Position information verification method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a position information verification method and device, a nonvolatile storage medium and electronic equipment. The method comprises the steps that the first position of a target cell in a target area is determined, the target cell is any cell in the target area, and the first position of the target cell is the cell center position contained in network engineering parameters of the target area; determining a plurality of pieces of measurement report data corresponding to the target cell; iteratively traversing the multiple pieces of measurement report data, randomly selecting two pieces of measurement report data in each iteration process, and determining a correction position according to a test position contained in the selected measurement report data to obtain a correction position set; and determining a second position according to the correction position set, and determining whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position. According to the invention, the technical problems of long checking period and low efficiency caused by manual checking in a traditional position checking method are solved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technologies, and in particular, to a method and apparatus for verifying location information, a non-volatile storage medium, and an electronic device. Background Art

[0002] Network engineering parameters are the basis for wireless network optimization. The quality of network optimization engineering parameter data has a great impact on work such as network optimization analysis, planning and simulation, complaint handling, and special analysis. During the application and maintenance of network engineering parameters, data quality problems are often found, such as errors in cell longitude and latitude, azimuth angle, etc. Due to data limitations and insufficient means, traditional solutions have problems such as long cycle, low efficiency, and high cost.

[0003] In view of the above problems, a method for quickly mining error information of network engineering parameters and correcting them is needed, especially for the longitude and latitude information of cells in engineering parameters. However, the traditional manual verification method mainly uses manual GPS measurement instruments to conduct on-site tests and verify the accuracy of engineering parameters. This traditional manual verification method of engineering parameters is difficult to timely detect problems existing in network engineering parameters in the case of a large number of network cells, and has problems such as long verification cycle, low efficiency, inability to be normalized, and inability to efficiently support daily network optimization.

[0004] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present application provide a method and apparatus for verifying location information, a non-volatile storage medium, and an electronic device, so as to at least solve the technical problem of long verification cycle and low efficiency caused by traditional location verification methods using manual verification.

[0006] According to one aspect of the embodiments of the present application, a method for verifying location information is provided, including: determining a first location of a target cell in a target area, where the target cell is any cell in the target area, and the first location of the target cell is the cell center location included in the network engineering parameters of the target area; determining a plurality of measurement report data corresponding to the target cell; iteratively traversing the plurality of measurement report data, and in each iteration, arbitrarily selecting two measurement report data, and determining a correction location based on the test locations included in the selected measurement report data to obtain a set of correction locations, where the correction location and the location information included in the corresponding measurement report data form a triangle, and the measurement data combination selected in each iteration is different from the combination selected in the previous iteration; determining a second location based on the set of correction locations, and determining whether the network engineering parameters corresponding to the target area need to be updated based on the first location and the second location.

[0007] Optionally, after determining the first position of the target cell in the target area, the method further includes: determining the coverage area corresponding to each cell according to the central position of each cell in the target area, where the central position of the cell is the cell central position included in the network engineering parameters of the cell; determining the neighboring cells of the target cell according to the coverage area corresponding to each cell, and determining the closed area corresponding to the target cell according to the neighboring cells, where the coverage area corresponding to the neighboring cell is adjacent to the coverage area corresponding to the target cell, and the closed area of the target cell is a polygon area determined with the centers of the neighboring cells of the target cell as vertices.

[0008] Optionally, determining the coverage area corresponding to each cell according to the central position of each cell in the target area includes: determining the Thiessen polygon area corresponding to each central position according to the central position of each cell, where the Thiessen polygon area corresponding to the central position of the cell is the coverage area of the cell.

[0009] Optionally, determining whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position includes: determining that the network engineering parameters of the target area do not need to be updated when the first position and the second position are in the same coverage area; determining that the network engineering parameters of the target area need to be updated when the first position and the second position are not in the same coverage area.

[0010] Optionally, after determining the multiple measurement report data corresponding to the target cell, the method further includes: determining the first proportion of the measurement report data whose location information included in the multiple measurement report data corresponding to the target cell falls into the closed area; determining that the network engineering parameters corresponding to the target area do not need to be updated when the first proportion is greater than a preset threshold; and continuing to iterate through the steps of the multiple measurement report data when the first proportion is less than the preset threshold.

[0011] Optionally, determining the calibration position according to the location information included in the selected measurement report data includes: determining the first test position and the first timing advance information included in the selected first measurement report data, and the second test position and the second timing advance information included in the selected second measurement report data; determining the length of the first side according to the first timing advance information, determining the length of the second side according to the second timing advance information, and determining the length of the third side according to the first test position and the second test position; and determining the target triangle according to the first test position, the second test position, the length of the first side, the length of the second side, and the length of the third side when the length of the first side, the length of the second side, and the length of the third side can form a triangle, where the first vertex of the target triangle is at the first test position, the second vertex is at the second test position, and the position of the third vertex is the calibration position.

[0012] Optionally, determining the second position based on the set of calibration positions includes: clustering the set of calibration positions to obtain multiple clusters; determining the cluster with the most calibration positions among the multiple clusters as the target cluster; and determining the central position of the target cluster as the second position.

[0013] Optionally, after determining multiple pieces of measurement report data corresponding to the target cell, the method further includes: determining the distribution of the measurement positions included in each of the multiple pieces of measurement report data; determining discrete measurement positions based on the distribution, and removing the measurement report data that includes the discrete measurement positions.

[0014] Optionally, after determining multiple pieces of measurement report data corresponding to the target cell, the method further includes: when the number of the measurement report data is greater than a first preset quantity threshold, determining the distribution of the measurement positions included in each of the multiple pieces of measurement report data; determining multiple grids with consistent size information based on the distribution; determining a second preset quantity threshold corresponding to each grid according to the first preset quantity threshold; when the number of the measurement report data corresponding to a grid is not greater than the second preset quantity threshold, retaining all the measurement report data corresponding to the grid, where the measurement report data corresponding to the grid is the measurement report data with the measurement position in the grid; and when the number of the measurement report data corresponding to a grid is greater than the second preset quantity threshold, performing random sampling processing on the measurement report data corresponding to the grid.

[0015] According to another aspect of the embodiments of the present application, there is also provided a location information verification device, including: a first processing module, configured to determine a first position of a target cell in a target area, where the target cell is any cell in the target area, and the first position of the target cell is the cell center position included in the network engineering parameters of the target area; a second processing module, configured to determine multiple pieces of measurement report data corresponding to the target cell; a third processing module, configured to iteratively traverse the multiple pieces of measurement report data, and in each iteration, randomly select two pieces of measurement report data, and determine a calibration position based on the test positions included in the selected measurement report data to obtain a set of calibration positions, where the calibration position and the position information included in the corresponding measurement report data form a triangle, and the combination of the measurement data selected each time is different from the combination selected in the previous iteration; and a fourth processing module, configured to determine a second position based on the set of calibration positions, and determine whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position.

[0016] According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where a program is stored in the non-volatile storage medium, and when the program runs, it controls the device where the non-volatile storage medium is located to execute the location information verification method.

[0017] According to another aspect of the embodiments of the present application, an electronic device is further provided, including: a memory and a processor, where the processor is configured to run a program stored in the memory, and when the program runs, it executes a location information verification method.

[0018] According to another aspect of the embodiments of the present application, a computer program product is further provided, including a computer program, and when the computer program is executed by a processor, it implements a location information verification method.

[0019] In the embodiments of the present application, the first position of the target cell in the target area is determined, where the target cell is any cell in the target area, and the first position of the target cell is the cell center position included in the network engineering parameters of the target area; multiple measurement report data corresponding to the target cell are determined; the multiple measurement report data are iteratively traversed, and in each iteration, any two measurement report data are selected, and the calibration position is determined based on the test positions included in the selected measurement report data to obtain a set of calibration positions. The calibration position and the position information included in the corresponding measurement report data form a triangle, and the combination of measurement data selected in each iteration is different from the combination selected in the previous iteration; the second position is determined based on the set of calibration positions, and whether the network engineering parameters corresponding to the target area need to be updated is determined based on the first position and the second position. By combining the 3σ algorithm to eliminate discrete measurement report data, the Thiessen polygon algorithm to calculate the closed area and the falling rate, the random sampling algorithm to reduce the calculation amount, the cosine theorem algorithm to iteratively calculate the calibration longitude and latitude sample set, and the clustering algorithm to extract the most likely calibration longitude and latitude, the purpose of quickly and accurately positioning and correcting the wrong longitude and latitude in the network engineering parameters is achieved, thereby realizing the technical effect of improving the network optimization efficiency and work quality, and further solving the technical problem of long verification period and low efficiency caused by manual verification in the traditional position verification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0021] Figure 1 is a schematic structural diagram of a computer terminal provided according to an embodiment of the present application;

[0022] Figure 2 is a schematic flowchart of a location information verification method provided according to an embodiment of the present application;

[0023] Figure 3 is an example diagram of a Thiessen polygon provided according to an embodiment of the present application;

[0024] Figure 4It is an example diagram of the 3σ principle provided according to an embodiment of the present application;

[0025] Figure 5 It is an example diagram of the effect of eliminating discrete longitude and latitude through the 3σ principle provided according to an embodiment of the present application;

[0026] Figure 6 It is an example diagram of the calculation principle of calibrating longitude and latitude samples provided according to an embodiment of the present application;

[0027] Figure 7 It is a schematic diagram of a location information verification system provided according to an embodiment of the present application;

[0028] Figure 8 It is a schematic diagram of a data acquisition and preprocessing process provided according to an embodiment of the present application;

[0029] Figure 9 It is a schematic diagram of a process for creating a Thiessen polygon and obtaining basic information provided according to an embodiment of the present application;

[0030] Figure 10 It is a schematic diagram of a falling rate calculation process provided according to an embodiment of the present application;

[0031] Figure 11 It is a schematic diagram of a falling rate provided according to an embodiment of the present application;

[0032] Figure 12 It is a schematic diagram of a preliminary judgment process provided according to an embodiment of the present application;

[0033] Figure 13 It is a schematic diagram of a random sampling process provided according to an embodiment of the present application;

[0034] Figure 14 It is a schematic diagram of a random sampling provided according to an embodiment of the present application;

[0035] Figure 15 It is a schematic diagram of a sample calculation process provided according to an embodiment of the present application;

[0036] Figure 16 It is a schematic diagram of a positioning analysis process provided according to an embodiment of the present application;

[0037] Figure 17 It is a schematic diagram of calibrating longitude and latitude sample clustering provided according to an embodiment of the present application;

[0038] Figure 18 It is a schematic diagram of rasterization provided according to an embodiment of the present application;

[0039] Figure 19It is a schematic diagram of the actual location of a cell provided according to an embodiment of the present application;

[0040] Figure 20 It is a schematic diagram of whether the engineering parameter longitude and latitude and the calibrated longitude and latitude are within the same Thiessen polygon area provided according to an embodiment of the present application;

[0041] Figure 21 It is a schematic diagram of a geographical presentation process provided according to an embodiment of the present application;

[0042] Figure 22 It is a schematic diagram of a geographical presentation provided according to an embodiment of the present application;

[0043] Figure 23 It is a schematic structural diagram of a location information verification device provided according to an embodiment of the present application. Detailed implementation manners

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present application.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0046] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0047] MR: Periodic measurement report reported by the terminal to the base station. The measurement report contains information such as the actual wireless environment of the user's location.

