Base station location calculation method, device and electronic equipment based on multi-center fusion

Abstract

The present invention discloses a method and apparatus for calculating the position of a base station based on multi-center fusion, and an electronic device, which relates to the field of mobile communication or other related technical fields. The method includes: obtaining a sampling data set of a target base station; calculating a global centroid and a regional centroid based on the data in the sampling data set; mapping each sampling point and sampling data into a geographic grid, and determining a plurality of regional centroids and regional strong centers of each coverage area based on the sampling points and sampling data within each geographic grid; combining each regional centroid and regional strong center of each coverage area to obtain a plurality of regional center combinations, and calculating candidate position coordinates of the target base station based on the global centroid and each regional center combination; calculating target position coordinates of the target base station based on the plurality of candidate position coordinates, and determining the target position of the target base station. The present invention solves the technical problem of low accuracy of the calculation result in the related art when automatically calculating the position of the base station.

Description

Technical Field

[0001] The present invention relates to the field of mobile communications or other related technical fields. Specifically, it relates to a method and device for calculating the position of a base station based on multi-center fusion, and an electronic device. Background Art

[0002] In modern communication networks, the accurate calculation of the base station position is crucial for network planning, optimization, and maintenance. Specifically, accurate base station position 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 reasonable allocation of network resources. At the same time, in case of emergencies, accurate base station position information can quickly help locate the approximate position of users, accelerating rescue and response speed, which is of great significance for ensuring public safety. Especially in aspects such as network coverage optimization, fault detection, and user experience improvement, the acquisition of accurate, reliable, and automated base station position information is the key to achieving efficient management.

[0003] Traditional base station positioning methods obtain the longitude and latitude of the base station by using GPS through manual on-site detection. Although data can be obtained intuitively, it depends on the device accuracy and the professionalism of the operators, wasting manpower and time, and may also lead to inaccurate measurement results, and it is difficult to meet the requirements of rapid network changes and automation.

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

[0005] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0006] Embodiments of the present invention provide a method and device for calculating the position of a base station based on multi-center fusion, and an electronic device, so as to at least solve the technical problem of low accuracy of the calculation result when automatically calculating the position of a base station in related technologies.

[0007] According to one aspect of an embodiment of the present invention, there is provided a method for calculating the position of a base station based on multi-center fusion, including: obtaining device data reported by user equipment in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, where 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 point and the reference signal received strength; determining a global centroid and the regional centroids 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 into a geographic grid, and determining N regional centers of gravity and M regional strong centers of each of the coverage areas based on the sampling points and sampling data in each geographic grid, where N and M are both positive integers, the regional center of gravity 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 received strength of the sampling points in each geographic grid; combining each regional center of gravity and regional strong center of each of the coverage areas to obtain multiple regional center combinations, configuring centroid weights for the regional centroids of each of the coverage areas based on the global centroid and each regional center combination, and calculating 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 centroids to obtain a set of candidate position coordinates; calculating the expected value of all candidate position coordinates in the set of candidate position coordinates to obtain 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.

[0008] Further, the step of determining the global centroid and the regional centroids 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 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 sampling points in each of the coverage areas, and obtaining the regional centroids of each of the coverage areas based on the regional average longitude and latitude values.

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

[0010] Further, the steps of determining the N regional centroids of each of the coverage areas based on the sampling points and sampling data within each geographical grid include: counting the number of sampling points within each geographical grid, and sorting the geographical grids within each coverage area based on the number of sampling points to obtain a first sorted list; screening the first sorted list based on a centroid distance threshold and a centroid angle threshold, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within the preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within the preset coverage area; selecting N geographical grids with the number of sampling points greater than a preset number threshold from the screened first sorted list, and taking the center point of each geographical grid as the regional centroid to obtain the N regional centroids.

[0011] Further, the steps of determining the M regional strong hearts of each of the coverage areas based on the sampling points and sampling data within each geographical grid include: calculating the average reference signal reception strength of all sampling points within each geographical grid based on the reference signal reception strength in the sampling data to obtain the average reference signal reception strength corresponding to each geographical grid; sorting the geographical grids within each coverage area based on the average reference signal reception strength corresponding to each geographical grid to obtain a second sorted list; screening the second sorted list based on a strong heart distance threshold and a strong heart angle threshold, where the strong heart distance threshold is the maximum value of the distance between the regional strong heart and the regional centroid within the preset coverage area, and the strong heart angle threshold is the maximum value of the angle between the regional strong heart and the regional centroid within the preset coverage area; selecting M geographical grids with the average reference signal reception strength greater than a preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographical grid as the regional strong heart to obtain the M regional strong hearts.

[0012] Further, the steps of configuring centroid weights for the regional centroids of each of the coverage areas by combining the global centroid and each regional center include: obtaining the reference line of each coverage area based on the ray of the line connecting the global centroid and the regional centroid within 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 within the coverage area; calculating the strong heart-related weight according to the reference line and the line connecting the regional strong heart and the regional centroid in the regional center combination within 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 heart-related weight corresponding to the regional center combination.

[0013] Further, the center-of-gravity related weights include: center-of-gravity distance weight and center-of-gravity angle weight. The steps of calculating the center-of-gravity related weights according to the reference line and the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area include: calculating the center-of-gravity distance weight according to the distance value of the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area; calculating the center-of-gravity angle weight according to the included angle between the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area and the reference line.

[0014] Further, the cardiac-strengthening related weights include: cardiac-strengthening distance weight and cardiac-strengthening angle weight. The steps of calculating the cardiac-strengthening related weights according to the reference line and the connection line between the regional cardiac-strengthening point and the regional centroid in the regional center combination within the coverage area include: calculating the cardiac-strengthening distance weight according to the distance value of the connection line between the regional cardiac-strengthening point and the regional centroid in the regional center combination within the coverage area; calculating the cardiac-strengthening angle weight according to the included angle between the connection line between the regional cardiac-strengthening point and the regional centroid in the regional center combination within the coverage area and the reference line.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a base station position measurement device based on multi-center fusion, including: an acquisition unit, configured to acquire device data reported by user equipment in a plurality of coverage areas corresponding to a target base station, and preprocess the device data to obtain a sampling data set, where the sampling data set includes sampling points 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; a determination unit, configured to determine a global centroid and the regional centroids of each of the coverage areas based on the position coordinates of the sampling points; a mapping unit, configured to map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centers of gravity and M regional cardiac-strengthening points of each of the coverage areas based on the sampling points and sampling data within each geographic grid, where N and M are both positive integers, the regional center of gravity is determined based on the number of sampling points within each geographic grid, and the regional cardiac-strengthening point is determined based on the reference signal reception strength of the sampling points within each geographic grid; a configuration unit, configured to combine each regional center of gravity and regional cardiac-strengthening point of each of the coverage areas to obtain a plurality of 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 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 centroids, to obtain a set of candidate position coordinates; a calculation unit, configured to calculate the expected value of all candidate position coordinates in the set of candidate position coordinates 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] Further, the determining unit includes: a first calculation module, configured to calculate a global average longitude and latitude value based on the position coordinates of all sampling points within all the coverage areas, and obtain the global centroid based on the global average longitude and latitude value; a second calculation module, configured to calculate a regional average longitude and latitude value based on the position coordinates of all sampling points within each of the coverage areas, and obtain the regional centroid of each of the coverage areas based on the regional average longitude and latitude value.

