Method for identifying different sites based on 5GMRO measurement

Abstract

The invention discloses a different network site identification method, device and equipment based on 5GMRO measurement and a storage medium, and relates to the technical field of communication, and the method comprises the steps: obtaining a plurality of different network adjacent cell measurement data; performing density clustering on the measurement data of the adjacent cells of the different networks to obtain a plurality of clustering groups; for each clustering group, grouping according to a primary service cell identifier, a neighbor cell frequency point and a neighbor cell identifier of different network neighbor cell measurement data to obtain a plurality of first small groups; for each first group, grouping the horizontal arrival angle and the time advance of the different-network cell corresponding to the first group according to a preset granularity to obtain a plurality of second groups; respectively calculating the characteristic index of each second group according to the measurement data of each different network adjacent region; and inputting each feature index in the first group into a preset classifier model to obtain an output home network cell identifier, and taking a position corresponding to the home network cell identifier as a predicted position of the different network cell. According to the method and the device, different website site positions can be predicted.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, equipment and storage medium for identifying foreign website URLs based on 5GMRO measurement. Background Art

[0002] Improving the sharing efficiency of 5G network tower resources and quickly and accurately determining the pricing system for shared towers are crucial to reducing operation and maintenance costs. Therefore, in the context of industry competition, it is particularly urgent and important to accurately identify the locations of sites from different manufacturers and carry out network optimization and planning work accordingly.

[0003] Currently, the identification of such scenarios mainly relies on manual grid-based investigation, by reporting the location information of foreign network base stations in each grid, or analyzing based on tower fee audit data. However, the manual investigation method is not only inefficient, but also difficult to guarantee accuracy, and it is impossible to effectively identify sites unique to foreign networks based on tower fees.

[0004] In summary, how to identify the location of foreign websites has become a problem that needs to be solved urgently in this field.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a method, device, equipment and storage medium for identifying foreign website addresses based on 5GMRO (Measurement Report Data) measurement, aiming to solve the technical problem of how to identify the location of foreign website addresses.

[0007] To achieve the above purpose, the present application proposes a method for identifying foreign website URLs based on 5GMRO measurement, the method comprising: Obtain multiple neighboring cell measurement data from different networks; Density clustering is performed on each of the heterogeneous network neighboring area measurement data according to the heterogeneous network parameters of each of the heterogeneous network neighboring area measurement data to obtain a plurality of cluster groups; For each cluster group, grouping each of the heterogeneous network neighboring cell measurement data according to the primary service cell identifier, the neighboring cell frequency point and the neighboring cell identifier of each heterogeneous network neighboring cell measurement data to obtain a plurality of first subgroups; For each of the first groups, the horizontal arrival angles and time advances of the foreign network cells corresponding to the first group are grouped according to a preset granularity to obtain a plurality of second groups; Calculate the characteristic index of each of the second groups respectively according to the measurement data of each of the neighboring cells of the different networks; For each of the first groups, each of the characteristic indicators in the first group is input into a preset classifier model to obtain an output local cell identifier, and the position corresponding to the local cell identifier is used as the predicted position of the foreign cell.

[0008] In one embodiment, before the step of inputting each of the feature indicators in the first group into a preset classifier model, the method further includes: Obtain multiple neighboring area measurement data of this network; Grouping each of the local network neighboring cell measurement data according to the primary service cell identifier, neighboring cell frequency point and neighboring cell identifier of each of the local network neighboring cell measurement data to obtain a plurality of local network groups; For each of the local network groups, the horizontal arrival angle and the time advance of the area of ​​the primary service cell corresponding to the local network group are regionally sliced ​​according to a preset granularity to obtain regional slices; Calculate the characteristic index of each of the regional slices according to the measurement data of the neighboring areas of each network; According to a preset time granularity, the characteristic index of each of the regional slices within a preset time is counted to obtain a plurality of slice feature sets; The classifier is trained with each of the primary serving cell identifiers as a unit, and each of the slice feature sets corresponding to each of the primary serving cell identifiers is used as a sample, and a preset classifier model is trained to obtain a trained classifier model.

