, characterized in that: In the data collection module, the geographic location entropy value H i The calculation formula is: Among them, H i is the geographic location entropy value of geographic data point i, n is the number of geographic data points divided in the preset geographic area, p i is the probability of the IP address of a data point in the open dataset appearing in geographic data point i.

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