Abnormal base station position detection and estimation method, apparatus, and electronic device

Abstract

The application relates to the technical field of base stations, and provides an abnormal base station position detection and estimation method, device and electronic equipment. The method comprises the following steps: acquiring a data set of a to-be-tested base station, wherein the data set comprises a plurality of measurement reports; determining that the to-be-tested base station is an abnormal base station according to a distance difference between a first distance and a second distance corresponding to the measurement reports; the first distance is used for representing the distance between the latitude and longitude position of a user equipment of the measurement report and the to-be-tested base station; the second distance is used for representing the distance between the user equipment estimated according to the time advance of the measurement report and the to-be-tested base station; deleting the measurement report with an abnormal latitude and longitude position under the abnormal base station based on the connection rule of the latitude and longitude position of the measurement report; and estimating the position estimation value of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the data set. The abnormal base station position detection and estimation method provided in the application can improve the ranging accuracy of the abnormal base station.

Description

Technical Field

[0001] This application relates to the field of base station technology, specifically to a method, apparatus, and electronic device for detecting and estimating the location of abnormal base stations. Background Technology

[0002] Currently, base stations, as fundamental equipment for data communication, are of paramount importance, and the detection of their actual engineering parameters has always been a key research focus. Existing technologies include methods for detecting the actual engineering parameters of base stations to determine the location of abnormal base stations.

[0003] The invention patent entitled "Base Station Latitude and Longitude Estimation Method Based on MDT Technology and RSRP Ranging" (application number: 202110072246.X) discloses a base station latitude and longitude estimation method based on MDT technology and RSRP ranging. Although this patent can locate the base station, the logarithmic loss model based on RSRP (Reference Signal Receiving Power) used in the patent for base station ranging is greatly affected by environmental factors, resulting in low ranging accuracy for abnormal base stations. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for detecting and estimating the location of abnormal base stations, in order to solve the technical problem of low ranging accuracy of existing solutions for abnormal base stations.

[0005] In a first aspect, embodiments of this application provide a method for detecting and estimating the location of abnormal base stations, including:

[0006] Obtain the dataset of the base station to be tested, which includes multiple measurement reports;

[0007] The base station under test is determined to be an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report; the first distance is used to characterize the distance between the user equipment's latitude and longitude location in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report.

[0008] Based on the connectivity rules of the latitude and longitude positions in the measurement reports, delete the measurement reports with abnormal latitude and longitude positions under the abnormal base stations;

[0009] Based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the estimated location of the abnormal base station is obtained.

[0010] In one embodiment, determining that the base station under test is an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report includes:

[0011] Measurement reports with a distance difference greater than a first preset threshold are identified as abnormal measurement reports.

[0012] If the ratio of the abnormal measurement report to all measurement reports in the dataset is greater than a second preset threshold, the base station under test is determined to be an abnormal base station.

[0013] In one embodiment, deleting measurement reports with abnormal latitude and longitude locations under the abnormal base station based on the connectivity rules of the latitude and longitude locations in the measurement report includes:

[0014] The latitude and longitude data from multiple measurement reports are converted into multiple two-dimensional grids;

[0015] Obtain grid connectivity rules; the grid connectivity rules include individual grid connectivity rules and inter-grid connectivity rules.

[0016] Based on the grid connectivity rules, multiple two-dimensional grids are clustered to obtain multiple cluster heads and subordinate grids corresponding to the cluster heads.

[0017] Based on preset rules, delete outlier cluster heads and their corresponding subordinate grids from multiple cluster heads.

[0018] In one embodiment, the step of deleting outlier cluster heads and the subordinate grids corresponding to the outlier cluster heads from multiple cluster heads based on preset rules includes:

[0019] Traverse each cluster head and identify the cluster head whose number of subordinate grids is less than a third set threshold as an outlier cluster head;

[0020] Delete the outlier cluster head and the corresponding subordinate raster.

[0021] In one embodiment, estimating the location estimate of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset includes:

[0022] Construct an objective function based on the mean square error between the first distance and the second distance;

[0023] The objective function is solved using the gradient descent method to obtain the location estimate of the abnormal base station.