[0009] In one embodiment, the step of obtaining multiple heterogeneous network neighboring cell measurement data includes: Collecting measurement data of neighboring cells of different networks at multiple preset sampling points; If there is an abnormal sampling point among the sampling points where the measurement data of the neighboring areas of different networks is missing, the measurement data of the neighboring areas of different networks of the sampling points adjacent to the abnormal sampling point are calculated to complete the measurement data of the neighboring areas of different networks.

[0010] In one embodiment, the step of grouping the horizontal angles of arrival and the timing advances of the foreign network cells corresponding to the first group according to a preset granularity includes: Determine the grouping granularity of the horizontal arrival angle and the timing advance amount respectively according to a preset granularity; The foreign network cells corresponding to the first group are grouped according to the grouping granularity.

[0011] In one embodiment, the step of respectively calculating the characteristic index of each of the second subgroups according to the measurement data of each of the neighboring cells of the different network includes: Determine the second group to which each of the heterogeneous network neighboring cell measurement data belongs according to the horizontal arrival angle and the time advance in each of the heterogeneous network neighboring cell measurement data; For each second group, a characteristic index of the second group is calculated according to the measurement data of the neighboring area of ​​the different network corresponding to the second group.

[0012] In one embodiment, after the step of using the position corresponding to the local network cell identifier as the predicted position of the foreign network cell, the method further includes: For each cluster group, determining the predicted position corresponding to each of the first subgroups in the cluster group; The weighted average of the predicted positions is performed to obtain the final predicted position.

[0013] In one embodiment, the step of grouping each of the own network neighboring area measurement data according to the primary serving cell identifier, the neighboring area frequency point and the neighboring area identifier of each of the own network neighboring area measurement data includes: Grouping each of the local network neighboring area measurement data according to the primary serving cell identifier of each of the local network neighboring area measurement data to obtain a plurality of first local network groups; For each first local network group, each local network neighboring cell measurement data is grouped according to the neighboring cell frequency point and the neighboring cell identifier of each local network neighboring cell measurement data.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for identifying foreign website URLs based on 5GMRO measurements as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the foreign website URL identification method based on 5GMRO measurement as described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the foreign website URL identification method based on 5GMRO measurement as described above.

[0017] The present application provides a method for identifying a foreign website address based on 5GMRO measurement. The present application first collects multiple foreign network neighboring area measurement data, and then clusters the foreign network neighboring area measurement data according to foreign network parameters, clusters the data that may belong to the same cell in a clustering group, and then for each clustering group, the foreign network neighboring area measurement data is grouped again according to the main service cell identifier, the neighboring area frequency point and the neighboring area identifier to obtain multiple first groups, and then the foreign network cell corresponding to each first group is divided into multiple second groups according to the horizontal arrival angle and the time advance amount, and the characteristic index of each second group can be calculated according to the foreign network neighboring area measurement data, and finally, the characteristic index is input into a preset classifier model to obtain the local network cell identifier corresponding to the foreign network cell, and the position corresponding to the local network cell identifier can be used as the predicted position of the foreign network cell.

[0018] In summary, the present application utilizes 5G heterogeneous network neighboring area measurement data to subdivide the heterogeneous network neighboring area measurement data, and utilizes the neighboring area classifier model of the own network to group the neighboring areas of the own network at the same location, and then obtains the characteristic indicators of each group. The classifier model is used to identify the own network cells that are similar to the heterogeneous network cells, and the location of the heterogeneous network cells is predicted based on the location of the own network cells, thereby realizing the prediction of the location of heterogeneous network sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flow chart of a first embodiment of a method for identifying a foreign website address based on 5GMRO measurement in this application; Figure 2 This is an overall implementation flow chart of the embodiment of the foreign website URL identification method based on 5GMRO measurement of this application; Figure 3 This is a schematic diagram of the positioning process involved in an embodiment of a method for identifying a foreign website address based on 5GMRO measurement in this application; Figure 4 A schematic diagram of a region slice involved in an embodiment of a foreign website URL identification method based on 5GMRO measurement of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the foreign website URL identification method based on 5GMRO measurement in an embodiment of the present application.

