Base station position measuring and calculating method and device based on multi-center fusion, and electronic equipment

Abstract

The invention discloses a base station position measuring and calculating method and device based on multi-center fusion and electronic equipment, and relates to the field of mobile communication or other related technical fields, and the method comprises the steps: obtaining a sampling data set of a target base station; calculating a global centroid and a regional centroid based on data of the sampled data set; mapping the sampling points and the sampling data into geographic grids, and determining a plurality of regional gravity centers and regional strong centers of the coverage regions based on the sampling points and the sampling data in the geographic grids; combining each area gravity center and area strong center of each coverage area to obtain a plurality of area center combinations, and calculating candidate position coordinates of the target base station based on the global centroid and each area center combination; and calculating a target position coordinate of the target base station based on the plurality of candidate position coordinates, and determining a target position of the target base station. According to the method and the device, the technical problem that the accuracy of a measurement result is relatively low when the position of the base station is automatically measured in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communications or other related technical fields, and in particular to a base station position calculation method based on multi-center fusion, a device thereof, and electronic equipment. Background Art

[0002] In modern communication networks, accurate measurement of base station locations is crucial for network planning, optimization, and maintenance. Specifically, accurate base station location data helps network operators optimize network coverage, reduce blind spots and overlapping coverage, and improve signal quality and network capacity. This includes adjusting parameters such as antenna direction, downtilt angle, and transmit power to ensure the rational allocation of network resources. At the same time, in an emergency, accurate base station location information can quickly help locate the approximate location of users, speed up rescue and response, and is of great significance to ensuring public safety. In particular, in terms of network coverage optimization, fault detection, and user experience improvement, accurate, reliable, and automated acquisition of base station location information is the key to achieving efficient management.

[0003] The traditional base station positioning method uses GPS to obtain the latitude and longitude of the base station through manual on-site detection. Although it can obtain data intuitively, it relies on the accuracy of the equipment and the professionalism of the operator, wastes manpower and time, and may also lead to inaccurate measurement results. It is also difficult to adapt to the rapid changes and automation needs of the network.

[0004] In related technologies, using signal strength for positioning (Received Signal Strength, RSS for short, positioning based on received signal strength indication) is one of the most common methods for automatically calculating the location of base stations. However, signal strength is greatly affected by environmental factors, such as buildings, vegetation, weather conditions, etc., resulting in insufficient positioning accuracy and stability. Although the RSS-based positioning algorithm is low-cost, the positioning accuracy will be significantly reduced under non-line-of-sight (NLOS) conditions and when there is a shadow effect.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a base station location calculation method based on multi-center fusion, a device thereof, and an electronic device, so as to at least solve the technical problem in the related art that when the base station location is automatically calculated, the accuracy of the calculation result is low.

[0007] According to one aspect of an embodiment of the present invention, a base station position measurement method based on multi-center fusion is provided, comprising: obtaining device data reported by user devices in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the position coordinates of the sampling points and the reference signal reception strength; determining the global centroid and the regional centroid of each of the coverage areas based on the position coordinates of the sampling points; mapping each sampling point and sampling data in the sampling data set to a geographic grid, and determining N regional centroids of each of the coverage areas and M regional strong centroids of each of the coverage areas based on the sampling points and sampling data in each geographic grid, wherein, N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each of the geographic grids, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each of the geographic grids; each regional centroid and regional strong centroid of each of the coverage areas are combined to obtain a plurality of regional center combinations, a centroid weight is configured for the regional centroid of each of the coverage areas based on the global centroid and each regional center combination, and the candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set; the expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0008] Furthermore, the step of determining the global centroid and the regional centroid of each of the coverage areas based on the position coordinates of the sampling points includes: calculating the global average longitude and latitude values ​​based on the position coordinates of all the sampling points in all the coverage areas, and obtaining the global centroid based on the global average longitude and latitude values; calculating the regional average longitude and latitude values ​​based on the position coordinates of all the sampling points in each of the coverage areas, and obtaining the regional centroid of each of the coverage areas based on the regional average longitude and latitude values.

[0009] Furthermore, the step of mapping each sampling point and sampling data in the sampling data set to a geographic grid includes: establishing K geographic grids for each of the coverage areas, where K is a positive integer; and mapping the sampling points and the sampling data to the geographic grids based on the position coordinates of the sampling points.

[0010] Furthermore, the step of determining N regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid includes: counting the number of sampling points in each geographic grid, and sorting the geographic grids in each coverage area based on the number of sampling points to obtain a first sorted list; filtering the first sorted list based on a centroid distance threshold and a centroid angle threshold to obtain the filtered first sorted list, wherein the centroid distance threshold is a preset maximum value of the distance between the regional centroid and the regional centroid in the coverage area, and the centroid angle threshold is a preset maximum value of the angle between the regional centroid and the regional centroid in the coverage area; selecting N geographic grids whose number of sampling points is greater than the preset number threshold from the filtered first sorted list, and taking the center point of each geographic grid as the regional centroid to obtain N regional centroids.

[0011] Furthermore, the step of determining M regional strong points of each coverage area based on the sampling points and sampling data in each geographic grid includes: calculating the average value of the reference signal reception strength of all sampling points in each geographic grid based on the reference signal reception strength in the sampling data, and obtaining the average value of the reference signal reception strength corresponding to each geographic grid; sorting the geographic grids in each coverage area based on the average value of the reference signal reception strength corresponding to each geographic grid, and obtaining a second sorted list; filtering the second sorted list based on the strong point distance threshold and the strong point angle threshold, and obtaining a filtered second sorted list, wherein the strong point distance threshold is the maximum value of the distance between the regional strong point and the regional centroid in the coverage area, and the strong point angle threshold is the maximum value of the angle between the regional strong point and the regional centroid in the coverage area; selecting M geographic grids whose average value of the reference signal reception strength is greater than the preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographic grid as the regional strong point, and obtaining M regional strong points.

[0012] Furthermore, the step of configuring centroid weights for the regional centroids of each of the coverage areas based on the global centroid and each regional center combination includes: obtaining a reference line for each of the coverage areas based on the ray connecting the global centroid and the regional centroids in each of the coverage areas; calculating the centroid-related weight according to the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; calculating the strong center-related weight according to the reference line and the line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; and obtaining the centroid weight of the regional centroid of each of the coverage areas under the regional center combination based on the centroid-related weight and the strong center-related weight corresponding to the regional center combination.

[0013] Furthermore, the centroid-related weights include: centroid distance weights, centroid angle weights, and the step of calculating the centroid-related weights based on the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area includes: calculating the centroid distance weight based on the distance value of the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; calculating the centroid angle weight based on the angle between the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area.

[0014] Furthermore, the strong heart related weights include: a strong heart distance weight and a strong heart angle weight. The step of calculating the strong heart related weights according to the reference line and the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area includes: calculating the strong heart distance weight according to the distance value of the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area; calculating the strong heart angle weight according to the angle between the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area and the reference line.

[0015] According to another aspect of an embodiment of the present invention, a base station position measurement device based on multi-center fusion is also provided, including: an acquisition unit, used to acquire device data reported by user devices in multiple coverage areas corresponding to a target base station, and preprocess the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the location coordinates of the sampling point, and the reference signal reception strength; a determination unit, used to determine the global centroid and the regional centroid of each of the coverage areas based on the location coordinates of the sampling point; a mapping unit, used to map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centroids of each of the coverage areas and M regional strong centroids of each of the coverage areas based on the sampling points and sampling data in each geographic grid. , wherein N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each of the geographic grids, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each of the geographic grids; a configuration unit is used to combine each regional centroid and regional strong centroid of each of the coverage areas to obtain multiple regional center combinations, configure centroid weights for the regional centroids of each of the coverage areas based on the global centroid and each regional center combination, and calculate the candidate position coordinates of the target base station under the regional center combination based on the centroid weights and the position coordinates of the regional centroid to obtain a candidate position coordinate set; a calculation unit is used to calculate the expected values ​​of all candidate position coordinates in the candidate position coordinate set to obtain the target position coordinates of the target base station, and determine the target position of the target base station based on the target position coordinates.

[0016] Furthermore, the determination unit includes: a first calculation module, used to calculate the global average longitude and latitude values ​​based on the position coordinates of all sampling points in all the coverage areas, and obtain the global centroid based on the global average longitude and latitude values; a second calculation module, used to calculate the regional average longitude and latitude values ​​based on the position coordinates of all sampling points in each of the coverage areas, and obtain the regional centroid of each of the coverage areas based on the regional average longitude and latitude values.