[0048] In the related art, location information verification is mainly carried out by two basic means. One is through traditional manual verification methods, and the other is calibration based on various algorithms of big data.

[0049] The traditional manual verification method mainly involves manually carrying GPS measurement instruments to conduct on-site tests and verify the accuracy of engineering parameters. In the case of a large number of existing network cells, it is difficult to promptly detect problems with network engineering parameters through this traditional manual verification method. It cannot promptly discover network engineering parameter problems, and this method has the problems of a long cycle, low efficiency, inability to be normalized, and inability to efficiently support daily network optimization.

[0050] Algorithms based on big data mainly rely on engineering parameters, MR measurement data, performance data, configuration data, etc. After data preprocessing, the corrected longitude and latitude of base stations or cells are obtained through various algorithms. Currently, the existing technology algorithms are roughly divided into the following three categories.

[0051] Algorithm 1: Mean algorithm. This type of algorithm is based on the collected MR measurement data, performs arithmetic averaging on the longitude and latitude in the MR, and uses the average value as the corrected longitude and latitude of the target base station or cell. This type of algorithm is not applicable to all scenarios, such as the situation of "black under the tower" and less MR measurement information, where the calculation results have large deviations, low accuracy, and may even lead to misjudgments.

[0052] Algorithm 2: Neighboring cell algorithm. Based on the neighboring cell frequency points (ltenceafrcn) and (ltencpci) measured by MR, the base station identifier and cell identifier of the neighboring cell are matched, and then the network engineering parameter longitude and latitude are corrected through triangulation based on the engineering parameter longitude and latitude of the neighboring cell. This type of algorithm is only suitable for roughly judging whether there are abnormalities in engineering parameters and cannot accurately calibrate the engineering parameter longitude and latitude. Moreover, this type of algorithm matches the neighboring cell base station identifier and cell identifier through the measured neighboring cell frequency points (ltenceafrcn) and (ltencpci) based on the correct network engineering parameter longitude and latitude, and this type of algorithm cannot achieve the purpose of truly correcting the network engineering parameter longitude and latitude.

[0053] Algorithm 3: Machine learning algorithm. Based on engineering parameter data, MR measurement data, performance data, configuration data, etc., a prediction model is established through algorithms such as random forest algorithm and neural network algorithm to achieve the prediction and positioning of the longitude and latitude of base stations or cells. This algorithm is greatly affected by factors such as the actual wireless environment, device frequency band, device performance, and user distribution. The prediction results have low accuracy and large deviations, and the algorithm is complex and not applicable to the actual application environment.

[0054] Therefore, current various technologies all have certain limitations. To solve the above problems, relevant solutions are provided in the embodiments of this application, which are described in detail below.

[0055] According to an embodiment of the present application, a method embodiment of a location information verification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0056] The method embodiment provided by the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing the location information verification method is shown. As Figure 1 shown, the computer terminal 10 may include one or more (shown as 102a, 102b,..., 102n in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0057] It should be noted that the above one or more processors 102 and / or other data processing circuits are generally referred to as "data processing circuits" in this article. The data processing circuit can be embodied as software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10. As involved in the embodiment of the present application, the data processing circuit is a processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0058] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the location information verification method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned location information verification method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0059] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0060] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10.

[0061] Under the above operating environment, an embodiment of the present application provides a location information verification method, as Figure 2 shown, the method includes the following steps:

[0062] Step S202, determine the first location of the target cell in the target area, where the target cell is any cell in the target area, and the first location of the target cell is the cell center location included in the network engineering parameters of the target area.

[0063] Optionally, before determining the first location of the target cell in the target area, the method further includes extracting network engineering parameters and MR measurement data from a professional network management system and performing preprocessing: First, organize the cell longitude and latitude in the engineering parameters, group them according to the base station identifier (enb), and uniformly set the engineering parameter longitude and latitude with a distance deviation within the group less than the threshold d thr of the group, such as setting them based on one of the cells in the group. The unified setting can avoid the influence of small longitude and latitude deviations caused by manual entry on the calculation results. Then perform unique base station coding code on the network engineering parametersk , that is, the unique identifier composed of the same eNodeB identifier (enb), cell longitude (cell_lon), and cell latitude (cell_lat), where k represents the kth unique identifier. Secondly, for the MR measurement report, extract key fields such as the eNodeB identifier (enb), cell identifier (cellid), serving cell reference signal power (ltescrsrp), timing advance (ltesctadv), reported longitude (longitude), reported latitude (latitude), etc., and associate them with the preprocessed network engineering parameters through the eNodeB identifier (enb) and cell identifier (cellid).

[0064] where d thr refers to the maximum distance deviation between the longitude and latitude of the cell engineering parameters corresponding to the same eNodeB identifier (enb). Cells within this error range are considered to belong to the same base station. The cell longitude (cell_lon) and cell latitude (cell_lat) of the target cell constitute the first position of the target cell.

[0065] In the technical solution provided in step S202, after determining the first position of the target cell in the target area, the method further includes: determining the coverage area corresponding to each cell according to the central position of each cell in the target area, where the central position of the cell is the central position included in the network engineering parameters of the cell; determining the adjacent cells of the target cell according to the coverage area corresponding to each cell, and determining the closed area corresponding to the target cell according to the adjacent cells, where the coverage area corresponding to the adjacent cell is adjacent to the coverage area corresponding to the target cell, and the closed area of the target cell is a polygon area determined with the centers of the adjacent cells of the target cell as vertices.

[0066] As an optional implementation manner, determining the coverage area corresponding to each cell according to the central position of each cell in the target area includes: determining the Thiessen polygon area corresponding to each central position according to the central position of each cell, where the Thiessen polygon area corresponding to the central position of the cell is the coverage area of the cell.

[0067] Optionally, after determining the first position of the target cell in the target area, create a Thiessen polygon. Based on the preprocessed network engineering parameters (i.e., the central positions of each cell), select the longitude and latitude of the cells with the base station type of macro station, and generate a longitude and latitude sequence coordarray after removing duplicates. Create a Thiessen polygon according to the longitude and latitude sequence (i.e., determine the Thiessen polygon area corresponding to each central position according to the central positions of each cell), and calculate the Thiessen polygon area region n of each longitude and latitude coord n in the longitude and latitude sequence coordarray, which is used to judge the unique base station code code in the subsequent stepsk Whether the engineering parameter longitude and latitude of the lower cell and the calibrated longitude and latitude are within the same Thiessen polygon area. Among them, coord n refers to the nth longitude and latitude in the longitude and latitude sequence coordarray after removing duplicates, and region n refers to the Thiessen polygon corresponding to the nth longitude and latitude.

[0068] Optionally, calculate the set of adjacent regions regionset of the Thiessen polygon for each longitude and latitude coord in the longitude and latitude sequence coordarray n to visually present the adjacent relationship between the lower cell of each unique base station code n and its surrounding neighboring cells. Calculate the set of adjacent longitudes and latitudes coordset for each longitude and latitude coord in the longitude and latitude sequence coordarray k The closed area border of the longitude and latitude coord n is formed by the adjacent longitudes and latitudes coordset n (i.e., the center of the neighboring cells of the target cell), which is used to calculate the MR measurement falling rate of each lower cell of each unique base station code n in subsequent steps. n (i.e., the closed area corresponding to the target cell). n Among them, regionset k refers to the set of Thiessen polygons of the adjacent longitudes and latitudes of the nth longitude and latitude, coordset

[0069] refers to the set of adjacent longitudes and latitudes of the nth longitude and latitude, and border n refers to the closed area formed by the longitudes and latitudes of coordset n For example, the illustration of the Thiessen polygon n is shown. Each blue area is the Thiessen polygon area corresponding to each center position determined according to the center position of each cell. The green dots are the center positions, and the red area is the closed area of the target cell. The minimum value of n is 1, and the maximum value depends on the number of macro station longitudes and latitudes in the network engineering parameters, and there is no theoretical upper limit. n Figure 3 As shown, each blue area is the Thiessen polygon area corresponding to each center position determined according to the center position of each cell. The green dots are the center positions, and the red area is the closed area of the target cell. The minimum value of n is 1, and the maximum value depends on the number of macro station longitudes and latitudes in the network engineering parameters, and there is no theoretical upper limit.

[0070] Step S204, determine multiple measurement report data corresponding to the target cell.

[0071] ​As an alternative implementation, after determining multiple pieces of measurement report data corresponding to the target cell, the method further includes: determining a first proportion of the measurement report data whose included location information falls within the closed area among the multiple pieces of measurement report data corresponding to the target cell; in the case where the first proportion is greater than a preset threshold, determining that there is no need to update the network engineering parameters corresponding to the target area; in the case where the first proportion is less than the preset threshold, continuing to iteratively traverse the multiple pieces of measurement report data.

