Method and device for identifying location of alien base station, electronic device and storage medium

Abstract

The present disclosure discloses a method and device for identifying the position of a foreign network base station, electronic equipment and a storage medium. Target measurement report data is obtained. A density clustering algorithm is called to cluster the target measurement report data, and at least three target clusters of foreign network adjacent areas are obtained. The target clusters are calculated to obtain first position information of the optimal network signal and second position information of the worst network signal of the target clusters. The first center position of the optimal network signal set and the second center position of the worst network signal set of the at least three foreign network adjacent areas are calculated respectively. The signal source direction line composed of the second center position pointing to the first center position in each foreign network adjacent area is determined. The intersection area is constructed through at least three signal source direction lines, and the center position information of the intersection area is determined. The center position is determined as the position of the target foreign network base station. The present disclosure is suitable for complex communication environment, reduces the limitations of foreign network base station position identification, and can accurately identify the position of the foreign network base station.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a method and device for identifying the position of a base station in a different network, an electronic device and a storage medium. BACKGROUND

[0002] With the development of mobile networks, 5G technology has been deployed on a large scale. The construction, maintenance and optimization of 5G base stations are important links in network operation. The position of a base station cell is an important parameter affecting network optimization and network planning. Evaluating and analyzing the basic data of a different network system, such as the position, number and construction speed of a site, is conducive to targeted promotion in strong areas of the network when formulating marketing strategies.

[0003] In related technologies, the identification technology of a base station in a different network has certain limitations and cannot be widely applied. For example, after clustering the measurement report (MR) data collected by a base station cell, the clustering results of each cell of the base station are fitted according to the position information and the level value of the base station cell to obtain the fitting results of each cell. The fitting results are a straight line in a two-dimensional coordinate system, which is a linear regression line. A shape is generated according to the linear regression lines of multiple cells, the center position of the shape is determined, and the center position of the shape is taken as the position information of the base station. The defects of this scheme are as follows: since the coverage direction of each cell rarely appears an ideal coverage scene of a flat land, various obstacles and reflectors such as buildings, buildings and trees will appear, and the field strength will appear multiple local good points and bad points as the distance from the base station becomes farther, which cannot be expressed by a linear regression line. Therefore, the fitting by a linear regression line has great limitations.

[0004] Therefore, the related method for identifying the position of a base station in a different network has certain limitations and cannot be applied to various communication environments, resulting in the inability to accurately identify the position of a base station in a different network. SUMMARY

[0005] The present disclosure provides a method and device for identifying the position of a base station in a different network, an electronic device and a storage medium. The main purpose is to solve the problem that the related method for identifying the position of a base station in a different network has certain limitations and cannot be applied to various communication environments, resulting in the inability to accurately identify the position of a base station in a different network.

[0006] According to a first aspect of the present disclosure, a method for identifying the position of a base station in a different network is provided, which comprises the following steps.

[0007] obtaining target measurement report data;

[0008] calling a density clustering algorithm to cluster the target measurement report data, obtaining target clusters of the at least three inter-network adjacent areas respectively, and performing calculation on the target clusters to obtain first position information of optimal network signals of the target clusters and second position information of worst network signals of the target clusters;

[0009] calculating first center positions of optimal network signal sets of the at least three inter-network adjacent areas and second center positions of worst network signal sets of the at least three inter-network adjacent areas respectively;

[0010] determining a signal source direction line constituted by the second center position pointing to the first center position in each inter-network adjacent area respectively;

[0011] constructing a cross region through at least three signal source direction lines and determining a barycentric position information of the cross region, and determining the barycentric position as a target inter-network base station position.

[0012] Optionally, the calculation on the target clusters to obtain the first position information of the optimal network signals of the target clusters and the second position information of the worst network signals of the target clusters comprises:

[0013] defining the reference signal received power as a first parameter and the reference signal received quality as a second parameter, and the target measurement report data comprising the reference signal received power and the reference signal received quality;

[0014] calling a cost function of a gradient descent algorithm, and performing differential calculation on the first parameter and the second parameter based on the cost function of the gradient descent algorithm respectively to obtain a differential first parameter and a differential second parameter respectively;

[0015] calculating partial derivatives of the differential first parameter and the differential second parameter to obtain a partial derivative first parameter and a partial derivative second parameter;

[0016] performing the gradient descent calculation on the partial derivative first parameter and the partial derivative second parameter to converge to obtain the first position information of the optimal network signals of the target clusters and the second position information of the worst network signals of the target clusters.