[0024] In one embodiment, solving the objective function using the gradient descent method to obtain the location estimate of the abnormal base station includes:

[0025] Solve for the partial derivatives of the objective function;

[0026] Determine the iterative formula for the gradient descent method;

[0027] Based on the partial derivatives and the iterative formula, the objective function is iteratively calculated using the gradient descent method to obtain the base station location data in the Mercator coordinate system.

[0028] The base station location data is converted into latitude and longitude format to obtain the location estimate.

[0029] Secondly, embodiments of this application provide an abnormal base station location detection and estimation apparatus, comprising:

[0030] The acquisition module is used to acquire the dataset of the base station under test, which includes multiple measurement reports;

[0031] An abnormal base station location detection module is used to determine that the base station under test is an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement report; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report.

[0032] The deletion module is used to delete measurement reports with abnormal latitude and longitude locations under the abnormal base station based on the connectivity rules of the latitude and longitude locations in the measurement report.

[0033] An abnormal base station location estimation module is used to estimate the location of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset.

[0034] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the abnormal base station location detection and estimation method described in the first or second aspect.

[0035] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the abnormal base station location detection and estimation method described in the first aspect.

[0036] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the abnormal base station location detection and estimation method described in the first aspect.

[0037] The abnormal base station location detection and estimation method, apparatus, and electronic device provided in this application determine that the base station under test is an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement report. Since the distance difference between the first distance and the second distance represents the difference between the distance estimated from the base station based on time advance and the distance estimated from the base station based on the Global Positioning System (GPS), and both the first distance and the second distance represent the distance from the user equipment to the base station antenna, the difference between the first distance and the second distance can determine that the base station under test is an abnormal base station. Furthermore, based on the connectivity rules of the latitude and longitude positions in the measurement reports, measurement reports with abnormal latitude and longitude positions under the abnormal base station are deleted; thereby deleting abnormal data in the measurement reports and reducing the impact of abnormal data on the location estimation value of the abnormal base station; and then, based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the location estimation value of the abnormal base station is estimated. Since the second distance is the distance between the user equipment and the base station under test estimated based on the time advance, and the time advance is calculated by the Ts granularity of the measurement data, and since the Ts granularity is a constant with a clear and fixed physical meaning, the second distance estimated based on the time advance is not easily affected by the environment. Therefore, the location estimate of the abnormal base station obtained based on the first distance and the second distance can improve the ranging accuracy of the abnormal base station. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is one of the flowcharts illustrating the abnormal base station location detection and estimation method provided in the embodiments of this application;

[0040] Figure 2 This is the second flowchart illustrating the abnormal base station location detection and estimation method provided in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram illustrating how the latitude and longitude data of multiple measurement reports are converted into multiple two-dimensional grids according to an embodiment of this application;

[0042] Figure 4 This is a schematic diagram of a single grid connectivity rule according to an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of the connectivity rules between grids in an embodiment of this application;

[0044] Figure 6 This is a schematic diagram of how, according to the grid connectivity rules, multiple two-dimensional grids are clustered to obtain multiple cluster heads and subordinate grids corresponding to the cluster heads.

[0045] Figure 7 This is the third flowchart illustrating the abnormal base station location detection and estimation method provided in the embodiments of this application;

[0046] Figure 8 This is a schematic diagram of the abnormal base station location detection and estimation device provided in the embodiments of this application;

[0047] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] Figure 1 This describes a method for detecting and estimating the location of abnormal base stations. Please refer to [link / reference]. Figure 1 This application provides a method for detecting and estimating the location of abnormal base stations, which may include:

[0050] Step 100: Obtain the dataset of the base station to be tested, which includes multiple measurement reports.

[0051] The electronic device acquires a dataset from the base station under test, which includes measurement reports uploaded by multiple user equipment. For example, the dataset for a particular base station under test might be {MR1, MR2, MR3, ..., MR...} N There are a total of N measurement reports (MR, short for Measurement Result). MR represents the measurement report. The latitude and longitude data of the i-th measurement report is (lon... i ,lat i ), where lon i Let lat represent the longitude of the i-th measurement report. i This represents the latitude of the i-th measurement report.

[0052] Step 200: Determine the base station under test as an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report.

[0053] The electronic device determines whether the base station under test is an abnormal base station based on the measurement report uploaded by the user equipment. Specifically, the electronic device determines the base station under test as an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report.

