Error correction method and device and electronic equipment

Abstract

The embodiment of the invention provides an error correction method and device and electronic equipment. The error correction method comprises the following steps: determining an iron tower number of each base station according to a base station code of each base station in a base station data set; performing clustering analysis on the first position information of the base stations belonging to the same iron tower number, and determining a first discrete base station and a first high-density base station; and updating the first position information of the first discrete base station according to the first position information of the first high-density base station.

Description

Technical Field

[0001] This application relates to the field of base station database information technology, and in particular to error correction methods, apparatus and electronic devices. Background Technology

[0002] The Base Station Database (BSDB), also known as the Base Station Database, is an electronic system used to centrally store and manage information related to all base stations in a mobile communication network, recording the location information of each base station.

[0003] The location information of base stations in the base station database is currently entered by the equipment deployment department during the installation and deployment of base stations. Due to the lack of understanding of geographical location by business personnel, the entered base station location information may be incorrect.

[0004] It is extremely difficult to detect such erroneous location information through manual review, and there is a serious delay in fixing the base station location information through post-incident inspection or customer complaints. Summary of the Invention

[0005] The purpose of this application is to provide error correction methods, devices, and electronic devices that can solve the problem of difficulty in discovering and correcting the location information of base stations in the base station database.

[0006] To solve the above-mentioned technical problems, the embodiments of this application are implemented through the following aspects.

[0007] In a first aspect, embodiments of this application provide an error correction method, the method comprising: determining the tower number of each base station based on the base station code of each base station in a base station dataset; performing cluster analysis on the first location information of base stations belonging to the same tower number to determine a first discrete base station and a first high-density base station; and updating the first location information of the first discrete base station based on the first location information of the first high-density base station.

[0008] Secondly, embodiments of this application provide an error correction device, the device comprising: a tower number determination module, configured to determine the tower number of each base station based on the base station code of each base station in the base station dataset; a clustering analysis module, configured to perform clustering analysis on the first location information of base stations belonging to the same tower number to determine a first discrete base station and a first high-density base station; and an update module, configured to update the first location information of the first discrete base station based on the first location information of the first high-density base station.

[0009] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and computer-executable instructions stored in the memory and executable on the processor, wherein the computer-executable instructions, when executed by the processor, implement the steps of the method described in the first aspect. Fourthly, embodiments of this application provide a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the steps of the method described in the first aspect. In this embodiment, the tower number eNBID / gNBID of each base station is first determined based on the base station code of each base station in the base station dataset. Then, cluster analysis is performed on the first location information of all base stations belonging to the same tower number to identify discrete samples (first discrete base stations) and high-density samples (first high-density base stations) among these base stations. The first discrete base station is identified as a base station whose first location information is abnormal and needs to be corrected. Therefore, the first location information of the first discrete base station is updated based on the first location information of the first high-density base station, so as to proactively and timely correct the erroneous first location information of the base station. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of 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 only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This illustration shows a flowchart of an error correction method provided in an embodiment of this application; Figure 2 This illustration shows another flowchart of the error correction method provided in an embodiment of this application; Figure 3 This illustration shows a structural schematic diagram of an error correction device provided in an embodiment of this application; Figure 4 A schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0013] The base station database is a database used by operators to record the locations of base stations. Records regarding base station locations in the database are categorized by data type, including: base station code, administrative division, and specific location. The base station code data field can include the Location Area Code (LAC, for 2G / 3G) or Tracking Area Code (TAC, for 4G / 5G), and the Cell Identifier (CI, for 2G / 3G) or E-UTRA Cell Identifier (ECI, for 4G / 5G) or NR Cell Identifier (NCI, for 5G). The administrative division data field can include: province (province name and code), city (city name and code), and district / county (district / county name and code). The specific location data field includes: longitude and latitude.

[0014] Currently, the base station database construction plan involves the equipment deployment department entering base station information during the installation and deployment process. Data accuracy is ensured through approval and review by higher authorities after the information is entered. During the data entry phase, due to some misunderstandings of geographical locations by business personnel, such as unclear provincial boundaries or incorrect referencing of cities with the same or similar names, the provincial, municipal, and county-level administrative divisions in the base station location information may be incorrect.

[0015] It is extremely difficult to detect erroneous location information of base stations through manual review, and there is a serious lag in fixing the location information of base stations through post-incident inspections or customer complaints.

