A method, apparatus, electronic device, and storage medium for estimating the azimuth angle of a residential area.

Abstract

This disclosure provides a cell azimuth estimation method, apparatus, electronic device, and storage medium, belonging to the field of wireless communication. A cell azimuth estimation method includes: rasterizing the MR sampling points of a cell to obtain a first grid of MR sampling points; extracting the second grid with the N largest signal strength from the target grid, where the target grid is the first grid whose distance from the cell is greater than a preset distance; and calculating the cell azimuth based on the latitude and longitude of the second grid. Implementing the technical solution of this disclosure can quickly and accurately estimate the cell azimuth, thereby reducing the difficulty of wireless network optimization.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more particularly to a cell azimuth estimation method, apparatus, electronic device, and storage medium. Background Technology

[0002] In wireless communication, antennas play a crucial role in transmitting and receiving signals. For a base station cell, the azimuth angle of the antenna is a critical parameter, determining the cell's primary coverage direction and consequently affecting the signal quality of terminals within that coverage area. Obtaining the actual azimuth angle of each cell is essential for optimizing wireless network coverage. Currently, cell azimuth angle data primarily comes from cell parameter tables. However, because cell antenna azimuth angles frequently require adjustment, the azimuth angles recorded in these tables may not match the actual values, increasing the difficulty of wireless network optimization and often necessitating significant manpower and time for verification. Summary of the Invention

[0003] To address at least one technical problem in the prior art, this disclosure provides a cell azimuth estimation method, apparatus, electronic device, and storage medium.

[0004] According to a first aspect of this disclosure, a method for estimating the azimuth of a residential area includes:

[0005] The MR sampling points of the cell are rasterized to obtain the first grid of the MR sampling points;

[0006] Extract the second grid cell with the N largest signal strength from the target grid, wherein the target grid cell is the first grid cell whose distance from the cell is greater than a preset distance;

[0007] The cell azimuth is calculated based on the latitude and longitude of the second grid.

[0008] Optionally, before performing rasterization processing on the MR sampling points of the cell, the method further includes:

[0009] Based on the RSRP value of the MR sampling point of the cell, calculate the first distance between the MR sampling point and the cell site;

[0010] Based on the latitude and longitude of the MR sampling point of the cell and the latitude and longitude of the cell, calculate the second distance between the MR sampling point and the cell;

[0011] MR sampling points whose difference between the first distance and the second distance is greater than a preset difference are filtered out.

[0012] Optionally, calculating the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell includes:

[0013] The first distance D is calculated using the following formula:

[0014]

[0015] In the formula, a and b are preset parameters.

[0016] Optionally, the step of rasterizing the MR sampling points of the cell to obtain the first grid of the MR sampling points includes:

[0017] Based on the location information of the MR sampling points, the MR sampling points are mapped to the corresponding grids in the rasterized map, and the corresponding grids are used as the first grids of the MR sampling points.

[0018] Optionally, the signal strength is the RSRP value, and / or N is any integer between 1 and M, where M is half the number of target grids.

[0019] Optionally, the preset distance is the median of the distances between each of the MR sampling points and the cell, or the preset distance is the average of the distances between each of the MR sampling points and the cell.

[0020] Optionally, calculating the cell azimuth angle based on the latitude and longitude of the second grid includes:

[0021] Calculate the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the cell;

[0022] The azimuth angle of the cell is obtained based on the relative position angle.

[0023] According to a second aspect of this disclosure, a cell azimuth estimation device includes:

[0024] The rasterization processing module is used to rasterize the MR sampling points of the cell to obtain the first grid of the MR sampling points;

[0025] A grid extraction module is used to extract the second grid with the N largest signal strength from the target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance;

[0026] The calculation module is used to calculate the cell azimuth angle based on the latitude and longitude of the second grid.

[0027] According to a third aspect of this disclosure, an electronic device includes:

[0028] Processor; and

[0029] Stored program memory,

[0030] The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of the first aspects of this disclosure.

[0031] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method according to any one of the first aspects of this disclosure.