[0054] The first distance is used to characterize the distance between the latitude and longitude location of the user equipment in the measurement report and the base station under test. It is expressed by the formula:

[0055]

[0056] Where, d AGPS Indicates the first distance, (x) i x y (C) indicates the latitude and longitude location of the user equipment in the measurement report; i C y () indicates the location of the base station to be tested.

[0057] Therefore, the distance between the user equipment's latitude and longitude location and the base station under test, as reported in the measurement report, can characterize the distance from the user equipment to the base station antenna, i.e., the distance from the user equipment to the base station.

[0058] The distance between the user equipment and the base station under test, estimated based on the time advance of the measurement report, is expressed by the formula:

[0059] d TA =4.88 * TA;

[0060] Where, d TA d represents the second distance, and TA represents the time lead. TA It represents the distance between the user equipment (UE) and the base station antenna port.

[0061] The time advance distance corresponding to 1Ts is equal to:

[0062] (3*10^8*1 / (15000*2048) / 2=4.88m. The meaning is that distance = propagation speed (speed of light) * 1Ts / 2 (uplink and downlink path sum). Where Ts=1 / (15000*2048), the unit is: seconds.)

[0063] Therefore, the distance between the user equipment and the base station under test, estimated based on the time advance of the measurement report, can characterize the distance from the user equipment to the base station antenna, i.e., the distance from the user equipment to the base station.

[0064] In other words, since both the first and second distances characterize the distance from the user equipment to the base station antenna, the difference between the first and second distances can determine whether the base station under test is an abnormal base station. Therefore, the first and second distances can mutually verify whether the distance from the user equipment to the base station is correct, thereby confirming whether the measurement report obtained by the electronic device is incorrect. When the difference between the first and second distances is sufficient to determine whether the base station under test is an abnormal base station...

[0065] Specifically, step 200, determining the base station under test as an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report, includes:

[0066] Step 210: Determine that a measurement report with a distance difference greater than a first set threshold is an abnormal measurement report;

[0067] The electronic device determines a measurement report where the distance difference is greater than a first preset threshold as an abnormal measurement report. This is because the first distance (i.e., d...) AGPS ) and the second distance (i.e., d) TA Both distances can characterize the distance from the terminal to the base station antenna, so the first distance and the second distance can corroborate each other. A large difference between the first distance and the second distance indicates that the corresponding measurement report is abnormal.

[0068] Specifically, the electronic device calculates the distance difference between the first and second distances in each measurement report. For the i-th measurement report, the distance difference d... i The calculation formula is as follows:

[0069]

[0070] Where, d i This represents the distance difference between the first and second distances in the i-th measurement report; This represents the first distance of the i-th measurement report; This represents the second distance of the i-th measurement report.

[0071] The first set threshold can be set according to actual conditions. For example, in one embodiment, the first set threshold can be 180. When the electronic device calculates the distance difference d... i If the value is greater than 180, the i-th measurement report is considered an abnormal measurement report.

[0072] Step 220: If the ratio of the abnormal measurement report to all the measurement reports in the dataset is greater than a second set threshold, the base station under test is determined to be an abnormal base station.

[0073] If the ratio of the abnormal measurement report to all the measurement reports in the dataset is greater than a second preset threshold, the electronic device determines that the base station under test is an abnormal base station.

[0074] The more measurement reports considered abnormal, the more abnormal the base station under test is considered. The second threshold can be set according to actual conditions; for example, in one embodiment, the second threshold could be 20%.

[0075] That is when When the base station under test is identified as an abnormal base station, N1 represents the number of abnormal measurement reports, and N represents the total number of all measurement reports in the dataset.

[0076] Step 300: Based on the connectivity rules of the latitude and longitude positions in the measurement report, delete the measurement report with abnormal latitude and longitude positions under the abnormal base station;

[0077] The electronic device deletes measurement reports with abnormal latitude and longitude locations based on the connectivity rules of the latitude and longitude locations in the measurement reports.

[0078] Please refer to Figure 2 In one embodiment, step 300, the deletion of measurement reports with abnormal latitude and longitude locations under the abnormal base station based on the connectivity rules of the latitude and longitude locations in the measurement report, includes:

[0079] Step 310: Convert the latitude and longitude data of the multiple measurement reports into multiple two-dimensional grids;

[0080] The electronic equipment first converts the latitude and longitude data contained in each measurement report into data in Mercator coordinates, and then converts the data in Mercator coordinates into two-dimensional gratings.