[0072] Optionally, based on the preprocessed MR measurement report, taking the unique base station code k as a unit, calculating whether the longitude and latitude of each MR measurement of the cell under this base station code fall within the closed area border k corresponding to the nth longitude and latitude n , if it falls within the corresponding closed area, increment the falling sampling counter mrin k by 1. After traversing all MR measurement information, the falling rate per k of all MRs under this unique base station code k (i.e., the first proportion) can be obtained, that is, the value of the falling sampling counter mrin k divided by the total number of MR measurements mrall k , which is used to judge the accuracy of the cell engineering parameter longitude and latitude under the unique base station code k . If the falling rate per k is greater than or equal to the threshold per thr (i.e., the preset threshold), it is considered that the engineering parameter longitude and latitude are accurate, and directly skip without performing positioning analysis to improve the algorithm efficiency. If the falling rate per k is less than the threshold per thr , further processing and analysis are performed.

[0073] Among them, the unique base station code k refers to the kth unique identifier composed of the base station identifier (enb), cell longitude (cell_lon), and cell latitude (cell_lat). mrin k refers to the number of MR measurement longitude and latitudes of the cell under the kth unique base station code k that fall within the closed area border n , mrall k refers to the total number of all MR measurements of the cell under the kth unique base station identifier code k . per k refers to the falling rate of the MR measurement longitude and latitude of the cell under the kth unique base station identifier code k falling within the closed area border nThe proportion of the number of MRs. The minimum value of k is 1, and the maximum value is determined by the network engineering parameters, with no theoretical upper limit, per thr Refers to the minimum percentage threshold of the falling-in rate, used to preliminarily judge the unique base station code code k The accuracy of the longitude and latitude of the cell engineering parameters below

[0074] As an optional implementation manner, after determining multiple measurement report data corresponding to the target cell, the method further includes: determining the distribution of the measurement positions included in each of the multiple measurement report data; determining discrete measurement positions according to the distribution, and removing the measurement report data including the discrete measurement positions

[0075] Optionally, for the unique base station code code k The discrete longitude and latitude of the MR measurement of the cell below are removed by the 3σ principle. The 3σ principle is as Figure 4 shown. The 3σ principle is a probability distribution index that conforms to the normal distribution. The probability that the value is distributed in (μ - σ, μ + σ) is 68.27%; the probability that the value is distributed in (μ - 2σ, μ + 2σ) is 95.45%; the probability that the value is distributed in (μ - 3σ, μ + 3σ) is 99.73%. It can be considered that the values outside this are discrete values. The effect of removing discrete longitude and latitude by the 3σ principle is as Figure 5 shown. The red dots in the figure are all MR measurement data under this unique base station code, and the green dots are discrete MR measurement data

[0076] As an optional implementation manner, after determining multiple measurement report data corresponding to the target cell, the method further includes: when the number of measurement report data is greater than the first preset number threshold, determining the distribution of the measurement positions included in each of the multiple measurement report data; determining multiple grids with consistent size information according to the distribution; determining a second preset number threshold corresponding to each grid according to the first preset number threshold; when the number of measurement report data corresponding to the grid is not greater than the second preset number threshold, retaining all the measurement report data corresponding to the grid, where the measurement report data corresponding to the grid is the measurement report data with the measurement position in the grid; when the number of measurement report data corresponding to the grid is greater than the second preset number threshold, performing random sampling processing on the measurement report data corresponding to the grid

[0077] Optionally, for the falling-in rate per k (The first proportion) less than the threshold per thr (Preset threshold) of the unique base station code code k For the MR measurement data of the cell below, first judge the total number of MR measurements mrall k (The number of measurement report data) and the threshold mrcnt thr(The first preset quantity threshold) relationship. If mrall k is less than the threshold mrcnt thr , then directly perform positioning analysis. If mrall k is greater than or equal to the threshold mrcnt thr , then perform positioning analysis after random sampling.

[0078] Random sampling is performed by rasterizing the MR measurement longitude and latitude of the cells under the unique base station code k in accordance with grid size meters (i.e., the size information), and calculating the number of raster grids grid number . Combining with the threshold mrcnt thr , the maximum number of MR measurements that need to be randomly sampled for each grid, samplecnt k (i.e., the second preset quantity threshold) can be obtained. The specific calculation formula is as follows:

[0079] samplecnt k = ceil(mrcnt thr / grid number )

[0080] If the number of MR measurements in the grid is less than samplecnt k , then all MR measurements in this grid will be sampled (retained). If the number of MR measurements in the grid is greater than or equal to samplecnt k , then randomly sample samplecnt k MR measurement data in this grid. Through this random sampling process, the amount of data calculation during positioning analysis can be reduced without affecting the calculation accuracy, improving the calculation effect.

[0081] Among them, grid size refers to the grid size, with the unit of meter. Grid number refers to the number of raster grids divided from the MR measurement longitude and latitude of all cells under the unique base station code k . Samplecnt k refers to the maximum number of randomly sampled MR measurements in each grid. Mrcnt thr refers to the theoretical maximum number of MRs participating in subsequent positioning analysis. Due to the special nature of the ceil function, the actual number of MR measurements participating in the calculation may be greater than mrcnt thr . Ceil is a mathematical function expression, and its function is to round up.

[0082] Step S206: Iteratively traverse multiple measurement report data. In each iteration, randomly select two pieces of measurement report data, and determine the calibration position based on the test positions included in the selected measurement report data to obtain a set of calibration positions. Among them, the calibration position and the position information included in the corresponding measurement report data form a triangle. The combination of measurement data selected in each iteration is different from the combination selected in the previous iteration.

[0083] As an optional implementation, determining the calibration position based on the position information included in the selected measurement report data includes: determining the first test position and the first timing advance information included in the selected first measurement report data, and the second test position and the second timing advance information included in the selected second measurement report data; determining the length of the first side based on the first timing advance information, determining the length of the second side based on the second timing advance information, and determining the length of the third side based on the first test position and the second test position; in the case where the length of the first side, the length of the second side, and the length of the third side can form a triangle, determining the target triangle based on the first test position, the second test position, the length of the first side, the length of the second side, and the length of the third side. Among them, the first vertex of the target triangle is at the first test position, the second vertex is at the second test position, and the position of the third vertex is the calibration position.

[0084] Optionally, determining the calibration position includes using the MR measurement data of the k-th unique base station code k in the lower cell. According to the cosine theorem principle, iteratively traverse and calculate the set of third vertices of the triangles formed by any two MR measurement data, that is, the set of calibration longitude and latitude samples p k of the lower cell corresponding to this unique base station code k (i.e., the set of calibration positions). The specific process of further performing positioning analysis to obtain the set of calibration positions is as follows:

[0085] Assume that the two MRs participating in the calculation in the MR measurement information of the k-th unique base station code k in the lower cell are respectively (i.e., the first measurement report data) and (the second measurement report data). Their corresponding measured longitudes, measured latitudes, serving cell reference signal powers, and timing advances are respectively (i.e., the first test position), (the first timing advance information), and (i.e., the second test position), (the second timing advance information). First, judge whether the distance and is greater than the distance threshold mrdis thr, if it is greater than, then proceed to the next calculation; otherwise, discard this calculation and continue to iteratively calculate the next pair of MR measurement data.

[0086] If and the distance is greater than the distance threshold mrdis thr , determine whether two MR measurement data can form a triangle, that is, and the distance and the equivalent distance whether it satisfies the triangle principle that the sum of the lengths of any two sides is greater than the length of the third side. If it satisfies, then proceed to the next calculation; otherwise, discard this calculation and continue to iteratively calculate the next pair of MR measurement data.

[0087] If a triangle can be formed, determine and the size relationship with and whether the size relationship is consistent. If it is consistent, then proceed to the next calculation; otherwise, discard this calculation and continue to iteratively calculate the next pair of MR measurement data.

[0088] and the size relationship with and are consistent, if is greater than or equal to then use as the starting point and as the ending point; otherwise, use as the starting point and as the ending point to proceed to the next calculation.

[0089] Assume is greater than and is greater than Calculate the included angle from the starting point to the ending point and the correction longitude and latitude sample k of the cell corresponding to the unique base station code code and by the cosine theorem At the same time, calculate the direction angle from the starting point to the ending point Combining and can obtain the starting point to the unique base station code k the calibrated longitude and latitude samples of the corresponding cell and the azimuth angle and Combined with the equivalent distance then the unique base station code can be obtained k the calibrated longitude and latitude samples of the corresponding cell and the longitude and latitude and Finally, the unique base station code is obtained through iterative calculation k all calibrated longitude and latitude sample sets p k , the calculation principle of the calibrated longitude and latitude samples is as Figure 6 shown.

[0090] Among them, and respectively refer to the i-th and j-th MR measurement data under the k-th unique base station code k ; and respectively refer to the measured longitude, measured latitude, serving cell reference signal power, and time advance in the i-th and j-th MR measurement data under the k-th unique base station code k ; mrdis thr refers to the minimum distance between two MRs to be involved in the calculation; refers to the distance between the i-th and j-th MRs under the k-th unique base station code k ; respectively refer to the i-th and j-th MRs under the k-th unique base station code k the equivalent distance in ; and refer to the calibrated longitude and latitude samples of the corresponding cell calculated from the i-th and j-th MR measurement data under the k-th unique base station code k ; refers to the angle between the direction from the i-th MR to the j-th MR and the direction from the i-th MR to the calibrated longitude and latitude samples of its corresponding cell k under the k-th unique base station code and ; refers to the azimuth angle from the i-th MR to the j-th MR under the k-th unique base station code k , with the due north direction of 0 degrees as the reference direction, and refers to the k-th unique base station code k the i-th MR to calibrated longitude and latitude sample and the azimuth angle; and respectively refer to the calibrated longitude and latitude sample and the longitude and latitude; p k refers to the k-th unique base station code k the set of all calibrated longitude and latitude samples under it (i.e., the calibrated position set).

[0091] Step S208, determine the second position according to the calibrated position set, and determine whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position.

[0092] As an optional implementation manner, determining the second position according to the calibrated position set includes: clustering the calibrated position set to obtain multiple clusters; determining the cluster with the most calibrated positions in the multiple clusters as the target cluster; determining the center position of the target cluster as the second position.

[0093] Optionally, based on the unique base station code k the calibrated longitude and latitude sample set p of the cell under it k , perform clustering on the calibrated longitude and latitude sample set p k through the adaptive radius dbscan algorithm, divide it into clusters of different sizes, and extract the center point of the largest cluster (i.e., the target cluster) as the calibrated longitude and latitude (correct_lon k , correct_lat k , correct_lat k )(i.e., the second position) of all cells under the k-th unique base station code.