[0017] Furthermore, the mapping unit includes: a first establishing module, used to establish K geographic grids for each of the coverage areas, wherein K is a positive integer; and a first mapping module, used to map the sampling points and the sampling data to the geographic grids based on the position coordinates of the sampling points.

[0018] Furthermore, the mapping unit also includes: a first statistical module, used to count the number of sampling points in each of the geographic grids, and sort the geographic grids in each coverage area based on the number of sampling points to obtain a first sorted list; a first screening module, used to screen the first sorted list based on a centroid distance threshold and a centroid angle threshold to obtain the first sorted list after screening, wherein the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within a preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within a preset coverage area; a first selection module, used to select N geographic grids whose number of sampling points is greater than a preset number threshold from the first sorted list after screening, and use the center point of each of the geographic grids as the regional centroid to obtain N regional centroids.

[0019] Furthermore, the mapping unit also includes: a third calculation module, which is used to calculate the average value of the reference signal reception strength of all sampling points in each of the geographic grids based on the reference signal reception strength in the sampling data, and obtain the average value of the reference signal reception strength corresponding to each of the geographic grids; a first sorting module, which is used to sort the geographic grids in each coverage area based on the average value of the reference signal reception strength corresponding to each of the geographic grids, and obtain a second sorting list; a second screening module, which is used to screen the second sorting list based on the strong heart distance threshold and the strong heart angle threshold, and obtain a screened second sorting list, wherein the strong heart distance threshold is the maximum value of the distance between the regional strong heart and the regional centroid in the coverage area, and the strong heart angle threshold is the maximum value of the angle between the regional strong heart and the regional centroid in the coverage area; a second selection module, which is used to select M geographic grids whose average reference signal reception strength is greater than the preset reference signal reception strength threshold from the second sorting list, and take the center point of each of the geographic grids as the regional strong heart, and obtain M regional strong hearts.

[0020] Furthermore, the configuration unit includes: a first acquisition module, used to obtain a reference line of each coverage area based on a ray connecting the global centroid and the regional centroid in each coverage area; a fourth calculation module, used to calculate a centroid-related weight based on the reference line and a line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; a fifth calculation module, used to calculate a strong-center-related weight based on the reference line and a line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; and a second acquisition module, used to obtain the centroid weight of the regional centroid of each coverage area under the regional center combination based on the centroid-related weight and the strong-center-related weight corresponding to the regional center combination.

[0021] Furthermore, the centroid-related weights include: centroid distance weights, centroid angle weights, and the fourth calculation module includes: a first calculation submodule, used to calculate the centroid distance weight according to the distance value of the line between the regional centroid and the regional centroid in the regional center combination in the coverage area; a second calculation submodule, used to calculate the centroid angle weight according to the angle between the line between the regional centroid and the regional centroid in the regional center combination in the coverage area and the reference line.

[0022] Furthermore, the strong heart related weights include: a strong heart distance weight and a strong heart angle weight, and the fifth calculation module includes: a third calculation submodule, used to calculate the strong heart distance weight according to the distance value of the line between the regional strong heart and the regional centroid in the regional center combination in the coverage area; a fourth calculation submodule, used to calculate the strong heart angle weight according to the angle between the line between the regional strong heart and the regional centroid in the regional center combination in the coverage area and the reference line.

[0023] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned base station position estimation methods based on multi-center fusion.

[0024] In the present application, the following steps are performed: obtaining device data reported by user devices in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the location coordinates of the sampling points, the reference signal reception strength, and determining the global centroid and the regional centroid of each coverage area based on the location coordinates of the sampling points, and then mapping each sampling point and sampling data in the sampling data set to a geographic grid, and determining N regional centroids of each coverage area and M regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers, and the area The domain centroid is determined based on the number of sampling points in each geographic grid, the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid, and each regional centroid and regional strong centroid of each coverage area are combined to obtain multiple regional center combinations, and the centroid weight is configured for the regional centroid of each coverage area based on the global centroid and each regional center combination, and the candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set, and finally the expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0025] In this application, by geo-rasterizing multiple sampling points for regional division, the sampling points can be analyzed more finely to obtain distribution characteristics, and the global centroid, regional centroid, regional center of gravity and regional strong center are defined by combining location information and signal reception strength data, so as to realize multi-center fusion measurement of base station location, comprehensively consider the mean value of base station signal coverage, the degree of user equipment aggregation and signal strength and other factors, and can more scientifically and reasonably measure the base station location, achieving the technical effect of improving the accuracy of base station location measurement. This solves the technical problem of low accuracy of the measurement results when the base station location is automatically measured in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for calculating a base station position is shown;

[0028] Figure 2 is a flow chart of an optional base station position calculation method based on multi-center fusion according to an embodiment of the present invention;

[0029] Figure 3is a schematic diagram of an optional multi-center-based base station position fitting and calculation process according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of an optional base station position calculation device based on multi-center fusion according to an embodiment of the present invention;

[0031] Figure 5 This is a hardware structure block diagram of an optional electronic device (or mobile device) for executing a base station position estimation method based on multi-center fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] It should be noted that the base station position calculation method based on multi-center fusion and the device thereof in the present application can be used in the mobile communication field when the base station position is calculated, and can also be used in any field other than the mobile communication field when the base station position is calculated. The present application does not limit the application field of the base station position calculation method based on multi-center fusion and the device thereof.

[0035] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions, providing users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0036] The following embodiments of the present invention can be applied to various base station location measurement systems / applications / devices. Based on the geographic data of the user equipment and data such as signal reception strength, the present invention defines the global centroid, regional centroid, regional strong center and regional center of gravity, and calculates the geographic location of the target base station through multi-center fusion. Compared with the positioning algorithm of a single factor, it can better cope with the complex situation of signal propagation in different scenarios, and the measurement method based on multi-center fusion can greatly improve the calculation accuracy.

[0037] The present invention performs geographic grid division on the sampled data to improve data processing accuracy. The grid accuracy can be set according to different scenarios, so that the position measurement method of the present invention can adapt to a variety of geographical environments, greatly improving the accuracy of the measurement results.

[0038] The present invention is described in detail below in conjunction with various embodiments.

[0039] Embodiment 1

[0040] According to an embodiment of the present invention, an embodiment of a base station location measurement method based on multi-center fusion 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 a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] The method embodiment provided in the first 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 (or mobile device) for implementing a base station location estimation method based on multi-center fusion is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) 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 device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0042] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0043] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the method for calculating the location of the base station in the embodiment 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, the method for calculating the location of the base station is realized. 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 memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0044] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0045] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0046] Under the above operating environment, this application provides Figure 2 The base station location calculation method shown in the figure is implemented by a base station location calculation system based on multi-center fusion.

[0047] Figure 2 is a flow chart of an optional base station location calculation method based on multi-center fusion according to an embodiment of the present invention, such as Figure 2 As shown, the method comprises the following steps:

[0048] Step S201: acquiring device data reported by user devices in a plurality of coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sample data set.

[0049] With the development of mobile network technology, especially the widespread application of Minimized Drive Test (MDT) data, base station positioning technology based on network data has received more and more attention. MDT data is automatically reported by user equipment (UE) in daily use, including received signal strength, signal reception strength, signal to interference plus noise ratio and other parameters. These data provide valuable real-time information for wireless network optimization.

[0050] In an embodiment of the present invention, the location information of the user equipment and the signal reception strength are taken into consideration, and the regional centroid, regional centroid, regional strong centroid and global centroid within each coverage range of the target base station are defined. The ray from the global centroid to the regional centroid is selected as the reference line, and the weights related to the regional centroid and the regional strong centroid are calculated based on this. The weight of the regional centroid is determined by taking into account the length of the line connecting the regional centroid and the global centroid, the angle between the line and the reference line, the length of the line connecting the regional strong centroid and the global centroid, and the angle between the line and the reference line. This process simulates the propagation path and direction of the signal in the actual environment. In actual situations, the signal propagation direction tends to be user-dense areas, and the calculation method of the regional centroid and the regional strong centroid is related to the user distribution and signal strength. Therefore, the factors considered in the positioning process of the embodiment of the present invention are closely combined with the actual propagation direction of the signal, which is more in line with the actual propagation direction, thereby improving the accuracy of base station positioning.