[0081] Here, we assume that the latitude and longitude of the i-th measurement report are (lon i ,lat i ), where lon i Let lat represent the longitude of the i-th measurement report. i This represents the latitude of the i-th measurement report, converted to two-dimensional coordinates in the Mercator coordinate system (x, y). i ,y i In addition, the latitude and longitude settings of this base station in the engineering parameters are (C). lon C lat ), where C lon Indicates the longitude of the base station, C latThe latitude of the base station is represented and converted to two-dimensional coordinates in the Mercator coordinate system (C). x C y ).

[0082] Specifically, the conversion formula from latitude and longitude (lon,lat) to Mercator coordinates (x,y) is as follows:

[0083]

[0084]

[0085] The grid size is 50 meters. Measurement reports from the current abnormal base station are grouped according to the two-dimensional grid. The grouping is based on the two-dimensional grid as the key value and the data of all measurement reports within the grid as the value, thus forming a structure like this: Figure 3 The relationship.

[0086] After the above transformation, the smallest unit we need to process now becomes a series of different 50-meter grids, instead of each measurement report, which greatly reduces the amount of data to be clustered.

[0087] Step 320: Obtain grid connectivity rules; the grid connectivity rules include individual grid connectivity rules and connectivity rules between grids;

[0088] Electronic devices acquire grid connectivity rules. Traditional density clustering algorithms, such as DBScan, require calculating the distance between the current point and all other points when performing density connectivity, resulting in a relatively large computational load. In this embodiment, the computational load is simplified by using individual grid connectivity rules and grid-to-grid connectivity rules, and the distance calculation method and density connectivity method are redefined.

[0089] Specifically, the connectivity rule for a single grid cell is that for the current grid cell, only the four grid cells above, below, left, and right of it are connected to it. Please refer to [reference needed]. Figure 4 As shown, only grids p1, p2, p3, and p4 are connected to grid p0.

[0090] The connectivity rules between graticles are as follows: For the current graticle p, if q is connected to p, and o is also connected to p, then graticle o is connected to graticle q. All connected graticles form a cluster together, and unconnected graticles form different clusters. Please refer to [reference needed]. Figure 5 As shown, the dashed box on the left contains a cluster C1 composed of grids, and the grids within it are all connected. In the right figure, the grids in cluster C1 are not connected to the grids in cluster C2 within the dashed box, but the grids in C1 are connected, and the same applies to C2.

[0091] Step 330: Based on the grid connectivity rules, cluster the multiple two-dimensional grids to obtain multiple cluster heads and subordinate grids corresponding to the cluster heads.

[0092] The electronic device clusters multiple two-dimensional graticles according to the grid connectivity rules, obtaining multiple cluster heads and corresponding subordinate graticles. That is, the electronic device traverses n graticles to obtain cluster heads and corresponding subordinate graticles. The specific process is represented by the following pseudocode:

[0093]

[0094] After the clustering process described above, the rasters are grouped, with the key for each group being the cluster head identifier and the value being a list of rasters within that cluster. This results in a structure like... Figure 6 The relationship diagram between the cluster head and the grid.

[0095] Step 340: Delete the outlier cluster heads and the subordinate grids corresponding to the outlier cluster heads from multiple cluster heads based on preset rules.

[0096] The electronic device deletes outlier cluster heads and their corresponding subordinate grids from multiple cluster heads based on preset rules.

[0097] In one embodiment, step 340, the deletion of outlier cluster heads and the subordinate grids corresponding to the outlier cluster heads based on preset rules, includes:

[0098] Step 341: Traverse each cluster head and determine the cluster head with the number of subordinate grids less than the third set threshold as the outlier cluster head;

[0099] Step 342: Delete the outlier cluster head and the subordinate grid corresponding to the outlier cluster head.

[0100] The electronic device identifies cluster heads with fewer than a third preset threshold number of subordinate grids as outlier cluster heads. Specifically, in one embodiment, the third preset threshold number can be 5. That is, the electronic device identifies cluster heads with fewer than 5 subordinate grids as outlier cluster heads. The electronic device traverses each cluster head and deletes the subordinate grids corresponding to clusters with fewer than 5 grids, as well as the measurement reports contained in those subordinate grids, thus completing the deletion of measurement reports with abnormal positions.