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

[0018] Further, the mapping unit further includes: a first statistics module, configured to count the number of sampling points within each of the geographical grids, and sort the geographical grids within each coverage area based on the number of sampling points to obtain a first sorted list; a first screening module, configured to screen the first sorted list based on a centroid distance threshold and a centroid angle threshold to obtain the screened first sorted list, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional center of gravity within a preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional center of gravity within a preset coverage area; a first selection module, configured to select N geographical grids with the number of sampling points greater than a preset number threshold from the screened first sorted list, and use the center point of each geographical grid as the regional center of gravity to obtain N regional centers of gravity.

[0019] Further, the mapping unit further includes: a third calculation module, configured to calculate the average reference signal reception strength of all sampling points within each of the geographical grids based on the reference signal reception strength in the sampling data, and obtain the average reference signal reception strength corresponding to each geographical grid; a first sorting module, configured to sort the geographical grids within each coverage area based on the average reference signal reception strength corresponding to each geographical grid to obtain a second sorted list; a second screening module, configured to screen the second sorted list based on a strong heart distance threshold and a strong heart angle threshold to obtain the screened second sorted list, where the strong heart distance threshold is the maximum value of the distance between the regional strong heart and the regional center of gravity within a preset coverage area, and the strong heart angle threshold is the maximum value of the angle between the regional strong heart and the regional center of gravity within a preset coverage area; a second selection module, configured to select M geographical grids with the average reference signal reception strength greater than a preset reference signal reception strength threshold from the second sorted list, and use the center point of each geographical grid as the regional strong heart to obtain M regional strong hearts.

[0020] Further, the configuration unit includes: a first acquisition module, configured to obtain a reference line of each of the coverage areas based on a ray of a connection line between the global centroid and the regional centroids in each of the coverage areas; a fourth calculation module, configured to calculate a centroid-related weight according to the reference line and a connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area; a fifth calculation module, configured to calculate a strongheart-related weight according to the reference line and a connection line between the strongheart of the region and the regional centroid in the regional center combination within the coverage area; a second acquisition module, configured to obtain a centroid weight of the regional centroids of each of the coverage areas under the regional center combination based on the centroid-related weight and the strongheart-related weight corresponding to the regional center combination.

[0021] Further, the centroid-related weight includes: a centroid distance weight and a centroid angle weight. The fourth calculation module includes: a first calculation sub-module, configured to calculate the centroid distance weight according to a distance value of a connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area; a second calculation sub-module, configured to calculate the centroid angle weight according to an angle between the connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area and the reference line.

[0022] Further, the strongheart-related weight includes: a strongheart distance weight and a strongheart angle weight. The fifth calculation module includes: a third calculation sub-module, configured to calculate the strongheart distance weight according to a distance value of a connection line between the strongheart of the region and the regional centroid in the regional center combination within the coverage area; a fourth calculation sub-module, configured to calculate the strongheart angle weight according to an angle between the connection line between the strongheart of the region and the regional centroid in the regional center combination within the coverage area and the reference line.

[0023] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including one or more processors and a memory. The memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement any one of the above base station location calculation methods based on multi-center fusion.

[0024] In this application, through the following steps: obtaining device data reported by user equipment in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, where 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 point and the reference signal received strength, and determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points, then mapping each sampling point and sampling data in the sampling data set into a geographic grid, 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, 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 center is determined based on the reference signal received strength of the sampling points in each geographic grid, and combining each regional centroid and regional strong center of each coverage area to obtain multiple regional center combinations, configuring centroid weights for the regional centroids of each coverage area based on the global centroid and each regional center combination, and calculating the candidate position coordinates of the target base station under this regional center combination based on the centroid weights and the position coordinates of the regional centroids to obtain a set of candidate position coordinates, and finally calculating the expected value of all candidate position coordinates in the set of candidate position coordinates to obtain 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.

[0025] In this application, through regional division by geographic grid-based multi-sampling points, the distribution characteristics of sampling points can be analyzed more finely. At the same time, by combining data such as position information and signal reception strength data to define the global centroid, regional centroid, regional centroid, and regional strong center, multi-center fusion is used to calculate the base station position. By comprehensively considering factors such as the mean of the base station signal coverage, the aggregation degree of user equipment, and the signal strength, the base station position can be calculated more scientifically and reasonably, achieving the technical effect of improving the accuracy of base station position calculation. Furthermore, it solves the technical problem of low accuracy of the calculation result in the related technology when automatically calculating the base station position. 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 schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

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

[0028] Figure 2 It is a flowchart of an optional method for calculating the position of a base station based on multi-center fusion according to an embodiment of the present invention;

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

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

[0031] Figure 5 It is a hardware structure block diagram of an electronic device (or mobile device) that executes the base station location calculation method based on multi - center fusion according to an embodiment of the present invention. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope 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 do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non - exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] It should be noted that the base station location calculation method and its device based on multi - center fusion in this application can be used in the case of calculating the base station location in the field of mobile communication, and can also be used in any field other than the field of mobile communication in the case of calculating the base station location. The application field of the base station location calculation method and its device based on multi - center fusion in this application is not limited.

[0035] It should be noted that the information collected in this application (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.) are information and data that have been authorized by the user or fully authorized by all parties. Moreover, for the processing of relevant data such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with relevant laws, regulations, and standards, necessary confidentiality measures are taken, it does not violate public order and good customs, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or institutions to provide 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 geographical data of user equipment and data such as signal reception strength, the present invention defines a global centroid, regional centroids, regional strong hearts, and regional centers of gravity, and calculates the geographical location of the target base station through multi-center fusion. Compared with positioning algorithms based on a single factor, it can better handle the complex situations of signal propagation in different scenarios, and the calculation accuracy can be greatly improved based on the multi-center fusion measurement method.

[0037] The present invention performs geographical grid division on sampling data to improve data processing accuracy. The grid accuracy can be set according to different scenarios, enabling the position measurement method of the present invention to adapt to diverse geographical environments and greatly improving the accuracy of measurement results.

[0038] The following will describe the present invention in detail in conjunction with each embodiment.

[0039] Embodiment 1

[0040] According to an embodiment of the present invention, an embodiment of a method for measuring the location of a base station 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0041] The method embodiment provided by the first embodiment of this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for measuring the location of a base station based on multi-center fusion is shown. As Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (illustrated as 102a, 102b, ……, 102n in the figure) (the processor 102 may include, but is not limited to, processing devices 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 further 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. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown in, or have a different configuration from Figure 1 that shown.

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

[0043] The memory 104 may be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for calculating the base station position in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned method for calculating the base station position. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise 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. Specific examples of the above-mentioned network may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a 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 can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0046] Under the above operating environment, the present application provides a method for calculating the position of a base station as shown in Figure 2 The implementation subject of this method is a base station position calculation system based on multi-center fusion.

[0047] Figure 2 It is a flowchart of an optional method for calculating the position of a base station based on multi-center fusion according to an embodiment of the present invention, as shown in Figure 2 shown, the method includes the following steps:

[0048] Step S201, obtain the device data reported by user equipment in multiple coverage areas corresponding to the target base station, and preprocess the device data to obtain a sampling data set.