[0017] Optionally, the calculation of the first center positions of the optimal network signal sets of the at least three inter-network adjacent areas and the second center positions of the worst network signal sets of the at least three inter-network adjacent areas comprises:

[0018] determining all optimal network signals as the optimal network signal set and determining all worst network signals as the worst network signal set;

[0019] The second-order Gaussian function is called to calculate the optimal network signal set and the worst network signal set respectively, and first center position information of the optimal network signal set and second center position information of the worst network signal set are obtained respectively.

[0020] Optionally, the determining of the source direction line constituted by the second center position pointing to the first center position in each inter-network neighbor cell comprises:

[0021] The direction of each inter-network neighbor cell from the second center position to the first center position is determined as the source direction.

[0022] A regression straight line with the first center position as the terminal point is calculated by calling a preset regression algorithm.

[0023] The source direction line is determined according to the source direction and the regression straight line.

[0024] Optionally, the determining of the barycentric position information of the intersection region and the determination of the barycentric position as the target inter-network base station position comprise:

[0025] The vertex position coordinates of the intersection region constructed by the at least three source direction lines are determined.

[0026] The barycentric position coordinates of the intersection region are calculated based on a geometric calculation method and the vertex position coordinates.

[0027] The barycentric position coordinates are determined as the position of the target inter-network base station.

[0028] Optionally, the obtaining of the target measurement report data comprises:

[0029] The sampling data are obtained based on a measurement report database.

[0030] The repeated data in the sampling data are processed to obtain the to-be-screened measurement report data.

[0031] The position information and the reference signal received power and the reference signal received quality of the inter-network neighbor cell in the to-be-screened measurement report data are screened to obtain the target measurement report data.

[0032] According to a second aspect of the present disclosure, an inter-network base station position identification device is provided, comprising:

[0033] An obtaining unit is configured to obtain target measurement report data.

[0034] A clustering unit is configured to call a density clustering algorithm to cluster the target measurement report data, and obtain target clusters of the at least three inter-network neighbor cells.

[0035] The first computing unit is configured to compute the target cluster to obtain first position information of optimal network signals and second position information of worst network signals of the target cluster;

[0036] The second computing unit is configured to respectively compute first center positions of optimal network signal sets and second center positions of worst network signal sets of the at least three different network neighbor cells;

[0037] The first determining unit is configured to respectively determine a source direction line constituted by the second center position pointing to the first center position in each different network neighbor cell;

[0038] The constructing unit is configured to construct a cross region through the at least three source direction lines;

[0039] The second determining unit is configured to determine a barycentric position information of the cross region, and determine the barycentric position as a target different network base station position.

[0040] Optionally, the first computing unit comprises:

[0041] The defining module is configured to define the reference signal received power as a first parameter, and the reference signal received quality as a second parameter; and the target measurement report data comprises the reference signal received power and the reference signal received quality.

[0042] The first computing module is configured to call a cost function of a gradient descent algorithm, and respectively perform differential calculation on the first parameter and the second parameter based on the cost function of the gradient descent algorithm to obtain a differential first parameter and a differential second parameter.

[0043] The second computing module is configured to compute partial derivatives of the differential first parameter and the differential second parameter to obtain a partial derivative first parameter and a partial derivative second parameter.

[0044] The third computing module is configured to perform the gradient descent calculation on the partial derivative first parameter and the partial derivative second parameter, and converge to obtain the first position information of the optimal network signals and the second position information of the worst network signals of the target cluster.

[0045] Optionally, the second computing unit comprises:

[0046] The first determining module is configured to determine all the optimal network signals as the optimal network signal set, and determine all the worst network signals as the worst network signal set.

[0047] The fourth computing module is configured to call a second-order Gaussian function to respectively compute the optimal network signal set and the worst network signal set to respectively obtain the first center position information of the optimal network signal set and the second center position information of the worst network signal set.

[0048] Optionally, the first determining unit comprises:

[0049] The second determining module is configured to determine a signal source direction of each inter-network adjacent cell in a direction from the second center position to the first center position.

[0050] The fifth calculating module is configured to calculate a regression straight line with the first center position as a terminal point by invoking a preset regression algorithm.