[0101] Based on the connectivity rules of the latitude and longitude positions in the measurement report, this application embodiment deletes measurement reports with abnormal latitude and longitude positions under the abnormal base station; thereby deleting abnormal data in the measurement report and reducing the impact of abnormal data on the location estimation value of the abnormal base station.

[0102] Step 400: Based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, estimate the location of the abnormal base station.

[0103] Based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the estimated location of the abnormal base station is obtained.

[0104] Since the second distance is the distance between the user equipment and the base station under test estimated based on the time advance (i.e., TA), and the time advance is calculated based on the Ts granularity of the measurement data, and since the Ts granularity is a constant with a clear and fixed physical meaning, the second distance estimated based on the time advance is not easily affected by the environment. Therefore, the location estimation value of the abnormal base station estimated based on the first distance and the second distance can improve the ranging accuracy of the abnormal base station.

[0105] For details, please refer to Figure 7 Step 400, estimating the location estimate of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, includes:

[0106] Step 410: Construct an objective function based on the mean square error between the first distance and the second distance.

[0107] The electronic device constructs an objective function based on the mean squared error between the first distance and the second distance. Specifically, in one embodiment, the objective function is defined as follows:

[0108]

[0109]

[0110] Where, d AGPS Indicates the first distance, (x) i x y () represents the latitude and longitude location of the user equipment in the measurement report; (Cx, Cy) represents the location of the base station to be measured. This represents the first distance of the i-th measurement report; w represents the second distance of the i-th measurement report; i This represents the weight of the first distance; the larger the time lead (TA), the higher the weight (w). i The smaller; TA i This represents the lead time for the i-th measurement report.

[0111] Step 420: Solve the objective function using the gradient descent method to obtain the location estimate of the abnormal base station.

[0112] After constructing the objective function based on the mean square error between the first distance and the second distance, the electronic device solves the objective function using the gradient descent method to obtain the location estimate of the abnormal base station.

[0113] In one embodiment, step 420, solving the objective function using the gradient descent method to obtain the location estimate of the abnormal base station, includes:

[0114] Step 421: Solve for the partial derivatives of the objective function;

[0115] The partial derivatives of the objective function were obtained by solving the electronic device. Based on the above objective function, the partial derivatives of the objective function are as follows:

[0116]

[0117]

[0118] Step 422: Determine the iterative formula for the gradient descent method;

[0119] Specifically, in one embodiment, the iterative formula is as follows:

[0120]

[0121] Step 423: Based on the partial derivatives and the iterative formula, the objective function is iteratively calculated using the gradient descent method to obtain the base station location data in the Mercator coordinate system;

[0122] The electronic device iteratively calculates the objective function based on the partial derivatives and the iterative formula using the gradient descent method to obtain base station location data in the Mercator coordinate system.

[0123] Specifically, the base station location data in the Mercator coordinate system can be calculated using the following algorithm. The pseudocode for the algorithm is as follows:

[0124]

[0125]

[0126] Step 424: Convert the base station location data into latitude and longitude format to obtain the location estimate.

[0127] The electronic device converts the base station location data into latitude and longitude format to obtain the estimated location value.

[0128] Specifically, the location estimate can be obtained by converting the base station location data into latitude and longitude format using the following formula:

[0129]

[0130]

[0131] Among them, (C) x C y ) represents the base station location data in the Mercator coordinate system; C lon C represents the longitude of the location estimate. lat The latitude represents the estimated location value.

[0132] This application embodiment constructs an objective function based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset. By minimizing the mean square error of the first distance and the second distance, the base station location estimation problem is transformed into an optimization problem, which is solved using the gradient descent method. Since the second distance is the distance between the user equipment and the base station under test estimated based on the time advance, and the time advance is calculated at the Ts granularity of the measurement data, and since the Ts granularity is a constant with a clear and fixed physical meaning, the second distance estimated based on the time advance is not easily affected by the environment. Therefore, the location estimation value of the abnormal base station obtained based on the first distance and the second distance can improve the ranging accuracy of the abnormal base station.