[0032] One or more technical solutions provided in the embodiments of this application can quickly and accurately estimate the cell azimuth angle, thereby reducing the difficulty of wireless network optimization.

[0033] One or more technical solutions provided in this application embodiment obtain a first grid of MR sampling points through rasterization processing, extract the second grid with the N largest signal strengths from the first grid that is at a distance greater than a preset distance from the cell, and calculate the cell azimuth angle based on the latitude and longitude of the second grid. This allows a small number of second grids to accurately represent the main coverage area for calculating the cell azimuth angle, and reduces the negative impact of the horizontal offset of the second grid on the cell azimuth angle estimation. Therefore, when estimating the cell azimuth angle based on the latitude and longitude of the second grid, both the estimation speed and accuracy of the cell azimuth angle can be considered. Attached Figure Description

[0034] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0035] Figure 1 The flowchart of a cell azimuth estimation method according to an exemplary embodiment of the present disclosure is shown. Figure 1 ;

[0036] Figure 2 The flowchart of a cell azimuth estimation method according to an exemplary embodiment of the present disclosure is shown. Figure 2 ;

[0037] Figure 3 A schematic diagram of angle estimation deviation under the same horizontal deviation according to an exemplary embodiment of the present disclosure is shown;

[0038] Figure 4 The flowchart of a cell azimuth estimation method according to an exemplary embodiment of the present disclosure is shown. Figure 3 ;

[0039] Figure 5 A schematic block diagram of a cell azimuth estimation apparatus according to an exemplary embodiment of the present disclosure is shown;

[0040] Figure 6A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0041] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0042] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0043] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0044] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0045] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0046] The present disclosure is described below with reference to the accompanying drawings.

[0047] See Figure 1 A method for estimating the azimuth of a residential area, comprising:

[0048] S101, perform rasterization processing on the MR (Measurement Report) sampling points of the cell to obtain the first raster of the MR sampling points.

[0049] MR sampling points represent the location of a terminal device or dedicated test equipment. The cell in which an MR sampling point is located represents the serving cell of the terminal device or dedicated test equipment corresponding to that MR sampling point. MR sampling points can be obtained from MR data; for example, MR data can be obtained from the network management system, and the location information and cell in which the MR sampling point is located can be obtained from the MR data. MR data includes fields such as eNodeBID, PCI, arfcn, longitude, latitude, and RSRP. Among them, eNodeBID represents the base station identifier, PCI represents the physical cell identifier, arfcn represents the cell antenna frequency, longitude represents the user's longitude in the cell, latitude represents the latitude, and RSRP represents the reference signal received power, used to describe the reference signal received strength. For the above fields, the eNodeBID, PCI, and arfcn fields can uniquely identify a cell. As for the cell's latitude and longitude, they can be directly queried from the cell's operating parameters table, or their latitude and longitude can be estimated through the distribution characteristics of the MR data.

[0050] In one implementation, step S101 can map the MR sampling points to corresponding grids on a rasterized map based on their location information, using the corresponding grid as the first grid for the MR sampling points. Specifically, the MR sampling points can be mapped to corresponding grids on the rasterized map based on their location information and the location information of the grids in the rasterized map. For example, based on geographical distribution, square grids of specific side lengths are divided on the map, and each MR sampling point is mapped to a specific grid. The side length of the grid is set according to actual needs, for example, 20 meters. Other rasterization methods can also be used to obtain the first grid for the MR sampling points.

[0051] After rasterization, the main coverage area can be extracted by replacing MR sampling points with the first raster as the granularity.

[0052] Before performing step S101, data preprocessing and filtering steps can also be performed.

[0053] In one implementation, see Figure 2 Before executing step S101, the method includes:

[0054] S201, Calculate the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point in the cell.

[0055] In this step, the first distance can be calculated using the RSRP value of the MR sampling points.

[0056] For example, the first distance is calculated based on a simplified SPM (Standard Propagation Model), where the simplified SPM model is: RSRP = a × lg(D) + b, where D is the distance from the MR sampling point to the cell site, and a and b are parameters to be estimated, which can be obtained by fitting existing historical data. Based on the SPM model, the first distance is calculated according to the RSRP value.