[0022] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of the embodiment of the present application is: obtaining multiple heterogeneous neighboring area measurement data; density clustering each heterogeneous neighboring area measurement data according to the heterogeneous network parameters of each heterogeneous network neighboring area measurement data to obtain multiple clustering groups; for each clustering group, grouping each heterogeneous network neighboring area measurement data according to the primary service cell identifier, neighboring area frequency point and neighboring area identifier of each heterogeneous network neighboring area measurement data to obtain multiple first groups; for each first group, grouping the horizontal arrival angle and time advance of the heterogeneous network cell corresponding to the first group according to a preset granularity to obtain multiple second groups; calculating the characteristic indicators of each second group according to each heterogeneous network neighboring area measurement data; for each first group, inputting each of the characteristic indicators in the first group into a preset classifier model to obtain an output local network cell identifier, and using the position corresponding to the local network cell identifier as the predicted position of the heterogeneous network cell.

[0026] In this embodiment, for the convenience of description, the electronic device is used as the execution subject for explanation below.

[0027] Improving the sharing efficiency of 5G network tower resources and quickly and accurately determining the pricing system for shared towers are crucial to reducing operation and maintenance costs. Therefore, in the context of industry competition, it is particularly urgent and important to accurately identify the locations of sites from different manufacturers and carry out network optimization and planning work accordingly.

[0028] Currently, the identification of such scenarios mainly relies on manual grid-based investigation, by reporting the location information of foreign network base stations in each grid, or analyzing based on tower fee audit data. However, the manual investigation method is not only inefficient, but also difficult to guarantee accuracy, and it is impossible to effectively identify sites unique to foreign networks based on tower fees.

[0029] In summary, how to identify the location of foreign websites has become a problem that needs to be solved urgently in this field.

[0030] In response to the above problems, the present application provides a method for identifying a foreign website address based on 5GMRO measurement. The present application first collects multiple foreign network neighboring area measurement data, and then clusters the foreign network neighboring area measurement data according to the foreign network parameters, clusters the data that may belong to the same cell in a clustering group, and then for each clustering group, the foreign network neighboring area measurement data is grouped again according to the main service cell identifier, the neighboring area frequency point and the neighboring area identifier to obtain multiple first groups, and then the foreign network cell corresponding to each first group is divided into multiple second groups according to the horizontal arrival angle and the time advance amount. The characteristic indicators of each second group can be calculated according to the foreign network neighboring area measurement data. Finally, the characteristic indicators are input into a preset classifier model to obtain the local cell identifier corresponding to the foreign network cell, and the position corresponding to the local cell identifier can be used as the predicted position of the foreign network cell.

[0031] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes an electronic device as an example to illustrate this embodiment and the following embodiments.

[0032] Based on this, the embodiment of the present application provides a method for identifying a foreign website URL based on 5GMRO measurement, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the foreign website URL identification method based on 5GMRO measurement of the present application.

[0033] In this embodiment, the foreign website URL identification method based on 5GMRO measurement includes steps S10 to S50: Step S10, obtaining multiple neighboring cell measurement data of different networks; In this embodiment, the device collects measurement data of neighboring cells of different networks at multiple sampling points, and the data includes multiple parameters about different cells of different networks.

[0034] Furthermore, in a feasible implementation manner, the above step S10 may further include steps S11-S12: Step S11, collecting measurement data of neighboring cells of different networks at multiple preset sampling points; In this embodiment, the device collects measurement data of neighboring cells of different networks at a plurality of preset sampling points.

[0035] Step S12: If there is an abnormal sampling point in each of the sampling points where the measurement data of the neighboring area of ​​the different network is missing, the measurement data of the neighboring area of ​​the different network of the abnormal sampling point is calculated according to the measurement data of the neighboring area of ​​the different network of the sampling points adjacent to the abnormal sampling point, so as to complete the measurement data of each of the neighboring areas of the different network.

[0036] In this embodiment, during the collection process, the measurement data of the neighboring areas of different networks of some sampling points may be missing due to various reasons (such as equipment failure, signal interference, etc.). At this time, the device will calculate and complete the data of the abnormal sampling point based on the data of other sampling points adjacent to the abnormal sampling point through interpolation, fitting or other mathematical methods.