[0133] The base station under test is determined to be an abnormal base station by the distance difference between the first distance and the second distance corresponding to the measurement report. Since the distance difference between the first distance and the second distance represents the difference between the distance estimated from the base station based on the time advance and the distance estimated from the base station based on the Global Positioning System, and since both the first distance and the second distance represent the distance from the user equipment to the base station antenna, the base station under test can be determined to be an abnormal base station based on the distance difference between the first distance and the second distance. Then, based on the connectivity rules of the latitude and longitude positions in the measurement report, measurement reports with abnormal latitude and longitude positions under the abnormal base station are deleted; thereby deleting abnormal data in the measurement reports and reducing the impact of abnormal data on the location estimation value of the abnormal base station; and then, based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the location estimation value of the abnormal base station is estimated. Since the second distance is the distance between the user equipment and the base station under test estimated based on the time advance, and the time advance is calculated by the Ts granularity of the measurement data, and since the Ts granularity is a constant with a clear and fixed physical meaning, the second distance estimated based on the time advance is not easily affected by the environment. Therefore, the location estimate of the abnormal base station obtained based on the first distance and the second distance can improve the ranging accuracy of the abnormal base station.

[0134] The abnormal base station location detection and estimation apparatus provided in the embodiments of this application is described below. The abnormal base station location detection and estimation apparatus described below can be referred to in correspondence with the abnormal base station location detection and estimation method described above.

[0135] Please refer to Figure 8 An anomalous base station location detection and estimation device, comprising:

[0136] The acquisition module 201 is used to acquire the dataset of the base station to be tested, the dataset including multiple measurement reports;

[0137] The abnormal base station location detection module 202 is used to determine that the base station under test is an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement report; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report.

[0138] The deletion module 203 is used to delete measurement reports with abnormal latitude and longitude positions under the abnormal base station based on the connectivity rules of the latitude and longitude positions of the measurement report.

[0139] The abnormal base station location estimation module 204 is used to estimate the location of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset.

[0140] The base station under test is determined to be an abnormal base station by the distance difference between the first distance and the second distance corresponding to the measurement report. Since the distance difference between the first distance and the second distance represents the difference between the distance estimated from the base station based on the time advance and the distance estimated from the base station based on the Global Positioning System, and since both the first distance and the second distance represent the distance from the user equipment to the base station antenna, the base station under test can be determined to be an abnormal base station based on the distance difference between the first distance and the second distance. Then, based on the connectivity rules of the latitude and longitude positions in the measurement report, measurement reports with abnormal latitude and longitude positions under the abnormal base station are deleted; thereby deleting abnormal data in the measurement reports and reducing the impact of abnormal data on the location estimation value of the abnormal base station; and then, based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the location estimation value of the abnormal base station is estimated. Since the second distance is the distance between the user equipment and the base station under test estimated based on the time advance, and the time advance is calculated by the Ts granularity of the measurement data, and since the Ts granularity is a constant with a clear and fixed physical meaning, the second distance estimated based on the time advance is not easily affected by the environment. Therefore, the location estimate of the abnormal base station obtained based on the first distance and the second distance can improve the ranging accuracy of the abnormal base station.

[0141] In one embodiment, the abnormal base station location detection module includes:

[0142] An abnormal measurement report determination module is used to determine that a measurement report in which the distance difference is greater than a first preset threshold is an abnormal measurement report;

[0143] An abnormal base station determination module is used to determine that the base station under test is an abnormal base station when the ratio of the abnormal measurement report to all the measurement reports in the dataset is greater than a second preset threshold.

[0144] In one embodiment, the deletion module includes:

[0145] A conversion module is used to convert the latitude and longitude data of multiple measurement reports into multiple two-dimensional graticules;

[0146] The rule acquisition module is used to acquire grid connectivity rules; the grid connectivity rules include individual grid connectivity rules and inter-grid connectivity rules.

[0147] The clustering module is used to cluster multiple two-dimensional graticles according to the graticle connectivity rules to obtain multiple cluster heads and subordinate graticles corresponding to the cluster heads;

[0148] The final deletion module is used to delete outlier cluster heads and their corresponding subordinate grids from multiple cluster heads based on preset rules.

[0149] In one embodiment, the final deletion module is specifically used to: traverse each cluster head, determine cluster heads whose number of dependent grids is less than a third preset threshold as outlier cluster heads; and delete the outlier cluster head and the dependent grids corresponding to the outlier cluster head.