[0051] In the above step S101, the target base station to be measured can be determined based on the base station location measurement request. The base station location measurement request may come from the network optimization team of the network operator, the base station maintenance department or the third-party network analysis service provider. When the network has coverage problems, low signal-to-noise ratio or poor user experience, the problem can be located through base station location measurement, so as to take corresponding optimization measures. The location measurement request usually contains the following key information: target base station identification, request source information, attribute parameters, performance indicator requirements, where the attribute parameters may include: service provider, communication technology (ie, network standard), signal transmission range (ie, frequency band), coverage parameters (such as urban and rural areas) and usage scenario parameters (such as macro stations and indoor substations).

[0052] After receiving the base station location measurement request, the system first parses the various parameters in the request and determines the target base station to be measured based on this. And obtain the MDT data (i.e., device data) in all coverage areas under the target base station. The "coverage area" here refers to the geographical range that the target base station signal can reach, which is usually determined by the base station's transmission power, antenna configuration, and surrounding environment. For example, a base station can cover multiple cells, and each cell can be used as a coverage area to obtain the MDT data of all user devices in the cell. The MDT data is reported by the user device, and the MDT data includes the latitude and longitude of the user device and the reference signal received strength (RSRP). The user device exchanges data with network infrastructure such as base stations through wireless signals to achieve voice communication, data transmission, multimedia services and other functions. User devices cover various types of mobile and fixed devices, including but not limited to: smartphones, tablets, laptops, IoT devices, vehicle communication systems, and wearable devices. The latitude and longitude of the user device are the current location coordinates reported by its built-in positioning module; the reference signal received strength (RSRP) is the strength of the target base station signal received by the user device, reflecting the distance of signal transmission and environmental impact.

[0053] The preprocessing stage aims to clean and organize the original device data to remove outliers, fill missing data, standardize signal strength, etc. At the same time, it removes discrete points in the original data to obtain sampling points and sampling data of each sampling point, and finally obtains a sampling data set.

[0054] In an optional embodiment, the step of preprocessing the device data includes: performing data cleaning on the device data to obtain cleaned device data, wherein the data cleaning includes: missing value processing and outlier processing; setting a data valid interval, and filtering the cleaned device data based on the data valid interval to obtain filtered device data, wherein the filtered device data are all within the data valid interval.

[0055] Specifically, when preprocessing the device data, the device data is first cleaned to remove or adjust missing values ​​and outliers in the device data. For missing values, interpolation, statistical prediction methods, or reference to the average value of neighboring device data can be used to fill in, thereby ensuring the integrity of the data set and the feasibility of subsequent analysis. Outliers refer to points in the data set that deviate significantly from normal values, which may be caused by equipment failure, measurement errors, or extreme environmental conditions. These outliers are identified and removed by statistical analysis methods, such as calculating the mean and standard deviation of the data set. Further, after obtaining the cleaned device data, a data valid interval is set according to the value of the reference signal reception strength, and the device data within the interval is retained through the data valid interval, and the data outside the interval is removed to remove inaccurate sampling points. For example, the RSRP valid interval is set to [-120, 160], and the sampling points within the interval are screened out. The screened data set not only removes abnormal and unreasonable data, but also ensures the integrity of the data by filling in missing values, thereby improving the accuracy and reliability of base station location calculation.

[0056] In an optional embodiment, the step of preprocessing the device data also includes: for the filtered device data, sorting all the device data according to the reference signal reception strength to obtain a sorted list; splitting the sorted list according to the coverage area to which the sampling points corresponding to the device data in the sorted list belong, to obtain a sorted sub-list corresponding to each coverage area; intercepting the device data in the sorted sub-list corresponding to each coverage area according to a preset interception ratio to obtain intercepted device data.

[0057] Specifically, for the filtered device data, the preprocessing operation also includes sorting and partitioning all devices according to the reference signal reception strength. First, for the device data that has been screened and confirmed to be in the valid interval, the system sorts all device data according to the reference signal reception strength (RSRP) to create a sorted list. The purpose of this step is to highlight the sampling points with the best signal quality, because in wireless communications, a higher RSRP value usually means better communication quality between the user device and the base station, a more direct signal path, and less impact from external interference and multipath effects. Therefore, based on RSRP sorting, data points with high signal quality and more reliable location information can be given priority, thereby improving the accuracy of base station location calculation. Subsequently, according to the coverage area to which the sampling point corresponding to the device data in the sorted list belongs, the sorted list is split into sub-lists corresponding to multiple coverage areas, namely, sorted sub-lists.

[0058] Finally, according to the preset interception ratio (for example, it can be set to the first 80%), the device data in the sorted sublist corresponding to each coverage area is intercepted to obtain the intercepted device data. The selection of the interception ratio should be based on a comprehensive consideration of signal quality and location measurement requirements, aiming to retain the data points with the highest signal strength and the least impact of environmental factors, while reducing the amount of calculation and improving processing efficiency. The intercepted device data set is more refined, focusing on the sampling points with the highest signal quality, providing high-quality input data for subsequent positioning algorithms.

[0059] By preprocessing the device data, a sampling data set is obtained, which includes sampling points and sampling data of each sampling point. One user device corresponds to one sampling point, and the sampling data at least includes: location coordinates of the sampling point and reference signal reception strength.

[0060] Step S202: determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points.

[0061] In the above step S202, the longitude and latitude coordinates of the sampling points are extracted from the sample data set, and the global centroid is first calculated based on the longitude and latitude coordinates of all the sampling points. The global centroid represents the geometric center position of all the sampling points within the coverage range of the entire target base station, and can reflect the central trend of the entire signal coverage area. Then, the regional centroid is calculated based on the longitude and latitude coordinates of the sampling points in each coverage area, which more accurately represents the center of signal propagation and user distribution in each coverage area, and can capture more detailed spatial distribution characteristics.

[0062] Furthermore, the step of determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points includes: calculating the global average longitude and latitude values ​​based on the position coordinates of all sampling points in all coverage areas, and obtaining the global centroid based on the global average longitude and latitude values; calculating the regional average longitude and latitude values ​​based on the position coordinates of all sampling points in each coverage area, and obtaining the regional centroid of each coverage area based on the regional average longitude and latitude values.

[0063] Specifically, the global centroid, as the center of the entire signal coverage area, can be used as a key reference point in subsequent calculations. When calculating the impact of the centroid and strong centroid on the cell centroid, a directional reference is provided for the analysis of the signal propagation path based on the direction of the ray from the global centroid to the regional centroid. The global centroid is determined based on the average longitude and latitude of all sampling points, and the longitude and latitude coordinate information of all sampling points is summarized. These longitude and longitude values ​​are summed up, that is, the sum of all longitude values ​​and the sum of all latitude values. Then, these sums are divided by the total number of sampling points to obtain the average values ​​of longitude and latitude, thereby obtaining the position coordinates of the global centroid. The global average longitude and latitude values ​​represent the center position of all signal receiving points in the coverage area, providing a macroscopic position reference point for subsequent positioning analysis.

[0064] The regional centroid in each coverage area accurately reflects the center position of the signal activity within the area, and provides more detailed location information than the global centroid. The regional centroid can more accurately estimate the signal radiation center of the base station for different cells, which helps to improve the accuracy of positioning. For each coverage area of ​​the target base station, the average longitude and latitude of all sampling points in the coverage area is calculated to obtain the regional centroid of the coverage area.

[0065] Step S203, mapping each sampling point and sampling data in the sampling data set to a geographic grid, and determining N regional centroids and M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid.

[0066] In the above step S203, each sampling point in the sampled data set is mapped to a geographic grid system. Specifically, a coverage area is divided into more refined geographic grids. The accuracy of the geographic grid can be set according to the approximate geographical environment where the target base station is located. Through geographic grid division, the continuous geographic space is divided into manageable small units, which is conducive to the statistics and comparison of data points.

[0067] Secondly, based on the statistics of the number of sampling points in the geographic grid and the calculation of the signal reception strength, multiple centers corresponding to each area range can be determined, namely N regional centroids and M regional strong centers. The regional centroid is determined based on the number of sampling points in each geographic grid, reflecting the most densely populated geographical location of user equipment in the coverage area. These locations are often the areas with the best signal propagation effect, so they can effectively indicate the possible location of the base station. The regional strong center is determined based on the reference signal reception strength of the sampling points in each geographic grid. The regional strong center emphasizes the peak area of ​​signal strength, which is usually close to the base station, so it helps to accurately locate the signal source.