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

[0050] In the embodiments of the present invention, by taking the location information and signal reception strength of the user equipment as consideration factors, the regional centroid, regional barycenter, regional strong center, and global centroid within the coverage range of each target base station are defined. By selecting the ray from the global centroid to the regional centroid as the reference line, and based on this, the weights related to the regional barycenter and regional strong center are calculated. Considering the length of the line connecting the regional barycenter and the global centroid, and the angle between this line and the reference line, as well as the length of the line connecting the regional strong center and the global centroid, and the angle between this line and the reference line, the weight of the regional centroid is determined. This process simulates the propagation path and direction of signals in the actual environment. In actual situations, the signal propagation direction tends to be towards areas with dense users, and the calculation methods of the regional barycenter and regional strong center are related to user distribution and signal strength. Therefore, the factors considered in the positioning process in the embodiments of the present invention are closely combined with the actual signal propagation direction, which is more in line with the actual propagation direction, and thus can improve the accuracy of base station positioning.

[0051] In step S101 above, 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 originate from the network optimization team of the network operator, the base station maintenance department, or a third-party network analysis service provider, etc. When there are problems such as network coverage, low signal-to-noise ratio, or poor user experience, base station location measurement can be used to locate the problem, and then corresponding optimization measures can be taken. The location measurement request usually includes the following key information: target base station identifier, request source information, attribute parameters, performance index requirements. Among them, the attribute parameters can include: service provider, communication technology (i.e., network mode), signal transmission range (i.e., frequency band), coverage range parameters (such as urban and rural areas), and usage scenario parameters (such as macro base stations and in-building base stations).

[0052] Upon receiving a request for estimating the base station location, the system first analyzes each parameter in the request and, based on this, determines the target base station to be estimated. And it obtains the MDT data (i.e., device data) within all coverage areas of the target base station. Here, the "coverage area" refers to the geographical range that the target base station's signal can reach, which is usually determined by the base station's transmission power, antenna configuration, and the surrounding environment. For example, a base station can cover multiple cells, and each cell can be regarded as a coverage area, and the MDT data of all user devices within the cell is obtained. The MDT data is reported by the user devices, and the MDT data includes the longitude and latitude of the user devices and the Reference Signal Received Power (RSRP for short). User devices exchange data with network infrastructures such as base stations through wireless signals to achieve various functions such as voice communication, data transmission, and multimedia services. User devices cover various types of mobile and fixed devices, including but not limited to: smartphones, tablets, laptops, Internet of Things devices, vehicle communication systems, and wearable devices. The longitude and latitude of the user device are the current position coordinates reported by its built-in positioning module; the Reference Signal Received Power (RSRP) is the strength of the signal of the target base station received by the user device, which reflects 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 in missing data, standardize signal strength, etc. At the same time, discrete points in the original data are removed to obtain sampling points and the sampling data of each sampling point, and finally a sampling data set is obtained.

[0054] An optional embodiment, the steps of preprocessing the device data include: performing data cleaning on the device data to obtain the cleaned device data, where data cleaning includes: missing value processing and outlier processing; setting a data valid interval, and screening the cleaned device data based on the data valid interval to obtain the screened device data, where the screened device data are all within the data valid interval.

[0055] Specifically, when preprocessing device data, first perform data cleaning on the device data, aiming to remove or adjust missing values and outliers in the device data. For missing values, interpolation methods, statistics-based prediction methods, or filling them by referring to the average value of adjacent device data can be used to ensure the integrity of the dataset and the feasibility of subsequent analysis. Outliers refer to points in the dataset that deviate significantly from the normal values, which may be caused by device failures, measurement errors, or extreme environmental conditions. Through statistical analysis methods, such as calculating the mean and standard deviation of the dataset, these outliers are identified and removed. Further, after obtaining the cleaned device data, set a data valid interval according to the value of the reference signal received power. Retain the device data within the interval and remove the data outside the interval through the data valid interval to remove inaccurate sampling points. For example, set the valid interval of RSRP to [-120, 160], and filter out the sampling points within this interval. The filtered dataset not only removes abnormal and unreasonable data but also ensures the integrity of the data by filling in the missing values, thereby improving the accuracy and reliability of the base station location calculation.

[0056] An optional embodiment further includes the following steps for preprocessing device data: For the filtered device data, sort all the device data according to the reference signal received power to obtain a sorted list; split the sorted list according to the coverage areas to which the sampling points corresponding to the device data in the sorted list belong to obtain sorted sub-lists corresponding to each coverage area; intercept the device data in the sorted sub-lists corresponding to each coverage area according to a preset interception ratio to obtain the intercepted device data.

[0057] Specifically, for the filtered device data, the preprocessing operation also includes sorting and partitioning screening according to the reference signal received power. First, for the device data that has been filtered and confirmed to be within the valid interval, the system sorts all the device data according to the reference signal received power (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 communication, a higher RSRP value usually means better communication quality between the user equipment and the base station, a more direct signal path, and less influence from external interference and multipath effects. Therefore, based on the RSRP sorting, data points with high signal quality and more reliable location information can be given priority, improving the accuracy of the base station location calculation. Subsequently, according to the coverage areas to which the sampling points corresponding to the device data in the sorted list belong, the sorted list is split into sub-lists corresponding to multiple coverage areas, that is, sorted sub-lists.

[0058] Finally, according to the preset truncation ratio (for example, it can be set to the first 80%), the device data in the sorted sub-lists corresponding to each coverage area is truncated to obtain the truncated device data. The selection of the truncation ratio should be based on a comprehensive consideration of signal quality and location calculation requirements, aiming to retain the data points with the highest signal strength and the least influence from environmental factors, while reducing the computational amount and improving the processing efficiency. The truncated 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. The sampling data set includes sampling points and the sampling data of each sampling point. One user device corresponds to one sampling point, and the sampling data includes at least: the position coordinates of the sampling point and the reference signal received strength.

[0060] Step S202: Determine the global centroid and the regional centroids 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 sampling data set. First, the global centroid is calculated based on the longitude and latitude coordinates of all sampling points. The global centroid represents the geometric center position of all sampling points within the coverage range of the entire target base station, and can reflect the central tendency of the entire signal coverage area. Then, the regional centroids are calculated based on the longitude and latitude coordinates of the sampling points within each coverage area, which more precisely represents the center of signal propagation and user distribution within each coverage area, and can capture more detailed spatial distribution characteristics.

[0062] Furthermore, the step of determining the global centroid and the regional centroids 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 within 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 within each coverage area, and obtaining the regional centroids 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 influence of the centroid and the strong centroid on the cell centroid, based on the ray direction from the global centroid to the regional centroid, it provides a directional reference for the analysis of the signal propagation path. The global centroid is determined based on the average longitude and latitude of all sampling points, summarizing the longitude and latitude coordinate information of all sampling points. The longitude and latitude values are summed up, that is, the sum of all longitude values and the sum of all latitude values. Then, these sums are respectively 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 central position of all signal receiving points within the coverage area, providing a macroscopic position reference point for subsequent positioning analysis.

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

[0065] Step S203: Map each sampling point and sampling data in the sampling data set into a geographic grid, and determine the N area centroids and the M area strong centers of each coverage area based on the sampling points and sampling data within each geographic grid.