[0051] The third determining module is configured to determine the signal source direction line according to the signal source direction and the regression straight line.

[0052] Optionally, the second determining unit comprises:

[0053] The fourth determining module is configured to determine a vertex position coordinate of an intersection region constructed by the at least three signal source direction lines.

[0054] The sixth calculating module is configured to calculate a barycentric position coordinate of the intersection region based on a geometric calculation method and the vertex position coordinate.

[0055] The fifth determining module is configured to determine the barycentric position coordinate as a position of a target inter-network base station.

[0056] Optionally, the obtaining unit comprises:

[0057] The obtaining module is configured to obtain sampling data based on a measurement report database.

[0058] The deduplication module is configured to perform deduplication processing on repeated data in the sampling data to obtain to-be-screened measurement report data.

[0059] The screening module is configured to screen position information and reference signal received power and reference signal received quality of inter-network adjacent cells in the to-be-screened measurement report data to obtain target measurement report data.

[0060] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0061] at least one processor; and

[0062] a memory connected with the at least one processor in communication; wherein

[0063] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0064] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause a computer to perform the method of the first aspect.

[0065] According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program configured to implement the method of the first aspect when executed by a processor.

[0066] The method and device for identifying the position of a foreign network base station, the electronic device and the storage medium provided by the present disclosure obtain target measurement report data, call a density clustering algorithm to cluster the target measurement report data, obtain target clusters of the at least three foreign network adjacent areas respectively, and calculate the target clusters to obtain first position information of an optimal network signal and second position information of a worst network signal of the target clusters. The first center position of a set of optimal network signals and the second center position of a set of worst network signals of the at least three foreign network adjacent areas are calculated respectively. A signal source direction line constituted by the second center position pointing to the first center position in each foreign network adjacent area is determined respectively. An intersection area is constructed through at least three signal source direction lines, and the barycentric position information of the intersection area is determined. The barycentric position is determined as the position of a target foreign network base station. Compared with other related technologies, the present disclosure can be applied to various complex communication environments, reduces the limitations of identifying the position of a foreign network base station, and can accurately identify the position of a foreign network base station.

[0067] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0068] The accompanying drawings are used to better understand the present scheme and do not limit the present disclosure. Among them:

[0069] Figure 1 A flowchart of a method for identifying the position of a foreign network base station provided by an embodiment of the present disclosure;

[0070] Figure 2 An ideal clustering model diagram provided by an embodiment of the present disclosure;

[0071] Figure 3 An actual clustering model diagram provided by an embodiment of the present disclosure;

[0072] Figure 4 A diagram of a foreign network base station estimated position provided by an embodiment of the present disclosure;

[0073] Figure 5A structural schematic diagram of an identification device for a location of a base station in a different network is provided for an embodiment of the present disclosure.

[0074] Figure 6 A structural schematic diagram of another identification device for a location of a base station in a different network is provided for an embodiment of the present disclosure.

[0075] Figure 7 A schematic block diagram of an example electronic device 300 is provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0076] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, which should be considered in a descriptive sense only. Thus, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of brevity and clarity, descriptions of well-known functions and constructions are omitted from the following description.

[0077] An identification method, device, electronic device, and storage medium for a location of a base station in a different network are described below with reference to the accompanying drawings for an embodiment of the present disclosure.

[0078] Figure 1 A flowchart of an identification method for a location of a base station in a different network is provided for an embodiment of the present disclosure.

[0079] As shown in Figure 1 , the method includes the following steps:

[0080] Step 101: Obtain target measurement report data.

[0081] In an embodiment of the present disclosure, the measurement report (MR) refers to information that data is sent once every 480 ms (470 ms on a signaling channel) on a service channel, which can be used for network evaluation and optimization. The target measurement report includes measurement data of useful different-network neighbor cells of at least three different-network neighbor cells, and the measurement data includes information such as a longitude of a reported location of a user equipment (UE), a latitude of the reported location of the UE, a frequency point of the different-network neighbor cell, a physical cell identifier (PCI), a reference signal receiving power (RSRP), and a reference signal receiving quality (RSPQ). The target measurement report data is obtained by data deduplication and screening on the measurement report, and specific embodiments of the present disclosure are not limited.

[0082] At step 102, a density clustering algorithm is invoked to cluster the target measurement report data, to obtain target clusters of the at least three foreign network neighbor cells respectively, and to calculate the target clusters to obtain first position information of an optimal network signal and second position information of a worst network signal of the target clusters.