[0068] In an urban environment, buildings are densely packed and users are often concentrated in buildings. Buildings can block and reflect signal propagation, making signal propagation complicated. The embodiment of the present invention takes this actual situation into full consideration. During the calculation process, the data in the grid is statistically analyzed to remove discrete points to reduce the interference of abnormal data on positioning. When determining the center of gravity and the strong center, the regional center of gravity and the regional strong center are defined according to the number of sampling points in the grid and the average RSRP, and the distance threshold and the angle threshold are set to select the appropriate grid center longitude and latitude. This method can highlight the role of user-dense areas and areas with high signal strength, and give greater weight to rays with "cleaner" propagation paths (i.e., fewer reflections, closer to line-of-sight conditions) and more users. In other words, when locating a base station, by considering the effect of user aggregation in a building and the effect of signal reception strength, the positioning result can more accurately reflect the position of the base station in an actual complex environment, thereby improving the positioning accuracy.

[0069] Furthermore, the step of mapping each sampling point and sampling data in the sampling data set to a geographic grid includes: establishing K geographic grids for each coverage area, where K is a positive integer; and mapping the sampling points and sampling data to the geographic grid based on the location coordinates of the sampling points.

[0070] Specifically, rasterizing the sampled data is actually dividing each sampling point into a two-dimensional geographic grid according to the device longitude and latitude reported by the user device. The geographic grid divides the geographic space into a series of uniform, non-overlapping grid cells, and each cell (grid) represents a specific geographic area. For example, a 50m x 50m square grid can be used to correspond the longitude and latitude coordinates of the device data to these grids. The purpose of this is to achieve spatial aggregation, merging sampling points with similar geographical locations into the same grid, facilitating subsequent data statistical analysis based on geographic regions, reducing the amount of data, and improving processing efficiency. At the same time, it can also capture the signal strength change characteristics of local areas. When establishing a geographic grid, a plane rectangular coordinate system can also be constructed synchronously to convert the longitude and latitude coordinates of each sampling point into plane rectangular coordinates, which is convenient for the unification of parameters in the calculation process.

[0071] In an optional embodiment, after the sampling data is rasterized, for each geographic grid, the number of sampling points in the geographic grid is counted, and the geographic grid with a number of sampling points less than a preset number range is regarded as a grid containing sporadic discrete points, and the identified sporadic discrete points are deleted from the sampling data set.

[0072] Furthermore, the step of determining N regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid includes: counting the number of sampling points in each geographic grid, and sorting the geographic grids in each coverage area based on the number of sampling points to obtain a first sorted list; filtering the first sorted list based on a centroid distance threshold and a centroid angle threshold to obtain a filtered first sorted list, wherein the centroid distance threshold is a preset maximum value of the distance between the regional centroid and the regional centroid in the coverage area, and the centroid angle threshold is a preset maximum value of the angle between the regional centroid and the regional centroid in the coverage area; selecting N geographic grids whose number of sampling points is greater than the preset number threshold from the filtered first sorted list, and taking the center point of each geographic grid as the regional centroid to obtain N regional centroids.

[0073] Specifically, the regional centroid is calculated based on the number of sampling points in the geographic grid. When selecting the regional centroid, first, the number of sampling points in each geographic grid is counted to identify the area with dense user equipment signal reception in the coverage area. According to the number of sampling points in each geographic grid obtained by statistics, the geographic grids in each coverage area are arranged in descending order to form a first sorting list. The descending order ensures that the grid with the largest number of sampling points is given priority, which provides a basis for identifying the regional centroid. On the basis of the first sorting list, two pre-set thresholds, the centroid distance threshold and the centroid angle threshold, are applied for further screening. The centroid distance threshold limits the maximum allowable distance between the regional centroid and the regional centroid, ensuring that the selected regional centroid is within the effective range of signal coverage. The centroid angle threshold limits the maximum angle between the line connecting the regional centroid and the regional centroid and the preset reference line. The preset reference line is the ray connecting the global centroid and the regional centroid. The centroid angle threshold ensures that the direction of the line is consistent with the main direction of signal propagation, thereby improving the accuracy of positioning. Select N geographic grids with a larger number of sampling points from the filtered first sorted sublist, and use the center point of the geographic grid as the regional center to obtain N regional centroids.

[0074] Furthermore, the step of determining M regional strong points in each coverage area based on the sampling points and sampling data in each geographic grid includes: calculating the average value of the reference signal reception strength of all sampling points in each geographic grid based on the reference signal reception strength in the sampling data, and obtaining the average value of the reference signal reception strength corresponding to each geographic grid; sorting the geographic grids in each coverage area based on the average value of the reference signal reception strength corresponding to each geographic grid, and obtaining a second sorted list; filtering the second sorted list based on the strong point distance threshold and the strong point angle threshold, and obtaining a filtered second sorted list, wherein the strong point distance threshold is the maximum value of the distance between the regional strong point and the regional centroid in the coverage area, and the strong point angle threshold is the maximum value of the angle between the regional strong point and the regional centroid in the coverage area; selecting M geographic grids whose average value of the reference signal reception strength is greater than the preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographic grid as the regional strong point, and obtaining M regional strong points.

[0075] Specifically, the regional strong center is determined based on the average signal reception strength of the geographic grid. When determining the regional strong center of each regional range, first collect and calculate the RSRP average of all sampling points in each geographic grid. This process aims to quantify the average strength of the signal in different regions and provide a basis for the subsequent grid sorting and determination of the regional strong center. According to the RSRP average corresponding to each geographic grid, the second sorting list is generated. In this way, the geographic grid with the strongest signal, that is, the focal area of ​​signal propagation, is identified, laying the foundation for the selection of the regional strong center. The second sorting list is screened using the preset strong center distance threshold and strong center angle threshold. The strong center distance threshold limits the maximum allowable distance between the regional strong center and the regional centroid, ensuring that the selected strong center is within the effective range of signal coverage. The strong center angle threshold controls the maximum allowable angle between the line connecting the strong center and the regional centroid and the main direction of signal propagation, ensuring that the selection of the strong center matches the actual mode of signal propagation. From the filtered second sorted list, the first M geographic grids with the largest RSRP average values ​​are selected, and the center points of these grids are defined as regional strong centers within the region, thereby obtaining M regional strong centers.

[0076] The settings of the above-mentioned centroid distance threshold, centroid angle threshold, strong center distance threshold and strong center angle threshold further avoid the influence of unreasonable centroids and strong centers on the calculation of base station coordinates. At the same time, weights are assigned to each regional centroid based on the screened regional strong centers and regional centroids, thereby giving greater weights to regional centroids with "cleaner" propagation paths on the regional reference line (i.e., less reflections, closer to line-of-sight conditions) and more users.

[0077] Step S204, combine each regional centroid and regional strong centroid of each coverage area to obtain multiple regional center combinations, configure centroid weights for the regional centroids of each coverage area based on the global centroid and each regional center combination, and calculate the candidate position coordinates of the target base station under the regional center combination based on the centroid weights and the position coordinates of the regional centroid to obtain a set of candidate position coordinates.

[0078] In the above step S204, by combining the regional centroids and regional strong centroids of each coverage area, a centroid weight is configured for the regional centroid according to each combination, and the candidate position coordinates of the target base station are calculated accordingly, and the target base station is positioned based on multiple candidate position coordinates to improve the accuracy of the position measurement of the target base station. Each regional centroid and each regional strong centroid screened out within each regional range are combined, and multiple possible interpretations of the base station position in the coverage area are constructed by combining the highest point of signal coverage density (regional centroid) and the peak point of signal strength (regional strong centroid). For each regional center combination, the distance and angle from the global centroid to the regional centroid in the combination, as well as the distance and angle from the regional centroid to the regional centroid and the regional strong centroid are calculated. Based on these distances and angles, a centroid weight is assigned to the regional centroid. Thus, a greater weight is given to the regional centroid with a "cleaner" propagation path on the reference line in the coverage area (i.e., less reflection, closer to the line of sight condition) and more users. The candidate position coordinates of the target base station are calculated based on the centroid weight and the position coordinates of the regional centroid, and the above steps are repeated until all regional center combinations are selected, thereby generating a candidate position coordinate set.

[0079] Furthermore, the step of configuring centroid weights for the regional centroids of each coverage area based on the global centroid and each regional center combination includes: obtaining a reference line for each coverage area based on the ray connecting the global centroid and the regional centroids in each coverage area; calculating the centroid-related weight according to the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; calculating the strong center-related weight according to the reference line and the line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; and obtaining the centroid weights of the regional centroids of each coverage area under the regional center combination based on the centroid-related weights and strong center-related weights corresponding to the regional center combination.