[0037] The data filling step ensures the integrity and continuity of the data and avoids analysis errors caused by missing data. By using the data from adjacent sampling points to fill in the missing data, the authenticity and accuracy of the data can be maintained to the greatest extent.

[0038] Step S20, density clustering each of the heterogeneous network neighboring area measurement data according to the heterogeneous network parameters of each of the heterogeneous network neighboring area measurement data to obtain a plurality of cluster groups; In this embodiment, the device processes the heterogeneous network neighboring cell measurement data using a density clustering algorithm, and divides the data into multiple cluster groups according to the similarity of heterogeneous network parameters (such as signal strength, frequency, etc.).

[0039] Specifically, obtain the measurement data of the neighboring cells of the 5GMR sampling point in different networks, perform location density clustering according to the main service cell NCI (NR CellIdentifier, 5G network cell identifier), longitude, latitude, neighboring cell frequency, and neighboring cell PCI (Physical CellIdentifier, physical cell identifier), and obtain multiple ncNci, MR.NRNcArfcn (NRAbsolute Radio Frequency Channel Number, 5G absolute radio frequency channel number) clustering groups, where the longitude and latitude can use the site longitude and latitude of the main service cell of this network, or the MDT (Minimization Drive Test) longitude and latitude in MR. The measurement results of the same frequency and PCI clustered together can be considered to be the same 5G cell of different networks.

[0040] Step S30, for each cluster group, grouping each of the heterogeneous network neighboring cell measurement data according to the primary service cell identifier, neighboring cell frequency point and neighboring cell identifier of each of the heterogeneous network neighboring cell measurement data to obtain a plurality of first subgroups; In this embodiment, within each cluster group, the device further subdivides the measurement data according to parameters such as the primary serving cell identifier, the neighboring cell frequency point and the neighboring cell identifier to form a plurality of first subgroups.

[0041] Specifically, for each cluster group, grouping is performed according to the NCI of the primary service cell, the frequency point of the neighboring cell, and the PCI of the neighboring cell.

[0042] Step S40, for each of the first groups, grouping the horizontal angle of arrival and the time advance of the foreign network cells corresponding to the first group according to a preset granularity to obtain a plurality of second groups; In this embodiment, within each first group, the device groups the data again according to parameters such as the horizontal arrival angle and the time advance, and at a preset granularity (such as an angle range, a time interval, etc.), to form a plurality of second groups.

[0043] Further, in a feasible implementation manner, the above step S40 includes steps S41-S42: Step S41, determining the grouping granularity of the horizontal angle of arrival and the timing advance respectively according to a preset granularity; In this embodiment, before grouping, the device first presets the grouping granularity of the horizontal arrival angle (such as angle range, angle interval, etc.) and the time advance (such as time interval, time unit, etc.) according to actual needs and analysis objectives. These granularities determine the accuracy and detail of data division in subsequent grouping.

[0044] Step S42: grouping the foreign network cells corresponding to the first group according to the grouping granularity.

[0045] In this embodiment, after the grouping granularities are determined, the device groups the foreign network cells in the first group according to the granularities.

[0046] Step S50, calculating characteristic indicators of each of the second subgroups according to the measurement data of each of the neighboring cells of the different networks; In this embodiment, the device calculates the characteristic index according to the measurement data of the neighboring cells of the different networks in each second group.

[0047] Furthermore, in a feasible implementation manner, the above step S50 may further include steps S51-S52: Step S51, determining the second group to which each of the heterogeneous network neighboring cell measurement data belongs according to the horizontal arrival angle and the time advance in each of the heterogeneous network neighboring cell measurement data; In this embodiment, after the grouping granularity is determined and the grouping operation is completed, the device will assign each heterogeneous network neighboring cell measurement data to the corresponding second group according to the horizontal arrival angle and time advance in the data.

[0048] Step S52: for each second group, calculating the characteristic index of the second group according to the measurement data of the neighboring area of ​​the different network corresponding to the second group.