[0150] In one embodiment, the abnormal base station location estimation module includes:

[0151] The objective function construction module is used to construct an objective function based on the mean square error between the first distance and the second distance;

[0152] The gradient descent method solution module is used to solve the objective function according to the gradient descent method to obtain the location estimate of the abnormal base station.

[0153] In one embodiment, the gradient descent method solution module includes:

[0154] The partial derivative solving module is used to solve for the partial derivatives of the objective function;

[0155] The iterative formula determination module is used to determine the iterative formula for the gradient descent method.

[0156] An iterative module is used to iteratively calculate the objective function based on the partial derivatives and the iterative formula using the gradient descent method to obtain base station location data in Mercator coordinates.

[0157] The location estimation module is used to convert the base station location data into latitude and longitude format to obtain the location estimation value.

[0158] Figure 9 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 9 As shown, the electronic device may include a processor 910, a communication interface 920, a memory 930, and a communication bus 940, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other via the communication bus 940. The processor 910 can call a computer program in the memory 930 to perform steps for abnormal base station location detection and estimation, such as: acquiring a dataset of the base station to be tested, the dataset including multiple measurement reports; determining the base station to be tested as an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement reports; the first distance being used to characterize the distance between the latitude and longitude position of the user equipment in the measurement reports and the base station to be tested; the second distance being used to characterize the distance between the user equipment and the base station to be tested estimated based on the time advance of the measurement reports; deleting measurement reports with abnormal latitude and longitude positions under the abnormal base station based on the connectivity rules of the latitude and longitude positions in the measurement reports; and estimating the location estimate of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset.

[0159] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the abnormal base station location detection and estimation method provided in the above embodiments, such as: acquiring a dataset of a base station to be tested, the dataset including multiple measurement reports; determining that the base station to be tested is an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement reports; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station to be tested; the second distance is used to characterize the distance between the user equipment and the base station to be tested estimated based on the time advance of the measurement reports; deleting measurement reports with abnormal latitude and longitude positions under the abnormal base station based on the connectivity rules of the latitude and longitude positions in the measurement reports; and estimating the location estimate of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset.

[0161] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program, the computer program being used to cause a processor to execute the steps of the abnormal base station location detection and estimation methods provided in the above embodiments, such as including:

[0162] A dataset of the base station to be tested is obtained, the dataset including multiple measurement reports; the base station to be tested is determined to be an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement reports; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station to be tested; the second distance is used to characterize the distance between the user equipment and the base station to be tested estimated based on the time advance of the measurement reports; based on the connectivity rules of the latitude and longitude positions in the measurement reports, measurement reports with abnormal latitude and longitude positions under the abnormal base station are deleted; based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the location estimate of the abnormal base station is estimated.

[0163] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0164] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0165] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for detecting and estimating the location of abnormal base stations, characterized in that, include: Obtain the dataset of the base station to be tested, which includes multiple measurement reports; The base station under test is determined to be an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report. Based on the connectivity rules of the latitude and longitude positions in the measurement reports, delete the measurement reports with abnormal latitude and longitude positions under the abnormal base stations; Based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset, the estimated location of the abnormal base station is obtained. The step of deleting measurement reports with abnormal latitude and longitude locations under abnormal base stations based on the connectivity rules of the measurement reports includes: The latitude and longitude data from multiple measurement reports are converted into multiple two-dimensional grids; Obtain grid connectivity rules; the grid connectivity rules include individual grid connectivity rules and grid-to-grid connectivity rules; the individual grid connectivity rule is that for the current grid, only the four grids above, below, left, and right of it are connected to it; the grid-to-grid connectivity rules are: for the current grid, if the first grid is connected to the current grid and the second grid is also connected to the current grid, then the first grid and the second grid are connected, and all connected grids form a cluster together, while unconnected grids form different clusters; Based on the grid connectivity rules, multiple two-dimensional grids are clustered to obtain multiple cluster heads and subordinate grids corresponding to the cluster heads. Based on preset rules, delete outlier cluster heads and their corresponding subordinate grids from multiple cluster heads; The estimation of the location of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset includes: Construct an objective function based on the mean squared error between the first distance and the second distance; wherein the objective function is defined as follows: ； in, d AGPS Indicates the first distance, ( x i , x y () indicates the latitude and longitude location of the user equipment in the measurement report; C x , C y () indicates the location of the base station to be tested; Indicates the first i The first distance of the measurement report; Indicates the first i The second distance of the measurement report; w i The weight representing the difference between the first distance and the second distance. TA i Indicates the first i The time lead time of each measurement report; the larger the time lead time, the smaller the weight. The objective function is solved using the gradient descent method to obtain the location estimate of the abnormal base station.