[0094] Optionally, determining whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position includes: determining that there is no need to update the network engineering parameters of the target area when the first position and the second position are in the same coverage area; determining that it is necessary to update the network engineering parameters of the target area when the first position and the second position are not in the same coverage area.

[0095] Optionally, determining whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position includes determining the distance between the first position and the second position, and determining that the network engineering parameters corresponding to the target area need to be updated when the distance between the first position and the second position is greater than the preset distance threshold.

[0096] Optionally, calculate the unique base station code kThe longitude and latitude of the cell engineering parameters of the lower cell (cell_lon k , cell_lat k )(i.e., the first position) and the distance deviation dis_offset k , correct_lat k ) between the corrected longitude and latitude (correct_lon k , and determine whether the engineering parameter longitude and latitude (cell_lon k , cell_lat k ) and the corrected longitude and latitude (correct_lon k , correct_lat k ) are within the same Thiessen polygon area (i.e., within the same coverage area). If they are within the same Thiessen polygon area, set the flag same_region k to 1, otherwise set it to 0, which is convenient for visually and preliminarily judging the deviation degree between the engineering parameter longitude and latitude and the corrected longitude and latitude. When same_region k is 0, it indicates a serious deviation, and network optimization personnel need to pay key attention and update the network engineering parameters of the target area.

[0097] Among them, cell_lon k , cell_lat k refers to the engineering parameter longitude and latitude of the cell under the kth unique base station code code k ; correct_lon k , correct_lat k refers to the corrected longitude and latitude of the cell under the kth unique base station code code k ; dis_offset k refers to the distance deviation between the engineering parameter longitude and latitude and the corrected longitude and latitude of the cell under the kth unique base station code code k ; same_region k refers to the flag indicating whether the engineering parameter longitude and latitude and the corrected longitude and latitude of the cell under the kth unique base station code code k are within the same Thiessen polygon area.

[0098] Optionally, for the convenience of optimization personnel to visually analyze the correction results, the relevant information corresponding to the unique base station code code k is presented geographically, including the grid coverage of the serving cell reference signal power of the cell under the unique base station code code k , the position of the engineering parameter longitude and latitude and the corrected longitude and latitude of the cell under the unique base station code code k , the unique base station code code kLower cell sector and the nearest top m Sector layer of the lower cell of a base station and the unique base station code "code" k Closed area "border" of the nth longitude and latitude in the longitude and latitude sequence "coordarray" corresponding to the engineering parameters of the lower cell n And the unique base station code "code" k Voronoi polygon "region" of the nth longitude and latitude in the longitude and latitude sequence "coordarray" corresponding to the engineering parameters of the lower cell n And the set of adjacent Voronoi polygons "regionset" n .

[0099] Among them, top m Refers to the number of the nearest m base stations to the cell corresponding to the unique base station code "code". k

[0100] An embodiment of the present application provides a location information verification system, as Figure 7 shown, the system includes the following modules:

[0101] Data acquisition and preprocessing module 701, which extracts network engineering parameters and MR measurement reports from a professional network management respectively. First, preprocess the cell longitude and latitude in the engineering parameters, group them according to the base station identifier (enb), and uniformly set the engineering parameter longitude and latitude with a distance deviation less than the threshold d thr in the group. For example, set them based on one of the cells in the group. Uniform setting can avoid the influence of small longitude and latitude deviations caused by manual entry on the calculation results. Then, perform unique base station coding "code" on the network engineering parameters k , that is, the kth unique identifier composed of the same base station identifier (enb), cell longitude (cell_lon), and cell latitude (cell_lat). Secondly, for the MR measurement report, extract key fields such as base station identifier (enb), cell identifier (cellid), serving cell reference signal power (ltescrsrp), timing advance (ltesctadv), measurement reported longitude (longitude), measurement reported latitude (latitude), etc. After associating with the preprocessed network engineering parameters through the base station identifier (enb) and cell identifier (cellid), remove the discrete longitude and latitude of the MR measurement data of the lower cell under the unique base station coding "code" k by the 3σ principle.

[0102] ​Create a Thiessen polygon module 702. Based on the preprocessed network engineering parameters in module 701, select the longitude and latitude of the cells with the base station type being macro station, and generate a longitude and latitude sequence coordarray after removing duplicates. Create Thiessen polygons according to the longitude and latitude sequence, and calculate the Thiessen polygon area region of each longitude and latitude coord in the longitude and latitude sequence coordarray n for judging whether the engineering parameter longitude and latitude of the cell under the unique base station code n code and the calibrated longitude and latitude are within the same Thiessen polygon area in module 707. Calculate the set of adjacent regions regionset of the Thiessen polygon of each longitude and latitude coord in the longitude and latitude sequence coordarray k for visually presenting the adjacent relationship between the cell under each unique base station code n code and its surrounding neighboring cells. Calculate the set of adjacent longitudes and latitudes coordset of each longitude and latitude coord in the longitude and latitude sequence coordarray n which consists of the adjacent longitudes and latitudes coordset k to form the closed area border of the longitude and latitude coord n for calculating the falling-in rate of all MR measurements of the cell under each unique base station code n in module 703. n n n k

[0103] Analysis of falling-in rate module 703. Based on the preprocessed MR measurement data in module 701, calculate whether each MR measurement longitude and latitude of the cell under the unique base station code k code falls into the closed area border of the nth longitude and latitude corresponding to this base station code k If it falls into the corresponding closed area, the falling-in sampling counter mrin n is incremented by 1. After traversing each MR measurement information, the falling-in rate per of all MRs under the unique base station code k code can be obtained k , that is, the ratio of the value of the falling-in sampling counter mrin k to the total number of MR measurements mrall k for preliminarily judging the accuracy of the engineering parameter longitude and latitude of the cell under the unique base station code k code in module 704. k

[0104] Preliminary judgment module 704. Based on the falling-in rate per of the MR measurements of the cell under the base station code k code obtained in module 703k Perform an analysis. If the falling rate per k is greater than or equal to the threshold per thr , it is considered that the engineering parameter longitude and latitude are accurate, and it is directly skipped without further location analysis to improve the algorithm efficiency. If the falling rate per k is less than the threshold per thr , it is further processed and analyzed through module 705 and module 706.

[0105] The random sampling module 705, based on the falling rate per in the z module 704 k less than the threshold per thr for all cell MR measurement data of the only base station code code k , judge the relationship between the MR measurement quantity and the threshold mrcnt thr . If it is less than the threshold mrcnt thr , it is directly analyzed according to module 706. If it is greater than or equal to the threshold mrcnt thr , it is analyzed through module 706 after random sampling. Random sampling is to rasterize the MR measurement longitude and latitude of the cells under the only base station code code k by grid size meters, and calculate the number of raster grids grid number . Combining with the threshold mrcnt thr , the maximum number of MR measurements that need to be randomly sampled for each grid on average, samplecnt k , can be obtained. Its calculation formula is as follows:

[0106] samplecnt k =ceil(mrcnt thr / grid number )

[0107] The sample calculation module 706, for the MR measurement data of the cells under the k-th only base station code code obtained after processing by module 704 and module 705 k , according to the cosine theorem principle, iteratively traverse and calculate the set of the third vertices of the triangles formed by any two MR measurement data, that is, the set of corrected longitude and latitude samples p k corresponding to the cells of the only base station code code k , and then perform location analysis through module 707.

[0108] The location analysis module 707, based on all the corrected longitude and latitude sample sets p obtained in module 706 under the only base station code code k k, cluster the corrected longitude and latitude sample set p through the adaptive radius DBSCAN algorithm k to divide it into clusters of different sizes, and extract the center point of the largest cluster as the k-th unique base station code code k for all the corrected longitude and latitude (correct_lon k , correct_lat k ) of the cells. At the same time, calculate the distance deviation dis_offset k between the cell engineering parameter longitude and latitude (cell_lon k , cell_lat k ) corresponding to the unique base station code code k and the corrected longitude and latitude (correct_lon k , correct_lat k ), and determine whether the cell engineering parameter longitude and latitude (cell_lon k , cell_lat k ) and the corrected longitude and latitude (correct_lon k , correct_lat k ) are in the same Thiessen polygon area. If they are in the same Thiessen polygon area, set the flag same_region k to 1, otherwise set it to 0, which is convenient for visually and preliminarily judging the deviation degree between the cell engineering parameter longitude and latitude and the corrected longitude and latitude.

[0109] Geographical presentation module 708, for the convenience of the optimization personnel to visually analyze the correction results, geographically present the relevant information corresponding to the unique base station code code k , including the grid coverage of the serving cell reference signal power of the cells corresponding to the unique base station code code k , the positions of the cell engineering parameter longitude and latitude and the corrected longitude and latitude corresponding to the unique base station code code k , the sector layer of the cells corresponding to the unique base station code code k and the sectors of the cells of the top m nearest base stations, the closed area border k of the n-th longitude and latitude in the longitude and latitude sequence coordarray corresponding to the cell engineering parameter longitude and latitude corresponding to the unique base station code code n and the Thiessen polygon region k of the n-th longitude and latitude in the longitude and latitude sequence coordarray corresponding to the cell engineering parameter longitude and latitude corresponding to the unique base station code code n and the set of adjacent Thiessen polygons regionset n .

[0110] Among them, the data acquisition and preprocessing module 701 is used to obtain network engineering parameters and MR measurement data from the engineering parameter network management system and the MR server, parse and clean them, and then store them in the database. Figure 8 shows a schematic diagram of the data acquisition and preprocessing process, as Figure 8 shown, the data acquisition and preprocessing process includes:

[0111] Step 70101: Obtain the in-network engineering parameter data from the engineering parameter network management system. The engineering parameter data mainly includes key fields such as base station identifier (enb), cell identifier (cellid), cell longitude (cell_lon), cell latitude (cell_lat), downlink frequency point (dlearfcn), antenna azimuth (azimuth), and cell type (cover_type), as shown below.