[0066] In the above step S203, each sampling point in the sampling data set is mapped into a geographic grid system. Specifically, a coverage area is divided into more refined geographic grids, and the accuracy of the geographic grid can be set according to the general geographical environment where the target base station is located. Through the division of the geographic grid, the continuous geographical 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 within the geographic grid and the calculation of the signal reception strength, multiple centers corresponding to each area range can be determined, namely N area centroids and M area strong centers. The area centroid is determined based on the number of sampling points within each geographic grid, reflecting the most dense geographical locations of the 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 area strong center is determined based on the reference signal reception strength of the sampling points within each geographic grid. The area strong center emphasizes the peak area of the signal intensity and is usually close to the base station, so it helps to accurately locate the signal source.

[0068] In an urban environment, buildings are dense and users are often concentrated inside buildings. Buildings can have effects such as blocking and reflection on signal propagation, making signal propagation complex. Embodiments of the present invention fully consider this actual situation. During the calculation process, by statistically analyzing the data within the grid, discrete points are removed to reduce the interference of abnormal data on positioning. When determining the center of gravity and the strong point, the regional center of gravity and the regional strong point are defined respectively according to the number of sampling points within the grid and the average RSRP, and distance thresholds and angle thresholds are set to screen the appropriate longitude and latitude of the grid center. This way can highlight the roles of areas with dense users and areas with higher signal strength, and assign greater weights to rays with a "cleaner" propagation path (i.e., less reflection and closer to the line-of-sight condition) and more users. That is to say, when positioning the base station, by considering the effects of user aggregation in buildings and the signal reception strength effect, the positioning result can more accurately reflect the position of the base station in the actual complex environment, thereby improving the positioning accuracy.

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

[0070] Specifically, rasterizing the sampling data actually means dividing each sampling point into a two-dimensional geographical grid according to the longitude and latitude of the user equipment reported. The geographical grid divides the geographical space into a series of uniform and non-overlapping grid cells, and each cell (grid) represents a specific geographical area. For example, square grids of 50 meters × 50 meters can be used to correspond the longitude and latitude coordinates of the device data with these grids. The purpose of doing this is for spatial aggregation, grouping sampling points with close geographical locations into the same grid, facilitating subsequent data statistical analysis according to geographical regions, reducing the data volume, improving the processing efficiency, and at the same time being able to capture the signal strength change characteristics of local areas. When establishing the geographical 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, facilitating the unification of parameters during the calculation process.

[0071] In an alternative embodiment, after rasterizing the sampling data, for each geographical grid, the number of sampling points within the geographical grid is statistically analyzed, and the geographical grid with the number of sampling points less than the 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] Further, the steps of determining the N regional centroids of each coverage area based on the sampling points and sampling data within each geographical grid include: counting the number of sampling points within each geographical grid, and sorting the geographical grids within each coverage area based on the number of sampling points to obtain a first sorted list; screening the first sorted list based on a centroid distance threshold and a centroid angle threshold, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within the preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within the preset coverage area; selecting N geographical grids with the number of sampling points greater than a preset quantity threshold from the screened first sorted list, and taking the center point of each geographical grid as the regional centroid to obtain N regional centroids.

[0073] Specifically, the regional centroid is calculated based on the number of sampling points within the geographical grid. When selecting the regional centroid, first, count the number of sampling points within each geographical grid to identify the area where the user equipment signal reception is dense within the coverage area. According to the counted number of sampling points within each geographical grid, sort the geographical grids within each coverage area in descending order to form a first sorted list. The descending order ensures that the grid with the largest number of sampling points is considered first, providing a basis for identifying the regional centroid. Based on the first sorted list, apply two preset thresholds, the centroid distance threshold and the centroid angle threshold, 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 this line is consistent with the main direction of signal propagation, thereby improving the positioning accuracy. Select N geographical grids with a relatively large number of sampling points from the screened first sorted sub-list, and take the center point of the geographical grid as the regional center, and then obtain N regional centroids.

[0074] Further, the steps of determining the M regional strong points of each coverage area based on the sampling points and sampling data within each geographical grid include: calculating the average reference signal reception strength of all sampling points within each geographical grid based on the reference signal reception strength in the sampling data to obtain the average reference signal reception strength corresponding to each geographical grid; sorting the geographical grids within each coverage area based on the average reference signal reception strength corresponding to each geographical grid to obtain a second sorted list; screening the second sorted list based on a strong point distance threshold and a strong point angle threshold, where the strong point distance threshold is the maximum value of the distance between the regional strong point and the regional centroid within the preset coverage area, and the strong point angle threshold is the maximum value of the angle between the regional strong point and the regional centroid within the preset coverage area; selecting M geographical grids with an average reference signal reception strength greater than a preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographical grid as the regional strong point to obtain M regional strong points.

[0075] Specifically, the regional strong point is determined according to the average signal reception strength of the geographical grid. When determining the regional strong point of each regional scope, first, for each geographical grid, collect and calculate the average RSRP of all sampling points within the grid. This process aims to quantify the average intensity of the signal in different regions and provide a basis for subsequent grid sorting and determination of the regional strong point. Sort the geographical grids in descending order according to the average RSRP corresponding to each geographical grid to generate a second sorted list. In this way, identify the geographical grid with the strongest signal, that is, the focal area of signal propagation, and lay a foundation for the selection of the regional strong point. Use the preset strong point distance threshold and strong point angle threshold to screen the second sorted list. The strong point distance threshold limits the maximum allowable distance between the regional strong point and the regional centroid, ensuring that the selected strong point is within the effective range of signal coverage. The strong point angle threshold controls the maximum allowable angle between the line connecting the strong point and the regional centroid and the main direction of signal propagation, ensuring that the selection of the strong point matches the actual mode of signal propagation. From the screened second sorted list, select the first M geographical grids with a larger average RSRP, and define the center points of these grids as the regional strong points within the regional scope to obtain M regional strong points.

[0076] The setting of the above centroid distance threshold, centroid angle threshold, strong point distance threshold, and strong point angle threshold further avoids the influence of unreasonable centroids and strong points on calculating the base station coordinates. At the same time, based on the selected regional strong points and regional centroids, weights are assigned to each regional centroid, so as to assign greater weights to the regional centroids in the areas where the propagation path on the regional reference line is "cleaner" (that is, less reflection and closer to the line-of-sight condition) and there are more users.

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

[0078] In the above step S204, by combining the area centroids and the strong centers of each coverage area, configure centroid weights for the area centroids according to each combination, and calculate the candidate position coordinates of the target base station accordingly. Locate the target base station based on multiple candidate position coordinates to improve the accuracy of the position measurement of the target base station. Combine each area centroid and each area strong center selected within each area range. By combining the highest signal coverage density point (area centroid) and the signal strength peak point (area strong center), construct multiple possible explanations for the position of the base station within the coverage area. For each area center combination, calculate the distance and angle from the global centroid to the area centroid in the combination, as well as the distance and angle from the area centroid to the area centroid and the area strong center. Based on these distances and angles, assign centroid weights to the area centroids. Thus, greater weights are given to the area centroids in the coverage area where the propagation path on the reference line is "cleaner" (i.e., less reflection and closer to the line-of-sight condition) and there are more users. Calculate the candidate position coordinates of the target base station based on the centroid weights and the position coordinates of the area centroids. Repeat the above steps until all area center combinations are selected, thereby generating a set of candidate position coordinates.