[0049] In this embodiment, after determining the group to which each heterogeneous network neighboring cell measurement data belongs, the device calculates the characteristic index for each second group according to the heterogeneous network neighboring cell measurement data corresponding to the second group.

[0050] Specifically, for the data of the first grouping, the MR.hAOA (AzimuthofArrival, horizontal angle of arrival) and MR.NRScTadv (Timing Advance, the time advance between the sampling point and the main service cell) values ​​of the main service cell are grouped and counted at a certain granularity. For example, MR.hAOA can be divided into 18 parts of 10 degrees, and the MR.NRScTadv value is grouped according to a step size of 2. Then, the characteristic indicators of each secondary grouping are calculated, including: scNci, ncNci, MR.NRNcArfcn, MR.NRNcPci, MR.hAOA segment identifier, MR.NRScTadv segment identifier, number of sampling points, MR.NRScSSRSRP (Reference Signal Receiving Power, reference signal received power) mean, MR.NRNcSSRSRP mean, MR.NRScSSRSRP and MR.NRNcSSRSRP correlation coefficient, overlap coverage (MR.NRNcSSRSRP-MR.NRScSSRSRP>=-6dB) coefficient, and field strength difference (MR.NRNcSSRSRP-MR.NRScSSRSRP) standard deviation.

[0051] Step S60, for each of the first groups, input each of the characteristic indicators in the first group into a preset classifier model to obtain an output local cell identifier, and use the position corresponding to the local cell identifier as the predicted position of the foreign cell.

[0052] In this embodiment, the device inputs the characteristic index of each first group into the pre-trained classifier model, and the model outputs the corresponding local cell identifier according to the characteristic index. The device searches for the corresponding position according to the local cell identifier as the predicted position of the foreign cell.

[0053] In the overall plan, please refer to Figure 2 , Figure 2 This is an overall implementation flow chart of the embodiment of the foreign website URL identification method based on 5GMRO measurement of this application, such as Figure 2 As shown, when locating a neighboring cell of a different network, firstly, the slice width table index of the 5G neighboring cell of the own network is calculated, and then the classification model of the 5G neighboring cell of the own network is built to obtain the 5G neighboring cell classifier model, and then the 5G neighboring cell of the different network is DBSCAN clustered, and then the slice width table index of the 5G neighboring cell of the different network is calculated, and then the 5G neighboring cell of the different network is classified through the 5G neighboring cell classifier model, and finally the location of the neighboring cell of the different network is output.

[0054] For details, please refer to Figure 3 , Figure 3This is a schematic diagram of the positioning process involved in the embodiment of the foreign website URL identification method based on 5GMRO measurement of this application, such as Figure 3 As shown, a set of data with the same scNci, ncEarfcn (E-UTRA Absolute Radio Frequency Channel Number, 4G absolute radio frequency channel number), and ncPci is loaded into the scNci classifier model, and the model is used to classify the data to obtain the classification result. If the classification result is valid, it is determined that the classification result is the ECI of the local cell that is closest to the characteristics of the current foreign cell. The location of the ECI of the local cell can be used as the predicted location of the foreign cell. If the classification result is invalid, the model needs to be retrained or more data needs to be obtained.

[0055] Furthermore, after the above step S60, the method may further include steps S70 to S80: Step S70, for each cluster group, determining the predicted position corresponding to each of the first subgroups in the cluster group; In this embodiment, for each cluster group, it is necessary to determine the predicted position corresponding to each first subgroup (these first subgroups are obtained by preliminary grouping according to the measurement data of neighboring cells of different networks) therein.

[0056] Step S80, performing weighted averaging on the predicted positions to obtain a final predicted position.

[0057] In this embodiment, after determining the predicted position of the first group in each cluster group, we use a weighted average method to calculate the final predicted position. The weight of the weighted average can be determined based on the data quality, reliability, quantity or other relevant factors of each first group in the cluster group. In this way, we can comprehensively consider the information of all the first groups in the cluster group and obtain a more accurate and reliable final predicted position.

