Method, device and electronic equipment for tuning a base station antenna

Abstract

The application discloses a kind of base station antenna's tuning method, device and electronic equipment.Therein, the method includes: obtaining multiple types of acquisition data in the antenna feeder system;According to the distribution region of the measurement data in multiple types of acquisition data, create grid, and match measurement data with grid;From the grid, the grid that meets the first preset condition is determined as problem grid, and according to problem grid, determine problem area;Determine the target tuning scheme of the cell to be tuned corresponding to problem area, and determine the target tuning scheme of the cell to be tuned;According to target tuning scheme, generate tuning instruction, and send tuning instruction to base station, wherein, base station adjusts the angle of base station antenna according to tuning instruction.The application solves the technical problem that the optimization method of related technology in antenna feeder problem is mainly completed by artificial, there is low efficiency, and poor accuracy.

Description

Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a method, apparatus, and electronic device for optimizing a base station antenna. Background Technology

[0002] In current mobile communication networks, improper antenna feeder parameter settings will lead to a series of problems such as weak or no coverage in user distribution areas, increased interference, uneven call load, deterioration of performance KPIs, and decreased user experience.

[0003] In related technologies, the discovery, analysis, location, and optimization of antenna and feeder problems are mainly done manually, which is inefficient, costly, and poses safety hazards. Antenna and feeder optimization solutions rely heavily on engineers' personal experience and intuition, lack scientific data support, have poor accuracy, and require repeated on-site optimization and verification.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for optimizing base station antennas, which at least solves the technical problems of low efficiency and poor accuracy in the optimization methods for antenna feed problems in related technologies, which are mainly completed manually.

[0006] According to one aspect of the embodiments of this application, a method for optimizing a base station antenna is provided, comprising: acquiring multiple types of collected data in an antenna feeder system; creating a grid based on the distribution area of ​​measurement data in the multiple types of collected data, and matching the measurement data with the grid; identifying grids that meet a first preset condition from the grid as problem grids, and identifying problem areas based on the problem grids; determining the cell to be optimized corresponding to the problem area, and determining a target optimization scheme for the cell to be optimized; generating an optimization instruction based on the target optimization scheme, and sending the optimization instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instruction.

[0007] Optionally, a grid is created based on the distribution area of ​​the measurement data in the multi-class collected data, including: obtaining grid size information, wherein the size information includes the length and width of the grid; determining first boundary information of the distribution area of ​​the measurement data, wherein the first boundary information includes the maximum latitude, minimum latitude, maximum longitude and minimum longitude; determining the number of grids based on the boundary information and size information; and creating a two-dimensional array to represent the grid based on the number of grids.

[0008] Optionally, matching measurement data with a grid includes: acquiring the latitude and longitude information of a first sampling point, wherein the first sampling point is any one of multiple sampling points used to collect measurement data; determining the row and column number of the target grid to which the first sampling point belongs in a two-dimensional array based on the latitude and longitude information and the first boundary information; and adding the first sampling point and the first measurement data corresponding to the first sampling point to the corresponding target grid.

[0009] Optionally, determining the cells to be optimized corresponding to the problem area includes: obtaining the cells associated with the sampling points in the problem area to obtain the cells to be optimized; sorting the cells to be optimized in descending order according to the number of sampling points contained in the cells to be optimized, and determining the priority of the sorted cells to be optimized.

[0010] Optionally, determining the target optimization scheme for the cell to be optimized includes: obtaining a first cell to be optimized from the cells to be optimized, wherein the first cell to be optimized is any one of the cells to be optimized; determining multiple optimization schemes for the first cell to be optimized, and determining the first optimal scheme among the multiple optimization schemes, wherein each optimization scheme corresponds to different antenna parameter adjustments; scoring the first optimal scheme for each cell to be optimized, and determining the first optimal scheme with the highest score as the target optimization scheme.

[0011] Optionally, the first optimal solution for each cell to be optimized is scored, including: determining a first score for the first optimal solution in a first dimension for each cell to be optimized, wherein the first dimension represents the antenna adjustment range, which is determined by parameters before and after antenna adjustment; determining a second score for the first optimal solution in a second dimension for each cell to be optimized, wherein the second dimension represents path loss, including multipath loss and free space path loss; determining a third score for the first optimal solution in a third dimension for each cell to be optimized, wherein the third dimension represents signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; determining a fourth score for the first optimal solution in a fourth dimension for each cell to be optimized, wherein the fourth dimension represents optimal multipath effect; and determining a total score for the first optimal solution of each cell to be optimized based on the first, second, third, and fourth scores.

[0012] Optionally, the first optimal scheme among multiple optimization schemes is determined, including: acquiring relevant data of a GIS 3D map, the problem area, and the first cell to be optimized; constructing a 3D environment model based on the relevant data, the GIS 3D map, and the problem area; starting from the antenna of the first cell to be optimized, emitting a ray towards the problem area according to the antenna parameters in the first optimization scheme among multiple optimization schemes, and determining the propagation path of the ray in the 3D environment model, wherein the first optimization scheme is any one of the multiple optimization schemes; evaluating the rank of the channel based on the number of propagation paths, the number of transmitting antennas of the ray, and the number of receiving antennas of the ray, and obtaining a first evaluation result; when the first evaluation result does not meet the second preset condition, iteratively adjusting the antenna parameters in the next optimization scheme until the evaluation result of the adjusted optimization scheme meets the second preset condition, and stopping the iteration; and determining the last optimization scheme as the first optimization scheme.