[0112] Field Type Description Example enb INT Base station identifier 96831 cellid INT Cell identifier 55 dlearfcn INT Downlink frequency point 1850 cell_lon FLOAT Cell longitude 115.686182 cell_lat FLOAT Cell latitude 37.768103 azimuth FLOAT Antenna azimuth angle 0 cover_type NVARCHAR2(5) Base station type (macro station / in-building distribution) Macro station

[0113] Group the data according to the base station identifier (enb), and uniformly set the engineering parameter longitude and latitude within the group where the distance deviation is less than the threshold d thr . For example, set it based on one of the cells within the group. Uniform setting can avoid the influence of minor longitude and latitude deviations caused by manual entry on the calculation results. The following is an example: For two cells under the base station identifier (96831), the latitudes are slightly different due to manual entry, which are 37.768103 and 37.768133 respectively, and the distance error between the two longitudes and latitudes is 3 meters, less than the threshold d thr , so the longitudes and latitudes of the two cells can be uniformly set to 115.686182 and 37.768103.

[0114] enb cellid dlearfcn cell_lon cell_lat azimuth cover_type 96831 55 1850 115.686182 37.768103 0 Macro station 96831 57 1850 115.686182 37.768133 130 Macro station

[0115] Then perform a unique base station coding code on the network engineering parameters k , and set a unified identifier code for a group of cells with the same base station identifier (enb), cell longitude (cell_lon), and cell latitude (cell_lat) k , as shown in the following example:

[0116]

[0117] Step 70102: Collect MR measurement data from the MR server and extract key fields, such as information like base station identifier (enb), cell identifier (cellid), serving cell reference signal power (ltescrsrp), timing advance (ltesctadv), measurement reporting longitude (longitude), and measurement reporting latitude (latitude).

[0118] Field Type Description Example enb INT Base station identifier 96831 cellid INT Cell identifier 55 ltescrsrp INT Serving cell reference signal power -83 ltesctadv INT Timing advance 4 longitude FLOAT MR measurement reported longitude 115.689532 latitude FLOAT MR measurement reported latitude 37.758421

[0119] Combined with the preprocessed network engineering parameter data in step 70101, associate the data with the MR measurement data through the eNodeB identifier (enb) and cell identifier (cellid). An example of the table structure after association is as follows:

[0120]

[0121]

[0122] Eliminate the discrete longitude and latitude through the 3σ principle, that is, for the unique base station code code k under the following cell, eliminate the discrete longitude and latitude of the MR longitude and latitude respectively through the 3σ principle. The 3σ principle is a probability distribution index that conforms to the normal distribution. The probability that the numerical value is distributed in (μ - σ, μ + σ) is 68.27%; the probability that the numerical value is distributed in (μ - 2σ, μ + 2σ) is 95.45%; the probability that the numerical value is distributed in (μ - 3σ, μ + 3σ) is 99.73%. It can be considered that the values outside this range are discrete values, as Figure 4 shown.

[0123] Taking the MR measurement data of two cells under the unique base station code 96831_115.686182_37.768103 as an example, the effect of eliminating discrete longitude and latitude through the 3σ principle is as Figure 5 shown. In the figure, the red dots are all MR measurement data under this unique base station code, and the green dots are discrete MR measurement data.

[0124] Optionally, create a Thiessen polygon module 702, which is used to create a Thiessen polygon based on the longitude and latitude of the preprocessed network engineering parameters in module 701 and calculate relevant information, as Figure 9 shown. The specific steps for creating a Thiessen polygon and obtaining basic information are as follows:

[0125] Step 70201: Based on the preprocessed network engineering parameters in module 701, select the longitude and latitude of the cells with the base station type of macro station, and generate a longitude and latitude sequence coordarray after removing duplicates. Create a Thiessen polygon based on the longitude and latitude sequence, where the longitude and latitude sequence is as follows:

[0126] coordarray = {coord1, coord2, …, coord n-1 , coord n}

[0127] coord n = (cell_lon n , cell_lat n )

[0128] Step 70202: Calculate the Thiessen polygon region region of each longitude and latitude in the longitude and latitude sequence coordarray based on the Thiessen polygon vor created in Step 70201 n , as Figure 12 shown, the black dot is the nth longitude and latitude coord n , region n is the blue closed polygon containing coord n , and its calculation function is as follows:

[0129] region n = getRegionShapeByLonLat(vor, coord n )

[0130] where vor is the Thiessen polygon generated from the longitude and latitude sequence coordarray, and coord n is the longitude and latitude cell_lon k and cell_lat k of the cell under the unique base station code code k .

[0131] Step 70203: Calculate the set of adjacent Thiessen polygon regions regionset n of region n , as Figure 12 shown, the multiple Thiessen polygons adjacent to region n , and its calculation function is as follows:

[0132] regionset n = findNeighbourReions(vor, coord n )

[0133] Step 70204: Calculate the closed area border k of the nth longitude and latitude coord in the longitude and latitude sequence coordarray corresponding to the longitude and latitude of the cell under the unique base station code code n , border n is composed of multiple longitudes and latitudes coordset n adjacent to coord n , as n shown, the red closed polygon in Figure 3 is the closed area border n , and the multiple longitudes and latitudes coordset n adjacent to the green dot, and its calculation function is as follows:

[0134] border n=getNeighBourRelationForNeedSearchPoint(vor,coord n )

[0135] Optionally, as Figure 10 shown, the falling-in rate module 703, based on the preprocessed MR measurement report in module 701, calculates, with the unique base station code k as the unit, whether each MR measurement longitude and latitude of the cell under this base station code falls within the closed area k border corresponding to the nth longitude and latitude. n If it falls within the corresponding closed area, the falling-in sampling counter mrin k is incremented by 1. After traversing each MR measurement information, the falling-in rate per k of all MRs under this unique base station code k can be obtained, that is, the ratio of the value of the falling-in sampling counter mrin k to the total number of MR measurements mrall k , which is used for the preliminary judgment of the accuracy of the cell engineering parameter longitude and latitude under the unique base station code k . The specific steps are as follows:

[0136] Step 70301: Extract the MR longitude and latitude of the cell with the unique base station code k as shown in the bolded two columns of data in the following table.

[0137]

[0138]

[0139] Step 70302: Based on the longitude and latitude of the cell with the unique base station code k , find its corresponding closed area n border for the nth longitude and latitude coord n , as Figure 3 shown, the closed area corresponding to the nth longitude and latitude coord k of the cell with the unique base station code n is the area range formed by the red closed polygon in the figure.

[0140] Step 70303: Based on the MR measurement longitude and latitude k of the cell under the unique base station code obtained in steps 70301 and 70302 and and the unique base station code k ​Closed area border corresponding to the latitude and longitude of the lower cell n , through loop iteration analysis, determine whether each MR measurement latitude and longitude is within the closed area border n The calculation formula is as follows:

[0141]

[0142] InOrNot is the result obtained from the above formula, and its value range is true or false. If InOrNot is true, it falls into the sampling counter mrin k is incremented by 1, otherwise it remains unchanged. The initial value of mrin k is 0. After traversing the MR measurement latitudes and longitudes of the lower cell with the unique base station code code k , finally obtain the total number of MR measurements falling into the closed area border n . At the same time, during the traversal process, for each traversed MR measurement latitude and longitude, the total number of MR measurements mrall k is incremented by 1. The initial value of mrall k is 0. Thus, the total MR measurement data of the lower cell with the unique base station code code k is obtained. Finally, the falling rate per k of all MRs under the unique base station code code k can be obtained, that is, the ratio of the value of the sampling counter mrin k to the total number of MR measurements mrall k . Its expression is as follows:

[0143] per k = mrin k / mrall k

[0144] As Figure 11 shown, it can be seen that the number of MR measurement latitudes and longitudes of the lower cell under this base station code falling into the red area border n is 0, so the falling rate is 0.

[0145] Optionally, as Figure 12 shown, the preliminary judgment module 704 analyzes based on the falling rate per k of the MR measurements of the lower cell obtained in module 703 with the base station code code k . If the falling rate per k is greater than or equal to the threshold per thr , it is considered that the engineering parameter latitude and longitude are accurate, and it is directly skipped without further location analysis to improve the algorithm efficiency. If the falling rate per k is less than the threshold per thr, then further processing and analysis is performed through modules 705 and 706, and the specific steps are as follows:

[0146] Step 70401, based on the base station code obtained in module 703 k The MR measurement of the lower cell falls into the rate per k With the threshold per thr For comparison, if the fall rate per k Greater than or equal to the threshold per thr , the latitude and longitude of the engineering reference are considered accurate, and no positioning analysis is performed, and the next base station code is iterated in a loop k+1 The MR measurement data of the lower cell were analyzed.

[0147] Step 70402, if the fall rate per k Less than the threshold per thr , then determine the unique base station code k Total number of MR measurements k With the threshold mrcnt thr Compare, if mrall k Less than mrcnt thr , then directly proceed to module 706 for further processing. If mrall k Greater than or equal to mrcnt thr , then after random sampling by module 705 , further processed by module 706 .

[0148] Alternatively, if Figure 13 As shown, random sampling module 705, for the unique base station code code in module 704 k Lower cell MR measurement fall rate per k Less than the threshold per thr , and the total number of MR measurements mrall k Greater than or equal to the threshold mrcnt thr Random sampling is performed by passing the unique base station code k The MR measurement latitude and longitude of the next cell, according to the grid size Meters are rasterized and the number of grids divided is calculated. number , combined with the threshold mrcnt thr , we can get the maximum number of MR measurements samplecnt that needs to be randomly sampled per grid on average k Random sampling can reduce the amount of data calculation in module 706 and improve the calculation effect without affecting the calculation accuracy. The specific steps are as follows:

[0149] Step 70501, encode the unique base station code "code" k Gridify the longitude and latitude of the MR measurements of the lower cell, with the grid size being "grid" size meters, as shown by the blue square in Figure 14 Figure

[0150] Step 70502, based on the longitude and latitude of the grid centers generated in Step 70501, count the number of longitude and latitude points to obtain the number of grids "grid" number .