[0058] Specifically, the predicted positions of multiple scNci in the cluster group for the neighboring areas of the different networks are weighted averaged to obtain the positions corresponding to MR.NRNcArfcn and MR.NRNcPci. First, determine the weight distribution method of the weighted average. The weight can be determined according to the reliability, accuracy or other relevant factors of each scNci. Based on the results of multiple predictions, the weight of the different network position with higher accuracy can be increased. Then, for each scNci predicted position, multiply its corresponding weight. Then, sum all the weighted predicted positions. Finally, divide the sum result by the sum of all weights to obtain the weighted average position, that is, the position corresponding to MR.NRNcArfcn and MR.NRNcPci. Through this weighted average method, the prediction results of multiple scNci can be integrated to improve the accuracy and reliability of the position, providing more accurate data support for subsequent network analysis and optimization.

[0059] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, before the above step S60, the method further includes steps A10 to A60: Step A10, obtaining multiple neighboring cell measurement data of the network; In this embodiment, the device collects measurement data of multiple neighboring cells of the own network.

[0060] Step A20, grouping each of the local network neighboring cell measurement data according to the primary serving cell identifier, neighboring cell frequency point and neighboring cell identifier of each of the local network neighboring cell measurement data to obtain a plurality of local network groups; In this embodiment, the device groups the neighboring cell measurement data of the local network according to the parameters such as the primary service cell identifier, the neighboring cell frequency point and the neighboring cell identifier to form multiple local network groups. The data in each local network group has high similarity and relevance.

[0061] Specifically, obtain the neighboring cell measurement data of the 5GMR sampling point and group them according to the main service cell NCI, MR.NRNcArfcn, and MR.NRNcPci.

[0062] Grouping is done according to the NCI of the primary serving cell (PC). All MR data of each cell will be assigned to its corresponding NCI group.

[0063] In each NCI group, secondary grouping is further performed based on the frequency (MR.NRNcArfcn) and physical cell identifier (MR.NRNcPci) of the neighboring cells to obtain multiple local network groups. In this way, the MR data of each cell will be subdivided into more specific groups based on the different characteristics of its neighboring cells.

[0064] Step A30, for each of the local network groups, the horizontal arrival angle and the time advance of the area of ​​the primary service cell corresponding to the local network group are regionally sliced ​​according to a preset granularity to obtain regional slices; In this embodiment, in each local network group, the device slices the area of ​​the main service cell according to parameters such as the horizontal arrival angle and the time advance, and forms multiple area slices according to a preset granularity (such as an angle range, a time interval, etc.).

[0065] Step A40, calculating characteristic indicators of each of the area slices according to the measurement data of the neighboring areas of each network; In this embodiment, the device calculates the characteristic index based on the measurement data of the neighboring areas of the network in each area slice.

[0066] Specifically, for the data of a group, the MR.hAOA and MR.NRScTadv values ​​are sliced ​​into regions according to a certain granularity, and then the characteristic indicators of each slice are calculated, including: scNci, ncNci, MR.NRNcArfcn, MR.NRNcPci, MR.hAOA segment identifier, MR.NRScTadv segment identifier, number of sampling points, MR.NRScSSRSRP mean, MR.NRNcSSRSRP mean, MR.NRScSSRSRP and MR.NRNcSSRSRP co-correlation coefficient, overlap coverage (MR.NRNcSSRSRP-MR.NRScSSRSRP>=-6dB) coefficient, field strength difference (MR.NRNcSSRSRP-MR.NRScSSRSRP) standard deviation, etc. For example, please refer to Figure 4 , Figure 4 This is a schematic diagram of a region slice involved in an embodiment of a foreign website URL identification method based on 5GMRO measurement in this application, such as Figure 4 As shown, haoa can be divided into 6 parts with a size of 30 degrees, and the TA value is grouped according to the step size 1. The coverage area of ​​the main service cell can be divided into multiple slices, where the shaded area is the TA range [1,2) and the AOA segment identification range [4,5).

[0067] Step A50, counting the characteristic index of each of the region slices within a preset time according to a preset time granularity, to obtain a plurality of slice feature sets; In this embodiment, the device counts the characteristic index of each regional slice within a preset time according to a preset time granularity (such as hours, days, etc.) to form multiple slice feature sets. Each slice feature set contains the characteristic index statistical information of the regional slice within a period of time.