[0016] This application provides an error correction method to solve the above-mentioned technical problems. Figure 1 This diagram illustrates a flowchart of an error correction method provided in an embodiment of this application. This method can be executed by an error correction device or by an electronic device, such as a terminal device or a server device. In other words, the method can be executed by software or hardware installed on the terminal device or server device. Servers include, but are not limited to, single servers, server clusters, cloud servers, or cloud server clusters. Figure 1 As shown, the method may include the following steps.

[0017] Step S102: Determine the tower number (E-UTRA NodeB Identifier, eNBID, and / or NR NodeB Identifier, gNBID) of each base station based on the base station code of each base station in the base station data set. Step S104: Perform cluster analysis on the first location information of base stations belonging to the same tower number eNBID / gNBID to determine the first discrete base station and the first high-density base station; Step S106: Update the first location information of the first discrete base station based on the first location information of the first high-density base station.

[0018] In this embodiment, the tower number eNBID / gNBID of each base station is first determined based on the base station code of each base station in the base station dataset. Then, cluster analysis is performed on the first location information of these base stations belonging to the same tower number to identify discrete samples (first discrete base stations) and high-density samples (first high-density base stations) among these base stations. The first discrete base stations are identified as base stations with abnormal first location information that need to be corrected, while the first high-density base stations are base stations with normal first location information. Therefore, the first location information of the first discrete base stations is updated based on the first location information of the first high-density base stations, so as to proactively and timely correct the erroneous first location information of the base stations.

[0019] In one possible implementation, cluster analysis can be performed on the first location information of all base stations belonging to the same tower number, or cluster analysis can be performed on the first location information of a subset of all base stations as needed.

[0020] The base station data set centrally records the data of each base station, which may include the base station code of each base station, the first location information of each base station (that is, the first location information corresponding to each base station code), and the second location information of each base station, etc.

[0021] The construction of base stations requires the use of towers, and the base station code of each base station can be mapped to the tower number where each base station is located. Therefore, the tower number where each base station is located can be determined based on the base station code of each base station in the base station data set.

[0022] In one implementation, step S102 may include steps A1 and A2: Step A1: If the base station is encoded as a 4G / 5G cell identification code and the 4G / 5G cell identification code is a decimal identification code, convert the decimal identification code to determine the tower number eNBID / gNBID where the base station is located.

[0023] The conversion can be done by shifting the decimal 4G / 5G cell identifier code 8 bits to the right, or by dividing the decimal 4G / 5G cell identifier code by 256, thus determining the tower number. This conversion method assumes that the base station coding rules comply with 3GPP.

[0024] For example, the decimal 4G / 5G cell identifier ECI is 184126177, and the tower number eNBID / gNBID = ECI >> 8 (right shift by 8 bits), or eNBID / gNBID = ECI / / 256 (decimal division by decimal), after this conversion, the tower number eNBID / gNBID is 719242.

[0025] Step A2: If the base station is encoded as a 4G / 5G cell identification code and the 4G / 5G cell identification code is a four-field original identification code, take the third segment of the original identification code as the tower number eNBID / gNBID where the base station is located.

[0026] When the base station is encoded as a 4G / 5G cell identification code, and this 4G / 5G cell identification code is a four-field original identification code, the tower number where the base station is located can be directly determined from the third field of the four-field original identification code, since the third field is the tower number where the base station is located. The four-field original identification code is in the form of "Country Code-Network Code-Base Station ID-Cell ID". For example, the original identification code is 460-00-719242-225. The first segment 460: Country Code (China's Mobile Country Code MCC is fixed at 460); the second segment 00: Operator's Network Code (MNC), China Mobile's 4G network MNC is 00 / 02, also represented in decimal; the third segment 719242: Base Station Identifier (eNBID, 4G / 5G base station number); the fourth segment 225: Cell ID under the base station, ranging from 0 to 255.

[0027] Generally, the original identification code of these four fields should conform to 3GPP. Therefore, the third segment of the four fields can be determined as the tower number where the base station is located.

[0028] In some embodiments, if the original identification code of the four fields does not comply with 3GPP, the tower number where the base station is located can be determined according to the pre-established mapping relationship between base station codes and tower numbers.