[0151] Step 70503, based on the number of grids "grid" obtained in Step 70502 number , and the threshold "mrcnt" in Module 704 thr , calculate the maximum number of MR measurements for random sampling in each grid, "samplecnt" k , and its calculation formula is as follows:

[0152] samplecnt k = ceil(mrcnt thr / grid number )

[0153] Step 70504, based on the maximum number of MR measurements for random sampling in each grid, "samplecnt", obtained in Step 70503 k , perform random sampling on the MR measurement data of the lower cell with the unique base station code "code" k . If the number of MR measurements in the grid is less than "samplecnt" k , then all MRs in the grid are sampled. If the number of MR measurements in the grid is greater than or equal to "samplecnt" k , then "samplecnt" k MR measurement data will be randomly sampled from the grid. The result is shown by the red dots in Figure 14 Figure

[0154] sampleMr = sample(mr k , samplecnt k )

[0155] where "mr" k is the MR measurement data of the lower cell with the unique base station code "code" k .

[0156] Optionally, as shown in Figure 15 Figure k , the sample calculation module 706 processes the k-th unique base station code "code" obtained after the processing of module 704 and module 705MR measurement data of the lower cell. According to the cosine theorem principle, iteratively traverse to calculate the set of the third vertices of the triangles formed by any two MR measurement data, that is, the unique base station code k The set p of calibrated longitude and latitude samples of the corresponding cell k , and then perform positioning analysis through module 707. The specific steps are as follows:

[0157] Step 70601, based on the kth unique base station code k MR measurement data of the lower cell, iteratively calculate the distance between any two MR measurement data As Figure 6 shown. Compare it with the threshold mrdis thr . If is greater than mrdis thr , then analyze through step 70602. Otherwise, loop and iterate the next pair of MRs for analysis. The main purpose is to avoid the influence of fluctuations of MR measurement data with close distances on the calculation effect. The distance calculation formula between

[0158]

[0159] is as follows: where k are respectively the measured longitude and measured latitude in the ith MR measurement data under the unique base station code are respectively the measured longitude and measured latitude in the jth MR measurement data under the unique base station code k .

[0160] Step 70602, if is greater than mrdis thr , then judge whether the distance between the two MR measurements and the equivalent distances and from the two MR measurement data to the cell satisfy the triangle principle, that is, the relationship that the sum of the side lengths of any two sides is greater than the length of the third side. If it is satisfied, further analyze through step 70603. Otherwise, loop and iterate the next pair of MRs for analysis. and As Figure 6 shown, its calculation formula is as follows:

[0161]

[0162] where β is the step coefficient of the timing advance of the serving cell. is the unique base station code k The timing advance in the i-th MR measurement is the unique base station code k The timing advance in the j-th MR measurement.

[0163] Step 70603, if the distance between two MR measurements and the equivalent distances of the two MRs to the cell and satisfy the triangle principle, then compare and the magnitude relationship with and to see if they are consistent. If they are, analyze through step 70604; otherwise, loop and iterate to analyze the next pair of MRs. That is, if is greater than or equal to and is greater than or equal to or is less than and is less than then analyze through step 70604; otherwise, loop and iterate to analyze the next pair of MRs.

[0164] Step 70604, if and the magnitude relationship is consistent with and then calculate the angle between the direction from the MR with a larger serving cell reference signal power to the other MR and the direction to the cell calibration longitude and latitude sample of the unique base station code k as shown in For example, Figure 6 assuming is greater than and is greater than the calculation formula is:

[0165]

[0166] where acos is the arccosine function and PI is the value of pi.

[0167] Step 70605, based on the assumed conditions in step 70604, calculate the direction from the MR with a larger serving cell reference signal power to the other MR, that is, from to the direction angle with the due north direction as the reference 0-degree direction angle. For example, Figure 6As shown in, its calculation formula is:

[0168]

[0169] Wherein is the unique base station code code k The longitude and latitude of the i-th MR measurement of the lower cell, is the unique base station code code k The longitude and latitude of the j-th MR measurement of the lower cell.

[0170] Step 70606, based on the assumed conditions in Step 70604, calculate the direction angle of the MR with a larger reference signal power of the serving cell to the unique base station code code k The direction angle of the corrected longitude and latitude samples of the lower cell and As Figure 6 As shown in, its calculation formula is:

[0171]

[0172] Step 70607, based on the assumed conditions in Step 70604, calculate the unique base station code code k The corrected longitude and latitude samples of the lower cell and The longitude and latitude of and As Figure 6 As shown in, its calculation formula is:

[0173]

[0174] Wherein, is is is the unique base station code code k The longitude and latitude of the i-th MR measurement of the lower cell, is the unique base station code code obtained in Step 70606 k The direction angle from the longitude and latitude of the i-th MR measurement of the lower cell to the corrected longitude and latitude samples.

[0175] Through the above method, after the iterative calculation of the MR measurement data of the k-th unique base station code code k obtained after being processed by Module 704 and Module 705, the corrected longitude and latitude sample set p of the unique base station code code k of the lower cell is obtained k .

[0176]

[0177] Optionally, asFigure 16 As shown in Figure 16 , the positioning analysis module 707, based on the unique base station code code obtained in module 706 k and the set p of calibrated longitude and latitude samples of the lower cell k , clusters the intersection set through the adaptive radius DBSCAN algorithm, divides it into clusters of different sizes, and extracts the center point of the largest cluster as the unique base station code code k The calibrated longitude and latitude (correct_lon k , correct_lat k ) of the lower cell. At the same time, calculate the distance deviation dis_offset k between the cell engineering parameter longitude and latitude (cell_lon k , cell_lat k ) of the lower cell corresponding to the unique base station code code and the calibrated longitude and latitude (correct_lon k , correct_lat k ), and determine whether the engineering parameter longitude and latitude (cell_lon k , cell_lat k ) and the calibrated longitude and latitude (correct_lon k , correct_lat k ) are within the same Thiessen polygon region. If they are within the same Thiessen polygon region, set the flag same_region k to 1, otherwise set it to 0, which is convenient for visually and preliminarily judging the deviation degree between the engineering parameter longitude and latitude and the calibrated longitude and latitude. The specific steps are as follows: k Step 70701, based on the set p of calibrated longitude and latitude samples obtained in module 706

[0178] , clusters the set p of calibrated longitude and latitude samples through the adaptive radius DBSCAN algorithm k , divides it into clusters of different sizes, that is, divides the calibrated longitude and latitude samples into different groups according to the adaptive radius, and sets the group number, with the starting value of the number being 0, as shown by the points of different colors in k Figure 17 . Each color represents a cluster, and its calculation function is as follows: Figure 17 As shown by the points of different colors, each color represents a cluster, and its calculation function is as follows:

[0179] Clusters = getCluster(p k )

[0180] The result example is as follows, and ClusterId represents the cluster identifier assigned to each calibrated longitude and latitude sample.

[0181]

[0182] Step 70702: Based on the clustering result in Step 70701, extract the longitude and latitude of the center of the largest cluster as the unique base station code "code". k The corrected longitude and latitude (correct_lon k , correct_lat k ) of the lower cell are as shown in the following statistical table of the number of corrected longitude and latitude samples within the cluster. The number of corrected longitude and latitude samples in the cluster numbered 0 is the largest. Therefore, this unique base station code "code" k The corrected longitude correct_lon of the lower cell k and latitude correct_lat k are 115.69214 and 37.758533 respectively, and their actual positions are as shown by the red icon in Figure 18 .

[0183] Cluster center longitude Cluster center latitude Cluster number Number of longitude and latitude of calibration samples within the cluster 115.69214 37.758533 0 37927 115.698312 37.76159 1 3502 115.696906 37.766373 2 218 115.694648 37.766014 3 851 115.685981 37.758894 4 1412 115.701036 37.762654 5 62 115.702949 37.760189 6 88 115.70265 37.764785 7 7 115.692812 37.752475 8 171 115.698823 37.752724 9 254

[0184] Step 70703: Based on the unique base station code "code" obtained in Step 70702 k The corrected longitude and latitude (correct_lon k , correct_lat k ) of the lower cell, calculate the distance deviation "dis_offset" between it and the engineering parameter longitude and latitude of the unique base station code k of the lower cell. The calculation formula is as follows: k

[0185] dis_offset k = dis(cell_lon k , cell_lat k , correct_lon k , correct_lat k )

[0186] Through the above formula, the distance deviation between the engineering parameter longitude and latitude and the corrected longitude and latitude of the lower cell with the unique base station code 96831_115.686182_37.768103 listed in this example is 1176 meters. After on-site verification, the deviation between the position of the corrected longitude and latitude and the actual longitude and latitude position of the lower cell with the unique base station code 96831_115.686182_37.768103 is only 1 meter. As shown in Figure 19 , the base station position shown is the actual position of the lower cell with the unique base station code 96831_115.686182_37.768103.

[0187] Step 70704: Based on the unique base station code "code" obtained in Step 70702 k ​For the corrected longitude and latitude of the lower cell and the engineering parameter longitude and latitude, calculate the Thiessen polygon regions where the two longitude and latitude values are located respectively, and determine whether they are within the same Thiessen polygon region. If they are within the same Thiessen polygon region, set the identifier same_region k to 1, otherwise set it to 0, the identifier same_region k is used to indicate whether the distance deviation affects the change of the network structure. If same_region k is 1, it means that although there are errors in the engineering parameter longitude and latitude of the unique base station code k for the lower cell, it has little impact on the network structure. If it is 0, it means that there are serious problems with the engineering parameter longitude and latitude of the unique base station code k for the lower cell, and network optimization personnel need to pay key attention. As Figure 20 shown, the engineering parameter longitude and latitude and the corrected longitude and latitude are clearly in different Thiessen polygon regions, and its calculation formula is the same as the formula shown in step 70202.