[0057] Specifically, formula (1) is obtained from the SPM model, and the first distance is calculated based on formula (1).

[0058]

[0059] S202, calculate the second distance between the MR sampling point and the cell based on the latitude and longitude of the MR sampling point of the cell and the latitude and longitude of the cell.

[0060] In this step, the second distance can be calculated using relevant methods for calculating horizontal distance based on latitude and longitude.

[0061] For example, using the semi-sine formula (2), the second distance is calculated based on the latitude and longitude (x, y) of the MR sampling point and the latitude and longitude (x0, y0) of the cell:

[0062]

[0063] Where R is the Earth's radius and d is the second distance.

[0064] S203, filter out MR sampling points where the difference between the first distance and the second distance is greater than a preset difference.

[0065] The difference between the first and second distances can be a forward or reverse difference. Specifically, the absolute value of the difference between the first and second distances can be used. A preset difference can be set according to specific needs, such as 150 meters. When the difference between the first and second distances of an MR sampling point exceeds this preset difference, the MR sampling point is filtered out. This removes abnormal MR sampling points and makes the signal strength distribution more consistent with wireless propagation patterns, reducing the impact of abnormal data on azimuth estimation.

[0066] S102, extract the second grid with the N largest signal strength from the target grid, where the target grid is the first grid whose distance from the cell is greater than a preset distance.

[0067] The second grid, serving as the grid for the main coverage area (i.e., the grid in the azimuth angle), is used to calculate the cell azimuth angle. However, since the extracted second grid may not be completely on the normal to the cell azimuth angle and may have a certain horizontal offset, this step extracts the second grid from the first grid, which is farther from the cell, to reduce the negative impact of the horizontal offset on the cell azimuth angle calculation. The grid with the N largest signal strengths among the target grids is then extracted as the second grid, ensuring that the second grid is within the main coverage area. The preset distance can be set according to actual needs, for example, as the median distance between each MR sampling point and the cell, or the average distance between each MR sampling point and the cell. N can be set according to actual needs; for example, N can be any integer between 1 and M, where M can be half the number of target grids, or 1 / 10 of the number of target grids, etc. Specifically, N can be set to 2, 3, 4, etc. Signal strength can be expressed as RSRP value, etc. For example, the distance *r* between the grid and the cell is calculated using the mean latitude and longitude of the grid and the cell latitude and longitude according to the semi-versus formula. Grids with *r* greater than a preset distance are selected, and the three grids with the highest RSRP values ​​within each grid are selected as the main coverage area grids for extraction. If there are multiple MR sampling points within a grid, the mean of the MR sampling points can be used as the grid value.

[0068] See Figure 3 Using the latitude and longitude of the residential area as the center, according to the arc length formula... Under the same horizontal deviation Δs, the greater the distance r from the cell, the smaller the angle estimation deviation Δθ, and the more accurate the azimuth angle estimation. Therefore, when calculating the cell azimuth angle based on the second grid obtained in this embodiment, the problem of inaccurate cell azimuth angle caused by horizontal offset can be overcome to a certain extent, and the estimation accuracy of cell azimuth angle can be improved.

[0069] In this embodiment, in order to calculate the azimuth angle, the grid under the main coverage area, that is, the grid at the azimuth angle, is extracted from the grid under the cell based on signal strength characteristics such as RSRP. The angle is calculated based on the mean latitude and longitude of the MR sampling points in the grid and the latitude and longitude of the cell.

[0070] S103 calculates the cell azimuth angle based on the latitude and longitude of the second grid.

[0071] In one implementation, see Figure 4 S103 includes:

[0072] S401, calculate the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the cell.

[0073] For two points on the map with latitude and longitude of (x0, y0) and (x, y), their relative position angle can be determined based on the inverse trigonometric function relationship between their horizontal distance and longitude distance. The horizontal distance r has already been calculated in step four; only the longitude distance L needs to be determined, and its calculation formula (3) is:

[0074] L = R·arccos[cos 2 (y0)cos(x0-x)+sin 2 (y0)]·π / 180 (3)

[0075] For example, for the three extracted grid cells, their horizontal distance and longitude distance are summed respectively, and then the relative position angle α is calculated using the inverse trigonometric function (4):

[0076]

[0077] S402, based on the relative position angle, obtain the azimuth angle of the cell.