[0013] According to another aspect of the embodiments of this application, a base station antenna tuning device is also provided, comprising: an acquisition module for acquiring multiple types of collected data in an antenna feeder system; a matching module for creating a grid based on the distribution area of ​​measurement data in the multiple types of collected data, and matching the measurement data with the grid; a first determination module for determining grids that meet a first preset condition from the grids as problem grids, and determining problem areas based on the problem grids; a second determination module for determining the cell to be tuned corresponding to the problem area, and determining a target tuning scheme for the cell to be tuned; and a sending module for generating tuning instructions based on the target tuning scheme, and sending the tuning instructions to the base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instructions.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory for storing program instructions; and a processor connected to the memory for executing program instructions to perform the following functions: acquiring multiple types of collected data in an antenna feeder system; creating a grid based on the distribution area of ​​measurement data in the multiple types of collected data, and matching the measurement data with the grid; identifying grids that meet a first preset condition from the grid as problem grids, and identifying problem areas based on the problem grids; determining the cell to be optimized corresponding to the problem area, and determining the target optimization scheme for the cell to be optimized; generating optimization instructions based on the target optimization scheme, and sending the optimization instructions to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instructions.

[0015] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned base station antenna tuning method by running the computer program.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the above-described method for optimizing a base station antenna.

[0017] In this embodiment, multiple types of data are acquired from the antenna feeder system; a grid is created based on the distribution area of ​​the measurement data in the multiple types of data, and the measurement data is matched with the grid; grids that meet the first preset conditions are identified as problem grids, and problem areas are identified based on the problem grids; cells to be optimized corresponding to the problem areas are identified, and target optimization schemes for the cells to be optimized are identified; optimization instructions are generated based on the target optimization scheme, and the optimization instructions are sent to the base station. The base station adjusts the angle of the base station antenna according to the optimization instructions, thereby achieving the purpose of automatically identifying and locating network problems in the antenna feeder system. This achieves the technical effect of intelligent optimization of antenna feeder parameters, and solves the technical problem that the optimization methods for antenna feeder problems in related technologies are mainly completed manually, which has low efficiency and poor accuracy. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a hardware structure block diagram of a computer terminal for implementing a base station antenna tuning method according to an embodiment of this application.

[0020] Figure 2 This is a flowchart of a base station antenna tuning method according to an embodiment of this application;

[0021] Figure 3 This is a structural diagram of a base station antenna tuning device according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] First, some nouns or terms that appear in the explanation of the embodiments of this application shall be interpreted as follows:

[0025] The Northbound Interface (NBI) is the interface through which manufacturers or operators access and manage the network. It provides an upward-facing interface, allowing upper-layer systems or third-party systems to access and manage the network. The NBI plays a crucial role in network management architecture, especially in carrier networks and NGN (Next Generation Network), where management is implemented in layers. This network management can be divided into three layers: the application layer, the data processing layer, and the data management layer. The data interaction between the application layer and the data processing layer defines the NBI. Because the application layer sits above the data processing layer, this interface is called the Northbound Interface.

[0026] Density clustering is a clustering analysis method that identifies clusters based on the density distribution of points in a data space. In density clustering, a cluster is defined as a group of high-density points separated by low-density regions. This clustering method does not require pre-specifying the number of clusters, is not limited by the geometry of the clusters, can discover clusters of arbitrary shapes, and is robust to noise and outliers.

[0027] Ray tracing technology refers to the prediction of signal coverage and quality by simulating the propagation of radio waves in three-dimensional space, including direct, reflected, diffracted, and transmitted rays. It can provide more accurate predictions than traditional empirical models because it takes into account the effects of buildings, terrain, and other obstacles on signal propagation.

[0028] Reference Signal Received Power (RSRP) is a standard for measuring downlink signal quality in LTE and NR (5G) systems. It reflects the average power of the cell reference signal received by the user equipment (UE). A higher RSRP value indicates a stronger signal, which generally means better network coverage and potentially better communication quality.

[0029] Signal-to-interference-plus-noise ratio (SINR): This is the ratio of signal to interference plus noise. It is a metric for measuring signal quality, taking into account not only the signal strength but also background noise and interference from other signal sources.

[0030] Channel Quality Indicator (CQI): This is measured by the UE and reported to the base station, reflecting the instantaneous quality of the wireless channel. The higher the CQI value, the better the channel quality.

[0031] Rank Indicator (RI): Associated with the channel rank of a MIMO (Multiple-Input Multiple-Output) system, it reflects the magnitude of the channel's spatial degrees of freedom. In a MIMO system, the RI value indicates the number of independent data streams a UE can receive simultaneously.

[0032] In related technologies, the discovery, analysis, location, and optimization of antenna and feeder problems are mainly done manually, which is inefficient, costly, and poses safety hazards. Antenna and feeder optimization solutions rely heavily on engineers' personal experience and intuition, lack scientific data support, have poor accuracy, and require repeated on-site optimization and verification.

[0033] Furthermore, with network evolution and increasingly higher demands for service experience speeds, antenna capabilities have evolved from ordinary antennas to multi-frequency antennas, MIMO antennas, and massive MIMO antennas (such as Massive MIMO antennas). Antenna optimization, while addressing coverage requirements, places greater emphasis on performance tuning (such as improving sensing speed and throughput), especially in 5G, 5G-A, and future millimeter-wave scenarios, where performance tuning of Massive MIMO antennas (such as creating multipath, improving RANK streams, and increasing speed and throughput) is particularly important. However, given the complex wireless environment, relying solely on human experience is insufficient for antenna performance tuning.

[0034] To address the problems existing in related technologies, embodiments of this application provide a method for optimizing base station antennas. This method can be implemented in... Figure 1The computer terminal shown is explained below.