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

[0080] Specifically, the weight data configured for the global centroid includes centroid-related weights and strong-heart-related weights. The centroid-related weights include: centroid distance weight and centroid angle weight. The strong-heart-related weights include strong-heart distance weight and strong-heart angle weight. Starting from the global centroid, the rays connecting to the regional centroids within each coverage area are defined as the reference lines for each coverage area, simulating the propagation path and direction of the analog signal in the actual environment. The signal propagation direction tends to be towards areas with dense users and higher signal strength, and the calculation methods of the centroid and strong heart are related to the user distribution and signal strength. By configuring relevant weights for the regional centroid through the regional centroid and regional strong heart, the factors considered in the process of positioning the target base station are closely combined with the actual signal propagation direction, more in line with the actual situation of the propagation path and direction, and thus the accuracy of base station positioning can be improved.

[0081] Further, the centroid-related weights include: centroid distance weight, centroid angle weight. The steps for calculating the centroid-related weights according to the reference line and the connection line between the regional centroid and the regional centroid in the regional center combination within the coverage area include: calculating the centroid distance weight according to the distance value between the regional centroid and the regional centroid in the regional center combination within the coverage area; calculating the centroid angle weight according to the angle between the connection line between the regional centroid and the regional centroid in the regional center combination within the coverage area and the reference line.

[0082] Specifically, the configuration of the centroid-related weights considers the effect of user aggregation. The centroid distance weight is calculated according to the distance value between the regional centroid and the regional centroid. The calculation formula of the centroid distance weight is expressed as: , where is the centroid distance weight, is the length of the reference line in this coverage area. Secondly, the centroid angle weight is configured according to the angle between the connection line between the regional centroid and the regional centroid and the reference line within the coverage area. The calculation formula of the centroid angle weight is: , where is the centroid angle weight, is the angle between the connection line between the regional centroid and the regional centroid and the reference line, is the angle threshold between the connection line between the regional centroid and the regional centroid and the reference line, that is, the centroid angle threshold.

[0083] Further, the strong-heart-related weights include: strong-heart distance weight, strong-heart angle weight. The steps for calculating the strong-heart-related weights according to the reference line and the connection line between the regional strong heart and the regional centroid in the regional center combination within the coverage area include: calculating the strong-heart distance weight according to the distance value between the regional strong heart and the regional centroid in the regional center combination within the coverage area; calculating the strong-heart angle weight according to the angle between the connection line between the regional strong heart and the regional centroid in the regional center combination within the coverage area and the reference line.

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

[0085] Further, according to a group of regional center combinations, a weight value can be configured for the regional centroid. The weight value of the regional centroid can be expressed as: where is used to distinguish different physical characteristics, is used to refine the parameter attributes: when it represents the weight related to the center of gravity. When it represents the weight related to the strong heart; when it corresponds to the parameter related to the angle, when it corresponds to the parameter related to the distance.

[0086] Based on the weight value of the regional centroid and the position coordinates of the regional centroid under multiple regional center combinations within the regional range, the candidate position coordinates of the target base station can be calculated, so as to determine 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] where is the abscissa of the regional centroid, is the ordinate of the regional centroid. Calculate the abscissa and ordinate of the candidate position respectively to obtain the candidate position coordinates.

[0090] Step S205: 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.

[0091] In the above step S205, the potential positions of the target base station are obtained according to different combinations of regional centroids and regional strong points. Then, the expectations of all potential positions are calculated to obtain the final measured position of the target base station. Finally, the planar coordinates are converted into longitude and latitude coordinates as the final target position of the base station. Thus, the measurement of the base station position based on multi-center fusion is realized, significantly improving the positioning accuracy and stability.

[0092] Through the above steps, obtain the device data reported by user equipment in multiple coverage areas corresponding to the target base station, and preprocess the device data to obtain a sampling data set. The sampling data set includes sampling points and sampling data of each sampling point. The sampling data includes at least: the position coordinates of the sampling point and the reference signal reception strength. Determine the global centroid and the regional centroids of each coverage area based on the position coordinates of the sampling points. Then, map each sampling point and sampling data in the sampling data set to a geographic grid. Determine N regional centroids and M regional strong points of each coverage area based on the sampling points and sampling data in each geographic grid, 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 point is determined based on the reference signal reception strength of the sampling points in each geographic grid. Combine each regional centroid and regional strong point 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. Calculate the candidate position coordinates of the target base station under this regional center combination based on the centroid weights and the position coordinates of the regional centroids to obtain a candidate position coordinate set. Finally, 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.

[0093] In this embodiment, by performing regional division on multiple sampling points through geographic gridization, the distribution characteristics of sampling points can be analyzed more finely. At the same time, define the global centroid, regional centroid, regional centroid, and regional strong point by combining data such as position information and signal reception strength data to realize the measurement of the base station position through multi-center fusion. Consider factors such as the mean of the base station signal coverage, the aggregation degree of user equipment, and signal strength, etc., more scientifically and reasonably measure the base station position, and achieve the technical effect of improving the accuracy of base station position measurement. Furthermore, it solves the technical problem of low accuracy of measurement results in the related technology when automatically measuring the base station position.

[0094] The following is a detailed description in combination with another alternative specific implementation manner.

[0095] In the embodiments of the present invention, through the multi-center fusion sector fitting positioning algorithm, the whole process involves multiple links such as data preprocessing, clear screening, geographical rasterization, statistical analysis, and calculation of centroid, barycenter, strong center, and weight calculation. By collecting and cleaning the MDT data reported by user equipment, including longitude, latitude, and RSRP, the data is pre-classified and geographically rasterized. Subsequently, the data within the grid is statistically analyzed to remove discrete points, and the global centroid of all data sampling points, the centroid, barycenter, and strong center of each cell are calculated. Then, 3 barycenters and strong centers are respectively selected according to the threshold for subsequent combined calculation of the cell centroid weight. Then, a multi-center connection line is constructed. Each time a combination of barycenter and strong center is selected, by connecting the cell centroid and the cluster centroid, the barycenter and the global centroid, and the strong center and the global centroid, the centroid-centroid cell reference line l_i, the barycenter-centroid line segment l_z, and the strong center-centroid line segment l_q are obtained. Further, the cell centroid weight is determined by the influence effects of the barycenter and the strong center, and the weighted average of all cells is used to determine the potential position of the target site under the corresponding barycenter-strong center combination. Finally, the final estimated coordinates are determined by the expectation of all potential positions.

[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, latitude, and RSRP. During the data processing process, the data is geographically rasterized, and an appropriate grid accuracy is selected according to different regional scenarios. This method enables the algorithm to adapt to diverse 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, during the calculation process, multiple factors such as centroid, barycenter, and strong center are comprehensively considered. Compared with the positioning algorithm with a single factor, it can better handle the complex situations of signal propagation in different scenarios, greatly improving the positioning accuracy and applicability in various application scenarios and enhancing the universality of the application scenarios.

[0097] In wireless communication, signal propagation is not an ideal straight-line propagation and is affected by multiple factors such as building occlusion and terrain. When the present invention determines the position of the target base station, a ray from the global centroid to the cell centroid is selected as the reference line, and the weights of the barycenter and the strong center are calculated based on this. Considering the length of the line connecting the barycenter and the global centroid, and the angle between this line and the reference line, the length of the line connecting the strong center and the global centroid, and the angle between this line and the reference line to determine the cell centroid weight. 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 the user-dense area, and the calculation methods of the barycenter and the strong center are related to the user distribution and signal strength. Therefore, the factors considered in the positioning process of this algorithm are closely combined with the actual signal propagation direction, which is more in line with the actual propagation direction, and thus can improve the accuracy of base station positioning.