[0080] Specifically, the weight data configured for the global centroid include centroid-related weights and strong-center-related weights. The centroid-related weights include: centroid distance weights and centroid angle weights. The strong-center-related weights include strong-center distance weights and strong-center angle weights. Starting from the global centroid, the rays connecting the regional centroids in each coverage area are defined as reference lines for each coverage area, simulating the propagation path and direction of the signal in the actual environment. The signal propagation direction will tend to areas with dense users and areas with high signal strength, and the calculation method of the centroid and strong-center is related to the user distribution and signal strength. By configuring relevant weights for the regional centroid through the regional centroid and the regional strong-center, the factors considered in the process of locating the target base station are closely integrated with the actual propagation direction of the signal, which is more in line with the actual situation of the propagation path and propagation direction, thereby improving the accuracy of base station positioning.

[0081] Furthermore, the centroid-related weights include: centroid distance weights, centroid angle weights, and the steps of calculating the centroid-related weights based on the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area include: calculating the centroid distance weight based on the distance value of the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; calculating the centroid angle weight based on the angle between the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area.

[0082] Specifically, the configuration of the gravity center related weight takes into account the effect of user aggregation. The gravity center distance weight is calculated according to the distance value between the regional gravity center and the regional centroid. The calculation formula of the gravity center distance weight is expressed as: ,in, is the centroid distance weight, The reference line in the coverage area Secondly, the weight of the center of gravity angle is configured according to the angle between the line connecting the center of gravity of the region and the center of shape of the region and the reference line in the coverage area. The calculation formula of the weight of the center of gravity angle is: ,in, is the weight of the centroid angle, is the angle between the line connecting the regional centroid and the regional centroid and the reference line, It is the angle threshold between the line connecting the regional centroid and the regional centroid and the reference line, that is, the centroid angle threshold.

[0083] Furthermore, the strong heart related weights include: strong heart distance weights, strong heart angle weights, and the steps of calculating the strong heart related weights according to the reference line and the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area include: calculating the strong heart distance weight according to the distance value of the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area; calculating the strong heart angle weight according to the angle between the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area and the reference line.

[0084] Specifically, considering the influence of the signal with less reflection, the strong heart correlation weight is defined, and the strong heart distance threshold is calculated according to the distance value between the regional strong heart and the regional centroid. The calculation formula of the strong heart distance threshold can be expressed as: ,in, is the strong heart distance threshold, is the distance between the strong center of the region and the centroid of the region, is the path propagation loss distance. Further, the strong center angle weight is calculated according to the angle between the line between the strong center of the region and the center of the region and the reference line in the coverage area. The calculation formula of the strong center angle weight can be expressed as: ,in, is the strong center angle weight, is the angle between the line between the regional strong center and the regional centroid and the reference line in the coverage area, It is the angle threshold between the line between the regional strong center and the regional centroid and the reference line in the coverage area, that is, the strong center angle threshold.

[0085] Furthermore, according to a set of regional center combinations, a weight value can be configured for the regional centroid, and the weight value of the regional centroid can be expressed as: in, Used to distinguish different physical properties, Used to refine parameter properties: hour Characterizes the weight related to the center of gravity, when When , it represents the strong heart related weight; , which corresponds to the parameters related to the angle. , which corresponds to the distance-related parameters.

[0086] Based on the weight value of the regional centroid under the combination of multiple regional centers within the regional range and the position coordinates of the regional centroid, the candidate position coordinates of the target base station can be calculated, thereby determining the potential position of the target base station. Specifically, the candidate position coordinates of the target base station can be expressed as:

[0087]

[0088]

[0089] in, is the abscissa of the region centroid, is the ordinate of the region centroid, and the abscissa and ordinate of the candidate position are calculated respectively to obtain the coordinates of the candidate position.

[0090] Step S205 , calculating expected values ​​of all candidate position coordinates in the candidate position coordinate set, obtaining the target position coordinates of the target base station, and determining the target position of the target base station based on the target position coordinates.

[0091] In the above step S205, the potential position of the target base station is obtained according to different combinations of regional centroids and regional strong centers. The expectation is calculated for all potential positions to obtain the final measured position of the target base station, and finally the plane coordinates are converted into longitude and latitude coordinates as the final target position of the base station. In this way, the base station position measurement based on multi-center fusion is realized, which significantly improves the positioning accuracy and stability.

[0092] Through the above steps, the device data reported by the user devices in the multiple coverage areas corresponding to the target base station are obtained, and the device data are preprocessed to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the position coordinates of the sampling points, the reference signal reception strength, and the global centroid and the regional centroid of each coverage area are determined based on the position coordinates of the sampling points, and then the sampling points and sampling data in the sampling data set are mapped to the geographic grid, and the N regional centroids of each coverage area and the M regional strong centroids of each coverage area are determined based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers, and the regional centroid It is determined based on the number of sampling points in each geographic grid, the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid, and each regional centroid and regional strong centroid of each coverage area are combined to obtain multiple regional center combinations, and the centroid weight is configured for the regional centroid of each coverage area based on the global centroid and each regional center combination, and the candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set, and finally the expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0093] In this embodiment, by geo-rasterizing multiple sampling points for regional division, the sampling points can be analyzed more finely to obtain distribution characteristics, and the global centroid, regional centroid, regional center of gravity and regional strong center can be defined by combining location information and signal reception strength data, so as to realize multi-center fusion measurement of base station location, comprehensively consider the mean value of base station signal coverage, the degree of aggregation of user equipment and signal strength and other factors, so as to more scientifically and reasonably measure the base station location, and achieve the technical effect of improving the accuracy of base station location measurement. This solves the technical problem in the related technology that the accuracy of the measurement result is low when the base station location is automatically measured.

[0094] Another optional specific implementation is described in detail below.

[0095] In the embodiment of the present invention, based on the multi-center fusion sector fitting positioning algorithm, the whole process involves data preprocessing, clear screening, geographic gridding, statistical analysis, and centroid, center of gravity, strong center calculation and weight calculation. By collecting and cleaning the MDT data reported by the user equipment, including longitude and latitude and RSRP, the data is pre-classified and geo-gridded. Subsequently, the data in the grid is statistically analyzed, the discrete points are removed, and the global centroid, the centroid, the center of gravity, and the strong center of all data sampling points are calculated. Then, three centers of gravity and strong centers are respectively selected according to the threshold value for subsequent combination calculation of the cell centroid weight. Then, a multi-center connection line is constructed, and a combination of center of gravity and strong center is selected each time. By connecting the cell centroid and the cluster centroid, the center of gravity and the global centroid, the strong center and the global centroid, the centroid-centroid cell reference line l_i, the center of gravity-centroid line segment l_z and the strong center-centroid line segment l_q are obtained. Furthermore, the weight of the cell centroid is determined by the influence of the centroid and the strong centroid, and the potential location of the target site under the corresponding centroid-strong centroid combination is determined by weighted average of all cells. Finally, the final estimated coordinates are determined by the expectation of all potential locations.

[0096] The multi-center fusion sector fitting positioning algorithm of the present invention collects a large amount of user MDT data, covering rich information such as longitude and latitude, RSRP, etc. During the data processing process, the data is geo-rasterized, and the appropriate grid accuracy is selected according to different regional scenarios. This method enables the algorithm to adapt to a variety of geographical environments, whether it is an area with high-rise buildings in the city, a mountainous area with complex terrain, or an open plain area, it can effectively process and analyze data. Moreover, in the calculation process, multiple factors such as the centroid, center of gravity, and strong center are comprehensively considered. Compared with the positioning algorithm of a single factor, it can better cope with the complex situation of signal propagation in different scenarios, greatly improve the positioning accuracy and applicability in various application scenarios, and enhance the universality of application scenarios.

[0097] In wireless communication, signal propagation is not an ideal straight-line propagation and is affected by many factors, such as building obstruction, topography, etc. When determining the location of the target base station, the present invention selects the ray from the global centroid to the cell centroid as a reference line, and calculates the weights of the centroid and the strong center based on this. The cell centroid weight is determined by taking into account the length of the line connecting the centroid and the global centroid, the angle between the line and the reference line, the length of the line connecting the strong center and the global centroid, and the angle between the line and the reference line. This process simulates the propagation path and direction of the signal in the actual environment. In actual situations, the signal propagation direction tends to be in areas with dense user populations, and the calculation method of the centroid and the strong center is related to user distribution and signal strength. Therefore, the factors considered by the algorithm in the positioning process are closely combined with the actual propagation direction of the signal, which is more in line with the actual propagation direction, thereby improving the accuracy of base station positioning.