[0035] The base station antenna optimization method provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a base station antenna tuning method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions connected via wired and / or wireless networks. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the base station antenna optimization method in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned base station antenna optimization method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The transmission module 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission module 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0039] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0040] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0041] In the above operating environment, this application provides an embodiment of a method for optimizing a base station antenna. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] Figure 2 This is a flowchart of a base station antenna optimization method according to an embodiment of this application, such as... Figure 2As shown, the method includes the following steps:

[0043] Step S202: Acquire various types of data from the antenna feeder system.

[0044] In step S202 above, data acquisition mainly includes two aspects: network management acquisition and external import. First, the data types and fields to be acquired are determined, primarily including northbound MR data (measurement reports, containing signal quality information such as RSRP and SINR), network management configuration data (such as antenna power settings and neighbor cell configuration), network management KPI indicators, base station engineering parameters (such as base station location and antenna height), antenna data (including antenna gain, polarization, etc.), and GIS 3D map data (including environmental information such as terrain, building distribution, and vegetation). Specific data types and fields are shown in Table 1 below.

[0045] Table 1

[0046]

[0047] For network management data, network operation indicators are captured in real time. By designing system interfaces and seamlessly integrating with the network management system, and utilizing API interfaces or direct database connection technology, MR data (including MR latitude and longitude, primary cell RSRP / SINR, neighbor cell RSRP / SINR, CQI, RANK, etc.), network management configuration parameters (including power configuration data, neighbor cell configuration data), and network management indicators (including user perceived rate, throughput, interference level, PRB utilization, etc.) are obtained instantly.

[0048] For external data import, base station engineering parameters (including station latitude and longitude, station name, cell name, CGI, frequency, PCI, azimuth, latitude and longitude, mechanical downtilt angle, electronic downtilt angle, antenna height), antenna data (including antenna type, gain, polarization, electrical parameters, etc.), and GIS data (third-party publicly available GIS map data) are imported through external interfaces.

[0049] After acquiring various types of collected data, data cleaning algorithms are used to clean the data, removing outliers, duplicates, and errors, correcting data formats, and normalizing KPI data (e.g., PRB utilization). The data cleaning rules are as follows:

[0050] (1) Outliers: Let X be a dataset, x be any data point in the dataset, μ be the mean of the dataset, and σ be the standard deviation. Outlier detection: x is an outlier if and only if x < μ - 2σ or x > μ + 2σx. The formulas for the mean and standard deviation are as follows:

[0051]

[0052]

[0053] Where n is the number of elements in the dataset, x i It is the i-th element in the dataset.

[0054] (2) Duplicate values: Let X be a dataset. The detection of duplicate values ​​x should satisfy: duplicate items = {x∈X|X.count(x)>1}.

[0055] (3) Error Values: Define a reasonable numerical range for the data. If the data exceeds this range, it is considered an error. If x is a data point, min_val and max_val are predefined minimum and maximum values. The error value detection rule is: x<min_val∨x> When using max_valx, x is incorrect.

[0056] (4) Format check: For each data item d in the dataset, use the corresponding validation function V to check its format: is_valid(d) = V(d). If is_valid(d) is True, then the data item d is correctly formatted; otherwise, the format is incorrect.

[0057] (5) Data Normalization: The PRB utilization rate index is linearly transformed to the range [0,1] using minimum-maximum normalization. Other data is left unprocessed. The formula is as follows:

[0058]

[0059] Among them, X morm X represents the normalized data. min X represents the minimum value of the PRB utilization rate index. max This represents the maximum value of the PRB utilization rate index.

[0060] Step S204: Create a grid based on the distribution area of ​​the measurement data in the multi-class collected data, and match the measurement data with the grid.

[0061] In step S204 above, by creating a grid, measurement data can be mapped onto geographic space, forming a gridded data distribution. This allows for a more intuitive identification of problem areas, namely areas with poor signal quality, insufficient coverage, or excessive interference. Each grid represents signal quality data within a certain geographic area. By matching MR data with the grid, the distribution of signal quality measurements within each grid can be statistically analyzed.

[0062] Step S206: Select grids that meet the first preset conditions from the grid as problem grids, and determine the problem area based on the problem grids.

[0063] In step S206 above, problem grids can be automatically identified according to the first preset conditions. By aggregating multiple adjacent problem grids into a problem area, the target area for network optimization can be determined more accurately, providing a basis for subsequent antenna feeder parameter adjustments.

[0064] Step S208: Determine the cells to be optimized corresponding to the problem area, and determine the target optimization scheme for the cells to be optimized.

[0065] In step S208 above, cells related to the problem area are further identified, i.e., cells to be optimized. For each cell to be optimized, the impact of antenna parameter adjustments on signal propagation and network performance is evaluated based on ray tracing technology and intelligent scoring algorithms, thereby determining the optimal target optimization scheme, including the adjustment values ​​of parameters such as antenna azimuth and downtilt.

[0066] Step S210: Generate tuning instructions based on the target tuning scheme and send the tuning instructions to the base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instructions.

[0067] In step S210 above, based on the determined target optimization scheme, the system automatically generates optimization instructions and sends them to the base station through the network management system. Upon receiving the optimization instructions, the base station automatically adjusts the angle of its antennas to implement the optimization scheme. Automated execution of the optimization instructions avoids the inefficiency and inaccuracy of manual on-site adjustments.

[0068] In step S204 of the above-mentioned base station antenna optimization method, creating a grid based on the distribution area of ​​measurement data in multiple types of collected data includes: obtaining grid size information, wherein the size information includes the length and width of the grid; determining the first boundary information of the distribution area of ​​the measurement data, wherein the first boundary information includes the maximum latitude, minimum latitude, maximum longitude and minimum longitude; determining the number of grids based on the boundary information and size information; and creating a two-dimensional array to represent the grid based on the number of grids.