[0078] Since the cell azimuth is defined as the clockwise angle between true north and the cell azimuth normal, and the relative position angle α in the previous step is the angle between the azimuth normal and the meridian, the angle can be corrected based on the relative position angle between the grid and the cell to obtain the cell azimuth. Let the grid latitude and longitude mean be... If the latitude and longitude of the community is (x0, y0), then the correction formula (5) is as follows:

[0079]

[0080] The corrected θ is the estimated cell azimuth. After obtaining the estimated cell azimuth, it can be compared with the azimuth in the cell engineering parameter table. For cells with large deviations, the azimuth can be checked on-site, and abnormal cell azimuths can be calibrated in a timely manner.

[0081] This step can also use other cell azimuth calculation methods based on latitude and longitude to calculate the cell azimuth based on the latitude and longitude of the second grid.

[0082] The technical solution disclosed herein reduces the impact of abnormal data on azimuth estimation by combining a wireless propagation model for data filtering. Existing solutions are relatively simplistic in data filtering, merely applying a threshold based on the number of users or signal strength. However, such thresholds often vary depending on the base station, making it difficult to select a universal threshold. The method disclosed herein, however, combines the SPM model with the fitting results of actual data. Based on the RSRP value of the sample points, it infers the distance from the base station. This distance is compared with the latitude and longitude distance from the base station, filtering out sample points with large differences. This makes the filtered data more consistent with wireless propagation patterns, with a more distinct signal strength distribution, while also retaining some sample points from line-of-sight scenarios. This reduces the impact of abnormal data on azimuth estimation and mitigates the influence of complex wireless environments on azimuth estimation. By combining a wireless propagation model for data filtering, the impact of abnormal data on azimuth estimation is reduced.

[0083] The technical solution disclosed herein combines rasterization processing with RSRP distribution to estimate azimuth angles, improving both efficiency and accuracy. Typically, a single cell can contain thousands to tens of thousands, or even more, of user data. Calculating latitude and longitude based on sampling points would significantly reduce algorithm efficiency. By employing rasterization, the main coverage area is extracted directly at the raster level, improving efficiency without sacrificing accuracy. Furthermore, the main coverage area can be extracted by combining RSRP indicators within the raster, and by incorporating signal strength characteristics, the accuracy of azimuth angle estimation can be further enhanced.

[0084] The technical solution disclosed herein selects grids farther from the base station to extract the main coverage area, reducing the impact of extraction deviations on the azimuth angle and improving the stability and accuracy of the algorithm. If the main coverage area is closer to the base station, the horizontal deviation in this area often leads to a large angular deviation. According to the arc length formula, for the same arc length deviation, a larger radius results in a smaller angular deviation. Therefore, the extraction of the main coverage area should select a more distant area or grid, which is beneficial for improving the stability and accuracy of the algorithm.

[0085] See Figure 5 A cell azimuth estimation device, comprising:

[0086] The rasterization processing module 501 is used to perform rasterization processing on the MR sampling points of the cell to obtain the first grid of the MR sampling points.

[0087] The grid extraction module 502 is used to extract the second grid with the N largest signal strength from the target grid, where the target grid is the first grid whose distance from the cell is greater than a preset distance.

[0088] The calculation module 503 is used to calculate the cell azimuth angle based on the latitude and longitude of the second grid.

[0089] In one embodiment, the apparatus further includes: a filtering module, configured to calculate a first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell; calculate a second distance between the MR sampling point and the cell based on the latitude and longitude of the MR sampling point and the latitude and longitude of the cell; and filter out MR sampling points whose difference between the first distance and the second distance is greater than a preset difference.

[0090] In one implementation, the filtering module, when calculating the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point in the cell, specifically performs the following:

[0091] Calculate the first distance D using the following formula:

[0092]

[0093] In the formula, a and b are preset parameters.