[0083] In an embodiment of the present application, the density clustering algorithm is based on the main idea of a density-based clustering algorithm, which is to add a neighborhood region to a cluster as long as its density (number of objects or data points) is above a certain threshold. That is, for each data point in a given cluster, a neighborhood region must contain at least a certain number of points. Density-based clustering algorithms include, for example, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the like.

[0084] The embodiment of the present application takes the DBSCAN algorithm as an example, and the specific implementation method of the clustering includes: based on the target measurement report data, using the DBSCAN algorithm to obtain data clustering in a region range, and the algorithm needs to input two parameters: one parameter is a radius (Eps), which represents the range of a circular neighborhood with a given point P (a base station position in the network) as the center; the other parameter is the number of minimum points (MinPts) in the neighborhood with point P as the center. If the number of points in the neighborhood with point P as the center and a radius of Eps is not less than MinPts, point P is called a core point. The DBSCAN clustering uses a concept of k-distance, given a data set P={p(i); i=0,1,…n}, for any point P(i), the distance between point P(i) and all points in the subset S={p(1), p(2),…,p(i-1), p(i+1),…,p(n)} of set P is calculated, the distance is sorted in ascending order, and it is assumed that the sorted distance set is D={d(1), d(2),…,d(k-1), d(k), d(k+1),…,d(n)}, then d(k) is called k-distance. That is, the k-distance is the distance between point p(i) and the kth closest distance between all points (except point p(i) itself). The k-distance of each point p(i) in the clustering set is calculated, and finally the k-distance set E={e(1), e(2),…,e(n)} of all points is obtained. The radius Eps is calculated according to experience: according to the obtained k-distance set E of all points, the k-distance set E is obtained after the set E is sorted in ascending order, a curve of the change of the k-distance in the sorted set E is fitted, then the curve is drawn, and by observation, the value of the k-distance corresponding to the position where the change occurs sharply is determined as the value of the radius Eps. By adjusting the values of Eps and MinPts, the target cluster is determined through multiple iteration calculations and comparisons.

[0085] In order to better understand the density clustering, the embodiment of the present application provides an ideal clustering model diagram, as shown in Figure 2 The embodiment of the present application also provides an actual clustering model diagram, as shown in Figure 3

[0086] After the target measurement report data is clustered based on the density clustering method to obtain at least three target clusters of different network adjacent areas, the target cluster is calculated to obtain the first position information of the optimal network signal and the second position information of the worst network signal of the target cluster. The calculation method includes but is not limited to calculating the target cluster based on the gradient descent (Gradient Descent Algorithm) algorithm. The gradient descent algorithm finds the minimum value (maximum value) of the fitting target function of the cluster through iteration, or converges to the minimum value (maximum value). The basic formula is: ​

[0087]

[0088] wherein J is a continuous differentiable function to be optimized with respect to θ, α is a learning rate or step size, and a set of initial values (true values).

[0089] The specific meaning of the above formula is that J is a function of θ, the current position is θ0point, and the minimum value point of J is to be reached from this point. First, determine the direction of advancement, that is, the inverse of the gradient, and then walk a distance step, that is, α (positive uphill, negative downhill), and walk this segment step to reach the θ1point.

[0090] In the embodiment of the present application, by means of the two-dimensional gradient function, θ0is determined as RSRP and θ1is determined as RSPQ, and the optimal value and the worst value of (θ0, θ1) are calculated to determine the first position information of the optimal network signal and the second position information of the worst network signal of the target cluster.

[0091] In the embodiment of the present application, based on the gradient descent algorithm, a plurality of optimal network signals and a plurality of worst network signals can be calculated based on the target cluster.

[0092] Step 103, respectively calculating the first center position of the optimal network signal set and the second center position of the worst network signal set of the at least three different network neighbor areas.

[0093] In the embodiment of the present application, after obtaining a plurality of optimal network signals or worst network signals of the target cluster, the optimal network signals are determined as the optimal network signal set, the worst network signals are determined as the worst network signal set, and the first center position of the optimal network signal set and the second center position of the worst network signal set are calculated respectively. In the embodiment of the present application, since the noise in the optimal network signal set and the worst network signal set belongs to Gaussian noise, a second-order Gaussian function can be used to calculate the optimal network signal set and the worst network signal set, for example, all optimal network signals are determined as the optimal network signal set, and all worst network signals are determined as the worst network signal set; a second-order Gaussian function is called to calculate the optimal network signal set and the worst network signal set respectively, and the first center position information of the optimal network signal set and the second center position information of the worst network signal set are obtained respectively.