[0098] In an urban environment, buildings are dense and users are often concentrated inside buildings. Buildings can block, reflect, etc. the signal propagation, making the signal propagation complex. The present invention fully considers this actual situation. During the calculation process, by statistically analyzing the data within the grid, discrete points are removed to reduce the interference of abnormal data on positioning. When determining the center of gravity and the strong heart, the cell center of gravity and the cell strong heart are respectively defined according to the number of sampling points and the average RSRP within the grid, and distance thresholds and angle thresholds are set to screen the appropriate longitude and latitude of the grid center. This method can highlight the roles of areas with dense users and areas with higher signal strength, and assign greater weights to rays with a more "clean" propagation path (i.e., less reflection and closer to the line-of-sight condition) and more users. That is to say, when positioning the base station, by considering the effect of user aggregation and use in the building, the positioning result can more accurately reflect the position of the base station in the actual complex environment, thereby improving the positioning accuracy.

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

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

[0101] Step 2, clean the data and screen the strong data to clean the RSRP sampling points.

[0102] Clean the RSRP sampling data to remove inaccurate sampling points. Set the effective interval of RSRP as [-120, 160], and retain the RSRP sampling points within this interval; the screened RSRP sampling points are sorted from strong to weak according to the signal strength, and then the top 80% of the strong RSRP sampling points are intercepted.

[0103] Step 3, rasterize the geographical plane.

[0104] Map the processed device data to a two-dimensional geographical grid system, and the grid accuracy can be selected according to the regional scenario.

[0105] Step 4, delete discrete points.

[0106] Among the rasterized data, count the number of sampling points in each grid. The number of sampling points is less than The grids are regarded as containing sporadic discrete points, and the identified sporadic discrete points are deleted from the dataset.

[0107] Step Five, calculate the global centroid and the cell centroid.

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

[0109] Step Six, screen the cell center of gravity and the cell strong center.

[0110] Calculate the cell center of gravity according to the number of sampling points in the grid. Sort the grids in descending order according to the number of sampling points in the grid, and screen the top N grids according to the distance threshold between the center of gravity and the cell centroid and the angle threshold ; The center point of the screened grid is the cell center of gravity; Define the cell strong center according to the average RSRP in the grid, sort the grids in descending order, and screen the top M grids according to the distance threshold between the strong center and the cell centroid and the angle threshold ; The center point of the screened grid is the cell strong center. By setting the above thresholds, the influence of unreasonable center of gravity and strong center on estimating the 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 more "clean" (i.e., less reflection and closer to the line-of-sight condition) propagation paths and more users on the cell reference line.

[0111] Step Seven, randomly combine the cell center of gravity and the cell strong center to calculate the suspected location.

[0112] Calculate the weight related to the center of gravity. Considering the effect of user aggregation, according to the length of the center of gravity - centroid line segment , and the included angle between it and the cell reference line , calculate the weight related to the center of gravity of the cell centroid. The calculation formula of the weight related to the center of gravity is as follows:

[0113] , ,

[0114] Where, 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 Nine: Determine the final location of the target base station based on the suspected location. Determine the potential locations of the target base station according to different combinations of cell centroids and cell strong centers. Calculate the expectation of all potential locations to obtain the location coordinates of the target base station, and convert the planar coordinates into longitude and latitude coordinates as the final target coordinates of the target base station.

[0126] Step Ten: End.

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

[0128] Embodiment 2

[0129] A base station location calculation device based on multi-center fusion provided in this embodiment includes multiple implementation units. Each implementation unit corresponds to each implementation step in Embodiment 1 above. The specific implementation manners and beneficial effects can be referred to the foregoing method embodiments and will not be elaborated here.

[0130] Figure 4 is a schematic diagram of an optional base station location calculation device based on multi-center fusion according to an embodiment of the present invention. As Figure 4 shown, the base station location calculation 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, where

[0131] The acquisition unit 41 is configured to acquire device data reported by user equipment in multiple coverage areas corresponding to the target base station, and preprocess the device data to obtain a sampling data set. The sampling data set includes sampling points and sampling data of each sampling point. The sampling data at least includes: the location coordinates of the sampling point and the reference signal reception strength;

[0132] The determination unit 42 is configured to determine the global centroid and the regional centroids of each coverage area based on the location coordinates of the sampling points;

[0133] The mapping unit 43 is configured to map each sampling point and sampling data in the sampling data set to a geographic grid, and determine N regional centers of gravity and M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid, where N and M are both positive integers. The regional center of gravity 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;

[0134] The configuration unit 44 is configured to combine each regional center of gravity and regional strong center 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 location coordinates of the target base station under the regional center combination based on the centroid weights and the location coordinates of the regional centroids to obtain a set of candidate location coordinates;

[0135] A calculation unit 45 is configured to calculate the expected values of all candidate location coordinates in the set of candidate location coordinates, obtain the target location coordinates of the target base station, and determine the target location of the target base station based on the target location coordinates.

[0136] The above-mentioned base station location measurement device obtains device data reported by user equipment in multiple coverage areas corresponding to the target base station through the acquisition unit 41, and preprocesses the device data to obtain a sampling data set, where the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data includes at least: the location coordinates of the sampling point 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 and M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid, 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 center is determined based on the reference signal reception strength of the sampling points in each geographic grid; the configuration unit 44 combines each regional centroid and regional strong center of each coverage area to obtain a plurality of regional center combinations, configures centroid weights for the regional centroids of each coverage area based on the global centroid and each regional center combination, and calculates the candidate location coordinates of the target base station under the regional center combination based on the centroid weights and the location coordinates of the regional centroids, obtaining a set of candidate location coordinates; the calculation unit 45 calculates the expected values of all candidate location coordinates in the set of candidate location coordinates, obtains the target location coordinates of the target base station, and determines the target location of the target base station based on the target location coordinates.

[0137] In this embodiment, by performing regional division through geographical grid-based multi-sampling points, the distribution characteristics of sampling points can be analyzed more precisely. At the same time, the global centroid, regional centroid, regional centroid, and regional strong center are defined by combining data such as location information and signal reception strength data, realizing multi-center fusion measurement of the base station location, comprehensively considering factors such as the mean of the base station signal coverage, the aggregation degree of user equipment, and signal strength, and can more scientifically and reasonably measure the base station location, achieving the technical effect of improving the accuracy of base station location measurement. Furthermore, it solves the technical problem of low accuracy of measurement results in the related art when automatically measuring the base station location.

[0138] Further, the determination unit 42 includes: a first calculation module configured to calculate the global average longitude and latitude value based on the location coordinates of all sampling points in all coverage areas, and obtain the global centroid based on the global average longitude and latitude value; a second calculation module configured to calculate the regional average longitude and latitude value based on the location 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 value.