[0094] In one implementation, the filtering module is used to perform rasterization processing on the MR sampling points of the cell to obtain the first grid of the MR sampling points. Specifically, it is used to: map the MR sampling points to the grid corresponding to the rasterized map according to the location information of the MR sampling points, and use the corresponding grid as the first grid of the MR sampling points.

[0095] In one implementation, the signal strength is the RSRP value.

[0096] In one implementation, the preset distance is the median or average distance between each MR sampling point and the cell.

[0097] In one implementation, N is any integer between 1 and M, and M is half the number of target grid cells.

[0098] In one implementation, the calculation module 503 is used to calculate the cell azimuth angle based on the latitude and longitude of the second grid, specifically by: calculating the relative position angle between the second grid and the cell according to the latitude and longitude of the second grid and the cell; and obtaining the cell azimuth angle according to the relative position angle.

[0099] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this disclosure.

[0100] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0101] Exemplary embodiments of this disclosure also provide a computer program product, including a computer program, wherein, when executed by a processor of a computer, the computer program is used to cause the computer to perform a method according to an embodiment of this disclosure.

[0102] refer to Figure 6 The present invention describes a structural block diagram of an electronic device 600 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0103] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0104] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 604 may include, but is not limited to, disk and optical disk. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0105] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the methods of the embodiments of this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. In some embodiments, the computing unit 601 can be configured to perform the methods of the embodiments of this disclosure by any other suitable means (e.g., by means of firmware).

[0106] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0108] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0110] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0111] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A method for estimating the azimuth of a residential area, characterized in that, include: Based on the RSRP value of the MR sampling point in the cell, calculate the first distance between the MR sampling point and the cell site; Based on the latitude and longitude of the MR sampling point and the cell, calculate the second distance between the MR sampling point and the cell; Filter out MR sampling points where the difference between the first distance and the second distance is greater than a preset difference; The MR sampling points of the cell are rasterized to obtain the first grid of the MR sampling points, wherein the MR sampling points are mapped to the grids corresponding to the rasterized map, and the corresponding grids are used as the first grids of the MR sampling points. Extract the second grid cell with the N largest signal strength from the target grid, where the target grid cell is the first grid cell whose distance from the cell is greater than a preset distance, and the signal strength is the RSRP value; The cell azimuth is calculated based on the latitude and longitude of the second grid.

2. The method according to claim 1, characterized in that, The calculation of the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point of the cell includes: The first distance D is calculated using the following formula: In the formula, a and b are preset parameters.

3. The method according to claim 1, characterized in that, N is any integer between 1 and M, where M is half the number of target grid cells.

4. The method according to claim 1, characterized in that, The preset distance is the median of the distances between each of the MR sampling points and the cell, or the preset distance is the average of the distances between each of the MR sampling points and the cell.

5. The method according to claim 1, characterized in that, The calculation of the cell azimuth angle based on the latitude and longitude of the second grid includes: Calculate the relative position angle between the second grid and the cell based on the latitude and longitude of the second grid and the cell; The azimuth angle of the cell is obtained based on the relative position angle.

6. A device for estimating the azimuth angle of a residential area, characterized in that, include: The filtering module is used to calculate the first distance between the MR sampling point and the cell site based on the RSRP value of the MR sampling point in the cell. Based on the latitude and longitude of the MR sampling point and the cell, calculate the second distance between the MR sampling point and the cell; filter out MR sampling points whose difference between the first distance and the second distance is greater than a preset difference; A rasterization processing module is used to rasterize the MR sampling points of the cell to obtain the first grid of the MR sampling points, wherein the MR sampling points are mapped to the grids corresponding to the rasterized map, and the corresponding grids are used as the first grids of the MR sampling points. The grid extraction module is used to extract the second grid with the N largest signal strength from the target grid, wherein the target grid is the first grid whose distance from the cell is greater than a preset distance, and the signal strength is the RSRP value; The calculation module is used to calculate the cell azimuth angle based on the latitude and longitude of the second grid.

7. An electronic device, characterized in that, include: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

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