[0029] In this process, there is a mapping relationship between the cell identification code, which is a decimal identification code in step A1, and the original identification code with four fields in step A2. Therefore, for the same base station encoding, the tower number determined according to either step A1 or step A2 is consistent.

[0030] The above describes an implementation method for determining the tower number where the base station is located when the base station code is a 4G / 5G cell identifier. Even when the base station code is not a 4G / 5G cell identifier, since there is a mapping relationship between the base station code and the tower number, the tower number where the base station is located can still be determined based on the base station code.

[0031] In one implementation, the base station coding may include at least one of the following: a location area code and a cell identification code. The location identifier may include at least one of the following: a 2G / 3G location area code (lac), or a 4G / 5G tracking area code (TAC). The cell identification code may include at least one of the following: a 4G / 5G cell identification code (E-UTRA Cell Identifier, ECI), a 5G cell identification code (NR Cell Identifier, NCI), or a 2G / 3G cell identification code (CI).

[0032] The aforementioned first location information may include at least one of the following: administrative division information, latitude and longitude information.

[0033] In one possible implementation, step S106 may include at least one of step B1 and step B2.

[0034] Step B1: Update the administrative division information of the first discrete base station based on the administrative division information of the first high-density base station.

[0035] Among them, within the same tower number, the administrative division information of the first high-density base station is within the reasonable range of high-density base stations within that tower number. Since the administrative division information of the first discrete base station deviates from that of the first high-density base station, it can be considered that the first discrete base station belongs to the base station with unreasonable location information under that tower number, and its administrative division information needs to be corrected. The administrative division information of the first discrete base station can be updated based on the administrative division information of the first high-density base station.

[0036] For example, if the tower code 719242 has a high-density center administrative division that should be Chang'an District, Xi'an City, Shaanxi Province, but it is found that under this tower code, there is an administrative division for a certain base station location that is Chengdu, Sichuan Province, Tianfu New Area, then the province, city, and district administrative division information of that base station can be identified as incorrect, and then the administrative division of that base station can be corrected to the aggregated high-density area administrative division data.

[0037] Step B2: Update the latitude and longitude information of the first discrete base station based on the latitude and longitude information of the first high-density base station.

[0038] In one implementation, the K-means algorithm can be used to perform the clustering analysis described above; of course, in other implementations, other algorithms can also be used for clustering analysis.

[0039] In one embodiment, step S104 above includes the following steps: Step C1: Determine the heat map center x of base stations belonging to the same tower number eNBID / gNBID.

[0040] Step C2: Draw a circle with radius r centered at the center x of the heatmap, and count the number of base stations n within the circle. Here, the number of base stations n is the number of samples used in the calculation.

[0041] Step C3: Calculate the average distance between the n base stations within the circle and the center x.

[0042] Step C4: If the average distance ≤ r, then the point density ρ(x) of the heatmap center x is the number of base stations n. The fact that the average distance ≤ r indicates that these n base stations are indeed around the center x, conforming to the physical coverage area of ​​the same tower. Therefore, the number of these base stations n is the point density ρ(x) of x.

[0043] Step C5: Determine the initial cluster centers for cluster analysis based on the point density ρ(x). The point density ρ(x) indicates that the denser the base stations around center x, the more suitable it is as the initial cluster center for K-means, thus avoiding selecting outliers or sparse regions. A threshold can be set for the point density ρ(x), selecting center x exceeding the threshold as the initial cluster center for cluster analysis. Alternatively, the center x corresponding to the maximum point density ρ(x) can be selected as the initial cluster center for cluster analysis.

[0044] Step C6: Based on the initial cluster centers, determine the first discrete base station and the first high-density base station belonging to the same tower number eNBID / gNBID. Based on the initial cluster centers, the high-density region and discrete region in this clustering algorithm can be identified. The base station located in the high-density region is the first high-density base station, and the base station located in the discrete region is the first discrete base station.

[0045] In one possible implementation, the point density ρ(x) can be calculated using the following formula:

[0046] In the above formula, x is the center, i refers to the i-th base station, n is the number of base stations within the radius of x as the center, r is the number of base stations, U is the base station dataset, and ECI is the 4G / 5G cell identifier. Based on this ECI, the data is processed with... After performing a bitwise AND operation, shifting the bits 8 bits to the right yields the tower number where the base station is located. This refers to using the center of the heatmap of base stations with the same tower number in the base station dataset U as the cluster initialization point. This represents the Euclidean distance between base station locations within the same data aggregation set, and similarly... An outlier is defined as an outlier.