[0069] In step S204 of the above-mentioned base station antenna tuning method, matching measurement data with the grid includes: obtaining the latitude and longitude information of the first sampling point, wherein the first sampling point is any one of multiple sampling points, and the multiple sampling points are used to collect measurement data; determining the row and column number of the target grid to which the first sampling point belongs in the two-dimensional array based on the latitude and longitude information and the first boundary information; and adding the first sampling point and the first measurement data corresponding to the first sampling point to the corresponding target grid.

[0070] In this embodiment of the application, the raster corresponding to the measurement data domain is matched through the following steps:

[0071] (1) Determine the grid size: Set the grid size information, including the grid length and width. If the grid is a square, then the side length of the grid can be determined. Taking a square grid as an example, assume that its side length is A meters.

[0072] (2) Determine the first boundary information of the distribution area of ​​MR data (i.e. measurement data): that is, minimum longitude λmin, maximum longitude λmax, minimum latitude φmin, and maximum latitude φmax.

[0073] (3) Calculate the number of grids: Calculate the number of grids in the longitude (Nλ) and latitude (Nφ) directions.

[0074]

[0075]

[0076] (4) Create a grid: Initialize a two-dimensional array or data structure to represent the grid. Each grid does not have MR sampling points initially.

[0077] (5) Calculate the grid boundaries: For each grid cell, calculate its latitude and longitude boundaries. For the grid cell in the i-th row and j-th column, its longitude boundary... and The following can be calculated: Latitude boundary and The following can be calculated: Where φavg is the average value of the latitude of the grid, which can be the average of φmin and φmax.

[0078] (6) Match MR sampling points to grids: For each MR sampling point, such as the first sampling point mentioned above, its latitude and longitude information is (λ,φ). Calculate the row and column number (i,j) of the grid to which it belongs. Add the MR sampling points to the corresponding target raster.

[0079] (7) Ensure that each grid has at least one MR sampling point: If a grid does not have an MR sampling point, delete the grid.

[0080] (8) Handling boundary cases: For MR sampling points located on the grid boundary line, they are assigned to adjacent grids according to the proximity principle. If two adjacent grids both satisfy the proximity principle, the MR sampling point is classified into any one of the adjacent grids.

[0081] After matching the measurement data and sampling points to the corresponding grids, the problem grid is determined according to a first preset condition, which may be, for example, a predefined threshold. Specifically, the predefined threshold includes:

[0082] (1) Weak coverage grid: A grid where the proportion of sampling points with 4G RSRP < A (e.g., < -110 dBm) or 5G RSRP < B (e.g., < -105 dBm) ≥ X (e.g., ≥ 20%).

[0083] (2) Poor quality grid: A grid where the proportion of sampling points with SINR < C (e.g., < -3 dB) ≥ D (e.g., ≥ 20%).

[0084] (3) Overlapping coverage grid: A grid where the proportion of sampling points where the level of the serving cell > E (e.g., < -103 dBm) and the difference between the levels of F (e.g., 3) neighboring cells and the level of the serving cell < G (e.g., < 5 dB) > H (e.g., > 10%).

[0085] (4) Low CQI grid: A grid where the proportion of sampling points with CQI < I (e.g., < 7) > J (e.g., > 20%).

[0086] (5) Low Rank grid: A grid where the proportion of sampling points with RI = 1 > K (e.g., > 20%).

[0087] When identifying problem grids, it is achieved through the following process:

[0088] (1) Define the problem identification function: ProblemGrid = IdentifyProblem(GridData, Conditions);

[0089] (2) Input parameters: GridData contains the measurement data of all sampling points within the grid, such as RSRP, SINR, CQI, RI, etc. Conditions contains a set of problem identification conditions, and each condition has a threshold value and a threshold percentage;

[0090] (3) Output: ProblemGrid: A boolean value, which is true (True) if the grid meets any problem condition, otherwise false (False);

[0091] (4) Algorithm steps: For each grid, check whether it meets any problem condition defined in Conditions;

[0092] (5) General algorithm formula: ProblemGrid = Any(Condition(GridData) ≥ ThresholdPercentage),

[0093] The Any() function checks if at least one of its arguments is true. The Condition() function returns the percentage of sampling points that meet a given threshold value. ThresholdPercentage is the threshold percentage used to determine whether a condition is met.

[0094] (6) Specific conditional functions:

[0095] ① Weak cover parts:

[0096]

[0097] ② Poor quality conditions:

[0098] ③ Overlapping coverage conditions:

[0099]

[0100] ④ Low CQI conditions:

[0101] ⑤ Low Rank condition:

[0102] The density clustering algorithm (DBSCAN) is used to cluster problem grids into problem regions. The detailed process of the algorithm is as follows:

[0103] (1) The core concepts of the DBSCAN algorithm include:

[0104] ① Core Point: A point is called a core point if it has at least min_samples of neighbors within a specified radius ε.

[0105] ②Border Point: If a point has fewer than min_samples of neighbors within a specified radius ε, but the point is located in the neighborhood of a core point, then the point is called a border point.

[0106] ③ Noise Point: A point that is neither a core point nor a boundary point is called a noise point.

[0107] (2) The working principle of the DBSCAN algorithm is as follows:

[0108] ① Initialization: Select a point (i.e., a grid cell) in the dataset corresponding to the problem grid cell as the starting point.

[0109] ②Density reachability: If a point is within the ε-neighborhood of another point, and there are at least min_samples points in that neighborhood, then the two points are considered to be density reachable.

[0110] ③ Density connectivity: If there exists a point such that two points are density-reachable from that point, then the two points are considered density-connected.

[0111] ④ Cluster formation: Based on the relationship between density reachability and density connectivity, all density-connected points are grouped into the same cluster.

[0112] ⑤ Iteration: Repeat the above process until all points have been visited.