[0188] region n = getRegionShapeByLonLat(vor, (cell_lon k , cell_lat k ))

[0189] region ′ n = getRegionShapeByLonLat(vor, (correct_lon k , correct_lat k ))

[0190] Among them, cell_lon k , cell_lat k are the engineering parameter longitude and latitude of the unique base station code k for the lower cell, region n is the nth Thiessen polygon region corresponding to the engineering parameter longitude and latitude of the unique base station code k for the lower cell, correct_lon k , correct_lat k are the corrected longitude and latitude of the unique base station code k for the lower cell, region ′ n is another Thiessen polygon region corresponding to the corrected longitude and latitude of the unique base station code k for the lower cell. If region n and region ′n If they are the same, set the identifier same_region k to 1, otherwise set it to 0. The example results are as follows:

[0191]

[0192] Optionally, as Figure 21 shown, the geospatial rendering module 708 geospatially renders the relevant information corresponding to the unique base station code code k for the convenience of the optimization personnel to visually analyze the calibration results, including the unique base station code code k the grid coverage of the serving cell reference signal power of the cell below, the unique base station code code k the engineering parameters longitude and latitude of the cell below and the calibrated longitude and latitude positions, the unique base station code code k the sectors of the cell below and the sector layers of the cells of the top m nearest base stations, the unique base station code code k the closed area border of the nth longitude and latitude in the longitude and latitude sequence coordarray corresponding to the engineering parameters longitude and latitude of the cell below n and the unique base station code code k the Thiessen polygon region of the nth longitude and latitude in the longitude and latitude sequence coordarray corresponding to the engineering parameters longitude and latitude of the cell below n and the set of adjacent Thiessen polygons regionset n .

[0193] Step 70801, create the underlying map layer based on the open-source map API interface.

[0194] Step 70802, add the grid coverage of the serving cell reference signal power of the cell below the unique base station code code k to the map layer. As Figure 22 shown, the grids of different colors represent different average values of the serving cell reference signal power measured by the MR of the cell below the unique base station code code k in the grid.

[0195] Step 70803, add the engineering parameters longitude and latitude and the calibrated longitude and latitude of the cell below the unique base station code code k to the map layer. As Figure 22 shown by the red and blue icons in, the red icon is the calibrated longitude and latitude position, and the blue icon is the engineering parameters longitude and latitude position.

[0196] Step 70804, the cell below the unique base station code code k and the top mAdd the cell sectors under a base station to the map layer, as Figure 22 shown. The color of the sector is related to the downlink frequency point (dlearfcn) in the cell engineering parameters, and different frequency points (dlearfcn) are distinguished by different colors.

[0197] Step 70805: Add the closed area border corresponding to the nth longitude and latitude in the longitude and latitude sequence coordarray of the cell engineering parameters under the unique base station code code k to the map layer, as n shown by the red polygon in Figure 22 .

[0198] Step 70806: Add the Thiessen polygon region corresponding to the nth longitude and latitude in the longitude and latitude sequence coordarray of the cell engineering parameters under the unique base station code code k and the set of adjacent Thiessen polygon regions regionset n to the map layer, as n shown. The blue polygon containing the engineering parameter longitude and latitude is region Figure 22 , and the other adjacent Thiessen polygons are regionset n . n .

[0199] Based on the method embodiment of this application, calibration analysis was performed on 8466 unique base station codes in the existing network, and the falling rate per k is less than the threshold per thr (90%), and the distance deviation dis_offset k is greater than 300 meters, and same_region k is 0. The engineering parameter longitude and latitude of the cells under 105 unique base station codes were verified on-site, and the calibration accuracy rate is as high as over 98.6%, and the calibration effect is good.

[0200] Through the above steps, an accurate parameter calibration method based on the cosine theorem and machine learning can be realized, which can solve the problem of incorrect longitude and latitude in network parameters, improve the application value of network parameters in practical work such as network optimization, simulation, complaint handling, and various special analyses, as well as the quality and usability of the output results, improve work efficiency, solve the problems of long cycle, low efficiency, and high cost of the existing traditional manual verification method for network parameters, and solve the problems of complex algorithm, low accuracy, large deviation, and inapplicability of the existing technology algorithm for calibrating the longitude and latitude of network parameters. Through the method embodiments of the present application, based on two types of data, namely network parameters and MR measurements, not only can the calibration of the longitude and latitude of network parameters be quickly and accurately realized, the costs of labor and equipment be reduced, and the practical application value of network parameters be improved, but also this algorithm is not affected by factors such as wireless environment and terminal user distribution and can be widely applied to various environmental scenarios. Specifically, the method embodiments of the present application have the following advantages:

[0201] 1. Solve the problem of incorrect longitude and latitude in network parameters, improve the application value of network parameters in practical work such as network optimization, simulation, complaint handling, and various special analyses, as well as the quality and usability of the output results, and improve work efficiency, etc.

[0202] 2. Solve the problems of long cycle, low efficiency, and high cost of the existing traditional manual verification method for network parameters.

[0203] 3. Solve the problems of complex algorithm, low accuracy, large deviation, and inapplicability of the existing technology algorithm for calibrating the longitude and latitude of network parameters.

[0204] 4. The method embodiments of the present application have characteristics such as low algorithm complexity, fast running speed, high accuracy, low cost, and wide applicability. The method embodiments of the present application are based on two types of data, namely network engineering parameters and MR measurements. Through algorithms such as the 3σ algorithm, Thiessen polygon algorithm, random sampling algorithm, cosine theorem algorithm, and clustering algorithm, the correction of longitude and latitude in network engineering parameters can be quickly and accurately achieved, improving the accuracy of engineering parameters, work efficiency, and the application value of network engineering parameters. This algorithm is not affected by environmental factors, the distribution of end-users, etc., and can be widely applied to various wireless environment scenarios. By using the 3σ algorithm to eliminate discrete MR measurement data, the error caused by a small amount of incorrect MR measurement data in the network engineering parameter correction result can be avoided, improving the accuracy of network engineering parameter correction. Through the Thiessen polygon algorithm, according to the Thiessen polygon, the closed area corresponding to the longitude and latitude of the cell engineering parameters under the unique base station code is calculated, and then the falling rate of MR measurements under this base station code is calculated to quickly make a preliminary judgment on the network engineering parameters. At the same time, it is possible to analyze whether the engineering parameter longitude and latitude and the corrected longitude and latitude are in the same Thiessen polygon area to make a basic judgment on the severity of the deviation between the two. The random sampling algorithm first rasterizes the MR measurement data of the cell under the unique base station code, and then randomly samples a small amount of MR measurement data from each grid. Compared with directly using all MRs for correction analysis, the calculation efficiency is significantly improved. And compared with directly randomly sampling the MR measurement data of the cell under the unique base station code, the problem of reduced calculation accuracy caused by the sampled MR data being concentrated in a narrow range can be avoided. Therefore, this random sampling method can reduce the data calculation amount without affecting the calculation accuracy, improving the calculation effect. By combining the cosine theorem algorithm, when various judgment conditions for MR measurement data are met, the corrected longitude and latitude sample set is calculated, which can not only improve the calculation efficiency but also ensure the accuracy of the calculation result. By using the adaptive radius clustering algorithm to cluster the corrected sample set and extracting the central longitude and latitude of the largest cluster in the clustering result as the corrected longitude and latitude, the adaptive radius clustering algorithm can automatically and flexibly set the corresponding clustering radius for the MR measurement data of each cell under the base station code. Compared with the traditional clustering algorithm with a fixed clustering radius, it has better applicability.

[0205] 5. Good applicability. The method embodiments of the present application only need to combine the above data and can quickly and accurately achieve the correction of longitude and latitude in network engineering parameters by calling relevant algorithm modules through a program, and can be better used in actual work.

[0206] In summary, the method embodiments of the present application are based on the above data and can quickly and accurately achieve the correction of longitude and latitude in network engineering parameters through algorithms such as the 3σ algorithm, Thiessen polygon algorithm, random sampling algorithm, cosine theorem algorithm, and clustering algorithm, improving work efficiency and the application value of network engineering parameters.

[0207] The embodiments of the present application provide a position information verification device.Figure 23 is a schematic structural diagram of the device, as Figure 23 shown, the device includes: a first processing module 230, configured to determine a first position of a target cell in a target area, where the target cell is any cell in the target area, and the first position of the target cell is the cell center position included in the network engineering parameters of the target area; a second processing module 232, configured to determine multiple measurement report data corresponding to the target cell; a third processing module 234, configured to iteratively traverse the multiple measurement report data, select any two measurement report data during each iteration, and determine a calibration position based on the test positions included in the selected measurement report data, to obtain a set of calibration positions, where a triangle is formed by the calibration position and the position information included in the corresponding measurement report data, and the combination of measurement data selected during each iteration is different from the combination selected during the previous iteration; a fourth processing module 236, configured to determine a second position based on the set of calibration positions, and determine whether the network engineering parameters corresponding to the target area need to be updated based on the first position and the second position.

[0208] In some embodiments of the present application, after determining the first position of the target cell in the target area, the first processing module 230 is further configured to: determine the coverage area corresponding to each cell based on the center positions of the cells in the target area, where the center position of the cell is the cell center position included in the network engineering parameters of the cell; determine the adjacent cells of the target cell based on the coverage areas corresponding to the cells, and determine the closed area corresponding to the target cell based on the adjacent cells, where the coverage areas corresponding to the adjacent cells are adjacent to the coverage area corresponding to the target cell, and the closed area of the target cell is a polygon area determined with the centers of the adjacent cells of the target cell as vertices.

[0209] In some embodiments of the present application, the first processing module 230 determines the coverage area corresponding to each cell based on the center positions of the cells in the target area, including: determining the Thiessen polygon area corresponding to each center position based on the center positions of the cells, where the Thiessen polygon area corresponding to the center position of the cell is the coverage area of the cell.

[0210] In some embodiments of the present application, the fourth processing module 236 determines whether the network engineering parameters corresponding to the target area need to be updated based on the first position and the second position, including: determining that the network engineering parameters of the target area do not need to be updated when the first position and the second position are in the same coverage area; determining that the network engineering parameters of the target area need to be updated when the first position and the second position are not in the same coverage area.

[0211] In some embodiments of the present application, after determining multiple pieces of measurement report data corresponding to the target cell, the second processing module 232 is further configured to: determine a first proportion of the measurement report data whose included location information falls within the closed area among the multiple pieces of measurement report data corresponding to the target cell; in the case where the first proportion is greater than a preset threshold, determine that it is not necessary to update the network engineering parameters corresponding to the target area; in the case where the first proportion is less than the preset threshold, continue to iteratively traverse the steps of the multiple pieces of measurement report data.