[0094] Step 104, respectively determining the source direction line constituted by the second center position pointing to the first center position in each different network neighbor area.

[0095] In the embodiment of the present application, the source direction refers to the propagation direction of the wireless network, i.e. the direction from the second center position to the first center position in each foreign network neighbor cell. Based on the second center position, the first center position and a preset regression algorithm, the source direction line can be calculated.

[0096] In step 105, an intersection region is constructed by at least three source direction lines, and the barycentric position information of the intersection region is determined, and the barycentric position is determined as the target foreign network base station position.

[0097] In one embodiment of the present application, after determining at least three source direction lines in different directions, an intersection region constructed by at least three source direction lines can be obtained. Taking a triangular intersection region constructed by three source direction lines as an example, please refer to Figure 4 , Figure 4 A schematic diagram of a foreign network base station position estimation method provided in the embodiment of the present application is shown in the figure, in which light color dots represent optimal network signals, black dots represent worst network signals, and dashed lines represent source direction lines. If there are three source direction lines, a triangular intersection region is constructed by the three source direction lines. After obtaining the intersection region, the barycentric position information of the intersection region can be calculated by a geometric algorithm, for example, the barycentric position of the triangular intersection region in Figure 4 is calculated, and the barycentric position is determined as the target position of the foreign network base station.

[0098] The foreign network base station position identification method provided in the present disclosure includes the following steps: obtaining target measurement report data; calling a density clustering algorithm to cluster the target measurement report data, obtaining target clusters of the at least three foreign network neighbor cells, and calculating the target clusters to obtain first position information of optimal network signals and second position information of worst network signals of the target clusters; calculating first center positions of optimal network signal sets of the at least three foreign network neighbor cells and second center positions of worst network signal sets; determining source direction lines composed of the second center positions and the first center positions in each foreign network neighbor cell; constructing an intersection region by at least three source direction lines, and determining barycentric position information of the intersection region, and determining the barycentric position as a target foreign network base station position. Compared with other related technologies, the present disclosure can be applied to various complex communication environments, reduces the limitations of foreign network base station position identification, and can accurately identify the position of the foreign network base station.

[0099] In some embodiments, in order to determine the first position information of the optimal network signal and the second position information of the worst network signal of the target cluster, the following can be used but are not limited to: defining the reference signal received power as a first parameter and the reference signal received quality as a second parameter; the target measurement report data includes the reference signal received power and the reference signal received quality; calling a cost function of a gradient descent algorithm, and respectively performing differential calculation on the first parameter and the second parameter based on the cost function of the gradient descent algorithm to obtain a differential first parameter and a differential second parameter; calculating the partial derivative of the differential first parameter and the differential second parameter to obtain a partial derivative first parameter and a partial derivative second parameter; and performing the gradient descent calculation on the partial derivative first parameter and the partial derivative second parameter to obtain the first position information of the optimal network signal and the second position information of the worst network signal of the target cluster.

[0100] The embodiments of the present application provide a two-dimensional gradient function to calculate the first position information of the optimal network signal. By the two-dimensional gradient function, θ0 is determined as RSRP and θ1 is determined as RSPQ. The optimal value and the worst value of (θ0, θ1) are calculated to determine the first position information of the optimal network signal and the second position information of the worst network signal of the target cluster, as shown in formula (2):

[0101]

[0102] In order to ensure that the function has a solution, a cost function is defined. The mean square error cost function (also known as the square error cost function) is selected in the present application, as shown in formula (3):

[0103]

[0104] In this formula:

[0105] m is the number of data points in the data set, that is, the sample number.

[0106] 1 / 2 is a constant. This is to offset the 2 multiplied by the square when calculating the gradient, so that there is no extra constant coefficient, which is convenient for subsequent calculation and does not affect the result.

[0107] y is the value of the true y coordinate of each point in the data set, that is, the class label.