[0113] The DBSCAN algorithm can group geographically adjacent grates with common problem characteristics into problem areas based on the density relationship between grates. This allows for the identification of areas in the network with coverage or signal quality issues requiring further optimization. On the other hand, a key feature of the DBSCAN algorithm is its ability to identify and exclude isolated points (i.e., noise points), preventing isolated problem grates that do not affect overall network performance from being included in the analysis of problem areas. This helps focus on continuous problem areas that truly impact user experience and network performance, improving the targeting and efficiency of optimization solutions. Through the DBSCAN algorithm, the system can accurately identify areas with severe overlapping coverage problems or generally poor signal quality. These areas may be caused by signal overlap from multiple cells or sectors, requiring optimization by adjusting antenna parameters (such as azimuth and downtilt).

[0114] In step S208 of the above-mentioned base station antenna tuning method, determining the cell to be tuned corresponding to the problem area includes: obtaining the cell associated with the sampling point in the problem area to obtain the cell to be tuned; sorting the cells to be tuned in descending order according to the number of sampling points contained in the cell to be tuned, and determining the priority of the sorted cells to be tuned.

[0115] In some embodiments of this application, the system identifies the cells associated with MR (Measurement Report) sampling points within the problem area; these cells are the cells to be optimized. The system sorts these cells according to the number of sampling points they contain. A higher number of sampling points indicates a potentially more severe problem with network coverage and signal quality, thus requiring priority optimization. By sorting from largest to smallest, the system can determine the priority of the cells to be optimized, prioritizing the cells with the most prominent problems, thereby improving optimization efficiency and targeting.

[0116] In step S208 of the above-mentioned base station antenna tuning method, determining the target tuning scheme for the cell to be tuned includes: obtaining the first cell to be tuned from the cells to be tuned, wherein the first cell to be tuned is any one of the cells to be tuned; determining multiple tuning schemes for the first cell to be tuned, and determining the first optimal scheme among the multiple tuning schemes, wherein each tuning scheme corresponds to different antenna parameter adjustments; scoring the first optimal scheme for each cell to be tuned, and determining the first optimal scheme with the highest score as the target tuning scheme.

[0117] In the above steps, the first optimal solution for each cell to be optimized is scored, including: determining the first score of the first optimal solution for each cell to be optimized in the first dimension, where the first dimension represents the antenna adjustment range, which is determined by the parameters before and after the antenna adjustment; determining the second score of the first optimal solution for each cell to be optimized in the second dimension, where the second dimension represents path loss, including multipath loss and free space path loss; determining the third score of the first optimal solution for each cell to be optimized in the third dimension, where the third dimension represents the signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; determining the fourth score of the first optimal solution for each cell to be optimized in the fourth dimension, where the fourth dimension represents the optimal multipath effect; and determining the total score of the first optimal solution for each cell to be optimized based on the first, second, third, and fourth scores.

[0118] In some embodiments of this application, for each cell to be optimized, the system first acquires the cell's data, including parameters such as the current antenna position, downtilt angle, and azimuth angle, as well as environmental information related to the cell, such as GIS 3D map data. The system generates multiple optimization schemes for each cell. Each scheme corresponds to different antenna parameter adjustments, such as adjusting the antenna's azimuth angle and downtilt angle, to attempt to improve signal coverage and reduce interference. For each cell, the system evaluates and determines the first optimal scheme from these optimization schemes. The evaluation process is based on 3D ray tracing technology and an intelligent scoring algorithm, which scores from four dimensions: antenna adjustment amplitude, path loss, signal interference intensity, and multipath effect. Antenna adjustment amplitude (first dimension): The smaller the adjustment amplitude, the smaller the change to the existing network configuration, which helps reduce the potential impact on other cells. The score for antenna adjustment amplitude is calculated based on the difference in parameters before and after adjustment. Path loss (second dimension): Path loss includes multipath loss and free space path loss. The system uses 3D ray tracing technology to evaluate path loss under each optimization scheme, including signal diffraction, reflection, and direct transmission, to ensure that the signal can effectively reach the target area. Signal interference intensity (third dimension): Signal interference intensity is determined by calculating the power ratio of the interfering signal to the desired signal. The goal is to find a scheme that minimizes interference, thereby improving signal clarity and user communication quality. Multipath effect (fourth dimension): The evaluation of multipath effect aims to optimize the performance of the MIMO (Multiple-Input Multiple-Output) system. The system uses ray tracing technology to evaluate multipath signals under different schemes, aiming to maximize the channel rank (RANK), i.e., increasing the number of independent data streams, thereby improving data transmission rate and network performance. The system comprehensively considers the scores of these four dimensions and calculates a total score for each optimal scheme. The scheme with the highest total score will be determined as the target optimization scheme for that cell.

[0119] Specifically, the four dimensions mentioned above are explained as follows:

[0120] (1) Adjustment range (A): The adjustment range can be calculated by the change of parameters before and after antenna adjustment. A = |Parameterafter-Parameterbefore|, where Parameter can be the antenna tilt angle, azimuth angle or other adjustable parameters.

[0121] (2) Path Loss (L): Path loss in the actual environment is calculated using a 3D ray tracing model, including multipath effects such as reflection, refraction, and diffraction. PathLossactual (dB) = FSPL (dB) + MultiPathLoss (dB), where MultiPathLoss (dB) is the multipath loss calculated by the 3D ray tracing model. FSPL is the free space path loss calculation: FSPL (dB) = 20log10(d) + 20log10(c×f) + 20log10(4×π×λ), where d is the distance (meters), f is the frequency (Hz), c is the speed of light (≈3×10⁸ m / s), and λ is the signal wavelength (meters).