[0068] Step A60: training a classifier with each primary serving cell identifier as a unit, using each slice feature set corresponding to each primary serving cell identifier as a sample, and training a preset classifier model to obtain a trained classifier model.

[0069] In this embodiment, the device takes each primary service cell ID as a unit and its corresponding slice feature set as a sample to train a preset classifier model. The trained classifier model can predict the corresponding local network cell ID according to the input feature index.

[0070] Specifically, the indicators of regional slices are counted according to a certain time granularity (such as 1 hour), and the data of N (>=7) days are continuously counted to construct a slice feature set, including the following fields: time, scNci, ncNci, MR.NRNcArfcn, MR.NRNcPci, MR.hAOA segment identifier, MR.NRScTadv segment identifier, number of sampling points, MR.NRScSSRSRP mean, MR.NRNcSSRSRP mean, MR.NRScSSRSRP and MR.NRNcSSRSRP co-correlation coefficient, overlap coverage (MR.NRNcSSRSRP-MR.NRScSSRSRP>=-6dB) coefficient, and field strength difference (MR.NRNcSSRSRP-MR.NRScSSRSRP) standard deviation. The classifier is trained with the primary service cell scNci as the unit, and the same secondary grouping data of all scNci is obtained to train the classifier model for ncEci.

[0071] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the foreign website URL identification method based on 5GMRO measurement in the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0072] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed 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 foreign website URL identification method based on 5GMRO measurement in the above-mentioned embodiment one.

[0073] Reference below Figure 5, which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0074] like Figure 5 As shown, the electronic 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: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0075] In particular, according to the embodiments disclosed in the present application, 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 application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. 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 method of the embodiment disclosed in the present application are executed.

[0076] The electronic device provided by the present application adopts the foreign website URL identification method based on 5GMRO measurement in the above-mentioned embodiment, which can solve the technical problem of how to identify the location of foreign website URLs. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the foreign website URL identification method based on 5GMRO measurement provided in the above-mentioned embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0077] It should be understood that the various parts disclosed in this application 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.

[0078] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0079] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the foreign website URL identification method based on 5GMRO measurement in the above-mentioned embodiment.

[0080] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0081] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0082] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains multiple heterogeneous network neighboring area measurement data; performs density clustering on each heterogeneous network neighboring area measurement data according to the heterogeneous network parameters of each heterogeneous network neighboring area measurement data to obtain multiple clustering groups; for each clustering group, groups each heterogeneous network neighboring area measurement data according to the primary service cell identifier, neighboring cell frequency point and neighboring cell identifier of each heterogeneous network neighboring area measurement data to obtain multiple first groups; for each first group, groups the horizontal arrival angle and time advance of the heterogeneous network cell corresponding to the first group according to a preset granularity to obtain multiple second groups; calculates the characteristic indicators of each second group according to each heterogeneous network neighboring area measurement data; for each first group, inputs each of the characteristic indicators in the first group into a preset classifier model to obtain an output local network cell identifier, and uses the position corresponding to the local network cell identifier as the predicted position of the heterogeneous network cell.

[0083] Computer program code for performing the operation of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0084] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0086] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned foreign website URL identification method based on 5GMRO measurement, and can solve the technical problem of how to identify the location of foreign website sites. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the foreign website URL identification method based on 5GMRO measurement provided in the above-mentioned embodiment, and will not be repeated here.

[0087] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for identifying foreign website URLs based on 5GMRO measurements.

[0088] The computer program product provided by the present application can solve the technical problem of how to identify the location of a foreign website. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the foreign website identification method based on 5GMRO measurement provided by the above embodiment, and will not be repeated here.