[0212] In some embodiments of the present application, the third processing module 234 determines the calibration position based on the location information included in the selected measurement report data, including: determining a first test position and first timing advance information included in the selected first measurement report data, and a second test position and second timing advance information included in the selected second measurement report data; determining a first side length based on the first timing advance information, determining a second side length based on the second timing advance information, and determining a third side length based on the first test position and the second test position; in the case where the first side length, the second side length, and the third side length can form a triangle, determining a target triangle based on the first test position, the second test position, the first side length, the second side length, and the third side length, where the first vertex of the target triangle is at the first test position, the second vertex is at the second test position, and the position of the third vertex is the calibration position.

[0213] In some embodiments of the present application, the fourth processing module 236 determines the second position based on the calibration position set, including: clustering the calibration position set to obtain multiple clusters; determining the cluster with the most calibration positions among the multiple clusters as the target cluster; determining the central position of the target cluster as the second position.

[0214] In some embodiments of the present application, after determining multiple pieces of measurement report data corresponding to the target cell, the second processing module 232 is further configured to: determine the distribution of the measurement positions included in each of the multiple pieces of measurement report data; determine discrete measurement positions based on the distribution, and exclude the measurement report data including the discrete measurement positions.

[0215] In some embodiments of the present application, after determining multiple pieces of measurement report data corresponding to a target cell, the second processing module 232 is further configured to: when the number of pieces of measurement report data is greater than a first preset number threshold, determine the distribution of the measurement positions included in each of the multiple pieces of measurement report data; determine multiple grids with consistent size information according to the distribution; determine a second preset number threshold corresponding to each grid according to the first preset number threshold; when the number of pieces of measurement report data corresponding to a grid is not greater than the second preset number threshold, retain the measurement report data corresponding to all grids, where the measurement report data corresponding to a grid is the measurement report data whose measurement position is in the grid; when the number of pieces of measurement report data corresponding to a grid is greater than the second preset number threshold, perform random sampling processing on the measurement report data corresponding to the grid.

[0216] It should be noted that each module in the above position information verification device may be a program module (for example, a set of program instructions for implementing a specific function), or a hardware module. For the latter, it may be presented in the following forms, but not limited to: the manifestation form of each of the above modules is a processor, or the functions of each of the above modules are implemented by a processor.

[0217] The embodiments of the present application provide a non-volatile storage medium, in which a program is stored. When the program runs, it controls the device where the non-volatile storage medium is located to execute the following position information verification method: determine the first position of a target cell in a target area, where the target cell is any cell in the target area, and the first position of the target cell is the cell center position included in the network engineering parameters of the target area; determine multiple pieces of measurement report data corresponding to the target cell; iteratively traverse the multiple pieces of measurement report data. In each iteration, randomly select two pieces of measurement report data, and determine a correction position according to the test positions included in the selected measurement report data to obtain a set of correction positions. A triangle is formed by the correction position and the position information included in the corresponding measurement report data. The combination of measurement data selected in each iteration is different from the combination selected in the previous iteration; determine a second position according to the set of correction positions, and determine whether the network engineering parameters corresponding to the target area need to be updated according to the first position and the second position.

[0218] An embodiment of the present application provides an electronic device, including: a memory and a processor, where the processor is configured to run a program stored in the memory. When the program runs, it executes the following location information verification method: determining a first location of a target cell in a target area, where the target cell is any cell in the target area, and the first location of the target cell is the cell center location included in the network engineering parameters of the target area; determining multiple measurement report data corresponding to the target cell; iteratively traversing the multiple measurement report data, and in each iteration, arbitrarily selecting two pieces of measurement report data, and determining a correction location based on the test locations included in the selected measurement report data to obtain a set of correction locations, where the correction location and the location information included in the corresponding measurement report data form a triangle, and the combination of measurement data selected in each iteration is different from the combination selected in the previous iteration; determining a second location based on the set of correction locations, and determining whether the network engineering parameters corresponding to the target area need to be updated based on the first location and the second location.

[0219] An embodiment of the present application provides a computer program product, including a computer program that, when executed by a processor, implements the following location information verification method: determining a first location of a target cell in a target area, where the target cell is any cell in the target area, and the first location of the target cell is the cell center location included in the network engineering parameters of the target area; determining multiple measurement report data corresponding to the target cell; iteratively traversing the multiple measurement report data, and in each iteration, arbitrarily selecting two pieces of measurement report data, and determining a correction location based on the test locations included in the selected measurement report data to obtain a set of correction locations, where the correction location and the location information included in the corresponding measurement report data form a triangle, and the combination of measurement data selected in each iteration is different from the combination selected in the previous iteration; determining a second location based on the set of correction locations, and determining whether the network engineering parameters corresponding to the target area need to be updated based on the first location and the second location.

[0220] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0221] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the units or modules can be in an electrical or other form.

[0222] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0223] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0224] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the related technology, or all or 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0225] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for verifying location information, characterized in that Including: Determine a first position of a target cell in a target area, where the target cell is any cell in the target area, and the first position of the target cell is the cell center position included in the network engineering parameters of the target area; Determine multiple measurement report data corresponding to the target cell; Iteratively traverse the multiple measurement report data. In each iteration, randomly select two pieces of the measurement report data, and determine a correction position based on the test positions included in the selected measurement report data to obtain a set of correction positions. The correction position and the position information included in the corresponding measurement report data form a triangle, and the combination of measurement data selected in each iteration is different from the combination selected in the previous iteration; Determine a second position based on the set of correction positions, and determine whether the network engineering parameters corresponding to the target area need to be updated based on the first position and the second position.

2. The location information verification method according to claim 1, wherein After determining the first position of the target cell in the target area, the method further includes: Determine the coverage areas corresponding to the respective cells in the target area based on the center positions of the respective cells in the target area, where the center position of the cell is the cell center position included in the network engineering parameters of the cell; Determine the neighboring cells of the target cell based on the coverage areas corresponding to the respective cells, and determine the closed area corresponding to the target cell based on the neighboring cells. The coverage area corresponding to the neighboring cell is adjacent to the coverage area corresponding to the target cell, and the closed area of the target cell is a polygon area determined with the centers of the neighboring cells of the target cell as vertices.

3. The location information verification method according to claim 2, characterized in that, Determining the coverage areas corresponding to the respective cells in the target area based on the center positions of the respective cells in the target area includes: Determine the Thiessen polygon area corresponding to each of the center positions based on the center positions of the respective cells, where the Thiessen polygon area corresponding to the center position of the cell is the coverage area of the cell.

4. The position information verification method according to claim 2, wherein Determining whether the network engineering parameters corresponding to the target area need to be updated based on the first position and the second position includes: In the case where the first position and the second position are in the same coverage area, determine that the network engineering parameters of the target area do not need to be updated; In the case where the first position and the second position are not in the same coverage area, determine that the network engineering parameters of the target area need to be updated.

5. The location information verification method according to claim 1, characterized in that After determining the multiple measurement report data corresponding to the target cell, the method further includes: Determine a first ratio of the measurement report data whose included position information falls within the closed area among the multiple measurement report data corresponding to the target cell; In the case where the first ratio is greater than a preset threshold, determine that the network engineering parameters corresponding to the target area do not need to be updated; In the case where the first ratio is less than the preset threshold, continue with the step of iteratively traversing the multiple measurement report data.

6. The location information verification method according to claim 1, wherein Determining a correction position based on the position information included in the selected measurement report data includes: Determine a first test position and a first timing advance information included in the selected first measurement report data, and a second test position and a second timing advance information included in the selected second measurement report data; Determine a first side length according to the first timing advance information, determine a second side length according to the second timing advance information, and determine a third side length according to the first test position and the second test position; When the first side length, the second side length and the third side length can form a triangle, the target triangle is determined based on the first test position, the second test position, the first side length, the second side length and the third side length, wherein the first vertex of the target triangle is at the first test position, the second vertex is at the second test position, and the position of the third vertex is the correction position.

7. The location information verification method according to claim 1, wherein Determining the second position according to the correction position set includes: Clustering the correction position set to obtain a plurality of clusters; Determine a cluster containing the most correction positions among the multiple clusters as a target cluster; The center position of the target cluster is determined as the second position.

8. The position information verification method according to claim 1, characterized in that After determining the multiple measurement report data corresponding to the target cell, the method further includes: Determining the distribution of measurement locations included in each of the plurality of measurement report data; Discrete measurement locations are determined according to the distribution, and measurement report data containing the discrete measurement locations are discarded.

9. The location information verification method according to claim 1, characterized in that After determining the multiple measurement report data corresponding to the target cell, the method further includes: When the number of the measurement report data is greater than a first preset number threshold, determining a distribution of measurement locations included in each of the plurality of measurement report data; Determine a plurality of grids having consistent size information according to the distribution; Determine a second preset number threshold corresponding to each of the grids according to the first preset number threshold; When the number of measurement report data corresponding to the grid is not greater than the second preset number threshold, retaining the measurement report data corresponding to all the grids, wherein the measurement report data corresponding to the grid is the measurement report data whose measurement position is in the grid; When the quantity of the measurement report data corresponding to the grid is greater than the second preset quantity threshold, random sampling processing is performed on the measurement report data corresponding to the grid.

10. A position information verification device, characterized in that, include: A first processing module, configured to determine a first position of a target cell in a target area, wherein the target cell is any cell in the target area, and the first position of the target cell is a cell center position included in a network engineering parameter of the target area; A second processing module, used to determine a plurality of measurement report data corresponding to the target cell; A third processing module is configured to iteratively traverse the multiple pieces of measurement report data, select any two of the measurement included data during each iteration, and determine a calibration position based on the test position included in the selected measurement report data, so as to obtain a set of calibration positions. Wherein, a triangle is formed by the calibration position and the position information included in the corresponding measurement report data, and the combination of measurement data selected during each iteration is different from the combination selected during the previous iteration; A fourth processing module is configured to determine a second position based on the set of calibration positions, and determine whether the network engineering parameters corresponding to the target area need to be updated based on the first position and the second position.

11. A non-volatile storage medium, characterized in that, A program is stored in the non-volatile storage medium. When the program runs, it controls the device where the non-volatile storage medium is located to execute the position information verification method according to any one of claims 1 to 9.

12. An electronic device, characterized in that, Comprising: A memory and a processor, the processor is configured to run the program stored in the memory. When the program runs, it executes the position information verification method according to any one of claims 1 to 9.

13. 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 method according to any one of claims 1 to 9.

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