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

[0140] Further, the mapping unit 43 further includes: a first statistical module, configured 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, configured to screen the first sorted list based on a centroid distance threshold and a centroid angle threshold to obtain a screened first sorted list, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within the preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within the preset coverage area; a first selection module, configured to select N geographic grids with the number of sampling points greater than a 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] Further, the mapping unit 43 further includes: a third calculation module, configured to calculate 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; a first sorting module, configured to sort 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; a second screening module, configured to screen the second sorted list based on a strong heart distance threshold and a strong heart angle threshold to obtain a screened second sorted list, where the strong heart distance threshold is the maximum value of the distance between the regional strong heart and the regional centroid within the preset coverage area, and the strong heart angle threshold is the maximum value of the angle between the regional strong heart and the regional centroid within the preset coverage area; a second selection module, configured to select M geographic grids with the average reference signal reception strength greater than a preset reference signal reception strength threshold from the second sorted list, and use the center point of each geographic grid as the regional strong heart to obtain M regional strong hearts.

[0142] Further, the configuration unit 44 includes: a first acquisition module, configured to obtain a reference line of each coverage area based on a ray of a connection line between the global centroid and the regional centroid within each coverage area; a fourth calculation module, configured to calculate a centroid-related weight according to the reference line and a connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area; a fifth calculation module, configured to calculate a strong-heart-related weight according to the reference line and a connection line between the strong heart of the region and the regional centroid in the regional center combination within the coverage area; a second acquisition module, configured to obtain a centroid weight of the regional centroid of each coverage area under the regional center combination based on the centroid-related weight and the strong-heart-related weight corresponding to the regional center combination.

[0143] Further, the centroid-related weight includes: a centroid distance weight and a centroid angle weight. The fourth calculation module includes: a first calculation sub-module, configured to calculate the centroid distance weight according to a distance value of a connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area; a second calculation sub-module, configured to calculate the centroid angle weight according to an included angle between the connection line between the centroid of the region and the regional centroid in the regional center combination within the coverage area and the reference line.

[0144] Further, the strong-heart-related weight includes: a strong-heart distance weight and a strong-heart angle weight. The fifth calculation module includes: a third calculation sub-module, configured to calculate the strong-heart distance weight according to a distance value of a connection line between the strong heart of the region and the regional centroid in the regional center combination within the coverage area; a fourth calculation sub-module, configured to calculate the strong-heart angle weight according to an included angle between the connection line between the strong heart of the region and the regional centroid in the regional center combination within the coverage area and the reference line.

[0145] It should be noted here that the above-mentioned acquisition unit 41, determination unit 42, mapping unit 43, configuration unit 44, and calculation unit 45 correspond to steps S201 to S205 in Embodiment 1. The examples and application scenarios implemented by the above units and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules or units may be hardware components or software components stored in a memory (for example, memory 104) and processed by one or more processors (for example, processors 102a, 102b,..., 102n). The above modules or units may also be part of a device and may run in the computer terminal 10 provided in Embodiment 1.

[0146] The following describes the present invention with reference to another optional embodiment.

[0147] Embodiment 3

[0148] The embodiment of the present invention may further provide an electronic device, Figure 5 which is a hardware structure block diagram of an electronic device (or mobile device) that optionally executes the measurement method of the base station position according to the embodiment of the present invention, asFigure 5 As shown, the electronic device may include: one or more ( Figure 5 only one is shown in the figure) processors 502, a memory 504, a storage controller, and a peripheral interface, where the peripheral interface is connected to a radio frequency module, an audio module, and a display.

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

[0150] The processor can call the information and application programs stored in the memory through a transmission device to perform the following steps: obtaining device data reported by user equipment in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, where 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 point, the reference signal received strength; determining a global centroid and the regional centroids of each coverage area based on the position coordinates of the sampling points; mapping each sampling point and sampling data in the sampling data set into a geographic grid, and determining N regional centers of gravity and M regional strong centers of each coverage area based on the sampling points and sampling data in each geographic grid, where N and M are both positive integers, the regional center of gravity 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 received strength of the sampling points in each geographic grid; combining each regional center of gravity and regional strong center of each coverage area to obtain a plurality of regional center combinations, configuring centroid weights for the regional centroids of each coverage area 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 weights and the position coordinates of the regional centroids to obtain a set of candidate position coordinates; calculating the expected value of all candidate position coordinates in the set of candidate position coordinates to obtain 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.

[0151] The processor can also call the information and application programs stored in the memory through the transmission device to execute the following steps: The steps of determining the global centroid and the regional centroids 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 within 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 within 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 programs stored in the memory through the transmission device to execute the following steps: The steps of mapping each sampling point and sampling data in the sampling data set to the geographical grid include: establishing K geographical grids for each coverage area, where K is a positive integer; mapping the sampling points and sampling data to the geographical grid based on the position coordinates of the sampling points.

[0153] The processor can also call the information and application programs stored in the memory through the transmission device to execute the following steps: The steps of determining the N regional centroids of each coverage area based on the sampling points and sampling data within each geographical grid include: counting the number of sampling points within each geographical grid, and sorting the geographical grids within each coverage area based on the number of sampling points to obtain the first sorted list; screening the first sorted list based on the centroid distance threshold and the centroid angle threshold, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid within the preset coverage area, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid within the preset coverage area; selecting N geographical grids with the number of sampling points greater than the preset number threshold from the screened first sorted list, and taking the center point of each geographical grid as the regional centroid to obtain N regional centroids.

[0154] 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 M regional strong centers of each coverage area based on the sampling points and sampling data within each geographic grid include: calculating the average reference signal reception strength of all sampling points within 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 within each coverage area based on the average reference signal reception strength corresponding to each geographic grid, to obtain a second sorted list; screening the second sorted list based on the strong center distance threshold and the strong center angle threshold, to obtain the screened second sorted list, where the strong center distance threshold is the maximum value of the distance between the regional strong center and the regional centroid within the preset coverage area, and the strong center angle threshold is the maximum value of the angle between the regional strong center and the regional centroid within the preset coverage area; selecting M geographic grids with an average reference signal reception strength 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 programs stored in the memory through the transmission device to perform the following steps: The steps of configuring the centroid weight for the regional centroid of each coverage area by combining the global centroid and each regional center include: obtaining the reference line of each coverage area based on the ray of the connection line between the global centroid and the regional centroid within each coverage area; calculating the centroid-related weight according to the reference line and the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area; calculating the strong center-related weight according to the reference line and the connection line between the regional strong center and the regional centroid in the regional center combination within 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 programs stored in the memory through the transmission device to perform the following steps: The centroid-related weight includes: the centroid distance weight, the centroid angle weight. The steps of calculating the centroid-related weight according to the reference line and the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area include: calculating the centroid distance weight according to the distance value of the connection line between the regional center of gravity and the regional centroid in the regional center combination within the coverage area; calculating the centroid angle weight according to the angle between the connection line between the regional center of gravity 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 execute the following steps: The cardiac strengthening related weights include: cardiac strengthening distance weight, cardiac strengthening angle weight. The steps of calculating the cardiac strengthening related weights according to the reference line and the connection line between the regional cardiac strengthening and the regional centroid in the regional center combination within the coverage area include: calculating the cardiac strengthening distance weight according to the distance value of the connection line between the regional cardiac strengthening and the regional centroid in the regional center combination within the coverage area; calculating the cardiac strengthening angle weight according to the included angle between the connection line between the regional cardiac strengthening and the regional centroid in the regional center combination within 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. Through regional division with geographical grid multi-sampling points, the distribution characteristics can be analyzed more finely by sampling points. At the same time, by combining data such as position information and signal reception strength data to define the global centroid, regional centroid, regional center of gravity, and regional cardiac strengthening, the multi-center fusion is used to calculate the base station location, comprehensively considering factors such as the mean of the base station signal coverage, the aggregation degree of user equipment, and the signal strength, etc., so that the base station location can be calculated more scientifically and reasonably, and the technical effect of improving the accuracy of base station location calculation is achieved. Furthermore, the technical problem of low accuracy of the calculation result in the related technology when automatically calculating the base station location is solved.