[0089] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for identifying foreign website URLs based on 5GMRO measurement, characterized in that: The method comprises: Obtain multiple neighboring cell measurement data from different networks; Density clustering is performed on each of the heterogeneous network neighboring area measurement data according to the heterogeneous network parameters of each of the heterogeneous network neighboring area measurement data to obtain a plurality of cluster groups; For each cluster group, grouping each of the heterogeneous network neighboring cell measurement data according to the primary service cell identifier, the neighboring cell frequency point and the neighboring cell identifier of each heterogeneous network neighboring cell measurement data to obtain a plurality of first subgroups; For each of the first groups, the horizontal arrival angles and time advances of the foreign network cells corresponding to the first group are grouped according to a preset granularity to obtain a plurality of second groups; Calculate the characteristic index of each of the second groups respectively according to the measurement data of each of the neighboring cells of the different networks; For each of the first groups, each of the characteristic indicators in the first group is input into a preset classifier model to obtain an output local cell identifier, and the position corresponding to the local cell identifier is used as the predicted position of the foreign cell.

2. The method for identifying foreign website addresses based on 5GMRO measurement according to claim 1, characterized in that: Before the step of inputting each of the feature indicators in the first group into a preset classifier model, the method further includes: Obtain multiple neighboring area measurement data of the network; Grouping each of the local network neighboring cell measurement data according to the primary service cell identifier, neighboring cell frequency point and neighboring cell identifier of each of the local network neighboring cell measurement data to obtain a plurality of local network groups; For each of the local network groups, the horizontal arrival angle and the time advance of the area of ​​the primary service cell corresponding to the local network group are regionally sliced ​​according to a preset granularity to obtain regional slices; Calculate the characteristic index of each of the regional slices according to the measurement data of the neighboring areas of each network; According to a preset time granularity, the characteristic index of each of the regional slices within a preset time is counted to obtain a plurality of slice feature sets; The classifier is trained with each of the primary serving cell identifiers as a unit, and each of the slice feature sets corresponding to each of the primary serving cell identifiers is used as a sample, and a preset classifier model is trained to obtain a trained classifier model.

3. The method for identifying foreign website addresses based on 5GMRO measurement according to claim 1, characterized in that: The step of obtaining multiple heterogeneous network neighboring area measurement data comprises: Collecting measurement data of neighboring cells of different networks at multiple preset sampling points; If there is an abnormal sampling point among the sampling points where the measurement data of the neighboring areas of different networks is missing, the measurement data of the neighboring areas of different networks of the sampling points adjacent to the abnormal sampling point are calculated to complete the measurement data of the neighboring areas of different networks.

4. The method for identifying foreign website addresses based on 5GMRO measurement according to claim 1, characterized in that: The step of grouping the horizontal arrival angles and time advances of the foreign network cells corresponding to the first group according to a preset granularity includes: Determine the grouping granularity of the horizontal arrival angle and the timing advance amount respectively according to a preset granularity; The foreign network cells corresponding to the first group are grouped according to the grouping granularity.

5. The method for identifying foreign website addresses based on 5GMRO measurement according to claim 1, characterized in that: The step of respectively calculating the characteristic index of each of the second subgroups according to the measurement data of each of the neighboring cells of the different networks comprises: Determine the second group to which each of the heterogeneous network neighboring cell measurement data belongs according to the horizontal arrival angle and the time advance in each of the heterogeneous network neighboring cell measurement data; For each second group, a characteristic index of the second group is calculated according to the measurement data of the neighboring area of ​​the different network corresponding to the second group.

6. The method for identifying foreign website addresses measured by 5GMRO as claimed in claim 1, characterized in that: After the step of using the position corresponding to the own network cell identifier as the predicted position of the foreign network cell, the method further includes: For each cluster group, determining the predicted position corresponding to each of the first subgroups in the cluster group; The weighted average of the predicted positions is performed to obtain the final predicted position.

7. The method for identifying foreign website addresses based on 5GMRO measurement according to claim 2, characterized in that: The step of grouping each of the local network neighboring cell measurement data according to the primary serving cell identifier, neighboring cell frequency point and neighboring cell identifier of each of the local network neighboring cell measurement data comprises: Grouping each of the local network neighboring area measurement data according to the primary serving cell identifier of each of the local network neighboring area measurement data to obtain a plurality of first local network groups; For each first local network group, each local network neighboring cell measurement data is grouped according to the neighboring cell frequency point and the neighboring cell identifier of each local network neighboring cell measurement data.

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