[0122] (3) Interference impact (I): Interference impact can be assessed by analyzing the signal interference in adjacent sectors. The calculation formula is: I = Pinterfering / Pdesired, where Pinterfering is the power of the interfering signal and Pdesired is the power of the desired signal.

[0123] ④ Multipath Effect (M): In antenna feeder optimization, by adjusting the azimuth and downtilt angles of the base station, the reflection from the ground and buildings is increased, which can distinguish the number of independent and uncorrelated channels in space, increase the spatial multiplexing capability of the channels, and improve the number of spatial multiplexing streams. 3D ray tracing technology, by simulating the propagation of radio waves in the real environment, including direct, reflected, refracted, and scattered rays, can accurately predict the propagation path and characteristics of signals.

[0124] The formula for the total score S(x) is as follows:

[0125] S(x) = w A ×A(x)+w L ×L(x)+w I ×I(x)+w M ×M(x)

[0126] Where A(x) is the antenna feeder adjustment range score of scheme x (i.e., the first optimal scheme mentioned above), i.e., the first score mentioned above; L(x) is the path loss score of scheme x, i.e., the second score mentioned above; I(x) is the interference impact score of scheme x, i.e., the third score mentioned above; M(x) is the multipath effect score of scheme x, i.e., the fourth score mentioned above; w A w L w I w M These are the weighting coefficients for each rating dimension.

[0127] In the above steps, determining the first optimal scheme among multiple optimization schemes includes: acquiring relevant data of a GIS 3D map, the problem area, and the first cell to be optimized; constructing a 3D environment model based on the relevant data, the GIS 3D map, and the problem area; starting from the antenna of the first cell to be optimized, emitting a ray towards the problem area according to the antenna parameters in the first optimization scheme among multiple optimization schemes, and determining the propagation path of the ray in the 3D environment model, wherein the first optimization scheme is any one of the multiple optimization schemes; evaluating the rank of the channel based on the number of propagation paths, the number of transmitting antennas of the ray, and the number of receiving antennas of the ray, and obtaining the first evaluation result; when the first evaluation result does not meet the second preset condition, iteratively adjusting the antenna parameters in the next optimization scheme until the evaluation result of the adjusted optimization scheme meets the second preset condition, and stopping the iteration; and determining the last optimization scheme as the first optimization scheme.

[0128] In this embodiment, for the determined first optimal solution, the system constructs a three-dimensional environment model, which includes GIS three-dimensional map data, the characteristics of the problem area, and the antenna configuration of the first cell to be optimized. Then, the system emits a ray starting from the antenna according to the first optimization scheme and traces the propagation path of the ray in the three-dimensional model to evaluate the channel rank (RANK). If the evaluation result (RANK value) does not meet the preset optimization conditions (i.e., the second preset condition), the system iteratively adjusts the antenna parameters to generate the next optimization scheme. This process is repeated until an optimization scheme that meets the optimization conditions (such as the RANK value reaching the target and signal coverage being improved) is found. The final determined scheme is the first optimization scheme, which is the optimal optimization scheme for that cell.

[0129] In the above process, the rank (RANK) of the channel is explained as follows:

[0130] RANK = min(Nt, Nr, number of independent paths), where Nt is the number of antennas at the transmitting end, Nr is the number of antennas at the receiving end, and the number of independent paths is the number of independent paths identified by 3D ray tracing technology (i.e., the number of propagation paths mentioned above).

[0131] Finally, the system generates optimization instructions based on the target optimization scheme for each cell to be optimized and sends these instructions to the base station. Upon receiving the instructions, the base station adjusts the azimuth and downtilt angles of its antennas to execute the optimization scheme. This process is automated, avoiding the inefficiency and inaccuracy of manual on-site adjustments. Simultaneously, the system monitors the adjusted network performance to ensure the optimization effect.

[0132] Figure 3This is a structural diagram of a base station antenna tuning device according to an embodiment of this application, as shown below. Figure 3 As shown, the device includes:

[0133] Acquisition module 30 is used to acquire various types of data from the antenna feeder system;

[0134] The matching module 32 is used to create a grid based on the distribution area of ​​the measurement data in the multi-class collected data, and to match the measurement data with the grid.

[0135] The first determining module 34 is used to determine the grid that meets the first preset condition as the problem grid from the grid, and to determine the problem area based on the problem grid;

[0136] The second determining module 36 is used to determine the cells to be optimized corresponding to the problem area, and to determine the target optimization scheme for the cells to be optimized.

[0137] The sending module 38 is used to generate an optimization instruction based on the target optimization scheme and send the optimization instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instruction.

[0138] In the matching module of the aforementioned base station antenna tuning device, the matching module is also used to acquire grid size information, wherein the size information includes the length and width of the grid; determine the first boundary information of the distribution area of ​​the measurement data, wherein the first boundary information includes the maximum latitude, minimum latitude, maximum longitude and minimum longitude; determine the number of grids based on the boundary information and size information; and create a two-dimensional array to represent the grid based on the number of grids.

[0139] In the matching module of the aforementioned base station antenna tuning device, the matching module is further used to acquire the latitude and longitude information of the first sampling point, wherein the first sampling point is any one of multiple sampling points, and the multiple sampling points are used to collect measurement data; based on the latitude and longitude information and the first boundary information, the row and column number of the target grid to which the first sampling point belongs in the two-dimensional array is determined; and the first sampling point and the first measurement data corresponding to the first sampling point are added to the corresponding target grid.

[0140] In the second determining module of the aforementioned base station antenna tuning device, the second determining module is further used to obtain the cells associated with the sampling points in the problem area to obtain the cells to be tuned; according to the number of sampling points contained in the cells to be tuned, the cells to be tuned are sorted in descending order, and the priority of the sorted cells to be tuned is determined.