[0047] Figure 2 This diagram illustrates yet another flowchart of the error correction method provided in this application. This method can be executed by an error correction device or by an electronic device, such as a terminal device or a server device. In other words, the method can be executed by software or hardware installed on the terminal device or server device. Servers include, but are not limited to, single servers, server clusters, cloud servers, or cloud server clusters. Figure 2 As shown, the method may include the following steps.

[0048] Step S202: Determine the tower number eNBID / gNBID of each base station based on the base station code of each base station in the base station data set.

[0049] Step S204: Perform cluster analysis on the first location information of base stations belonging to the same tower number eNBID / gNBID to determine the first discrete base station and the first high-density base station.

[0050] Step S206: Update the first location information of the first discrete base station according to the first location information of the first high-density base station. The first location information may include administrative division information or may be administrative division information.

[0051] Step S208: Perform cluster analysis on the second location information of all base stations accessed by IoT cards with the same card number and fixed location to determine the second discrete base station and the second high-density base station.

[0052] Due to the security management requirements of the Internet of Things (IoT), there are a large number of IoT cards in the current network whose locations are fixed in a small area (LAC / TAC level). The core network Radius signaling data of such IoT cards can be attached to neighboring base stations. Therefore, the Radius signaling data of the IoT can be associated with the base station dataset to determine the second location information of all base stations accessed by IoT cards with the same card number and fixed location.

[0053] Among them, the IoT SIM cards with fixed locations are not connected to fixed base stations. They can switch between different base stations due to factors such as network rules and environment.

[0054] Step S210: Update the second location information of the second discrete base station according to the second location information of the second high-density base station.

[0055] Steps S202-S206 can refer to steps S102-S106 above, and will not be repeated here. The main difference is that the first location information in step S206 may include or may be administrative division information.

[0056] In one embodiment, the second location information of this application is a more detailed location than the first location information. For example, when the first location information is administrative division information, the second location information can be latitude and longitude information.

[0057] In this embodiment of the application, after correcting the first location information (such as administrative division information) of some base stations in the base station dataset in step S206, further detailed location correction is required, that is, the second location information (such as latitude and longitude information) of the base stations in the base station dataset needs to be analyzed and corrected. To this end, in step S208, cluster analysis is performed on the second location information of all base stations accessed by IoT cards with the same card number and fixed location to determine the second high-density base stations whose second location information is located in a reasonable area and the second discrete base stations whose second location information is located in an unreasonable area. Finally, the second location information of the second discrete base stations is updated based on the second location information of the second high-density base stations, thereby completing the correction of the second location information of the base stations in the base station dataset.

[0058] In one possible implementation, cluster analysis can be performed on the second location information of all base stations accessed by IoT cards with the same card number and fixed location. Alternatively, cluster analysis can be performed on the second location information of a subset of all base stations accessed by IoT cards with the same card number and fixed location, as needed.

[0059] In one implementation, in order to accurately determine all base stations accessed by an IoT SIM card with the same card number and a fixed location, the following steps may be included before step S208: Step D1: Create consecutive snapshots based on all signaling data from the fixed-location IoT SIM card, arranged in time slices. Determine the signaling data set for each snapshot. Each snapshot includes the following elements: IoT SIM card number, signaling time, base station code of the accessed base station, and second location information. Here, n can be the total number of snapshots, and i can be the snapshot sequence number.

[0060] The IoT SIM card number can be the Mobile Station International Subscriber Directory Number (MSISDN).

[0061] The signaling data can be all RADIUS signaling data from the IoT SIM card, and the base station code can include the location area and cell identification code. Due to the massive amount of RADIUS signaling data, the signaling data set only needs to use snapshots formed within a certain time slice. Continuous snapshots are formed by taking the base station location points of all signaling data from the fixed-location IoT SIM card according to time slices. The snapshot length can be 24 hours, 12 hours, etc. Each SIM card will generate a snapshot. To form a data set Each snapshot The elements include [IoT SIM card number MSISDN, signaling time, base station code of the accessed base station, and second location information]. The base station code of the accessed base station may include the location area code and cell identification code. Therefore, each snapshot... The elements include [IoT SIM card number MSISDN, signaling time, location area code, cell identification code, and second location information].