[0108] h is a prediction function (hypothesis function). According to each input x, the predicted y value is calculated according to θ, that is,

[0109]

[0110] The gradient of the cost function is solved, that is, the two variables are differentiated respectively:

[0111]

[0112] The partial derivative of J is:

[0113]

[0114] Therefore, the partial derivatives of θ0 and θ1 are obtained as follows:

[0115]

[0116]

[0117] The partial derivatives of θ0 and θ0 are substituted into the gradient descent algorithm, and the iteration is continuously cycled until convergence is obtained. The cost function is usually not as concave and convex as the area in the figure, and there are many local minimum points. Instead, it is a convex function with only one global minimum point, which accelerates data convergence, speeds up calculation, and reduces computational complexity.

[0118] In some embodiments, α in formula (1) is changed to a negative value, which realizes the process of gradient ascent, and the final convergence is the local maximum point, that is, the worst network signal.

[0119] In some embodiments, the intersection area can be an intersection area formed by three source direction lines, or an intersection area formed by four or five source direction lines. However, in order to facilitate calculation, a triangular intersection area formed by three source direction lines is generally used to calculate the barycentric position of the triangular intersection area.

[0120] In some embodiments, the method for obtaining target measurement report data can use, but is not limited to, the following method: using the fields "UE longitude" + "UE latitude" + "neighbor cell of Reference Signal Receiving Power (NCRSRP)" in combination to resample the data, thereby reducing the computational complexity of subsequent inter-network neighbor cell continuous PCIs. Since the MR measurement is not full-network 5G cell opening, and considering the planning principle of 5G site 3-cell PCI continuity, the data with "neighbor PCI" + "neighbor frequency (NCFreq)" continuity is selected, and the outlier data is removed.

[0121] In summary, the embodiments of the present disclosure can achieve the following effects:

[0122] 1. Through the density clustering algorithm, the gradient descent algorithm, and the second-order Gaussian function algorithm, the intelligentization and scientization of data processing are realized.

[0123] 2. The method is suitable for various complex communication environments, reduces the limitations of identifying the positions of foreign network base stations, and can accurately identify the positions of foreign network base stations.

[0124] Corresponding to the above-mentioned method for identifying the positions of foreign network base stations, the application also provides a device for identifying the positions of foreign network base stations. Since the device embodiment of the application corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and the application will not be described in detail.

[0125] Figure 5 A structural schematic diagram of a device for identifying the positions of foreign network base stations provided by the embodiments of the present disclosure is shown in FIG. 1, which comprises an acquisition unit 21, a clustering unit 22, a first calculation unit 23, a second calculation unit 24, a first determination unit 25, a construction unit 26 and a second determination unit 27. Figure 5

[0126] The acquisition unit 21 is configured to acquire target measurement report data.

[0127] The clustering unit 22 is configured to call a density clustering algorithm to cluster the target measurement report data, and obtain target clusters of the at least three foreign network adjacent areas respectively.

[0128] The first calculation unit 23 is configured to calculate the target clusters to obtain first position information of optimal network signals and second position information of worst network signals of the target clusters.

[0129] The second calculation unit 24 is configured to calculate first center positions of optimal network signal sets and second center positions of worst network signal sets of the at least three foreign network adjacent areas respectively.

[0130] The first determination unit 25 is configured to determine a signal source direction line constituted by the second center position pointing to the first center position in each foreign network adjacent area.

[0131] The construction unit 26 is configured to construct a cross region through at least three signal source direction lines.

[0132] The second determination unit 27 is configured to determine the barycentric position information of the cross region, and determine the barycentric position as a target foreign network base station position.

[0133] ​The method for identifying the location of a foreign network base station provided by the present disclosure includes the following steps: obtaining target measurement report data; calling a density clustering algorithm to cluster the target measurement report data, obtaining target clusters of at least three foreign network adjacent areas, and calculating the target clusters to obtain first location information of optimal network signals and second location information of worst network signals of the target clusters; calculating first center positions of optimal network signal sets and second center positions of worst network signal sets of the at least three foreign network adjacent areas; determining a signal source direction line formed by the second center position pointing to the first center position in each foreign network adjacent area; constructing a cross area through at least three signal source direction lines and determining the barycentric position information of the cross area, and determining the barycentric position as the location of a target foreign network base station. Compared with other related technologies, the present disclosure can be applied to various complex communication environments, reduce the limitations of the location identification of foreign network base stations, and accurately identify the location of foreign network base stations.