[0098] In an urban environment, buildings are densely packed and users are often concentrated inside buildings. Buildings can block and reflect signal propagation, making signal propagation complicated. The present invention takes this actual situation into full consideration. During the calculation process, the data in the grid is statistically analyzed to remove discrete points to reduce the interference of abnormal data on positioning. When determining the center of gravity and the strong center, the center of gravity of the cell and the strong center of the cell are defined respectively according to the number of sampling points in the grid and the average RSRP, and the distance threshold and the angle threshold are set to select the appropriate longitude and latitude of the center of the grid. This method can highlight the role of areas with dense users and areas with high signal strength, and give greater weight to rays with "cleaner" propagation paths (i.e., fewer reflections and closer to line-of-sight conditions) and more users. In other words, when locating a base station, by considering the effect of user aggregation in a building, the positioning result can more accurately reflect the position of the base station in an actual complex environment, thereby improving the positioning accuracy.

[0099] Figure 3 is a schematic diagram of an optional multi-center based base station position fitting and calculation process according to an embodiment of the present invention, such as Figure 3 As shown in the figure, the position calculation process based on multi-center fusion specifically includes:

[0100] Step 1: collect MDT data of all cells under the target base station (in the embodiment of the present invention, the coverage area is specifically a cell, and other types of geographical features can also be used to characterize the coverage area). The MDT data is reported by the user equipment and includes longitude and latitude and RSRP, where RSRP represents reference signal reception strength.

[0101] Step 2: Clean the data and select strong data to clean RSRP sampling points.

[0102] The RSRP sampling data is cleaned to remove inaccurate sampling points. The RSRP valid interval is set to [-120, 160], and the RSRP sampling points within this interval are retained; the filtered RSRP sampling points are sorted from strong to weak according to signal strength, and then the top 80% strong RSRP sampling points are intercepted.

[0103] Step 3: rasterize the geographic plane.

[0104] The processed device data is mapped into a two-dimensional geographic grid system, and the grid accuracy can be selected according to the regional scenario.

[0105] Step 4: Delete discrete points.

[0106] In the gridded data, count the number of sampling points in each grid. The grid is considered to contain scattered discrete points, and the identified scattered discrete points are deleted from the dataset.

[0107] Step 5: Calculate the global centroid and cell centroid.

[0108] Get the average longitude and latitude of all sampling points as the global centroid ( , Get the cell The area centroid of , ). Find the ray emitted from the target base station that "cleanly" passes through the user-dense area. At the same time, considering the cumulative effect of users in the building, select the ray from the global centroid to the cell centroid as the cell. Reference line .

[0109] Step six, select the community center and community strong center.

[0110] Calculate the centroid of the cell based on the number of sampling points in the grid. Sort the grids in descending order of the number of sampling points in the grid, and calculate the centroid of the cell based on the distance threshold between the centroid and the centroid of the cell. and angle threshold Filter the first N grids, and the center point of the selected grid is the center of gravity of the cell; define the strong center of the cell according to the average RSRP in the grid, sort the grids from high to low, and use the distance threshold between the strong center and the cell centroid as the threshold. and angle threshold The first M grids are screened, and the center point of the screened grid is the strong center of the cell. By setting the above threshold, the influence of unreasonable center of gravity and strong center on the estimated base station coordinates is further avoided. At the same time, it is used to assign weights to each cell centroid, so as to give greater weights to the cell centroids with cleaner propagation paths on the cell reference line (i.e., less reflection, closer to line-of-sight conditions) and more users.

[0111] Step 7: Randomly combine the cell centroid and the cell strong center to calculate the suspected position.

[0112] Calculate the weights related to the center of gravity. Considering the effect of user aggregation, according to the center of gravity-centroid line segment Length ,as well as and cell reference line Angle , calculate the centroid-related weight of the cell centroid, and the calculation formula of the centroid-related weight is as follows:

[0113] , ,

[0114] in, represents the weight of the center of gravity angle, is the strong heart distance correlation weight, Reference line for the area length.

[0115] Calculate the strong heart correlation weight. Considering the influence of the signal with less reflection, according to the strong heart-centroid line segment Length ,as well as and cell reference line Angle , calculate the strong-heart correlation weight of the cell centroid, and the calculation formula of the strong-heart correlation weight is as follows:

[0116] , ,

[0117] in, is the strong center angle weight, is the strong heart distance weight, is the path propagation loss distance.

[0118] Calculate the weight of the cell centroid. Then we can get the weight of the cell centroid under the combination of a pair of cell centroid and cell strong center. The weight of the centroid is:

[0119]

[0120] in, Used to distinguish different physical properties, Used to refine parameter properties: hour Characterizes the weight related to the center of gravity, when When , it represents the strong heart related weight; , which corresponds to the parameters related to the angle. , which corresponds to the distance-related parameters.

[0121] Calculate the potential location of the target base station. The estimated potential target base station coordinate calculation formula is as follows:

[0122]

[0123]

[0124] Step 8, determine whether all combinations have been taken, if so, execute step 9, if not, repeat steps 7 to 8;

[0125] Step 9: Determine the final location of the target base station based on the suspected location. The potential location of the target base station is obtained based on different combinations of cell centroids and cell strong centers. Expect all potential locations to obtain the location coordinates of the target base station, and convert the plane coordinates into longitude and latitude coordinates as the final target coordinates of the target base station.

[0126] Step 10, end.

[0127] The following is a detailed description in conjunction with another embodiment.

[0128] Embodiment 2

[0129] A base station location measurement device based on multi-center fusion provided in this embodiment includes multiple implementation units, each implementation unit corresponds to each implementation step in the above-mentioned embodiment one. Its specific implementation method and beneficial effects can refer to the above-mentioned method embodiment and will not be repeated here.

[0130] Figure 4 is a schematic diagram of an optional base station position calculation device based on multi-center fusion according to an embodiment of the present invention, such as Figure 4 As shown, the base station position estimation device based on multi-center fusion may include: an acquisition unit 41, a determination unit 42, a mapping unit 43, a configuration unit 44, and a calculation unit 45, wherein:

[0131] The acquisition unit 41 is used to acquire device data reported by user devices in multiple coverage areas corresponding to the target base station, and pre-process the device data to obtain a sampling data set, wherein the sampling data set includes a sampling point and sampling data of each sampling point, and the sampling data at least includes: a location coordinate of the sampling point and a reference signal reception strength;

[0132] A determination unit 42, configured to determine the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points;

[0133] A mapping unit 43 is used to map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centroids and M regional strong centroids of each coverage area based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid;

[0134] The configuration unit 44 is used to combine each regional centroid and regional strong centroid of each coverage area to obtain multiple regional center combinations, configure a centroid weight for the regional centroid of each coverage area based on the global centroid and each regional center combination, and calculate the candidate position coordinates of the target base station under the regional center combination based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set;

[0135] The calculation unit 45 is used to calculate the expected values ​​of all candidate position coordinates in the candidate position coordinate set, obtain the target position coordinates of the target base station, and determine the target position of the target base station based on the target position coordinates.

[0136] The above-mentioned base station position measurement device obtains the device data reported by the user equipment in the multiple coverage areas corresponding to the target base station through the acquisition unit 41, and pre-processes the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the location coordinates of the sampling points and the reference signal reception strength; the determination unit 42 determines the global centroid and the regional centroid of each coverage area based on the location coordinates of the sampling points; the mapping unit 43 maps each sampling point and sampling data in the sampling data set to a geographic grid, and determines N regional centroids of each coverage area and M regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers. The regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid; each regional centroid and regional strong centroid of each coverage area are combined by the configuration unit 44 to obtain a plurality of regional center combinations, a centroid weight is configured for the regional centroid of each coverage area based on the global centroid and each regional center combination, and the candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set; the expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated by the calculation unit 45 to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0137] In this embodiment, by geo-rasterizing multiple sampling points for regional division, the sampling points can be analyzed more finely to obtain distribution characteristics, and the global centroid, regional centroid, regional center of gravity and regional strong center can be defined by combining location information and signal reception strength data, so as to realize multi-center fusion measurement of base station location, comprehensively consider the mean value of base station signal coverage, the degree of aggregation of user equipment and signal strength and other factors, so as to more scientifically and reasonably measure the base station location, and achieve the technical effect of improving the accuracy of base station location measurement. This solves the technical problem in the related technology that the accuracy of the measurement result is low when the base station location is automatically measured.