[0141] In the second determining module of the aforementioned base station antenna tuning device, the second determining module is further used to acquire a first cell to be tuned among the cells to be tuned, wherein the first cell to be tuned is any one of the cells to be tuned; determine multiple tuning schemes for the first cell to be tuned, and determine the first optimal scheme among the multiple tuning schemes, wherein each tuning scheme corresponds to different antenna parameter adjustments; score the first optimal scheme for each cell to be tuned, and determine the first optimal scheme with the highest score as the target tuning scheme.

[0142] In the second determining module of the aforementioned base station antenna optimization device, the second determining module is further configured to determine a first score of the first optimal solution for each cell to be optimized in a first dimension, wherein the first dimension represents the antenna adjustment range, which is determined by parameters before and after antenna adjustment; determine a second score of the first optimal solution for each cell to be optimized in a second dimension, wherein the second dimension represents path loss, which includes multipath loss and free space path loss; determine a third score of the first optimal solution for each cell to be optimized in a third dimension, wherein the third dimension represents signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; determine a fourth score of the first optimal solution for each cell to be optimized in a fourth dimension, wherein the fourth dimension represents optimal multipath effect; and determine the total score of the first optimal solution for each cell to be optimized based on the first score, second score, third score, and fourth score.

[0143] In the second determining module of the aforementioned base station antenna optimization device, the second determining module is further used to acquire relevant data of the GIS 3D map, the problem area, and the first cell to be optimized; construct a 3D environment model based on the relevant data, the GIS 3D map, and the problem area; starting from the antenna of the first cell to be optimized, emit a ray to the problem area according to the antenna parameters in the first optimization scheme among multiple optimization schemes, and determine the propagation path of the ray in the 3D environment model, wherein the first optimization scheme is any one of the multiple optimization schemes; evaluate the rank of the channel based on the number of propagation paths, the number of transmitting antennas of the ray, and the number of receiving antennas of the ray, and obtain a first evaluation result; when the first evaluation result does not meet the second preset condition, iteratively adjust the antenna parameters in the next optimization scheme until the evaluation result of the adjusted optimization scheme meets the second preset condition and the iteration stops; and determine the last optimization scheme as the first optimization scheme.

[0144] It should be noted that, Figure 3 The base station antenna tuning device shown is used to perform... Figure 2 The tuning method for the base station antenna shown above is also applicable to the tuning device for this base station antenna, and will not be repeated here.

[0145] This application embodiment also provides an electronic device, which includes a memory and a processor. The memory is used to store program instructions; the processor is connected to the memory and is used to execute program instructions to perform the following functions: acquiring multiple types of collected data in an antenna feeder system; creating a grid based on the distribution area of ​​measurement data in the multiple types of collected data, and matching the measurement data with the grid; identifying grids that meet a first preset condition as problem grids from the grids, and identifying problem areas based on the problem grids; determining the cell to be optimized corresponding to the problem area, and determining the target optimization scheme for the cell to be optimized; generating optimization instructions based on the target optimization scheme, and sending the optimization instructions to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instructions.

[0146] It should be noted that the aforementioned electronic equipment is used to perform Figure 2 The tuning method for the base station antenna shown above is also applicable to this electronic device, and will not be repeated here.

[0147] This application embodiment also provides a non-volatile storage medium, which includes a stored computer program, wherein the device containing the non-volatile storage medium executes the above-mentioned base station antenna tuning method by running the computer program.

[0148] This application also provides a computer program product, including computer instructions that, when executed by a processor, implement the steps of the base station antenna tuning method in various embodiments of this application.

[0149] This application also provides a computer program that, when executed by a processor, implements the steps of the base station antenna tuning method in various embodiments of this application.

[0150] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0151] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0155] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0156] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for optimizing a base station antenna, characterized in that, include: Acquire various types of data from the antenna feeder system; A grid is created based on the distribution area of ​​the measurement data in the multiple types of collected data, and the measurement data is matched with the grid. From the grid, the grids that meet the first preset condition are identified as problem grids, and the problem area is determined based on the problem grids; Identify the cells to be optimized corresponding to the problem area, and determine the target optimization scheme for the cells to be optimized; An optimization instruction is generated based on the target optimization scheme, and the optimization instruction is sent to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instruction; Determining the target optimization scheme for the cell to be optimized includes: obtaining a first cell to be optimized from the cells to be optimized, wherein the first cell to be optimized is any one of the cells to be optimized; determining multiple optimization schemes for the first cell to be optimized, and determining a first optimal scheme among the multiple optimization schemes, wherein each optimization scheme corresponds to different antenna parameter adjustments; scoring the first optimal scheme for each cell to be optimized, and determining the first optimal scheme with the highest score as the target optimization scheme; The first optimal solution for each cell to be optimized is scored, including: determining a first score for the first optimal solution in a first dimension, where the first dimension represents the antenna adjustment range, which is determined by parameters before and after antenna adjustment; determining a second score for the first optimal solution in a second dimension, where the second dimension represents path loss, including multipath loss and free space path loss; determining a third score for the first optimal solution in a third dimension, where the third dimension represents signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; determining a fourth score for the first optimal solution in a fourth dimension, where the fourth dimension represents optimal multipath performance; and determining a total score for the first optimal solution of each cell based on the first score, the second score, the third score, and the fourth score.

2. The method according to claim 1, characterized in that, Creating a raster based on the distribution area of ​​the measurement data in the multiple types of collected data includes: Obtain the size information of the grid, wherein the size information includes the length and width of the grid; First boundary information for the distribution area of ​​the measurement data is determined, wherein the first boundary information includes maximum latitude, minimum latitude, maximum longitude, and minimum longitude; The number of grid cells is determined based on the boundary information and the size information; A two-dimensional array is created to represent the grid based on the number of grid cells.