[0159] Those of ordinary skill in the art can understand that Figure 5 The structure shown is only for illustration, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, and a mobile Internet device (MID), a PAD, etc. Figure 5 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, a display device, etc.) than those shown in Figure 5 or have a different configuration from that shown in Figure 5 shown.

[0160] Those 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 relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc, etc.

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

[0162] Embodiment 4

[0163] An embodiment of the present invention also provides a computer-readable storage medium. Optionally, in the embodiment of the present invention, the above computer-readable storage medium may be used to store the program code executed by the method for calculating the base station location provided in the first embodiment above.

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

[0165] An embodiment of the present invention also provides a computer program product. When executed on a data processing device, it is adapted to execute a program for the steps of the method for calculating the base station location: obtaining device data reported by user equipment in multiple coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set, where 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 point and the reference signal received 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 sampling data in the sampling data set to a geographic grid, and determining N regional centroids and M regional strong hearts of each coverage area based on the sampling points and sampling data in each geographic grid, 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 heart is determined based on the reference signal received strength of the sampling points in each geographic grid; combining each regional centroid and regional strong heart of each coverage area to obtain a plurality of regional center combinations, configuring centroid weights for the regional centroids of each coverage area 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 weights and the position coordinates of the regional centroids to obtain a set of candidate position coordinates; calculating the expected value of all candidate position coordinates in the set of candidate position coordinates to obtain 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.

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

[0167] In the above embodiments of the present invention, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0168] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

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

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

[0171] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0172] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating the position of a base station based on multi-center fusion, characterized in that Including: Obtain device data reported by user equipment in multiple coverage areas corresponding to a target base station, and preprocess the device data to obtain a sampling data set, where the sampling data set includes sampling points and sampling data of each sampling point, and the sampling data includes at least: the position coordinates of the sampling point and the reference signal received strength; Determine the global centroid and the regional centroid of each of the coverage areas based on the position coordinates of the sampling points, including: calculating the global average longitude and latitude values based on the position coordinates of all sampling points in all the coverage areas, 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 of the coverage areas, and obtaining the regional centroid of each of the coverage areas based on the regional average longitude and latitude values; 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 of the coverage areas based on the sampling points and sampling data in each geographic grid, 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 center is determined based on the reference signal received strength of the sampling points in each geographic grid; Combine each regional centroid and regional strong center of each of the coverage areas to obtain multiple regional center combinations, configure centroid weights 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 weights and the position coordinates of the regional centroid, to obtain a set of candidate position coordinates; Calculate the expected value of all candidate position coordinates in the set of candidate position coordinates 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 mapping each sampling point and sampling data in the sampling data set to a geographic grid includes: Establish K geographic grids for each of the coverage areas, where K is a positive integer; Map the sampling points and the sampling data to the geographic grid based on the position coordinates of the sampling points.

3. The method according to claim 1, wherein The step of determining N regional centroids of each of the coverage areas based on the sampling points and sampling data in each geographic grid includes: 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; Filter the first sorted list based on a centroid distance threshold and a centroid angle threshold, where the centroid distance threshold is the maximum value of the distance between the regional centroid and the regional centroid in the coverage area set in advance, and the centroid angle threshold is the maximum value of the angle between the regional centroid and the regional centroid in the coverage area set in advance; Select N geographic grids with the number of sampling points greater than a preset number threshold from the filtered first sorted list, and use the center point of each geographic grid as the regional centroid to obtain N regional centroids.

4. The method according to claim 1, wherein The steps of determining M regional strong centers for each of the coverage areas based on the sampling points and sampling data within each geographical grid include: Calculating the average reference signal reception strength of all sampling points within each geographical grid based on the reference signal reception strength in the sampling data, to obtain the average reference signal reception strength corresponding to each geographical grid; Sorting the geographical grids within each coverage area based on the average reference signal reception strength corresponding to each geographical grid, to obtain a second sorted list; Filtering the second sorted list based on a strong center distance threshold and a strong center angle threshold, to obtain a filtered second sorted list, where the strong center distance threshold is the maximum value of the distance between the regional strong center and the regional centroid within the preset coverage area, and the strong center angle threshold is the maximum value of the angle between the regional strong center and the regional centroid within the preset coverage area; Selecting M geographical grids with an average reference signal reception strength greater than a preset reference signal reception strength threshold from the second sorted list, and taking the center point of each geographical grid as the regional strong center, to obtain M regional strong centers.

5. The method according to claim 1, characterized in that, The steps of configuring centroid weights for the regional centroids of each of the coverage areas based on the global centroid and each regional center combination include: Obtaining the reference line for each coverage area based on the ray of the line connecting the global centroid and the regional centroids within each coverage area; Calculating the centroid-related weights according to the reference line and the line connecting the regional centroid and the regional center within the regional center combination in the coverage area; Calculating the strong center-related weights according to the reference line and the line connecting the regional strong center and the regional center within the regional center combination in the coverage area; Obtaining the centroid weights of the regional centroids of each of the coverage areas under the regional center combination based on the centroid-related weights and the strong center-related weights corresponding to the regional center combination.

6. The method according to claim 5, wherein The centroid-related weights include: centroid distance weight, centroid angle weight. The steps of calculating the centroid-related weights according to the reference line and the line connecting the regional centroid and the regional center within the regional center combination in the coverage area include: Calculating the centroid distance weight according to the distance value of the line connecting the regional centroid and the regional center within the regional center combination in the coverage area; Calculating the centroid angle weight according to the angle between the line connecting the regional centroid and the regional center within the regional center combination in the coverage area and the reference line.

7. The method according to claim 5, wherein The strong center-related weights include: strong center distance weight, strong center angle weight. The steps of calculating the strong center-related weights according to the reference line and the line connecting the regional strong center and the regional center within the regional center combination in the coverage area include: Calculating the strong center distance weight according to the distance value of the line connecting the regional strong center and the regional center within the regional center combination in the coverage area; Calculating the strong center angle weight according to the angle between the line connecting the regional strong center and the regional center within the regional center combination in the coverage area and the reference line.

8. 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 equipment in multiple coverage areas corresponding to a target base station, and preprocess the device data to obtain a sampling data set, where 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 point and the reference signal received 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. The determination unit includes: a first calculation module, configured to calculate a global average longitude and latitude value 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 value; a second calculation module, configured to calculate a regional average longitude and latitude value 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 value; A mapping unit, configured to map each sampling point and sampling data in the sampling data set into a geographic grid, and determine N regional centroids and M regional strong centers of each of the coverage areas based on the sampling points and sampling data in each geographic grid, where both N and M are 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 received strength of the sampling points in each geographic grid; A configuration unit, configured to combine each regional centroid and regional strong center of each of the coverage areas to obtain a plurality of 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 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 centroids, to obtain a set of candidate position coordinates; A calculation unit, configured to calculate an expected value of all candidate position coordinates in the set of candidate position coordinates 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.

9. An electronic device, characterized in that, It includes one or more processors and a memory, and the memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the position of a base station based on multi-center fusion according to any one of claims 1 to 7.

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