[0134] Further, in a possible implementation manner of the embodiment, as shown in Figure 6 the first calculation unit 23 includes:

[0135] a definition module 231 configured to define the reference signal received power as a first parameter and the reference signal received quality as a second parameter, and the target measurement report data including the reference signal received power and the reference signal received quality;

[0136] a first calculation module 232 configured to call a cost function of a gradient descent algorithm and perform differential calculation on the first parameter and the second parameter based on the cost function of the gradient descent algorithm, respectively, to obtain a differential first parameter and a differential second parameter;

[0137] a second calculation module 233 configured to calculate partial derivatives of the differential first parameter and the differential second parameter to obtain a partial derivative first parameter and a partial derivative second parameter;

[0138] a third calculation module 234 configured to perform the gradient descent calculation on the partial derivative first parameter and the partial derivative second parameter, and converge to obtain the first location information of the optimal network signals and the second location information of the worst network signals of the target clusters.

[0139] Further, in a possible implementation manner of the embodiment, as shown in Figure 6 the second calculation unit 24 includes:

[0140] a first determination module 241 configured to determine all optimal network signals as the optimal network signal set and all worst network signals as the worst network signal set;

[0141] The fourth calculation module 242 is configured to call a second-order Gaussian function to calculate the optimal network signal set and the worst network signal set respectively, and obtain first center position information of the optimal network signal set and second center position information of the worst network signal set respectively.

[0142] Further, in a possible implementation of the embodiment, as shown in Figure 6 The first determination unit 25 includes:

[0143] The second determination module 251 is configured to determine a signal source direction of each foreign network neighbor cell in a direction from the second center position to the first center position.

[0144] The fifth calculation module 252 is configured to call a preset regression algorithm to calculate a regression straight line with the first center position as an end point.

[0145] The third determination module 253 is configured to determine the signal source direction line according to the signal source direction and the regression straight line.

[0146] Further, in a possible implementation of the embodiment, as shown in Figure 6 The second determination unit 27 includes:

[0147] The fourth determination module 271 is configured to determine vertex position coordinates of an intersection region constructed by the at least three signal source direction lines.

[0148] The sixth calculation module 272 is configured to calculate center of gravity position coordinates of the intersection region based on a geometric calculation method and the vertex position coordinates.

[0149] The fifth determination module 273 is configured to determine the center of gravity position coordinates as a position of a target foreign network base station.

[0150] Further, in a possible implementation of the embodiment, as shown in Figure 6 The acquisition unit 21 includes:

[0151] The acquisition module 211 is configured to acquire sampling data based on a measurement report database.

[0152] The deduplication module 212 is configured to perform deduplication processing on repeated data in the sampling data to obtain to-be-screened measurement report data.

[0153] The screening module 213 is configured to screen position information and reference signal received power and reference signal received quality of a foreign network neighbor cell in the to-be-screened measurement report data, and acquire target measurement report data.

[0154] It should be noted that the foregoing description of the method embodiments applies equally to the apparatus of this embodiment, and the principles are the same, which will not be repeated in this embodiment.

[0155] According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0156] Figure 7 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0157] As shown in Figure 7 The device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded into a RAM (Random Access Memory) 303 from a storage unit 308. Various programs and data required for the operation of the device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0158] Various components in the device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc., an output unit 307, such as various types of displays, speakers, etc., a storage unit 308, such as a magnetic disk, an optical disk, etc., and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0159] The computing unit 301 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above, such as the identification method of alien base station locations. For example, in some embodiments, the identification method of alien base station locations can be implemented as a computer software program, which is tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded onto the RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the aforementioned identification method of alien base station locations by any other appropriate means, such as by means of firmware.

[0160] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0161] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0162] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage medium can include but are not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of electrical wire, portable computer diskette, hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, fiber optics, CD-ROM (Compact Disc Read-Only Memory), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

[0163] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0164] The systems and techniques described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0165] The computer system can include clients and servers. The clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server is one of communication and distribution, with the server receiving requests from the client and transmitting responses via the communication network. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system. The server can also be a server of a distributed system, or a server combined with a blockchain.

[0166] It should be noted that artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors of people (such as learning, reasoning, thinking, planning, etc.), which has both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc. several major directions.