[0138] Furthermore, the determination unit 42 includes: a first calculation module, used to calculate the global average longitude and latitude values ​​based on the position coordinates of all sampling points in all coverage areas, and obtain the global centroid based on the global average longitude and latitude values; a second calculation module, used to calculate the regional average longitude and latitude values ​​based on the position coordinates of all sampling points in each coverage area, and obtain the regional centroid of each coverage area based on the regional average longitude and latitude values.

[0139] Furthermore, the mapping unit 43 includes: a first establishing module, used to establish K geographic grids for each coverage area, where K is a positive integer; and a first mapping module, used to map the sampling points and the sampling data to the geographic grids based on the position coordinates of the sampling points.

[0140] Furthermore, the mapping unit 43 also includes: a first statistical module, which is used to count the number of sampling points in each geographic grid, and sort the geographic grids in each coverage area based on the number of sampling points to obtain a first sorted list; a first screening module, which is used to screen the first sorted list based on the centroid distance threshold and the centroid angle threshold to obtain a screened first sorted list, wherein the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within a preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within a preset coverage area; a first selection module, which is used to select N geographic grids whose number of sampling points is greater than the preset number threshold from the screened first sorted list, and use the center point of each geographic grid as the regional centroid to obtain N regional centroids.

[0141] Furthermore, the mapping unit 43 also includes: a third calculation module, which is used to calculate the average value of the reference signal reception strength of all sampling points in each geographic grid based on the reference signal reception strength in the sampling data, and obtain the average value of the reference signal reception strength corresponding to each geographic grid; a first sorting module, which is used to sort the geographic grids in each coverage area based on the average value of the reference signal reception strength corresponding to each geographic grid, and obtain a second sorting list; a second screening module, which is used to screen the second sorting list based on the strong heart distance threshold and the strong heart angle threshold, and obtain a screened second sorting list, wherein the strong heart distance threshold is the maximum value of the distance between the regional strong heart and the regional centroid in the coverage area, and the strong heart angle threshold is the maximum value of the angle between the regional strong heart and the regional centroid in the coverage area; a second selection module, which is used to select M geographic grids whose average reference signal reception strength is greater than the preset reference signal reception strength threshold from the second sorting list, and take the center point of each geographic grid as the regional strong heart to obtain M regional strong hearts.

[0142] Furthermore, the configuration unit 44 includes: a first acquisition module, which is used to obtain a reference line of each coverage area based on a ray connecting the global centroid and the regional centroid in each coverage area; a fourth calculation module, which is used to calculate a centroid-related weight based on the reference line and a line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; a fifth calculation module, which is used to calculate a strong-center-related weight based on the reference line and a line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; and a second acquisition module, which is used to obtain the centroid weight of the regional centroid of each coverage area under the regional center combination based on the centroid-related weight and the strong-center-related weight corresponding to the regional center combination.

[0143] Furthermore, the centroid-related weights include: centroid distance weight, centroid angle weight, and the fourth calculation module includes: a first calculation submodule, used to calculate the centroid distance weight according to the distance value of the line between the regional centroid and the regional centroid in the regional center combination in the coverage area; a second calculation submodule, used to calculate the centroid angle weight according to the angle between the line between the regional centroid and the regional centroid in the regional center combination in the coverage area and the reference line.

[0144] Furthermore, the strong heart related weights include: strong heart distance weight, strong heart angle weight, and the fifth calculation module includes: a third calculation submodule, used to calculate the strong heart distance weight according to the distance value of the line between the regional strong heart and the regional centroid in the regional center combination in the coverage area; a fourth calculation submodule, used to calculate the strong heart angle weight according to the angle between the line between the regional strong heart and the regional centroid in the regional center combination in the coverage area and the reference line.

[0145] It should be noted that the acquisition unit 41, the determination unit 42, the mapping unit 43, the configuration unit 44, and the calculation unit 45 correspond to steps S201 to S205 in the first embodiment, and the examples and application scenarios implemented by the above units and the corresponding steps are the same, but are not limited to the contents disclosed in the first embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above modules or units can also be part of the device and can be run in the computer terminal 10 provided in the first embodiment.

[0146] The present invention is described below in conjunction with another optional embodiment.

[0147] Embodiment 3

[0148] An embodiment of the present invention may also provide an electronic device, Figure 5 is a hardware structure block diagram of an electronic device (or mobile device) for performing an optional method for calculating a base station position according to an embodiment of the present invention, such as Figure 5 As shown, the electronic device may include: one or more ( Figure 5 Only one is shown) processor 502, memory 504, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0149] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory 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 memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0150] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the device data reported by the user equipment in the multiple coverage areas corresponding to the target base station, and pre-process the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the position coordinates of the sampling points and the reference signal reception strength; determine the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points; map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centroids of each coverage area and M regional strong centroids of each coverage area based on the sampling points and sampling data in each geographic grid, wherein , N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid; each regional centroid and regional strong centroid of each coverage area are combined to obtain multiple regional center combinations, centroid weights are configured for the regional centroids of each coverage area based on the global centroid and each regional center combination, and candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weights and the position coordinates of the regional centroid to obtain a candidate position coordinate set; expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0151] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: The steps of determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points include: calculating the global average longitude and latitude values ​​based on the position coordinates of all sampling points in all coverage areas, and obtaining the global centroid based on the global average longitude and latitude values; calculating the regional average longitude and latitude values ​​based on the position coordinates of all sampling points in each coverage area, and obtaining the regional centroid of each coverage area based on the regional average longitude and latitude values.

[0152] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the step of mapping each sampling point and sampling data in the sampling data set to the geographic grid includes: establishing K geographic grids for each coverage area, where K is a positive integer; mapping the sampling points and sampling data to the geographic grid based on the location coordinates of the sampling points.

[0153] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the step of determining N regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid includes: counting the number of sampling points in each geographic grid, and sorting the geographic grids in each coverage area based on the number of sampling points to obtain a first sorted list; filtering the first sorted list based on the centroid distance threshold and the centroid angle threshold to obtain a filtered first sorted list, wherein the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid in the coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid in the coverage area; selecting N geographic grids whose number of sampling points is greater than the preset number threshold from the filtered first sorted list, and taking the center point of each geographic grid as the regional centroid to obtain N regional centroids.

[0154] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the step of determining M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid includes: calculating the average reference signal reception strength of all sampling points in each geographic grid based on the reference signal reception strength in the sampling data to obtain the average reference signal reception strength corresponding to each geographic grid; sorting the geographic grids in each coverage area based on the average reference signal reception strength corresponding to each geographic grid to obtain a second sorted list; filtering the second sorted list based on the strong center distance threshold and the strong center angle threshold to obtain a filtered second sorted list, wherein the strong center distance threshold is the maximum value of the distance between the regional strong center and the regional centroid in the coverage area, and the strong center angle threshold is the maximum value of the angle between the regional strong center and the regional centroid in the coverage area; selecting M geographic grids whose average reference signal reception strength is greater than the preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographic grid as the regional strong center to obtain M regional strong centers.

[0155] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the step of configuring the centroid weight for the regional centroid of each coverage area based on the global centroid and each regional center combination includes: obtaining the reference line of each coverage area based on the ray connecting the global centroid and the regional centroid in each coverage area; calculating the centroid-related weight according to the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination in the coverage area; calculating the strong center-related weight according to the reference line and the line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; obtaining the centroid weight of the regional centroid of each coverage area under the regional center combination based on the centroid-related weight and the strong center-related weight corresponding to the regional center combination.

[0156] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the centroid-related weights include: centroid distance weights, centroid angle weights, and the steps of calculating the centroid-related weights based on the reference line and the line connecting the regional centroid and the regional centroid in the regional center combination within the coverage area include: calculating the centroid distance weight based on the distance value of the line connecting the regional centroid and the regional centroid in the regional center combination within the coverage area; calculating the centroid angle weight based on the angle between the line connecting the regional centroid and the regional centroid in the regional center combination within the coverage area and the reference line.

[0157] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: the strong heart related weights include: strong heart distance weights, strong heart angle weights, and the steps of calculating the strong heart related weights according to the reference line and the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area include: calculating the strong heart distance weight according to the distance value of the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area; calculating the strong heart angle weight according to the angle between the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area and the reference line.

[0158] By adopting the embodiment of the present invention, a base station location calculation scheme based on multi-center fusion is provided. By geographically rasterizing multiple sampling points for regional division, the sampling points can be analyzed more finely to obtain distribution characteristics, and the global centroid, regional centroid, regional center of gravity and regional strong center can be defined by combining location information and signal reception strength data, so as to realize multi-center fusion calculation of base station location, comprehensively consider the mean value of base station signal coverage, the degree of aggregation of user equipment and signal strength and other factors, so as to calculate the base station location more scientifically and reasonably, and achieve the technical effect of improving the accuracy of base station location calculation. This solves the technical problem in the related technology that when the base station location is automatically calculated, the accuracy of the calculation result is low.