[0062] In this case, the IoT SIM card with a fixed location does not connect to a fixed base station. Due to factors such as network rules and environment, it can switch between different base stations. Therefore, the above steps can obtain all the signaling data of a fixed-location IoT SIM card and form continuous snapshots by time slices, thereby obtaining the base station codes of all base stations that the fixed-location IoT SIM card has connected to.

[0063] Step D2: Take the Cartesian product of the signaling data set S and the base station dataset U to generate a combined signaling and base station dataset.

[0064] In one implementation, step S208 can be: performing cluster analysis on the second location information of base stations accessed by IoT cards with the same card number and fixed location in the combined dataset to determine the second discrete base station and the second high-density base station. This latitude and longitude information includes longitude and latitude.

[0065] In one possible implementation, the second location information of the base stations accessed by the IoT card with the same card number and fixed location can be determined without using the above steps D1 and D2. For example, the second location information of the base stations accessed by the IoT card with the same card number and fixed location can be determined manually.

[0066] In one possible implementation, when the second location information is latitude and longitude information, the number of decimal places retained for the longitude and latitude can be set as needed. For example, if the accuracy requirement for latitude and longitude is low, the value can be taken to two decimal places, such as 38.38 North latitude; if the accuracy requirement for latitude and longitude is high, the value can be taken to six decimal places, such as 39.908823 North latitude.

[0067] In one possible implementation, the cluster analysis of the second location of the base station can be performed using the K-means algorithm; of course, in other embodiments, other algorithms can also be used for cluster analysis.

[0068] In one implementation, to improve the efficiency of distance calculation in latitude and longitude clustering analysis of base stations, before determining the signaling data set S, a geohash algorithm can be used to discretize the area where the IoT SIM card is located, determining a set of discrete rectangular grids. Each rectangular grid corresponds to a geohash grid code, which is a string. Then, a mapping relationship is established between the latitude and longitude information of the IoT SIM card's area and the grid code. Therefore, in determining the signaling data set S, the grid number can replace the second location information element in the snapshot. That is, the elements of each snapshot include: the IoT SIM card number (MSISDN), signaling time, base station code of the accessed base station, and grid number. This facilitates distance measurement using the grid number instead of latitude and longitude when performing clustering analysis on the second location information, thus improving the efficiency of distance measurement.

[0069] In one embodiment, step S212 above may include the following steps: Step E1: Determine the heat map center x1 of the base station belonging to the same IoT card number MSISDN.

[0070] Step E2: Draw a circle with radius r1 centered at the heatmap center x1, and count the number of base stations n1 within the circle. The number of base stations n1 is the number of samples used in the calculation.

[0071] Step E3: Calculate the average distance between the n1 base stations within the circle and the center x1.

[0072] Step E4: If the average distance ≤ r1, then the point density ρ(x1) of the heatmap center x is the number of base stations n1. Here, the average distance ≤ r1 indicates that these n1 base stations are indeed around the center x1, conforming to the physical coverage area of ​​the same tower. Therefore, the number of these base stations n1 is the point density ρ(x1) of x1.

[0073] Step E5: Determine the initial cluster centers for cluster analysis based on the point density ρ(x1). The point density ρ(x1) indicates that the denser the base stations around center x1, the more suitable it is as the initial cluster center for K-means, thus avoiding selecting outliers or sparse regions. A threshold can be set for the point density ρ(x1), selecting centers x1 exceeding the threshold as the initial cluster centers to be determined for cluster analysis, and then determining the initial cluster centers from these initial cluster centers according to preset rules; alternatively, the center x1 corresponding to the maximum point density ρ(x1) can be selected as the initial cluster center for cluster analysis.

[0074] Step E6: Based on the initial cluster center, determine the first discrete base station and the first high-density base station belonging to the same IoT SIM card number MSISDN. Based on the initial cluster center, the high-density region and discrete region in this clustering algorithm can be identified. The base station located in the high-density region is the second high-density base station, and the base station located in the discrete region is the second discrete base station.

[0075] The technical solution of this application embodiment can continue to correct and repair the second location information of the second discrete base station in the base station dataset after correcting and repairing the first location information of the first discrete base station in the base station dataset. Since the first location information and the second location information are different in detail, the specific error correction and repair methods for the two are different. Thus, by the cooperation of the two, the accuracy of the location information of each base station in the base station database is greatly improved.