2. The method for detecting and estimating the location of abnormal base stations according to claim 1, characterized in that, The step of determining the base station under test as an abnormal base station based on the distance difference between the first distance and the second distance corresponding to the measurement report includes: Measurement reports with a distance difference greater than a first set threshold are identified as abnormal measurement reports. If the ratio of the abnormal measurement report to all measurement reports in the dataset is greater than a second preset threshold, the base station under test is determined to be an abnormal base station.

3. The method for detecting and estimating the location of abnormal base stations according to claim 1, characterized in that, The step of deleting outlier cluster heads and their corresponding subordinate grids from multiple cluster heads based on preset rules includes: Traverse each cluster head and identify the cluster head whose number of subordinate grids is less than a third set threshold as an outlier cluster head; Delete the outlier cluster head and the corresponding subordinate raster.

4. The method for detecting and estimating the location of abnormal base stations according to claim 1, characterized in that, The step of solving the objective function using the gradient descent method to obtain the location estimate of the abnormal base station includes: Solve for the partial derivatives of the objective function; Determine the iterative formula for the gradient descent method; Based on the partial derivatives and the iterative formula, the objective function is iteratively calculated using the gradient descent method to obtain the base station location data in the Mercator coordinate system. The base station location data is converted into latitude and longitude format to obtain the location estimate.

5. An abnormal base station location detection and estimation device, characterized in that, include: The acquisition module is used to acquire the dataset of the base station under test, which includes multiple measurement reports; An abnormal base station location detection module is used to determine that the base station under test is an abnormal base station based on the distance difference between a first distance and a second distance corresponding to the measurement report; the first distance is used to characterize the distance between the latitude and longitude position of the user equipment in the measurement report and the base station under test; the second distance is used to characterize the distance between the user equipment and the base station under test estimated based on the time advance of the measurement report. The deletion module is used to delete measurement reports with abnormal latitude and longitude locations under the abnormal base station based on the connectivity rules of the latitude and longitude locations in the measurement report. An abnormal base station location estimation module is used to estimate the location of the abnormal base station based on the first distance and the second distance corresponding to the remaining measurement reports in the dataset. The deletion module includes: A conversion module is used to convert the latitude and longitude data of multiple measurement reports into multiple two-dimensional graticules; The rule acquisition module is used to acquire grid connectivity rules. The grid connectivity rules include individual grid connectivity rules and grid-to-grid connectivity rules. The individual grid connectivity rule is that for the current grid, only the four grids above, below, left, and right of it are connected to it. The grid-to-grid connectivity rules are: for the current grid, if the first grid is connected to the current grid and the second grid is also connected to the current grid, then the first grid and the second grid are connected, and all connected grids form a cluster together; unconnected grids form different clusters. The clustering module is used to cluster multiple two-dimensional graticles according to the graticle connectivity rules to obtain multiple cluster heads and subordinate graticles corresponding to the cluster heads; The final deletion module is used to delete outlier cluster heads and the subordinate grids corresponding to the outlier cluster heads from multiple cluster heads based on preset rules. The abnormal base station location estimation module is specifically used for: Construct an objective function based on the mean squared error between the first distance and the second distance; wherein the objective function is defined as follows: ； in, d AGPS Indicates the first distance, ( x i , x y () indicates the latitude and longitude location of the user equipment in the measurement report; C x , C y () indicates the location of the base station to be tested; Indicates the first i The first distance of the measurement report; Indicates the first i The second distance of the measurement report; w i The weight representing the difference between the first distance and the second distance. TA i Indicates the first i The time lead time of each measurement report; the larger the time lead time, the smaller the weight. The objective function is solved using the gradient descent method to obtain the location estimate of the abnormal base station.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the abnormal base station location detection and estimation method as described in any one of claims 1 to 4.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the abnormal base station location detection and estimation method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the abnormal base station location detection and estimation method according to any one of claims 1 to 4.

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