[0159] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the electronic device may also be a terminal device such as a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), a PAD, etc. Figure 5 The structure of the electronic device is not limited. Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 5 Different configurations are shown.

[0160] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0161] The present invention is described below in conjunction with another optional embodiment.

[0162] Embodiment 4

[0163] The embodiment of the present invention further provides a computer-readable storage medium. Optionally, in the embodiment of the present invention, the computer-readable storage medium can be used to store the program code executed by the method for calculating the base station position provided in the first embodiment.

[0164] Optionally, in an embodiment of the present invention, the above-mentioned storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0165] The embodiment of the present invention further provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the method for calculating the position of a base station: obtaining device data reported by user devices in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, wherein the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data at least includes: the position coordinates of the sampling points and the reference signal reception strength; determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points; mapping each sampling point and the sampling data in the sampling data set to a geographic grid, and determining N regional centroids of each coverage area and M regional centroids of each coverage area based on the sampling points and the sampling data in each geographic grid. regional strong centroids, where N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid; each regional centroid and regional strong centroid of each coverage area are combined to obtain multiple regional center combinations, centroid weights are configured for the regional centroids of each coverage area based on the global centroid and each regional center combination, and candidate position coordinates of the target base station under the regional center combination are calculated based on the centroid weights and the position coordinates of the regional centroid to obtain a candidate position coordinate set; expected values ​​of all candidate position coordinates in the candidate position coordinate set are calculated to obtain the target position coordinates of the target base station, and the target position of the target base station is determined based on the target position coordinates.

[0166] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0167] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] In the several embodiments provided in this 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 schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the 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 this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0172] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating base station position based on multi-center fusion, characterized in that: include: Acquire device data reported by user devices in multiple coverage areas corresponding to the target base station, and pre-process the device data to obtain a sampling data set, wherein the sampling data set includes a sampling point and sampling data of each sampling point, and the sampling data at least includes: position coordinates of the sampling point and reference signal reception strength; Determine the global centroid and the regional centroid of each of the coverage areas based on the position coordinates of the sampling points; Mapping each sampling point and sampling data in the sampling data set to a geographic grid, and determining N regional centroids and M regional strong centroids of each coverage area based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong centroid is determined based on the reference signal reception strength of the sampling points in each geographic grid; Combining each regional centroid and regional strong centroid of each of the coverage areas to obtain a plurality of regional center combinations, configuring a centroid weight for the regional centroid of each of the coverage areas based on the global centroid and each regional center combination, and calculating the candidate position coordinates of the target base station under the regional center combination based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set; Calculate expected values ​​of all candidate position coordinates in the candidate position coordinate set to obtain the target position coordinates of the target base station, and determine the target position of the target base station based on the target position coordinates.

2. The method according to claim 1, characterized in that The step of determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points comprises: Calculate the global average longitude and latitude value based on the position coordinates of all sampling points in the coverage area, and obtain the global centroid based on the global average longitude and latitude value; The regional average longitude and latitude values ​​are calculated based on the position coordinates of all sampling points in each of the coverage areas, and the regional centroid of each of the coverage areas is obtained based on the regional average longitude and latitude values.

3. The method according to claim 1, characterized in that The steps of mapping each sampling point and sampling data in the sampling data set to a geographic grid include: Establishing K geographic grids for each of the coverage areas, where K is a positive integer; The sampling points and the sampling data are mapped to the geographic grid based on the location coordinates of the sampling points.

4. The method according to claim 1, characterized in that: The step of determining the N regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid comprises: Counting the number of sampling points in each of the geographic grids, and sorting the geographic grids in each coverage area based on the number of sampling points to obtain a first sorting list; The first sorted list is screened based on a centroid distance threshold and a centroid angle threshold to obtain the screened first sorted list, wherein the centroid distance threshold is a preset maximum value of the distance between the regional centroid and the regional centroid in the coverage area, and the centroid angle threshold is a preset maximum value of the angle between the regional centroid and the regional centroid in the coverage area; N geographic grids whose number of sampling points is greater than a preset number threshold are selected from the filtered first sorted list, and the center point of each of the geographic grids is used as the regional centroid to obtain N regional centroids.

5. The method according to claim 1, characterized in that The step of determining the M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid comprises: Calculate the average value of the reference signal reception strength of all sampling points in each of the geographic grids based on the reference signal reception strength in the sampled data, and obtain the average value of the reference signal reception strength corresponding to each of the geographic grids; Sort the geographic grids in each coverage area based on the average value of the reference signal reception strength corresponding to each of the geographic grids to obtain a second sorting list; The second sorted list is screened based on a strong-center distance threshold and a strong-center angle threshold to obtain a screened second sorted list, wherein the strong-center distance threshold is a maximum value of a distance between a regional strong center and a regional centroid in a preset coverage area, and the strong-center angle threshold is a maximum value of an angle between a regional strong center and a regional centroid in a preset coverage area; Select M geographic grids whose average reference signal reception strength is greater than a preset reference signal reception strength threshold from the second sorting list, and use the center point of each geographic grid as the regional strong point to obtain M regional strong points.

6. The method according to claim 1, characterized in that The step of configuring centroid weights for the regional centroids of the respective coverage areas based on the combination of the global centroid and each regional center comprises: Obtaining a reference line for each of the coverage areas based on a ray connecting the global centroid and the regional centroids within each of the coverage areas; Calculate the gravity center related weight according to the reference line and the line connecting the regional gravity center and the regional centroid in the regional center combination in the coverage area; Calculate the strong center related weight according to the reference line and the line connecting the regional strong center and the regional centroid in the regional center combination in the coverage area; Based on the centroid-related weight and the strong-centroid-related weight corresponding to the regional center combination, the centroid weight of the regional centroid of each of the coverage areas under the regional center combination is obtained.

7. The method according to claim 6, characterized in that The barycenter-related weights include: barycenter distance weights and barycenter angle weights. The steps of calculating the barycenter-related weights according to the reference line and the line connecting the regional barycenter and the regional centroid in the regional center combination in the coverage area include: Calculate the centroid distance weight according to the distance value of the line between the regional centroid and the regional centroid in the regional center combination in the coverage area; The centroid angle weight is calculated according to the angle between the reference line and the line connecting the regional centroid and the regional shape centroid in the regional center combination in the coverage area.

8. The method according to claim 6, characterized in that The strong-heart related weights include: strong-heart distance weights and strong-heart angle weights. The steps of calculating the strong-heart related weights according to the reference line and the line connecting the regional strong heart and the regional centroid in the regional center combination in the coverage area include: Calculate the strong center distance weight according to the distance value of the line between the strong center of the region and the center of the region in the combination of the regional centers in the coverage area; The strong center angle weight is calculated according to the angle between the reference line and the line connecting the strong center of the region and the region centroid in the combination of regional centers in the coverage area.

9. A base station location calculation device based on multi-center fusion, characterized in that: include: an acquisition unit, configured to acquire device data reported by user devices in a plurality of coverage areas corresponding to a target base station, and preprocess the device data to obtain a sampling data set, wherein the sampling data set includes a sampling point and sampling data of each sampling point, and the sampling data includes at least: a location coordinate of the sampling point and a reference signal reception strength; A determination unit, configured to determine a global centroid and a regional centroid of each of the coverage areas based on the position coordinates of the sampling points; A mapping unit, used to map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centroids and M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid, wherein N and M are both positive integers, the regional centroid is determined based on the number of sampling points in each geographic grid, and the regional strong center is determined based on the reference signal reception strength of the sampling points in each geographic grid; A configuration unit, configured to combine each regional centroid and regional strong centroid of each of the coverage areas to obtain a plurality of regional center combinations, configure a centroid weight for the regional centroid of each of the coverage areas based on the global centroid and each regional center combination, and calculate the candidate position coordinates of the target base station under the regional center combination based on the centroid weight and the position coordinates of the regional centroid to obtain a candidate position coordinate set; The calculation unit is used to calculate the expected values ​​of all candidate position coordinates in the candidate position coordinate set, obtain the target position coordinates of the target base station, and determine the target position of the target base station based on the target position coordinates.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the base station location estimation method based on multi-center fusion as described in any one of claims 1 to 8.

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