[0076] Figure 3 The diagram shows the structure of the error correction device provided in the embodiment of this application. The device 100 may include: a tower number determination module 110, a cluster analysis module 120, and an update module 130.

[0077] The tower number determination module 110 is used to determine the tower number of each base station based on the base station code of each base station in the base station data set. Clustering analysis module 120 is used to perform clustering analysis on the first location information of the base stations belonging to the same tower number, and to determine the first discrete base station and the first high-density base station. The update module 130 is used to update the first location information of the first discrete base station based on the first location information of the first high-density base station.

[0078] In one embodiment, the tower number determination module 110, wherein determining the tower number of each base station based on the base station code of each base station in the base station dataset, includes: When the base station is encoded as a 4G / 5G cell identification code and the 4G / 5G cell identification code is a decimal identification code, the tower number where the base station is located is determined by converting the decimal identification code. When the base station is encoded as a 4G / 5G cell identification code and the 4G / 5G cell identification code is a four-field original identification code, the third segment of the original identification code is taken as the tower number where the base station is located.

[0079] In one embodiment, the base station code includes at least one of the following: location area code, cell identification code; The location distinction includes at least one of the following: 2G / 3G Location Area Code (lac), 4G / 5G Tracking Area Code (TAC). The cell identifier includes at least one of the following: 4G / 5G cell identifier (E-UTRA CellIdentifier, ECI), 5G cell identifier (NR CellIdentifier, NCI), and 2G / 3G cell identifier (CellIdentifier, CI).

[0080] In one implementation, the clustering analysis module 102, which performs clustering analysis on the first location information of base stations belonging to the same tower number to determine the first discrete base station and the first high-density base station, includes: Determine the heat map center x of the base stations belonging to the same tower number; Draw a circle with radius r centered at the center x of the heat map, and count the number n of base stations within the circle; Calculate the average distance between the n base stations within the circle and x; If the average distance is less than or equal to r, then the point density ρ(x) at the center x of the heat map is the number of base stations. Based on the point density ρ(x), determine the initial cluster centers for cluster analysis; Based on the initial cluster center, the first discrete base station and the first high-density base station belonging to the same tower number are determined.

[0081] In one embodiment, the updating module 103, which updates the first location information of the first discrete base station based on the first location information of the first high-density base station, includes at least one of the following: Update the administrative division information of the first discrete base station based on the administrative division information of the first high-density base station; Update the latitude and longitude information of the first discrete base station based on the latitude and longitude information of the first high-density base station.

[0082] In one embodiment, the device 100 is further configured to: Cluster analysis is performed on the second location information of all base stations accessed by IoT cards with the same card number and fixed location to determine the second discrete base station and the second high-density base station; The second location information of the second discrete base station is updated based on the second location information of the second high-density base station.

[0083] In one embodiment, before performing cluster analysis on the second location information of all base stations accessed by IoT cards with the same card number and fixed location, the device is further configured to: Continuous snapshots are generated based on the signaling data of IoT cards with fixed locations, arranged in time slices. Determine the signaling data set Each of the aforementioned snapshots The elements include: the IoT card number, signaling time, base station code of the accessed base station, and second location information; where n is the total number of snapshots and i is the snapshot sequence number; The signaling data set S and the base station dataset U are combined by taking the Cartesian product to generate a combined signaling and base station dataset.

[0084] In one embodiment, the device 100 is further configured to: In the combined dataset, cluster analysis is performed on the second location information of the base stations accessed by the IoT cards with the same card number to determine the second discrete base station and the second high-density base station.

[0085] In one implementation, the second location information is latitude and longitude information; Before determining the signaling data set S, the device 100 is further configured to: The Geohash algorithm is used to discretize the area where the IoT card is located in a fixed location, and determine a set of discrete rectangular grids. Each rectangular grid corresponds to a Geohash grid code. Establish a mapping relationship between the latitude and longitude information of the area where the IoT card is located and the grid code; In the device 100, the step of performing cluster analysis on the second location information of the base stations accessed by the IoT cards with the same card number in the combined dataset includes: in the cluster analysis, using the grid code for distance measurement.