[0167] It should be understood that various forms of flow shown above can be used to reorder, add or delete steps. For example, each step described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0168] The above detailed description does not limit the scope of the disclosure. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the disclosure shall be included in the scope of the disclosure.

Claims

1. A method for identifying the location of a cross-network base station, characterized in that, The method comprises the following steps: obtaining target measurement report data; calling a density clustering algorithm to cluster the target measurement report data, obtaining target clusters of at least three foreign network adjacent areas respectively, and calculating the target clusters to obtain first position information of optimal network signals and second position information of worst network signals of the target clusters; calculating first center positions of optimal network signal sets and second center positions of worst network signal sets of the at least three foreign network adjacent areas respectively; determining a signal source direction line constituted by the second center position pointing to the first center position in each foreign network adjacent area respectively; constructing a cross region through at least three signal source direction lines, and determining a barycentric position information of the cross region, and determining the barycentric position as a target foreign network base station position.

2. The method of claim 1, wherein, The calculation of the target clusters to obtain the first position information of the optimal network signals and the second position information of the worst network signals of the target clusters comprises the following steps: defining a reference signal received power as a first parameter and a reference signal received quality as a second parameter, and the target measurement report data comprising the reference signal received power and the reference signal received quality; calling a cost function of a gradient descent algorithm, and differentiating the first parameter and the second parameter based on the cost function of the gradient descent algorithm to obtain differentiated first parameter and differentiated second parameter respectively; calculating partial derivatives of the differentiated first parameter and the differentiated second parameter to obtain partial differentiated first parameter and partial differentiated second parameter; performing the gradient descent calculation on the partial differentiated first parameter and the partial differentiated second parameter to obtain the first position information of the optimal network signals and the second position information of the worst network signals of the target clusters.

3. The method of claim 1, wherein, The calculation of the first center positions of the optimal network signal sets and the second center positions of the worst network signal sets of the at least three foreign network adjacent areas comprises the following steps: determining all optimal network signals as the optimal network signal set and all worst network signals as the worst network signal set; calling a second-order Gaussian function to calculate the optimal network signal set and the worst network signal set respectively to obtain the first center position information of the optimal network signal set and the second center position information of the worst network signal set respectively.

4. The method of claim 1, wherein, The determination of the signal source direction line constituted by the second center position pointing to the first center position in each foreign network adjacent area comprises the following steps: determining the signal source direction of each foreign network adjacent area from the second center position to the first center position respectively; calling a preset regression algorithm to calculate a regression straight line with the first center position as the terminal point; determining the signal source direction line according to the signal source direction and the regression straight line.

5. The method of claim 4, wherein, The determination of the barycentric position information of the cross region and the determination of the barycentric position as the target foreign network base station position comprise the following steps: determining vertex position coordinates of the cross region constructed by the at least three signal source direction lines; calculating barycentric position coordinates of the cross region based on a geometric calculation method and the vertex position coordinates; determining the barycentric position coordinates as the position of the target foreign network base station.

6. The method according to any one of claims 1-5, characterized in that, The obtaining of the target measurement report data comprises the following steps: Obtaining sampling data based on a measurement report database; De-duplication processing is performed on repeated data in the sampling data to obtain to-be-screened measurement report data; The location information and the reference signal receiving power and the reference signal receiving quality of the foreign network neighbor cell in the to-be-screened measurement report data are screened to obtain target measurement report data.

7. A device for identifying the location of a cross-network base station, characterized in that, Comprise: An acquisition unit is configured to acquire target measurement report data; A clustering unit is configured to call a density clustering algorithm to cluster the target measurement report data to obtain target clusters of at least three foreign network neighbor cells; A first calculation unit is configured to calculate the target clusters to obtain first location information of optimal network signals and second location information of worst network signals of the target clusters; A second calculation unit is configured to calculate first center positions of optimal network signal sets and second center positions of worst network signal sets of the at least three foreign network neighbor cells, respectively; A first determination unit is configured to determine a signal source direction line constituted by the second center position pointing to the first center position in each foreign network neighbor cell; A construction unit is configured to construct a cross region through at least three signal source direction lines; A second determination unit is configured to determine a barycentric position information of the cross region, and determine the barycentric position as a target foreign network base station position.

8. An electronic device, comprising: Comprise: At least one processor; And A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-6.

10. A computer program product, characterised in that, Comprise a computer program, the computer program is executed by the processor to realize the method of any one of claims 1-6.

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