3. The method according to claim 2, characterized in that, Matching the measurement data with the grid includes: Obtain the latitude and longitude information of the first sampling point, wherein the first sampling point is any one of a plurality of sampling points, and the plurality of sampling points are used to collect the measurement data; Based on the latitude and longitude information and the first boundary information, the row and column numbers of the target grid to which the first sampling point belongs in the two-dimensional array are determined; The first sampling point and the first measurement data corresponding to the first sampling point are added to the corresponding target grid.

4. The method according to claim 1, characterized in that, Identifying the cells to be optimized corresponding to the problem area includes: Obtain the cells associated with the sampling points in the problem area to obtain the cells to be optimized; Based on the number of sampling points contained in the cell to be optimized, the cells to be optimized are sorted in descending order, and the priority of the sorted cells to be optimized is determined.

5. The method according to claim 1, characterized in that, Determining the first optimal solution among the multiple optimization schemes includes: Obtain relevant data from the GIS 3D map, the problem area, and the first cell to be optimized; A three-dimensional environment model is constructed based on the relevant data, the GIS 3D map, and the problem area; Starting from the antenna of the first cell to be optimized, a ray is emitted toward the problem area according to the antenna parameters in the first optimization scheme among the multiple optimization schemes, and the propagation path of the ray in the three-dimensional environment model is determined. The first optimization scheme is any one of the multiple optimization schemes. Based on the number of propagation paths, the number of transmitting antennas of the ray, and the number of receiving antennas of the ray, the rank of the channel is evaluated to obtain a first evaluation result; If the first evaluation result does not meet the second preset condition, the antenna parameters in the next optimization scheme are iteratively adjusted until the evaluation result of the adjusted optimization scheme meets the second preset condition, at which point the iteration stops. The final optimization scheme is determined as the first optimization scheme.

6. A tuning device for a base station antenna, characterized in that, include: The acquisition module is used to acquire various types of data from the antenna feeder system; A matching module is used to create a grid based on the distribution area of ​​the measurement data in the multi-type collected data, and to match the measurement data with the grid. The first determining module is used to determine the grid that meets the first preset condition as the problem grid from the grid, and to determine the problem area based on the problem grid; The second determining module is used to determine the cell to be optimized corresponding to the problem area, and to determine the target optimization scheme for the cell to be optimized. The transmitting module is used to generate an optimization instruction based on the target optimization scheme and send the optimization instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instruction; The second determining module is further configured to acquire a first cell to be optimized from the cells to be optimized, wherein the first cell to be optimized is any one of the cells to be optimized; determine multiple optimization schemes for the first cell to be optimized, and determine a first optimal scheme among the multiple optimization schemes, wherein each optimization scheme corresponds to different antenna parameter adjustments; score the first optimal scheme for each cell to be optimized, and determine the first optimal scheme with the highest score as the target optimization scheme; The second determining module is further configured to determine a first score for the first optimal solution of each cell to be optimized in a first dimension, wherein the first dimension represents the antenna adjustment range, which is determined by parameters before and after antenna adjustment; determine a second score for the first optimal solution of each cell to be optimized in a second dimension, wherein the second dimension represents path loss, which includes multipath loss and free space path loss; determine a third score for the first optimal solution of each cell to be optimized in a third dimension, wherein the third dimension represents signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; determine a fourth score for the first optimal solution of each cell to be optimized in a fourth dimension, wherein the fourth dimension represents optimal multipath effect; and determine a total score for the first optimal solution of each cell to be optimized based on the first score, the second score, the third score, and the fourth score.

7. An electronic device, characterized in that, include: Memory, used to store program instructions; The processor, connected to the memory, is configured to execute program instructions to perform the following functions: acquire multiple types of data collected from the antenna system; create a grid based on the distribution area of ​​the measurement data in the multiple types of data collected, and match the measurement data with the grid; identify grids that meet a first preset condition from the grid as problem grids, and determine problem areas based on the problem grids; Identify the cells to be optimized corresponding to the problem area, and determine the target optimization scheme for the cells to be optimized; An optimization instruction is generated based on the target optimization scheme, and the optimization instruction is sent to the base station, wherein the base station adjusts the angle of the base station antenna according to the optimization instruction; Determining the target tuning scheme for the cell to be tuned includes: obtaining a first cell to be tuned from the cells to be tuned, wherein the first cell to be tuned is any one of the cells to be tuned; determining multiple tuning schemes for the first cell to be tuned, and determining a first optimal scheme among the multiple tuning schemes, wherein each tuning scheme corresponds to different antenna parameter adjustments; scoring the first optimal scheme for each cell to be tuned, and determining the first optimal scheme with the highest score as the target tuning scheme; scoring the first optimal scheme for each cell to be tuned includes: determining a first score for the first optimal scheme of each cell to be tuned in a first dimension, wherein the first dimension is used to represent the antenna adjustment amplitude, and the antenna adjustment amplitude is determined by... The parameters before and after antenna adjustment are determined; a second score is determined for the first optimal solution of each cell to be optimized in the second dimension, where the second dimension represents path loss, including multipath loss and free space path loss; a third score is determined for the first optimal solution of each cell to be optimized in the third dimension, where the third dimension represents signal interference intensity, which is determined by the ratio of the power of the interfering signal to the power of the desired signal; a fourth score is determined for the first optimal solution of each cell to be optimized in the fourth dimension, where the fourth dimension represents optimal multipath performance; based on the first score, the second score, the third score, and the fourth score, a total score for the first optimal solution of each cell to be optimized is determined.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the base station antenna tuning method according to any one of claims 1 to 5 by running the computer program.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the base station antenna tuning method according to any one of claims 1 to 5.

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