[0086] The device 100 provided in this application embodiment can execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0087] Figure 4The diagram illustrates the hardware structure of an electronic device implementing the embodiments of this application. Referring to the diagram, at the hardware level, the electronic device includes a processor and optionally, an internal bus, a network interface, and a memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0088] The processor, network interface, and memory can be interconnected via an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus.

[0089] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0090] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a device at the logical level that locates the target user. The processor executes the program stored in memory and specifically performs the following: Figure 1-2 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0091] The above is as stated in this application. Figure 1-2The methods disclosed in the illustrated embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0092] The electronic device can also execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0093] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0094] This application also proposes a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1-2 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0095] The computer-readable storage medium mentioned above includes read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0096] Furthermore, embodiments of this application also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, implement the following process: Figure 1-3 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0097] In summary, the above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0098] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0099] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0101] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. An error correction method characterized by, The method comprises: According to the base station code of each base station in the base station data set, determine the tower number of each base station; The first position information of the base station belonging to the same tower number is clustered and analyzed to determine the first discrete base station and the first high-density base station; According to the first position information of the first high-density base station, update the first position information of the first discrete base station.

2. The error correction method of claim 1, wherein, According to the base station code of each base station in the base station data set, determine the tower number of each base station, comprising: In the case of the base station code being a 4G / 5G cell identifier and the 4G / 5G cell identifier being a decimal identifier, the decimal identifier is converted to determine the tower number of the base station; In the case of the base station code being a 4G / 5G cell identifier and the 4G / 5G cell identifier being a four-field original identifier, the third segment of the original identifier is taken as the tower number of the base station.

3. The error correction method of claim 1, wherein, The base station code includes at least one of the following: location area code, cell identifier; Among them, the location area code includes at least one of the following: 2G / 3G location area code ((Location Area Code, lac), 4G / 5G location area code (Tracking Area Code, TAC); The cell identifier includes at least one of the following: 4G / 5G cell identifier (E-UTRA Cell Identifier, ECI), 5G cell identifier (NR Cell Identifier, NCI), 2G / 3G cell identifier (Cell Identifier, CI).

4. The method of claim 1, wherein, The method further comprises: The second position information of all base stations accessed by the same card number and location fixed IOT card is clustered and analyzed to determine the second discrete base station and the second high-density base station; According to the second position information of the second high-density base station, update the second position information of the second discrete base station.

5. The method of claim 4, wherein, Before the second position information of all base stations accessed by the same card number and location fixed IOT card is clustered and analyzed, the method further comprises: All signaling data of the fixed-position internet-of-things card are time-sliced to form continuous snapshots , determine a signaling data set ; each element of the snapshot includes: card number of the internet-of-things card, signaling time, base station code of the accessed base station, and second location information; wherein n is the total number of snapshots, and i is the snapshot serial number Take the Cartesian product of the signaling data set S and the base station data set U to generate a combined data set of signaling and base stations; The second position information of all base stations accessed by the same card number and location fixed IOT card is clustered and analyzed, comprising: In the combined data set, the second position information of the base station accessed by the same card number of the IOT card is clustered and analyzed to determine the second discrete base station and the second high-density base station.

6. The method of claim 5, wherein, The second position information is latitude and longitude information; Before determining the signaling data set S, comprising: Using the geographic hash Geohash algorithm, the area where the location fixed IOT card is located is discretely decomposed to determine a group of discrete rectangular grids, each rectangular grid corresponds to a geographic hash Geohash grid code; The latitude and longitude information of the area where the IOT card is located is mapped to the grid code; The second position information of the base stations accessed by the IOT card with the same card number in the combined data set is subjected to cluster analysis, comprising: In the cluster analysis, the grid code is used for distance measurement.

7. An error correction device, characterized by comprising: The device comprises: a tower number determination module configured to determine the tower number of each base station according to the base station code of each base station in the base station data set; a cluster analysis module configured to perform cluster analysis on the first position information of the base stations belonging to the same tower number, and determine the first discrete base station and the first high-density base station; an update module configured to update the first position information of the first discrete base station according to the first position information of the first high-density base station.

8. An electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the steps of the error correction method of any one of claims 1-6.

9. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising multiple applications, cause the electronic device to perform the steps of the error correction method of any one of claims 1-6.

10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, implement the steps of the error correction method of any one of claims 1-6. ​

Images